PRODUCT MESSAGE GENERATION

Build customer relationships that thrive by delivering personalized product messaging to shoppers with first-to-market precision. customer data. scriptions that understand customer attitudes personalized manner.
Each word in a product description can be defined by a set of values – search value, information value, and resonance value, to name a few.  As shoppers interact with a product page, each word’s value reflects their on-page behavior and actions. 
Writesof specializes in computational linguistics for online retail.  We use customer-oriented Natural Language Processing (NLP) systems to calculate relational values between customer interactions and product page text.  These NLP systems are built with advanced machine learning capabilities, in a neural newtork environment, capable of evaluating millions of data points for each user-page interaction.  As new data is fed into the systems, Each user interaction  relays unique feedback to our systems.  As more interactions occur, we apply  language in relation to customer data.  that are built in a neural network that calculates relational values between customers and product page text.  Performance-based calculations   stomers interact with chunks of  product description language, using customer engagement and conversion metrics as quantitative feedback that automate product description writing and editing processes that generate content that speaks to individual users or customer segments.   
NLG
e by user interactions on the product pageBrands is communicated to each unique user.  These values operate in a confined space within a product page, which presents a challenge for writers in determining which words go  A word’s value is not determined by the writer, but by the reader.  When a shopper visits a product page, their experience is not limited to information available on that page.  They will shop around for the same or similar products and when they find a product that they believe in, they will complete the purchase.  That being said, if your product page provides the best information in a manner that resonates with their attitudes and interests, even if they don’t buy from you, they will remember your brand.  Online retail is not about converting shoppers into buyers, it’s about converting buyers into customers that develop a sense of familiarity with your brand.  These customers are more likely to return and purchase again, engage after the sale, and influence others to buy.  Therefore, product description personalization should not be about merely converting more customers, but about developing long-lasting relatoinships with customers who come back because they find value, feel appreciated and understood, and prefer shopping on your site because they simply trust that they will get what they need.   just . who are Consumers will shop around, with that product is limited by the information available to them on that page, combined with what they learn about the product off-site.  To keep the customer on the page, online retailers must write compelling detail about the product’s features and benefits and more importantly use everyThis is why it is so important for online retailers  As customer data has become    opportunity.  Much like customer data, language data can be analyzed with respect to product page performance and Writesof a can be analyzed An effective product page convey value in product features and benefits in a compelling manner.  Each customer visit will perceive the value differently.  intereaction with the product description differs by conveyed value perception.  in a way that resonates with cription will resonate with shoppers, conveying information and value and  familiarity with your brand.  When customers find sites with product information that speaks to their needs and desires, they are more likely to remember your brand.  Informative and personalized product descriptions, therefore, should be viewed as value-oriented opportunities to, not only convert shoppers into buyers, but also compel customers to return to a familiar place where they trust they will find products that speak to their needs and desires.  Each user visit is a valuable relationship opportunity, whereby customers may engage after purchase, leave positive reviews, and evagelize your brand in social media communities. 
mer experience, therefore, creates an opportunity to convert shoppers in to buyers, and much more.  Customers who experience informative product pages a sense of familiarity with product pages with familiarity and not lacking in detail are more likely to engage after the sale, return to purchase more, leave positive reviews, and buy, return, engage after the sale, leave positive reviews, and evangelize your brand.  This is why so many online retailers are investing in personalization. 
As access to customer data be, engagement, interests, affinities, and behavior has neverand more informed customers who are more likely to return that are more likely to  In order to optimize Every word of a product description is consumed by valuable space, and the relative position of each word reprresents an opportunity to present its communication value to online shoppers.   
Online shoppers are no longe defined merely by demographics, but by interests, behaviors, and actions.  Customer segments, once defined by demographics, today are defined by billions of relational data points.  Customer interests, behaviors, and actions help online retailers understand online shoppers with exceptional precision.  Each time a shopper lands on a product page, they have a desire to be spoken to in a way that appeals to their individuality.  For online retailers, the challenge is determining how to inject customer data into product description writing workflows.  For most retailers, particularly those thousands of product pages, its hard enough producing moderately enriched product descriptions, not to mention keeping it fresh and seasonably updated. content that is enriched, fresh, and persuasive.  product descriptions that speak to the customer.   their customer experience strategy.  W this unprecedented access to customer data into active performance   
online retailers that    with precision and clarity. that  online retailers, we have the capability of The problem is that most online retailers are simply not capable of producing the volume of content necessary to implement a diversified product description strategy.  diversify in order to  that many online retailers have is not about yet present significant production challenges for online retailers.   In most cases, product marketers can not realize optimum engagement and conversion rates merely on a basis of customer interests.  rely on keyword targeting and generic landing pages.  is experience is essential to izing significant gains in customer engagment, conversion and loaylty metrics.  Shoppers that   A product page that resonates  gains in customer engagement and conversionconversion rates, and enriching the customer experience is a critical function of success in online retail today.  and Among a range of   Choosing the right strategies and technologies that enable you to speak to increasingly diverse customer segments will determine will determine how Online retailers with customer experience improvement priority for online retailers today is improving customer experience in 
by enriching customer experience
Online retailsers responsible for managing and updating thousands of products that   and speak to your customersproduct page experience with content that  customer experience with unique product descriptions that speak to unique customer segments.  with more enriched product descriptions  uniquely personalized for each user, segment, users, segments, and channels.  Writesof  brand-tailored product descriptions systems for online retailers.  Our tec create personalized product descriptions  user, by channel, segment, or customer journey. 
As a Writesof partner, online retailers, product marketers and agencies can capitalize on customer relationship opportunities with product detail pages that convert more shoppers into loyal customers and brand influencers.   
Today, industry-leading Natural Language Generation technologies are, generally speaking, designed for statistics-based objective writing, capable of generating thousands of news articles and reports on topics such as finance, sports, and business.  In contrast, Writesof  has developed several innovations that enable our NLG systems to excel at subjective writing, capable of communicating a product’s features, benefits, and advantages (subjective attributes) to unique customer segments in a personalized manner.
Writesof has brought several innovations for computational linguistics, the science of using machines to analyze and manipulate language in its infinite forms.   that results in higher quality subjective writing capabilities, specifically for online retail.   that enable our NLG systems to understand how product description language influences customer behavior.   NLG systems that utilize incorporating customer-experience-driven machine learning and linguisticonline-retail-oriented machine learning that excels at subjective writing, capable of generating millions of unique, brand-tuned product descriptions and promotional messages, each uniquely positioned to persuasively communicate product features, benefits and advantages to a target audience.
software, Writesof ‘s brand of NLG excels at subjective writing, capable of generating millions of unique, brand-tuned product descriptions and promotional messages that persuasively communicate product features, benefits and advantages to specified audience segments.
for customers with enriched product descriptions that inform, engage, and persuade.   and  improvements in customer experience that  Customer experienceThere has never been a CultivateAs a Writesof partner, online retailers, product marketers and agencies can cultivate 
When Writesof first began developing our Natural Language Generation (NLG) software in 2014, the most sophisticated NLG technologies available at the time were built to linguistically interpret variable metric data, capable of generating thousands of unique articles and reports for subjects such as sports, finance, and statistics.  These pioneering NLG systems were designed on a core of objective writing production, which, as we have learned from several leading global retailers and consulting firms, has presented many challenges in their attempts at product description writing projects.  Despite these challenges, several online retailers find some value with NLG systems that are capable of producing minimally enriched content snippets for Product Informatoin Management (PIM) feeds, but they simply cannot produce full detailed product pages without massive amounts of production time or outsourcing.  have not found much success at by enriching existing product data for an online retailer’s Product Information Management (PIM) systems from these technologies by enriching existing product feature and benefit data feeds managed by integrated Product Informatoin Manaagmeent (PIM) systems, but with extreme limitations when it comes to building out content of a product detail page, with respect to target audiences.  
provide some value in enriching data feeds for Product Information Management (PIM) systems, but generally perform poorly at generating built out product detail page descriptons.  The problem is that these systems have difficultiy interpreting product attributes, such as features and benefits, an a subjective manner.  do not perform subjective writing or persuasive communications. 
Since our founding, Writesof has learned from some of the top Fortune 500 retailers and agencies, that our system architecture presents unique advantages for product marketers.  Today,telling stories about metric data.metric data storytelling, capable of writing high volumes of metric-based articles and reports about sports, finance, business, and sciences.  Since then, Writesof has learned from Fortune 500 retailers and agencies that these systems do  Existing NLG technologies, at their core, are systems designed to programmatically interpret changes in metric data in correlation with object data.   For example, they can write an article about a baseball team’s performance in today’s game, providing detailed statistic-based information about every stage of the game, even comparing details with respect to season and competitor performance.   While these systems excel at writing stories about sports, finance, and business, to name a few examples, they simply are not well suited to write about subjective attributes, for example a product’s features, benefits, uses, and advantages, and when forced to do so, the content tends to underperform in  perform poorly in .  Despite how impressive these leading NLG companies are at writing objective articlesHowwever,  with respect to seasonal performance data in the sprogrammed to interpret and insert language features derived from variable metric data.  These  content generators excelmetric data, inserting language features derived from the data into articles and reports.  These systems excel at objective writing, at high production volumes, on  sports, finance, and sciences.  However, many online retailers wouild agree that well leaders in the NLG software space have designed their system cores to  around dynamic template  software specialize in metric-based writing on subjects such as finance, sports, and statistics.  These companies have  content built out from a foundation of metric-based data
Leading natural language generation (NLG) providers excel at metric-based report writing, for example, news articles about sports and finance, but do not perform well at subjective writing production. 
Powered by advanced computational linguistics and machine learning systems, our NLG is uniqeuly capable of interpreting on-page language performance by analyzing user profile, engagement and conversion metrics in correlation with variable linguistic attributes.  This advancement in NLG technology enables our systems to write and edit product detail page content, on a continous improvement model, benchmarked by customer engagement and conversion metrics.  As publishing volume increases, data quality improves, improving our machine learning capabilities, which translates to a declining scale of human editor oversight requirements to meet quality and production objectives.  Unlike other NLG software companies, Writesof is powered by the highest quality user data available for machine learning – online retail. Thus, as a Writesof partner, online retailers, product marketers, and agencies are positioning their companies for significant competitive advantages: 
  • Shorter content production and publish-ready timelines
  • First-to-market publising capabilites
  • High quality seasonal and promotional editing
  • Diversified, chaannel / vendor product detail pages (PDP)
  • Version Split and A/B testing and multivariate analysis
  • Diversified landing pages by customer segmens
  • Enriched product descriptions improve on-site and off-site search visibillity, SEO, after-sale engagement, and create more brand-loyal fans and social media influeners, and syndicated and user-generated content
wherein product features, benefits, and advantages are persuasively communicated to targeted user segments.  The NLG writes informative and persuasive language that communicates product features, benefits, and advantages with your brand’s house style.  influence customer engagement and conversion behavior.  We are ready to produce millions of unique 100 to 500-word product descriptions, each individually performance benchmarked by engagement and conversion metrics.  Our product descriptions communicate product features, benefits, and advantages to your audience, in your brand’s voice.   benefits in your brand’s voice, emabe  Online Retailers, Product Marketers and Agencies write enriched and persuasive product descriptions. 

PRODUCT MESSAGE GENERATION

a a Build a thriving customer base with personalized product messaging that adapts to your customer’s unique interests and behaviors with first-to-market production speed.
With our brand-tailored Natural Language Generation (NLG) solutions, product marketers can create high-performing product messages that listen to and interact with each unique user, on an individual and personal basis.

Writesof helps organizations…

  1. Deploy communication streams that listen and learn about your customers.
  2. Target highly diverse user segments with personalized product messaging.
  3. Improve message resonance with customers on at the individual user level.
  4. Guarantee increases in customer engagement, response and conversion metrics.
  5. Cultivate customer relationships with language that speak to them, not at them.
  6. Create 10s or 100s of thousands of unique product descriptions in weeks.
  7. Edit thousands of unique product descriptions in hours – text and HTML.
  8. Build dozens, even hundreds, of unique product page versions for each SKU.
  9. A/B and multivariate test page language performance – over 1,000 variables.
  10. Get insights on how each word / phrase resonates with users and user groups.
  11. Learn more about how linguistic dimensions correlate with customer behavior.
  12. Track KPI values for phrases, grammars, sentence structures, and writing styles.
  13. Understand how different users and communities interact with language forms.
  14. Diversify product page content without cannibalizing text, titles or meta data.
  15. Ramp up personalization while maintianing brand voice and author styles.
  16. Publish unique product pages for each channel, marketplace and buyer journey.
  17. Distribute unique, publish-ready product descriptions to resellers and affiliates.
  18. Control how resellers impact your brand footprint – unique, enriched content
  19. Manage page text / HTML with any PIM – or by CSV, XML, JSON, TXT file.
  20. Create a language data set specifically designed for online retail and your brand.
  21. Generate fully enriched content with self-directed user targeting capabilities.
  22. Utilize powerful linguistics machine learning technology, built for online retail.
  23. Build a multi-dimensional language framework defined by user attributes.
  24. Leverage the power of our massive linguistic neural network engine.
  25. Learn how language data and customer data influences buyer journey actions.
  26. demographic, behavior, actions, interests, attitudes, affinity, community, social groups,
  27. Create a dynamic brand lexicon, by user segment
Create unique, fully enriched product descriptions, product ads, promotional
by listening to and interacting with shoppers. 
Powered by performance-driven machine learning, our NLG technology writes publish-ready product descriptions and can even autonomously edit existing product descriptions by analyzing customer interactions with product page messaging.

Your custom NLG system is tuned to generate product messaging in your brand’s unique voice.   At the first stage of tuning, our Natural Language Processing (NLP) systems analyze all customer-facing product page text, assigning qualitative and ordinal values to each word.  Product page text includes full product descriptions, bullets, on-page reviews, Q&A, and meta description text.  Each product page’s text, which includes on-page reviews, Q&A, and meta description text, is grouped by product name and category.  We then assign ordinal position values to each word of text, enabling our systems to know the exact location of a word within a section, paragraph, sentence, or phrase.

After this initial ordinal processing stage, our NLP classifier assigns qualitative attributes to language “chunks” – words, phrases and sentence fragments.  Our system uses hundreds of language attributes that define characteristics of words, phrases, and paragraphs.  At this language classification stage, our system begins to populate a large multi-dimension relational database of all product page text.  This database can be viewed as a brand communications schema, a comprehensive linguistics analysis of a brand’s product messaging.  Using a framework of several hundred language attributes, each word is assigned a plurality of attribute values that describe, not just static qualitative values, but also relational values, dependent on how a word is used in context within a phrase or sentence.  An simple standardized example of a qualitative attribute is part-of-speech.  A more complex example is the subjective tone of a word, which is dependent on how a word is used in relation to other words in a section of content.  To handle objective values, we use probability models which are tuned with a combination of human author tuning and content-performance-based machine learning which can be done at a later stage of NLG tuning.

word choice, tone, and style.  It is a large database of product messages parsed into chunks of words, phrases, and sentence structures.  Each language chunk is classified by hundreds of linguistic attributes, for example, static qualitative attributes such as part-of-speech and dynamic quantitative attributes such as probability of use by a specific user group or community.  Quantiative attribute values frequently change as published langage chunks are analyzed for performance.

 

without client   your NLG system is tuned by human writersThe NLG system makes decisions on what to write and howdecision-making on what content to write, wh driven by user interactions with product messages.  Each time a unique user visits a product page, our system takes note of on-page engagement metrics and conversion actions.   language analytics neural network – a large multi-demensional relational database that classifies and scores chunks of language into hundreds of linguistic attributes drives our NLG system’s decision making, determining

in correlation to user engagement data.   where a  each new cycle of product page interactions where  of language in a social media group. frequency (as a function of probability) within a particular social community.   Facebook group. , for example, age-readability, r, which ascribes  several hundred linghistic attributes.  When a customer visits a product page, this   Each published word on a single product page is individually scored for its weigted performance value.  If the same word is used more than once on a page, each word will have its own score.  Performance value is determined, in large part, by how different types of customers engage and respond to a product page.  Customer profile informatoin is recorded to help our systems learn how different types of customers engage with different types language forms and styles.

delivers publish-ready product detail, formatted and parsed for your PIM or feed files.
Understand how customers interact with product page language with product message analytics powered by machine learning and built specifically for online retail.
  , understanding exactly how users and user group segments interact with product messages, word by word.  .in your brand’s voice, powered by machine learning that understands how to personalize the .
Deliver more effective and interactive emails, in-product communications, and social media dialogue with machine-learning-generated language that is personalized for unique users and user groups.
beforeritten in your brand’s voice, tuned for your brand’s voice and adapted for audience with first-to-market production speed
ely personalized for each user, by channel, segment, or customer journey. 
Build customer relationships that thrive by delivering personalized product messaging to shoppers with first-to-market precision. customer data. scriptions that understand customer attitudes personalized manner.
Each word in a product description can be defined by a set of values – search value, information value, and resonance value, to name a few.  As shoppers interact with a product page, each word’s value reflects their on-page behavior and actions. 
Writesof specializes in computational linguistics for online retail.  We use customer-oriented Natural Language Processing (NLP) systems to calculate relational values between customer interactions and product page text.  These NLP systems are built with advanced machine learning capabilities, in a neural newtork environment, capable of evaluating millions of data points for each user-page interaction.  As new data is fed into the systems, Each user interaction  relays unique feedback to our systems.  As more interactions occur, we apply  language in relation to customer data.  that are built in a neural network that calculates relational values between customers and product page text.  Performance-based calculations   stomers interact with chunks of  product description language, using customer engagement and conversion metrics as quantitative feedback that automate product description writing and editing processes that generate content that speaks to individual users or customer segments.   
NLG
e by user interactions on the product pageBrands is communicated to each unique user.  These values operate in a confined space within a product page, which presents a challenge for writers in determining which words go  A word’s value is not determined by the writer, but by the reader.  When a shopper visits a product page, their experience is not limited to information available on that page.  They will shop around for the same or similar products and when they find a product that they believe in, they will complete the purchase.  That being said, if your product page provides the best information in a manner that resonates with their attitudes and interests, even if they don’t buy from you, they will remember your brand.  Online retail is not about converting shoppers into buyers, it’s about converting buyers into customers that develop a sense of familiarity with your brand.  These customers are more likely to return and purchase again, engage after the sale, and influence others to buy.  Therefore, product description personalization should not be about merely converting more customers, but about developing long-lasting relatoinships with customers who come back because they find value, feel appreciated and understood, and prefer shopping on your site because they simply trust that they will get what they need.   just . who are Consumers will shop around, with that product is limited by the information available to them on that page, combined with what they learn about the product off-site.  To keep the customer on the page, online retailers must write compelling detail about the product’s features and benefits and more importantly use everyThis is why it is so important for online retailers  As customer data has become    opportunity.  Much like customer data, language data can be analyzed with respect to product page performance and Writesof a can be analyzed An effective product page convey value in product features and benefits in a compelling manner.  Each customer visit will perceive the value differently.  intereaction with the product description differs by conveyed value perception.  in a way that resonates with cription will resonate with shoppers, conveying information and value and  familiarity with your brand.  When customers find sites with product information that speaks to their needs and desires, they are more likely to remember your brand.  Informative and personalized product descriptions, therefore, should be viewed as value-oriented opportunities to, not only convert shoppers into buyers, but also compel customers to return to a familiar place where they trust they will find products that speak to their needs and desires.  Each user visit is a valuable relationship opportunity, whereby customers may engage after purchase, leave positive reviews, and evagelize your brand in social media communities. 
mer experience, therefore, creates an opportunity to convert shoppers in to buyers, and much more.  Customers who experience informative product pages a sense of familiarity with product pages with familiarity and not lacking in detail are more likely to engage after the sale, return to purchase more, leave positive reviews, and buy, return, engage after the sale, leave positive reviews, and evangelize your brand.  This is why so many online retailers are investing in personalization. 
As access to customer data be, engagement, interests, affinities, and behavior has neverand more informed customers who are more likely to return that are more likely to  In order to optimize Every word of a product description is consumed by valuable space, and the relative position of each word reprresents an opportunity to present its communication value to online shoppers.   
Online shoppers are no longe defined merely by demographics, but by interests, behaviors, and actions.  Customer segments, once defined by demographics, today are defined by billions of relational data points.  Customer interests, behaviors, and actions help online retailers understand online shoppers with exceptional precision.  Each time a shopper lands on a product page, they have a desire to be spoken to in a way that appeals to their individuality.  For online retailers, the challenge is determining how to inject customer data into product description writing workflows.  For most retailers, particularly those thousands of product pages, its hard enough producing moderately enriched product descriptions, not to mention keeping it fresh and seasonably updated. content that is enriched, fresh, and persuasive.  product descriptions that speak to the customer.   their customer experience strategy.  W this unprecedented access to customer data into active performance   
online retailers that    with precision and clarity. that  online retailers, we have the capability of The problem is that most online retailers are simply not capable of producing the volume of content necessary to implement a diversified product description strategy.  diversify in order to  that many online retailers have is not about yet present significant production challenges for online retailers.   In most cases, product marketers can not realize optimum engagement and conversion rates merely on a basis of customer interests.  rely on keyword targeting and generic landing pages.  is experience is essential to izing significant gains in customer engagment, conversion and loaylty metrics.  Shoppers that   A product page that resonates  gains in customer engagement and conversionconversion rates, and enriching the customer experience is a critical function of success in online retail today.  and Among a range of   Choosing the right strategies and technologies that enable you to speak to increasingly diverse customer segments will determine will determine how Online retailers with customer experience improvement priority for online retailers today is improving customer experience in 
by enriching customer experience
Online retailsers responsible for managing and updating thousands of products that   and speak to your customersproduct page experience with content that  customer experience with unique product descriptions that speak to unique customer segments.  with more enriched product descriptions  uniquely personalized for each user, segment, users, segments, and channels.  Writesof  brand-tailored product descriptions systems for online retailers.  Our tec create personalized product descriptions  user, by channel, segment, or customer journey. 
As a Writesof partner, online retailers, product marketers and agencies can capitalize on customer relationship opportunities with product detail pages that convert more shoppers into loyal customers and brand influencers.   
Today, industry-leading Natural Language Generation technologies are, generally speaking, designed for statistics-based objective writing, capable of generating thousands of news articles and reports on topics such as finance, sports, and business.  In contrast, Writesof  has developed several innovations that enable our NLG systems to excel at subjective writing, capable of communicating a product’s features, benefits, and advantages (subjective attributes) to unique customer segments in a personalized manner.
Writesof has brought several innovations for computational linguistics, the science of using machines to analyze and manipulate language in its infinite forms.   that results in higher quality subjective writing capabilities, specifically for online retail.   that enable our NLG systems to understand how product description language influences customer behavior.   NLG systems that utilize incorporating customer-experience-driven machine learning and linguisticonline-retail-oriented machine learning that excels at subjective writing, capable of generating millions of unique, brand-tuned product descriptions and promotional messages, each uniquely positioned to persuasively communicate product features, benefits and advantages to a target audience.
software, Writesof ‘s brand of NLG excels at subjective writing, capable of generating millions of unique, brand-tuned product descriptions and promotional messages that persuasively communicate product features, benefits and advantages to specified audience segments.
for customers with enriched product descriptions that inform, engage, and persuade.   and  improvements in customer experience that  Customer experienceThere has never been a CultivateAs a Writesof partner, online retailers, product marketers and agencies can cultivate 
When Writesof first began developing our Natural Language Generation (NLG) software in 2014, the most sophisticated NLG technologies available at the time were built to linguistically interpret variable metric data, capable of generating thousands of unique articles and reports for subjects such as sports, finance, and statistics.  These pioneering NLG systems were designed on a core of objective writing production, which, as we have learned from several leading global retailers and consulting firms, has presented many challenges in their attempts at product description writing projects.  Despite these challenges, several online retailers find some value with NLG systems that are capable of producing minimally enriched content snippets for Product Informatoin Management (PIM) feeds, but they simply cannot produce full detailed product pages without massive amounts of production time or outsourcing.  have not found much success at by enriching existing product data for an online retailer’s Product Information Management (PIM) systems from these technologies by enriching existing product feature and benefit data feeds managed by integrated Product Informatoin Manaagmeent (PIM) systems, but with extreme limitations when it comes to building out content of a product detail page, with respect to target audiences.  
provide some value in enriching data feeds for Product Information Management (PIM) systems, but generally perform poorly at generating built out product detail page descriptons.  The problem is that these systems have difficultiy interpreting product attributes, such as features and benefits, an a subjective manner.  do not perform subjective writing or persuasive communications. 
Since our founding, Writesof has learned from some of the top Fortune 500 retailers and agencies, that our system architecture presents unique advantages for product marketers.  Today,telling stories about metric data.metric data storytelling, capable of writing high volumes of metric-based articles and reports about sports, finance, business, and sciences.  Since then, Writesof has learned from Fortune 500 retailers and agencies that these systems do  Existing NLG technologies, at their core, are systems designed to programmatically interpret changes in metric data in correlation with object data.   For example, they can write an article about a baseball team’s performance in today’s game, providing detailed statistic-based information about every stage of the game, even comparing details with respect to season and competitor performance.   While these systems excel at writing stories about sports, finance, and business, to name a few examples, they simply are not well suited to write about subjective attributes, for example a product’s features, benefits, uses, and advantages, and when forced to do so, the content tends to underperform in  perform poorly in .  Despite how impressive these leading NLG companies are at writing objective articlesHowwever,  with respect to seasonal performance data in the sprogrammed to interpret and insert language features derived from variable metric data.  These  content generators excelmetric data, inserting language features derived from the data into articles and reports.  These systems excel at objective writing, at high production volumes, on  sports, finance, and sciences.  However, many online retailers wouild agree that well leaders in the NLG software space have designed their system cores to  around dynamic template  software specialize in metric-based writing on subjects such as finance, sports, and statistics.  These companies have  content built out from a foundation of metric-based data
Leading natural language generation (NLG) providers excel at metric-based report writing, for example, news articles about sports and finance, but do not perform well at subjective writing production. 
Powered by advanced computational linguistics and machine learning systems, our NLG is uniqeuly capable of interpreting on-page language performance by analyzing user profile, engagement and conversion metrics in correlation with variable linguistic attributes.  This advancement in NLG technology enables our systems to write and edit product detail page content, on a continous improvement model, benchmarked by customer engagement and conversion metrics.  As publishing volume increases, data quality improves, improving our machine learning capabilities, which translates to a declining scale of human editor oversight requirements to meet quality and production objectives.  Unlike other NLG software companies, Writesof is powered by the highest quality user data available for machine learning – online retail. Thus, as a Writesof partner, online retailers, product marketers, and agencies are positioning their companies for significant competitive advantages: 
  • Shorter content production and publish-ready timelines
  • First-to-market publising capabilites
  • High quality seasonal and promotional editing
  • Diversified, chaannel / vendor product detail pages (PDP)
  • Version Split and A/B testing and multivariate analysis
  • Diversified landing pages by customer segmens
  • Enriched product descriptions improve on-site and off-site search visibillity, SEO, after-sale engagement, and create more brand-loyal fans and social media influeners, and syndicated and user-generated content
wherein product features, benefits, and advantages are persuasively communicated to targeted user segments.  The NLG writes informative and persuasive language that communicates product features, benefits, and advantages with your brand’s house style.  influence customer engagement and conversion behavior.  We are ready to produce millions of unique 100 to 500-word product descriptions, each individually performance benchmarked by engagement and conversion metrics.  Our product descriptions communicate product features, benefits, and advantages to your audience, in your brand’s voice.   benefits in your brand’s voice, emabe  Online Retailers, Product Marketers and Agencies write enriched and persuasive product descriptions. 

PUBLISH-READY PRODUCT DESCRIPTIONS AT SCALE

Leading natural language generation (NLG) providers excel at numeric-based objective writing, for example, financial and sports articles, but tend to underperform on subjective writing projects. 
In sharp contrast to existing natural language generation software, Writesof ‘s brand of NLG excels at subjective writing, capable of generating millions of unique, brand-tuned product descriptions and promotional messages that persuasively communicate product features, benefits and advantages to specified audience segments.
Powered by advanced computational linguistics and machine learning systems, our NLG is uniqeuly capable of interpreting on-page language performance by analyzing user profile, engagement and conversion metrics in correlation with variable linguistic attributes.  This advancement in NLG technology enables our systems to write and edit product detail page content, on a continous improvement model, benchmarked by customer engagement and conversion metrics.  As publishing volume increases, data quality improves, improving our machine learning capabilities, which translates to a declining scale of human editor oversight requirements to meet quality and production objectives.  Unlike other NLG software companies, Writesof is powered by the highest quality user data available for machine learning – online retail. Thus, as a Writesof partner, online retailers, product marketers, and agencies are positioning their companies for significant competitive advantages: 
  • Shorter content production and publish-ready timelines
  • First-to-market publising capabilites
  • High quality seasonal and promotional editing
  • Diversified, chaannel / vendor product detail pages (PDP)
  • A/B testing and multivariate analysis
  • Diversified landing pages by customer segmens
  • Enriched product descriptions improve on-site and off-site search visibillity, SEO, after-sale engagement, and create more brand-loyal fans and social media influeners, and syndicated and user-generated content
wherein product features, benefits, and advantages are persuasively communicated to targeted user segments.  The NLG writes informative and persuasive language that communicates product features, benefits, and advantages with your brand’s house style.  influence customer engagement and conversion behavior.  We are ready to produce millions of unique 100 to 500-word product descriptions, each individually performance benchmarked by engagement and conversion metrics.  Our product descriptions communicate product features, benefits, and advantages to your audience, in your brand’s voice.   benefits in your brand’s voice, emabe  Online Retailers, Product Marketers and Agencies write enriched and persuasive product descriptions. 

USER-DRIVEN NATURAL LANGUAGE GENERATION TECHNOLOGY

Writesof user-driven natural language generation (NLG) software enables online retailers to write high-quality, unique product descriptions with unmatched speed.  Natural language processing (NLP) and machine learning systems analyze user profile and behavior data to deliver personalized content to online shoppers.

Our technology enables product marketers to publish millions of personalized product detail pages (PDP), comprised of dozens, even hundreds of unique and persuasive product descriptions for a single product.

USER-DRIVEN NATURAL LANGUAGE GENERATION TECHNOLOGY

Writesof user-driven natural language generation (NLG) software enables online retailers to write high-quality, unique product descriptions with unmatched speed.  Natural language processing (NLP) and machine learning systems analyze user profile and behavior data to deliver personalized content to online shoppers.

Our technology enables product marketers to publish millions of personalized product detail pages (PDP), comprised of dozens, even hundreds of unique and persuasive product descriptions for a single product.

PROBLEMS WITH NATURAL LANGUAGE GENERATION FOR ONLINE RETAIL

Many retailers and agencies agree that existing natural language generation technologies, even the most advanced NLG systems, produce machine-generated product descriptions that offer little to no value to product marketers, with few exceptions.

Even the most advanced NLG systems produce minimally-enriched, low quality product descriptions.  They are lacking in the ability to describe features, persuade on benefits, and are riddled with duplication, regurgitating the same strings of text over and over.  While this may work for standardized language features of finance, business reporting, and sports articles, it is not suitable for detailed product descriptions.

While advanced NLG technologies are capable of producing high quality reports and articles about objective metric-based subject matter, such as with topics about finance, business analysis, sports, sciences, and statistics, leading NLG systems perform poorly when writing about subjective topics, such as product descriptions and persuasive narratives.

Narratives that report on metrics, statistics, and numeric-based values are typically described with highly standardized language.  Describing the change in value in a company’s stock price or a baseball pitcher’s earned run average is communicated with far more standardized language than, for example, describing a product’s colors, textures, uses, and customer benefits.

GENERATED PRODUCT DESCRIPTION QUALITY SCORE MODEL

Our natural language processing (NLP), computational linguistics and machine learning systems are built to analyze customer profile, engagement, and conversion data and how such user data and behavioral data correlates with linguistic attributes of a product description.

Each word in a product description is assigned a set of relevant variable values, including quantitative attribute values, and qualitative attribute values. Each published word is thereby interdependently analyzed for performance values in relationship to all other words within the same sentence, paragraph and page.  In a similar manner, Writesof analyzes PDP content for performance value correlations with all other published words in a category, on a website, and even compared to content published on off-site content that Writesof analyzes.

Our NLG systems select words, phrases, grammars and communication styles based on performance-based probabilities, benchmarked against engagement and conversion metrics, with respect to a customer segment.  Each published product description is benchmarked for performance against prior versions and all other product descriptions on a site, also factoring performance monitoring of language used on all other Writesof-generated PDP content.

Each word in a product description is evaluated on engagment and conversion metrics and our system, with minimal and declining editing oversight, decides how to choose and position words in a composition by analyzing these performance metrics in correlation to hundreds of qualitative and quantitiave linguistic attribute variables.

Language performance analyses can be managed by scheduled feed updates, extracting only relevant linguistic performance value data on a basis of metric relativity, or for more sophisticated solutions, for example semi-automated content editing and page version split tests and multivariate analysis, data can be updated in near-real-time, enabling our NLG editing to respond to changes in user behavior or by compeitive factors.  This robust linguistic performance scoring model enables our systems to calculate the effective performance value of individual product pages, given the language used on each page.

QUANTITATIVE ATTRIBUTE LANGUAGE VARIABLES

Each word of a product description is first analyzed by its variable quantitative values, such as a word’s usage frequency and its proximity and distance values in relationship to other words on a page, in a product category, and site-wide usage.  Quantitative language metrics enable our systems to understand exactly how words are used in relationship to other words are used on the entire website, and more specifically on each product page.

The position and location values of a word are foundational to quantitative language metrics inform our systems on where words are used in a website, within a category, on a page, and in sentences.  Other examples of quantitative values include word and phrase co-location values, word frequency usage, word-count totals and averages on page and sentences, and n-gram values.

N-grams are strings of grouped words where n is the integer that represents the number of words grouped together.  For example, the word-group string, change your, is a bi-gram, and change your world is a tri-gram.  Each word within in an n-gram is interdependently related by a factor of thousands, even hundreds of thousands of quantiative and qualitative language variables.  The quantitative attribute values inform our language processing analytics and machine learning systems about relevant metric relationships of the ways in which these words are used together.

With this scoring model, each word used in a product page is valued according to its unique location and distance-based position within the product description.  For example, the word change might be used in three instances within a single product description version, once as an imperative verb, once as an indicative verb and once as a noun.

The relative position of a word in sentence and within the product description as a whole is recorded in our system.  Positions and locations of all of the other words adjacent to the word change, to include bi-grams, tri-grams, phrase structures and complete sentences, are also recorded.

It should be noted that position and location values of words also correlate with grammar rules and author-tuned style attributes of the language.  For example, complex products with longer sentence structures tend to have higher sentence and page word counts, which correlates with author-tuned grammar rules and usage constraints configured in the system.

QUALITATIVE ATTRIBUTE LANGUAGE VARIABLES

Each word in a product description is assigned a set of constant and variable or dynamic qualitative values, with each word having as many as 100 recorded variables.  Each qualitative variable for a given word helps our system determine the ways in which language is used in a sentence, in the product description as a whole, and in relation to language usage on other pages.

Some simple examples of qualitative language attributes are part-of-speech, grammar structures, reading comprehension level, emotive qualities, and user profile resonance and affinity.  Whereas part-0f-speech is a static attribute of language, other attributes, such as reading-level are relative-constants with conditional and relational thresholds, and still others are purely subjective brand and author-tuned values, for example brand-voice, tone, and writing style.

As an example, the part-of-speech value of the imperative verb change, is a type of qualitative attribute.  The ways in which the word change is grouped with other words to form a sentence and a complete product description determines its other qualitative values.  For example, the word change might be used differently in three instances, in three different sentences, within a single product description,  once as an imperative verb, again as an indicative verb and also as a noun.

Such part-of-speech value represents one type of qualitative attribute of a word, in this case, the word change.  Writesof uses up to 120 different qualitative attribute classes to identify different values of a given word.  Each of these qualitative values may also bear static and weighted relationships to other qualitative attributes for the same word and also relationships to other words.

Relevant qualitative attributes also bear quantitative attribute relationships, for example, the imperative verb change proximal location compared to the indicative verb change in the product description text.  These are just a few of many types of interdependent language relationships that our systems analyze to determine granular and comprehensive qualitative values of words, phrases, sentences, and grammars that comprise a product description text.

The majority of qualitative attributes used in our systems are subject to author and brand inputs.  We use a combination of methods to extract such values from clients, but the majority of the work involved in assigning these values involves retrieval and extraction of published brand-approved content.  For example, our natural language processing tools are capable of extracting qualitative features of 100,000 live product pages (up to fifty million words) within a matter of hours.

By extracting existing product descriptions from public domain, we are able to internally evaluate and classify every published word, phrase, and sentence, by qualitative attributes.  It should be noted that this process does not necessarily fill in our requirements for qualitative language data for a brand, but when the data is filtered and cleaned correctly, it does serve as a linguistic foundation for building out a complete schema of qualitative language features.

To complete the process of building out qualitative language attributes, we provide the client with a lexicon of words and phrases, classified by product features, benefits, advantages, and uses, as well as customer-facing persuasive devices.  Each word and phrase is tagged with key values that represent its qualitative features.  We then give the client the tools to edit a relatively small sampling of words and phrases within each class and we then utilize their edits as inputs into our language processing algorithms to recode and edit the words and phrases and their respective qualitative attributes so that they can be used as brand-approved content within our system.

It should be noted that product features, benefits, and uses, unlike story-telling, operate with relative constancy in product sales communications.  In most cases, a product’s features and benefits have already been described, in detail, somewhere on the web, but it’s the way in which they are described that compels a shopper to find the product, read about it, and purchase it.

AUDIENCE AND USER PROFILE AND BEHAVIORAL ATTRIBUTES

PRODUCT DESCRIPTION TEXT PERFORMANCE VALUATION

Our NLG systems select words, phrases, grammars and communication styles based on performance-based probabilities, benchmarked against engagement and conversion metrics, with respect to a customer segment.  Each published product description is benchmarked for performance against prior versions and all other product descriptions on a site, also factoring performance monitoring of language used on all other Writesof-generated PDP content.

Each word in a product description is evaluated on engagment and conversion metrics and our system, with minimal and declining editing oversight, decides how to choose and position words in a composition by analyzing these performance metrics in correlation to hundreds of qualitative and quantitiave linguistic attribute variables.

GENERATED PRODUCT DESCRIPTION QUALITY SCORE MODEL

Product descriptions on a large retailer website are written, often with input from several authors and editors, to be individually unique, yet even with over 100,000 unique product pages, they share much of the same language elements and structures.  Aside from named entities, for example, brand and product names, singular entities of vocabulary (language elements) and grammar (language rules) usage even a large retail site can be broken down into a relatively small matrix of variables.  Of course, the combinatory ways in which these variables are used is what  (the elemental and rule-based inputs of language) are relatively infinite and the ways in which writers compose the elements of vocabulary and rules of grammar to form a detailed product description is, by definition, a form of artistry.  Yet, unlike other forms of art where laws of physics play a major role in the audience impression, with writing, there is a degree of standardization required, social norms and rules that enable writers to speak to people in a way in which their writing is understood and appreciated as intended.  A large retail website of over 100,000 product pages is composed of a relatively small vocabulary, often fewer than 10,000 uniqe words.  It is the way in which these words are composed together that gives each product page its own unique identity.  The “genetics” of a written composition are all of the unique ways in which words are pieced together to communicate a specific objective, about a specific subject, to a specific audience.

PERSONALIZED MESSAGING FOR PRODUCT DETAIL PAGES

Until recently, personalized product communications was not thought possible for product marketers.  Each unique customer and audience segment had to be addressed in one single narrative, making targeting strategies and split testing the only practicle path to engagment and conversion optimization.

With Writesof’s audience-driven natural language processing (NLP) analytics and machine learning systems, it is now possible for marketing teams to automate the message analytics process, placing all of the burden on machines to decide which words, phrases, and compositions produce higher performance in engagement and conversion metrics.

When product descripion page text is edited, our NLP and machine learning systems analyze thousands of linguistic variables to determine how the edited revision correlates with changes in shopping behaviors.  By analyzing millions of pages, these linguistic-performance correlations exhibit astonishingly predictive accuracy.

Of course, error does occur, but for most online retailers, even those with over 100,000 SKUs, our target vocabulary (lexemes) comprise fewer than 1,000 unique words (unigrams) and fewer than 5,000 unique phrases.  The system does not monitor keywords and product attribute values in the same manne that it monitors communicative, emotive, and verbal language.

Product marketers can deliver personalized communications to unique customers and segments, delivering a tailored expeience that fosters improved conversion rates and brand loaylty.

At Writesof, we believe that oprimizing message clarity and resonance for each customer is a function of profitability.  Increases in conversion rates, customer engagmeent, and customer loyalty occur in parity with improvements in communications quality.

A one-size-fits-all product description page is not an effective way to garner shopper engagement.  a disastrous approach for garnering customer loyalty.

Product description pages that communicate effectively to their target audience audiences are better positioned is a function of .   individual or segment-grouped customers.  with un for unique audience segments.

With minimal brand tuning, our software is capable of writing and editing audienc-driven, house-style product copy for tens of thousands of products in a matter of hours.

PERFORMANCE-DRIVEN NLG PRODUCT DESCRIPTION EDITING

All published content that we create is performance benchmarked against page conversion and engagement metrics, while our natural language processing (NLP) and machine learning systems are capable of semi-autonomously editing and improving published copy, based on performance evaluation.  Shoppers send signals to our systems, , performance metrics, using our writing   thousands of products, even tens of thousands of products,The personalized shopping experience is a burgeoning opportunity for online retailers and product marketers.  Personalization enables product marketers to improve the customer experience, thereby improving conversion, loyalty, and reputation metrics.

CUSTOMER ENGAGMENT THAT GENERATES CUSTOMER RELATIONSHIPS

In contrast, Writesof NLP systems analyze each word on a performance basis, against hundreds of millions of other published words.  Our system views each published word as a relationship opportunity.  Each word contains over 100 attribute values, both quantitative and qualitiative, for example, location-proximity of a published word in relationship to other words (quantitative) and the stylistic and tonal features of a word (qualitative).  The majority of these qualitative and quantitative values are dynamic and regularly change by performance on a given page and the same word used on two different pages will have mostly differing values.  These values are critically-correlated to the values of customer relationship opportunities, i.e., engagement, conversion, loyalty, and reputatioon.

of a word is  Each word on a page is a variable, even words that are unseen or overlooked.  This is why it is critically important for copywriters to choose words and configure them into consumable narratives that convert shoppers into customer relationships.  is a variable that influences the shopper’s behavior.  Message resonance is a function of customer mood, likes, needs, wants, product descriptions are online retailers should be communicating with customers.  The product message should follow the audience, The customer relationship begins at the product message and is sustained by the customer impression.  utility in the form of new technologies that  have enabled product marketers to improve customer conversion and retention rates by convert newOnline retailers are capturing this sitting on troves of high-value data that can fuel personaliation strategies.  , the opportunity to implement personalization strategiesdraw in profile and behavior data utility,  improve data-capture qualityamass troves of online shopper profile and behavior data, personalization opportunities abound. personalized product page experiences grows. in the form of data capture,   One of several opportunities aimed at improving online shopper experience is iprovide a better shopping experience to customers is product description page content diversification.  Product description pages are  can not afford to ignore the value in creating diversified and personalized customer experiences for online shoppers  will sudiversifying their approach to shopping  invest in personlized Each unique shoppermpower online retailers understand that each unique shopper  experience is everything and product page descriptions is a function of  of audience-driven language generation technology, a performance-driven natural language generation (NLG) software that  writes product copy.

USER-OPTIMIZED DETAILED PRODUCT DESCRIPTIONS

Our advanced natural language generation (NLG), natural language processing (NLP) and machine learning systems semi-autonomously write and edit product description page content with unrivaled speed and precision.  With this advancement in NLG technology, product markters can now publish dozens of product description variants for tens of thousands of products, unique pages for each audience segment (e.g., by shopper profile, location, or demographic), in each sales channel.  In fact,   This enables product marOur NLG content is   Our software can rapidly publish and edit product copy, keyword placement, even HTML, for each product page, in each channel.  It can deliver personalized customer experiences, redirecting unique customers or audience segments to specific product pages that fit their diverse group of content landing pages by user location, demographic profile, customer behavior, or communication profile)unique audience segments with a personalized content experience.  Writesof sers by location, demographic, customer behavior, or communications profile, Writesof can easily .  , keyword placement, and .  , for each SKU.  And since A/B, multivariate qualitiative analysis is an integral function of our system, Copywriters product pages to varied , benchmarked by customer engagement and conversion metrics.  With the copywriting bottleneck eliminated from your workflow,  to online shoppersand marketing teams to  to online shoppers with speed and precision.  In contrast with other NLG providers, Writesof uses proprietary natural language processing (NLP) systems which first classify a brand’s house-style of communication into subjective-type and objective-type language “chunks”, comprised of words, phrases, grammar structures, as well as narrative features and style values.  Each language chunk is classified into up to 100 different linguistic groupings, where it can be benchmarked for quality and performance in each respective grouping, and in relationship to other groupings.  We then train our NLG systems to select and combine language chunks in context with product attribute types, such as features, specifications, uses, and benefits, ultimately forming a short or long form product description page.

Finally, we employ our linguistic-analtyics machine learning system to learn how each language chunk resonates with individual customers and audience groups.  Thereby, when new or revised content is published or distributed, we can evaluate the performance value of every word, phrase, and grammar structure in correlation to customer behavior and profile data.  With this game-changing ability, our NLG systems can not only gather intelligence about copy writing quality on a granual scale, but importantly the NLG is capable of semi-autonomously editing content and creating new versions which can be published and distributed in a personalized manner to unique audience segments.

To offer a simple example,  a online retailer with 10,000 products can offer personalized product description pages for customers from each of the 50 U.S. states, for a total of 500,000 IP-conditional landing pages, with each customer visit resulting in site-wide content quality improvement. of three age in each of the 50 U.S. states can have their own unique experience with 100,000 products.This enables online retailers to publish high quality product page content to      to customers, brand editor, it is finally published as live or conditionally-dynamic product description pages (PDP).  Varied types of product contentor distributed to customers via promotional emails, ads, and notifications.  Our NLG system that can semi-autonomously edit published content for continuous improvement by measuring customer engagement and conversion data.  We do this by analyzing every published word, phrase, and grammar structure in correlation with on-page customer behaviors and profiles.

What sets Writesof apart from other natural language generation software?  With our artificial intelligence systems, text generation is performance driven.  Messages are constructed based on user behavior.  Keywords are positioned by search visibility and engagement.  Our NLG technology works by adapting to changes in search visibility, audience engagement, and competitor environments.   Text output can be customized by user or audience attributes, and can adapt to changes in performance data.  Each natural language system that we build is unique, writing with a particular style (voice).

We understand the importance of delivering highly unique content with strategically placed keywords and our systems can easily deliver millions of unique product description pages (PDP) and promotional content that is purely subjective, describing product attributes and how they relate to market audience attributes.

In scenarios where audiences are highly diverse, off-site and on-site customer behavior must likewise be addressed with pinpoint accuracy in a diversified manner.  Writesof, having learned from some of the leading global online retailers and consulting firm, is leading the way in natural language generation systems that help clients communicate directly to the individual customer.  With our technology, marketing teams can now implement strategies that deliver content to individual users and audience segments with machine learning algorithms that study how your customers engage with content and then semi-autonomously edit text to improve direct communication with customers.

The result is improved customer experience, benchmarked by customer engagement, conversion and loyalty metrics.  Customers that feel “at home” with your brand of content will click more, stay on-page longer, engage with push notifications, and come back for more.  With Writesof, you can cultivate meaningful customer relationships that find value with your brands and products.

 

At Writesof, we believe in the sanctity of protecting the customer relationship with the brand, which begins with each customer at all levels of communication, from ad impressions, to product description pages, to promotional emails.  Customer conversion, acquisition, and retention are, in fact, highly correlated with message resonance.  Message resonance, at optimization, is effectively communicating to the customer in such a way that evokes desired engagement and action.  Achieving an optimal level of persuasion, response, or conversion is still possible when message resonance is lacking, but the customer experience, as a function of customer relationship longevity, is constrained.  To put it simply, you marketing team knows your brand inside and out.  They know the product.  Aspeaking to the personalized preferences of each unique customer  Conversion  and your customer base is constantly evolving and diversifying.  Each prospective and current customer deserves an experience uniquely tailored to their preferences.  And showing your customers that you are paying attention to them individually is the best way to garner acquisition and retention.   deliver personalized content to customers with speed and precision, at scale.  Customer acquisition and retention is heavily dependent on content resonance and persuasion, one customer at a time.  Long gone are the days where content push serves the needs of increasingly diversified market segments.       With conventional marketing communications strategy, the message follows the market.  However, to be successful in today’s complex digital marketing landscape, the message must pay respect to the individual.  Customers have different wants, likes, and needs and marketing communications success is defined by how well the writer’s message resonates with each person in order to evoke the desired customer response.  At scale, pushing out semi-unique content can only be achieved with natural language generation.  But as a Writesof partner, your brand of content is guarded, as the language we produce adapts to audience segments and even unique users.  Audience and user attributes and customer behavior sends signals to our systems, whereby machine learning can make copy writing decisions in tune with your customers.  And the best part is, it’s all driven by engagement, resonance performance, and conversion data.   If the content that Writesof produces results in an increase in sales, each chunk of the message is analyzed in relationship to that performance metric.    are automatically adapted by signals from your audience.    is almost always limited by staff messages that resonate to individual customers and users.  We believe that personalized communication is more powerful when writers can focus on chunks of language for one audience segment at a time.  And we know thatr with personalized messaging.  And we have created a solution, with our audience-driven natural language generation systems, which deliver

dynamic, capable of self-editing with minimal and declining human oversight. PDPsfor tens of thousands of SKUs, managing content for millions of PDPs across multiple channels and in multiple languages.  Even still, the most powerful function of our NLG is not in its ability to output billions of words, but the way in which each published word is semi-autonomously monitored, analyzed, and edited based on performance with each customer segment.

PRODUCT ATTRIBUTE VARIANTS & DIVERSIFIED PRODUCT PAGES

Our NLP and machine learning systems analyze PDP customer engagement, with respect to user profile data.  Each published word is interdependently analyzed, using more than 100 scoring variables, in correlation with PDP engagement and conversion performance.

Our NLG systems select words, phrases, grammars and communication styles based on performance-based probabilities, benchmarked against engagement and conversion metrics, with respect to a customer segment.  Each published product description is benchmarked for performance against prior versions and all other product descriptions on a site, also factoring performance monitoring of language used on all other Writesof-generated PDP content.

UPDATE PRODUCT DESCRIPTIONS WITH FRESH CONTENT

Our NLP and machine learning systems analyze PDP customer engagement, with respect to user profile data.  Each published word is interdependently analyzed, using more than 100 scoring variables, in correlation with PDP engagement and conversion performance.

Our NLG systems select words, phrases, grammars and communication styles based on performance-based probabilities, benchmarked against engagement and conversion metrics, with respect to a customer segment.  Each published product description is benchmarked for performance against prior versions and all other product descriptions on a site, also factoring performance monitoring of language used on all other Writesof-generated PDP content.

PRODUCT DETAIL PAGE A/B TESTING AND MULTIVARIATE ANLYSIS

Our NLP and machine learning systems analyze PDP customer engagement, with respect to user profile data.  Each published word is interdependently analyzed, using more than 100 scoring variables, in correlation with PDP engagement and conversion performance.

Our NLG systems select words, phrases, grammars and communication styles based on performance-based probabilities, benchmarked against engagement and conversion metrics, with respect to a customer segment.  Each published product description is benchmarked for performance against prior versions and all other product descriptions on a site, also factoring performance monitoring of language used on all other Writesof-generated PDP content.

NLG WITH SUBJECTIVITY: FEATURES, BENEFITS, ADVANTAGES AND USES

Our NLP and machine learning systems analyze PDP customer engagement, with respect to user profile data.  Each published word is interdependently analyzed, using more than 100 scoring variables, in correlation with PDP engagement and conversion performance.

Our NLG systems select words, phrases, grammars and communication styles based on performance-based probabilities, benchmarked against engagement and conversion metrics, with respect to a customer segment.  Each published product description is benchmarked for performance against prior versions and all other product descriptions on a site, also factoring performance monitoring of language used on all other Writesof-generated PDP content.

ENRICHED PRODUCT DESCRIPTIONS WITH FULL ATTRIBUTES AND KEYWORDS

Our NLP and machine learning systems analyze PDP customer engagement, with respect to user profile data.  Each published word is interdependently analyzed, using more than 100 scoring variables, in correlation with PDP engagement and conversion performance.

Our NLG systems select words, phrases, grammars and communication styles based on performance-based probabilities, benchmarked against engagement and conversion metrics, with respect to a customer segment.  Each published product description is benchmarked for performance against prior versions and all other product descriptions on a site, also factoring performance monitoring of language used on all other Writesof-generated PDP content.

ROMANTICIZING AND STORY-TELLING FOR IMPROVED ENGAGEMENT

Our NLP and machine learning systems analyze PDP customer engagement, with respect to user profile data.  Each published word is interdependently analyzed, using more than 100 scoring variables, in correlation with PDP engagement and conversion performance.

Our NLG systems select words, phrases, grammars and communication styles based on performance-based probabilities, benchmarked against engagement and conversion metrics, with respect to a customer segment.  Each published product description is benchmarked for performance against prior versions and all other product descriptions on a site, also factoring performance monitoring of language used on all other Writesof-generated PDP content.

ROMANTICIZING AND STORY-TELLING FOR IMPROVED ENGAGEMENT

Our eading global retailers and consulting firms have informed us partiand other metric-based narratives p.  Then there are the “content generators” and other content automation “ai” systems that are nothing more than a joke.

When Writesof first began developing the first iterations of our systems back in 2015, we were approached by a leading luxury department store retailer and a prestigious global consulting firm.  After months of discussion with writers and marketers, we soon realized that our technology was a significant game-changing breaktrhough in NLG.  Other   retailers and consulting firms agenciesMachine generated product descriptions produce low-quality  generator, i.e., a template-based approach to machine generated content.  Our NLG software is an innovative Leading NLG software companies primarily specialize in objective writing subjects, such as sports, finance, and business analysis.  Their systems rely on brand-styled dynamic narrative templates, in which metric-derived content is inserted, in context, to output news articles, metric-based reports, and other data-feed driven analysis summaries.

CUSTOMER RELATIONSHIP AUTOMATION

retailers, agencies and brands

content at scale

clients can

fully-managed enterprise production team

product description, user reviews, buying guides, specifications, images, metadata

convert more shoppers into buyers

detailed product description, number one factor influencing online purchase decision

ton of other websites selling the exact same products… differentiate with unique high quality content… internal and external duplicate content… post same product description as the manufacturer (external) – google choose one for first page… no chance to compete or differentiate yourself… other ranking factors being equal.  manufacturers and large retailers like amazon and walmart, domain authority

for a site with thousands of skus

top sellers, highest margin, keyword research and then keyword optimize… system writers can follow to come up with keywords

[Brand} + [Model Name/Series] + [Descriptor] + [Generic product term]

50-350 words… simple products to high-value or complex products… avoid writing with fluff… cover all of the key features.  break it up into paragraphs.

window shopper in the awareness stage.. for ones in consideration or decision stage can scroll down and hopefully hit the buy button

mobile-first

updating content to keep it fresh… seasonable updates… new features, uses, benefits

use lifestyle/aspirational tone for high-end luxrury brand – more engaging and helps user understand features and benefits

listing the features and describing the benefit fo that feature

don’t frame anything in negative way – positive much more engaging

start with action verb – start strong (what is this product going to do for me) and stay in the present – right now

employ storytelling for high-value in-depth products to separate you from the competition

ahead of your competitors

optimizing for search engines but also the user – providing the right information, making it persuasive and engaging

bullet points – foundational…. then start to bulk out.  start with key features, benefits, and uses… then come back later and bulk up product descriptions after some history to analyze data.

start with top category pages and product pages…

product description bulks

live chat questions – feed to writers to base product descriptions on key questions – neat way to get data

writing optimized descriptions

buying stages, category pages and product pages

write 1000 words for a pair of pants – find blog article

enriching product content will improve visits to carts conversion by 15% or more… minimally enriched vs well-enriched…. more images, video, full attributes especially if product is more complex… all digital assets for that product, e.g. owner’s manual, warranty card, product title is optimized… unique and persuasive product description… the marketing copy has brought the customer into the site.. they’re on the product page.. first they look at the image and title for confirmaiton… then the glance at price and  then its up to the text and other assets to persuade them… product description essential to fostering relationship witht he customer.. convey enthusiasm that the retainer has for the product and for the customer… enthusiasm, expertise… description persuade the user

romantic product description – romanticize the reader – first paragraph… unique, persuasive story-telling

user optimization vs search engine optimization… category pages vs product description…. optimizing for the stage of the buyer journey… category-page content… step before… way that your speaking at the awareness,

product descriptions from a user perspective vs serch perspective (starts with keyword research)

create separate product pages that target different product names (keywords) – careful with product descriptions – watch out for cannibalization – next level stuff – so must be totally unique

story telling at the start to romanticize and get them to engage

more paragraph format if the features are complex and reserve the bullets (short and concise that can be easily scanned)

about 20% of content is read by avg visitor… but 100% is read by search engine

every single person that comes to website is different… some want romance content, some want bullet points… some want video… so must offer as many options… and scannable content

most users are going to read but where are they going to read about it… odds of them reading longer increase when the content is there… and with repeat visits, they grow to develop an expectation with that website as to whether it will provide the content that they need… find the info on amazon… find a review… and q&a.

retailer must determine to what level are they going to compete in the content space and how serious are they about creating that relationship with the content by way of content

look at common questions on amazon page and for competitor products to see what type of content that you hsould write

unique product description for each product version… each product description written by a different person.. emphasize different areas of that product…. use different minds working on different features of that product

writers are artists… the ability of one writer to be able to write to a specific audience and understand that niche that that audience is coming to… it is a. mix of science and artistry.  not everyone can write a solid compelling persuasive product description.

machine generated product descriptions… it is extremely difficult to synthesize something that is designed to establish a relationship with a living breathing customer…

the product descriptions are only as good as the writers who are working on them and it really does vary from writer to writer as

having different writers writing for the same product but a different variant…

for example, jackets… there might be search volume around certain sizes or colors in those situations you can stay away from cannibalization and actually have separate product pages… in other niches such as tshirts… there might not be volume around individual variants…. so better to put efforts into one single page.. depends on how much time you have

quality of writing is key… fluff is the worst thing that can happen to a website… demand for quality… still for high volume projects … save you money in the long run… complaints.. take it down and revise it.. low performing… take the time to properly formulate your strategy… right people in place to overseee it…. head of production…. implemented rigorous… high volume projects and ensure quality checks… deliver content that’s publish-ready

google rank brain algorithm – research this

search box

write for the customer… for the user… start out writing as if search engines don’t exist… after the fact better optimize the content for search… fine line … how we approach it

on-page seo – meet expectations of users by correctly using title tags… but make them the key points in the title… the search box will be able to see the keywords in the title

buyer guides (buying guide is better?) and reviews… apply to a category or group of products… value add for shoppers is giving them information to help them make their buying decision… customer looks at you as a resource… not just a place to buy… happy with the purchase… add more value and help them decide – you will increase conversion

go back to the buyer journey… what content lives in their

exit your website… can do retargeting to get them to return

buying guide is an opportunity to prove your authority… must be strong… worth their time to read this guide

customer reviews are essential to any retail site… and q and a section on site valuable… all part of the dialog… chances are someone will ask that 10 others would have asked… helps build trust

larger brands with thousands of products…

scale review generation – large volume of orders… follow-up touch point emails.. marketing automation… auto-responders… come back and leave a review… more personable and resonate better… also aggregate reviews… third party to collect reviews… internal email… mailshake emails from internal server… message from ceo… new website.. handwrite emails to customers personally… first 10 reviews starts to snowball

internal duplicate content or near duplicate content… search engines stop looking at it… crawlers not wanting to come back… differentiate and build unique content is best for your business… unique people write about it with different point of view… you’re not going to get the respect from the search box… why not talk about the uniqueness who, what, when, why, how, where… product reveiws best way to get unique content… if you have a lot of products… very expensive …

if item build system supports it try to build those items as variations so that they can share many of the same attributes… but when it comes to individual attributes you can affect those individually such as each can have its own product description…

new product range coming… get content on your site now (first) those pages are indexed… before the resellers… and give the resellers the basics…

romantic product description that really connects with the users on your site

use h1 for keywords rich product names… within content use h2 and h3 to focus the search bots crawls on your product page

images, video, full attributes, full digital assets, seo product title, seo product descritpoin, uinque and persuasive product description

images alt tags

IMPROVED CUSTOMER RELATIONSHIPS

In e-commerce, the customer relationship begins on the product detail page (PDP).  When an online shopper visits a product page, the interface, images and product description communicate specific values of the brand and the product in the form of messaging. Product description text comprises the greatest variability, and arguably the greatest value, within the PDP message, in terms of message resonance, on-page engagement, persuasion, conversion, and after-sale off-site engagement.

Each product page visit is an opportunity to expand your brand’s on-site and off-site influence, in the form of user-generated content, reviews, social media engagement, etc. Each product description has the potential for generating lasting impact on sales growth for your brand and its product assortment, via your broader omnichannel content footprint.  Personalized marketing communications, therefore, represents one of the greatest opportunities for long term success in online retail.  Product marketers that invest in personalization strategies will convert more customers and foster more valuable customer relationships and improved brand loaylty.

Each word in a product description carries an interdependent value by means of influencing customer engagement and conversion.  It is critically important to choose and compose high-value words that result in the greatest level of engagement with the target audience.  Speaking to your audience with compelling content is essential to building customer trust and familiarity with the product and the brand.  Content that resonates with the customer is a prerequisite to compelling the customer to engage and and purchase.

IMPROVED CUSTOMER RELATIONSHIPS

Rich content

unique and persuasive product description.  The marketing copy brings them to the page.  Then they look at the title

Most online retailers and product marketers are simply not staffed with enough copywriters to capitalize on a product message personalization strategy.  Even more challenging, most writers struggle to connect the data science with the copy and are given to A/B testing and experientially-based edit decisions.

writesof-favicon-light-gray

based on performance.  Messages are tuned by user behavior and .  systems are unlike anything else used in computational linguistics.  Each Writesof machine (NLG persona) that we deploy is in competition with every other Writesof machine in our arsenal.  Machines compete against other machines, and also compete indirectly against human writers and peripheral marketing objectives.  Unfortunately, due to certain legal obligations, we are not at liberty to disclose details of our AI with clients.  But essentially our natural language generation software is built on proprietary AI that “learns” and improves by analyzing measurable features of message performance, for example, user engagement, market visibility, and competitor metrics.

Each NLG machine, or “writer persona” that we deploy is constantly evaluating the quality of what it writes, learning and adapting to changes in audience engagement and competitive environments.  Collectively, NLG personas share performance information with one another,  also serving as performance benchmarks among various intersections of topics and audiences.  Personas can be customized and trained by one or more human writers or, in certain cases, they can be autonomously trained by other well established personas, along with user engagement data.   Each persona is either established with a specific “voice” (writing style), for example, a brand, or is built to be adaptable to alter its writing voice by analyzing audience engagement metrics.  Message performance is always determined by human oversight.  For example, message performance can be goal-oriented, rule-based, with restrictions, limitations, and risk-reward features controlled by human managers.  Human oversight of a persona’s writing voice is typically built around a set of audience characteristics, some of which are configured by human understanding, while others may be built on data analytics, typically a combination of both.

Personas can be cloned and reconfigured to target different audience segments or to make adjustments to content subject and topic features.  NLG personas semi-autonomously learn voice and writing style by modeling linguistic characteristics from sources of author and brand content.  Most crucial to our NLG AI capability is our proprietary data, to include retail consumer data and product message analytics data.  Our big data is like fuel that accelerates machine learning and computational linguistics processing, empowering our Writesof NLG personas to produce higher value content for online retailers, product manufacturers, B2B merchants, and long assortment consumer product brands.

NATURAL LANGUAGE GENERATION AI

What sets us apart from other natural language generation software?  Our NLG is built to integrate seamlessly with e-commerce systems and data.  Our AI utilizes a treasure trove of e-retail data and customer data.  Like other NLG systems, we employ advanced computational linguistics algorithms and natural language processing tools.  We use proprietary e-retail data and customer engagement data that  that reinforce our AI and machine learning.  And we use content engagement analytics t systems and data are trained  are trained to write product copy.  with AI and machine learning developed specifically for h NLG system, which we call NLG “personas”, to write high performing product copy with a distinct generative writing style.  A persona creates content by first performing deep analysis on relationships between word usage and customer behavior.  Millions of computations occur before a single word is written. Each generated message is self-monitored, word for word, for ROI and conversion metrics.  This and other customer data utilized in machine learning processes that help train and improve decision confidence, with every edit.  In most use cases, the client will already have tons of product pages, which means, at first, most of these can be used as static benchmarks for ROI, conversion rate,

Tbig data, and Our AI is powered by proprietary e-commerce data that helps our trainNLG make well-informed decisionscan create dozens of unique product descriptions, ads, and marketing notifications, for every product that you sell.  Similar to other automated personalization marketing software,  Writesof uses proprietary algorithms, machine learning, and data sets, each designed to interact with online customers.  The system interprets and evaluates customer data and linguistics data,  analyzing how each customer interacts and engages with each message.  This enables Writesof to deliver one of the most advanced natural langauge systems, one that has been formally trained to write good copy.  If you sell physical products online, you have a competitive duty to present them, in a personalized manner, to online customers.  Customers want to be convinced on the value of your products’ physical attributes, intangible qualities, use benefits, and quality assurance in a manner that makes the click “Buy” with a winning smile on their face.  Customers feel that they deserve special treatment and good copy, as well as informative content, are message drivers that help give customers a sense that they got a good deal.  Strategically worded messaging encourages customers to buy and makes them feel like they got a good deal.  Good information helps overcome objections.  And hitting the right tone on messaging makes them smile during checkout.   to buy and  that highlights physical attributes, qualitative features, customer benefitscopy about physical products.  online merchants, product brands, and manufacturers.  If you, and manufacturers  customers interacts with the messa of the generated text that it generates.  ustomer behavior analytics, to quantifiably match-make our messaging with predicted user intent.  Customer data, paricularly behavior and demographic data, when full value is extracted, translate to quality and performance scores for your landing pages and push messages.  Writesof harnesses the power of this data to train our natural language generation systems how to adapt to user intent, with respect to campaign settings.  We use machine learning and predictive decision models to perform measured edits of each narrative, rewarding our system with new expermiments when high-value edits are made and restricting or rolling back edits when poor edits are made.  As quality goes up, A/B testing of new and unique messages is broadened.  When quality goes down, messages either revert to the original benchmark or some other higher performing version.

writesof-favicon-light-gray

Tbig data, and Our AI is powered by proprietary e-commerce data that helps our trainNLG make well-informed decisionscan create dozens of unique product descriptions, ads, and marketing notifications, for every product that you sell.  Similar to other automated personalization marketing software,  Writesof uses proprietary algorithms, machine learning, and data sets, each designed to interact with online customers.  The system interprets and evaluates customer data and linguistics data,  analyzing how each customer interacts and engages with each message.  This enables Writesof to deliver one of the most advanced natural langauge systems, one that has been formally trained to write good copy.  If you sell physical products online, you have a competitive duty to present them, in a personalized manner, to online customers.  Customers want to be convinced on the value of your products’ physical attributes, intangible qualities, use benefits, and quality assurance in a manner that makes the click “Buy” with a winning smile on their face.  Customers feel that they deserve special treatment and good copy, as well as informative content, are message drivers that help give customers a sense that they got a good deal.  Strategically worded messaging encourages customers to buy and makes them feel like they got a good deal.  Good information helps overcome objections.  And hitting the right tone on messaging makes them smile during checkout.   to buy and  that highlights physical attributes, qualitative features, customer benefitscopy about physical products.  online merchants, product brands, and manufacturers.  If you, and manufacturers  customers interacts with the messa of the generated text that it generates.  ustomer behavior analytics, to quantifiably match-make our messaging with predicted user intent.  Customer data, paricularly behavior and demographic data, when full value is extracted, translate to quality and performance scores for your landing pages and push messages.  Writesof harnesses the power of this data to train our natural language generation systems how to adapt to user intent, with respect to campaign settings.  We use machine learning and predictive decision models to perform measured edits of each narrative, rewarding our system with new expermiments when high-value edits are made and restricting or rolling back edits when poor edits are made.  As quality goes up, A/B testing of new and unique messages is broadened.  When quality goes down, messages either revert to the original benchmark or some other higher performing version.

writesof-favicon-light-gray

toward the benchmark    Writesof harnesses the power of this data your landing pages and push notifications are working.   suchcustomer behavior analyticsombined with ,  , millions of powered by proprietary data on millions of unique products, transaction data and customer behavior data.  We use this data in conjuction with our online retail AI and machine learning customer profiles.  Our lexicons were developed in-house, from millions of live product descriptions.  We are capable of extracting and analyzing product information and messaging assoicated with any category or class of product.  as well as lexicons built from Online Retail  that no other natural language generation company has the ability to access.  We also maintain large libraries of product information and classified lexicons that help train our AI specifically to describe physical products.   retrieved from a of product information and data signals that accelerate machine-learning-driven decision algorithms.  Our NLG is the first available technology that can automatically develop “author personas”, or virtual machines that autonomously model their respective writing styles after human authors.  The system can actually predict how an author would write about a subject or topic that she has never written before.  Each NLG persona can learn how to interact with different people by assigning it with simple goal-oriented objectives, such as increasing time-on-page or conversion metrics.

Writesof’s mission is to empower writers to focus more on the interactive aspects of writing, such as angles, tone, and personalization, delegating the elemental and analytics-based writing workflow to Writesof.

writesof-favicon-light-gray

NATURAL LANGUAGE GENERATION AI

trains and improve decision confidence  processing text and analytics data, then automatically editing higher performing messages that The content that a persona creates is directly tied to a relatively finite depth and breadth of a “learned” brand voice, with a distinct writing style.  Writesof’s Pessage Message Analytics platform,  to write top-performing product copy.  We refer to our NLG systems as “personas”.  And yes, we give them names – sorry.  personas as a “persona”G “personas”  Second, each persona “learns” to write content in a specified brand voice by analyzing quality-validated source text from selected authors, domains, and brand websites.  Third, Writesof’s natural language generation seldom writes the same thing twice.  Each narrative that a persona generates is quantifiably unique.  Finally, Writesof uses the most sophisticated product message analytics platform in our field.  Most NLG software is focused on data-reporting narratives such as business reports, sports stories, and financial articles.  Online retail and product merchandising requires an immensely different set of data and analytics tools to achieve high quality content, particularly so for long form content.  Product assortments with a combined one-million-word online content footprint are nearly impossible to manage internally without some form of natural language generation technology, template-based listing generators at minimum.   But in today’s competitive e-commerce landscape, personalization is a critical component of your competitive market position.  Online merchants with fewer than 500 channel-wide product pages, those that primarily use prescribed brand content, are at a far greater advantage with personalization initiatives.    rely primarily on content spinning or pre-authored brand content, do not face this level of challenge.     finance, and online retail data and only produces unique, high ROI product descriptions, push messages, and ad copy.

writesof-favicon-light-gray

.  For example, one NLG persona might be modeled after a large retail website having 150,000 product listings authored by over 100 internal and third-party or brand writers.  Writesof’s message analytics platform can even, with a high degree of accuracy, classify which narratives are most likely to belong to a particular author.  We use this same text analytics technology to teach each unique persona how to predict what an author might write, if given access to certain types of linguistic data, for example subject and topic attributes, or user response goals.

subject and topic information.  has published copy from a total of 25 authors.  Another  while a different persona is modeled after two in-house product copywriters.

Writesof’s proprietary e-retail and campaign data, as well as user and behavior profile data.  data and esigned just to write product copy.   were developed as artificial intelligence that analyzes the behavior of individual consumers  for for the online consumer.  Each NLG system operates as independent computational writers, which we call “virtual personas”.  Powered by our proprietary online retail data and machinehow to write by “learning” from one or more human writers.  For example, Writesof can analyze every word of text published on a luxury retailer’s website and, in a short period of time, can predict how the writer(s) would describe and sell every product in a new collection launch.

writesof-favicon-light-gray

capable of learning how to write by analyzing large quantities of topic and subject-level copy.  writing at the same level as writers with a ssimilar-style authors.   Each NLG “pand communicates directly with Writesof’s Product Message Analytics platform, proprietary data and analytics systems. Writesof’s Product Message Analytics platform is a computational powerhouse, capable of calculating granular-to-aggregate values for every word published, in every channel where customer engagement happens.

., comprised of big data, natural language processing tools, and our machine learning platform.  All of our these systems were developed to process Writesof-developed computations specifically for online retail data and linguistics data.

from other  has a unique “NLG Persona”, a writing style similar to the human writers that “taught” them how to write.  Each virtual machine learns to write in a particular style, and within a narrow scope of subjects and topics, based on the author text that it analyzes and interprets.  A    related subjects and topics by analyzing after. have developed human-like writing capabilities ththat a human copywriter has, create dozens of variable product descriptions, ads, and marketing notifications, for every product that you sell.  Similar to other automated personalization marketing software,  Writesof’s proprietary algorithms and data sets, , each designed to interact with online customers.  Systems interpret and evaluate customer and linguistic data,  analyzing how each customer interacts and engages with each message.  The specific types and the breadth of data that we process  that  enables Writesof to deliver one of the most advanced natural langauge systems, machine personas that have each been uniquely trained to write good copy for any brand, any product.  If your company sells a large assortment of products online and have hard-working people that understand your content systems and data, and our team of engineers, data scientists, and e-commerce experts would like to hear from you.  are ready to  competitive advantage in personalized product messaging looks like.  Sho  your channelsyou have a competitive duty to present them, in a personalized manner, to online customers.

Automated Copywriting for Consumer Brands and e-Commerce

Automated Copywriting “AC” is the first NLG platform developed specifically for writing intelligent product descriptions that can be published, with precise message focus and content variability, to multiple channels. Click here to discover the full potential of the AC content generation platform. If you are interested in participating in the AC partner program, please visit the Automated Copywriting website, www.automatedcopywriting.com.

With Writesof’s recent launch of our first retail-tuned NLG system, Automated Copywriting, “AC”, online merchants and marketers of long product assortments can access the most advanced natural language generation platform in digital marketing.

Dynamic NLG

Our proprietary natural language processing tools analyze relationships between audience and message. Each NLG machine is built independently for each unique brand voice.

Writesof understands your highly competitive and costly content creation workflows, which is why we built a solution that relies on human-author-produced text, which means it requires minimal human oversight.

LEARN MORE

Consumer Behavior

Your customers certainly know more about your products than your copy writers do. Online shoppers may cumulatively spend hours browsing for features and benefits before they make a purchase in a category. In doing so, they reveal, via data signals, what they want, what interests them, what causes them to buy, and what causes them not to buy. Their behavior signals, actions, and inactions are recorded as browsing sessions, history, and cookies. Other signals can be retrieved from unrelated user group data on your competitor sites.

LEARN MORE

Message Engagement Analytics

All such data is quite valuable, especially if implemented in a natural language generation machine designed to write product copy. Writesof NLP and NLG systems have a unique ability to “learn” every jot and tittle about your content, your competitor’s content. User behavior, traffic signals, and ROI drive decision algorithms, all while self-monitoring with benchmarked performance metrics. This ensures that every new word that you publish or decide to keep as is undergoes continuous performance monitoring, with old product descriptions and content serving as benchmarked alternates.

LEARN MORE

AI-Powered Natural Language Generation

Natural language generation (NLG) technolgy is a form of artificial intelligence that employs natural language processing tools that finitely translate structured attribute data into template-based narratives about a set of events and or measurable facts. Audience-driven natural language generation, such as what Writesof’s AC NLG system uses, is AI that uses machine learning to learns how to write on a performance basis by analyzing current and historic author-audience interactions and goal performance.

LEARN MORE

Product Description Generation

Writesof’s Automated Copywriting platform, which we call “AC”, is developed specifically for online product marketing. systems are great for descriptive writing, they but have struggled with dynamic personalization and persuasive messaging. , typically measurable attributes and events. structured data and text templates into narratives that describe measurable attributes and events. that Your marketing team wants machines that write copy. just don’t know it yet. Our machines are naturally tuned to echo brand voice and human writing styles, each focused on unique campaign goals and audience segments. Empower your writers to shift focus to creative branding, message tuning, and personalization segments, rather than spending ROI-critical time on keyword placement, audience analytics, and ad experimentation.

LEARN MORE

Awareness Marketing Automation

Advertising automation vendors that use artificial intelligence and machine learning are on the rise. These companies help automate ad creation, distribution, and analysis, using text and image assets that generate unique ads.  Campaigns are spread across multiple channels, such as Facebook, Google, and Bing, in the form of text and display network ads. Often, text and images are swapped out at a particular frequency and machine learning algorithms learn how combinations of ad attributes perform in various channels, to various users. Ads are distributed with intelligent triggers and timing to each individual user.

LEARN MORE

Market Driven Message Valuation

Your marketing team wants machines that write copy, they just don’t know it yet. Our machines are naturally tuned to echo brand voice and human writing styles, each focused on unique campaign goals and audience segments. Empower your writers to shift focus to creative branding, message tuning, and personalization segments, rather than spending ROI-critical time on keyword placement, audience analytics, and ad experimentation.

LEARN MORE

Write Your Future With Us.

Thousands of online merchants will grow their business this year, but those with the right technology partners will be best positioned for growth in an increasingly competitive e-commerce market. Your business should be committed to providing real-time personalized product communications that adapt with customer browsing behavior. This is not some futuristic concept—it’s here, and the technologies that make it possible is developing fast.

One such technology in the mix is natural language generation. NLG, however, is currently only used by a handful of large firms for multi-user message personalization. Online sellers managing thousands of SKUs or more have relied on basic NLG templates which tend to produce content that lacks persuasive and personable characteristics. But in today’s competitive online commerce world, effective use of personalization makes the customer feel that the retailer knows what they want, particularly in the context of make-or-break text content in product pages, ads, and notifications.

LEARN MORE
  • Online Retailers
  • Product Brands
  • E-Commerce
  • Manufacturers
  • Wholesalers
  • B2B Online
  • Omni-Retail
  • Product Catalogs

Data Integration, Solved.

AC is best suited to integrate with long product assortments of 10,000 SKUs or more. Writesof’s natural language processing tools will retrieve, parse and classify structured data sets, extracting product information and keywords, by SKU. The NLP tools are equipped to process html, product information, competitor data, as well as traffic and user behavior analytics. If your firm uses a product information management (PIM) system, we transmit data by feed or scheduled transfer. Typical feed file formats include HTML, XML, CSV, JSON, and TXT. API or adapter integration is recommended, but is not necessary until you are ready.

PIM Analytics Channels
Oracle Google E-tail:
IBM Adobe Ads
RiverSand NetSuite E-mail
Salsify MuleSoft Push
inRiver Tableau Affiliate
*Integrated PIM and Analytics Platforms and E-commerce Channels.

Writesof Predictive NLP Analytics

Preempt your customers’ frame of thinking with Writesof’s predictive analytics tools.  This proprietary technology uses natural language processing tools and artificial intelligence to learn about the linguistic elements of your copy and how your customers interact with these elements.  With our NLP systems, your organization has the ability to process and “score” every published word.  Our NLG personas use this information to make decisions with a increasing levels of confidence, improving the overall performance of your channels and campaigns.

LEARN MORE
Personalized Natural Language Generation

More content equates to improved keyword placement, better search visibility, opportunities for improved engagement, as well as opportunities for testing and analysis.  Consumer brands, manufacturers, and online merchants that invest in personalized marketing automation will be among the best positioned for growth and profitability.  Companies that lag behind in this technology may still survive, but especially for companies with longer SKU lists, they will be at a significant disadvantage compared to competitors who are focused on automated their personalized selling strategy.

LEARN MORE

Automated Copywriting

It’s fun and easy to create your professional looking website using U-DESIGN. Give your website a unique style that helps you get Your message across.
Read more →

Message Analytics

It’s fun and easy to create your professional looking website using U-DESIGN. Give your website a unique style that helps you get Your message across.
Read more →

NLP For Brands

U-Design has been localized and it is 100% translation ready, it includes all necessary language files. It is WPML compatible and has been translated to many languages.
Read more →

SEE FULL LIST OF PRODUCTS

Speed of Implementation

Marketing automation technology is advancing at a rapid pace.  Never before has speed been so critical in implementing new technology, particularly for online merchants.  Online retail is unquestionably more competitive than any market outside of  the financial world.  Sell and buy decisions and signals are transmitted in a constant stream, with sell decisions increasing in volume and dynamic at an increasing rate.  Online retailers, for example, can send tens of thousands of unique buy signals in a given day and receive millions of customer behavior data points.

LEARN MORE

NEED A SOLID THEME YOU CAN COUNT ON? LOOK NO FURTHER!

U-Design is a very powerful theme which suits both users with no programming background as well as advanced developers.
It will help you build your site in no time to your liking with minimal effort.

Why choose us?

  • Colorpick each element to create a unique look and feel
  • Background uploader for 5 areas, box or fluid layout
  • Responsive and Mobile Ready with Retina Ready images
  • SEO Optimized and compilable with top SEO plugins
  • Solid and clean code and ongoing updates you can count on
  • Best support money can buy
  • Woo Commerce compatibility
  • 100% Translation & Multilingual Ready
LEARN MORE
BUY U-DESIGN THEME!
 

As the founder of one of the largest online retail sites in the flooring category, I recall our greatest challenge at the time was content personalization and keyword selection. Since being introduced to Writesof, nearly five years ago, we are not capable of producing millions of unique product descriptions for more than 100,000 SKUs. Just one meeting has enabled us to shift away from a competitive strategy that required us to hire analytics-trained copywriters to now having a strategy that produces valuable content around the clock, or whenever we want it.

Author Image

— Stephen Bair, Co-founder – floorstx.com

TESTIMONIALS

Perfect coding, flexible and great support!

“Perfect coding, flexible and great support! These guys respond in a second. I already have 2 website’s running uDesign and still recommend this theme to a lot of clients. This one won’t end with 2 website’s for me!”
– by colligro – Jan. 2018
ThemeForest →

Awesome theme!!!

“AWESOME THEME!!! I own many copies of this theme and it is my go-to theme, it is rock solid and SUPER FAST!!!!! !”
– by plumbpro – Jan. 2018
ThemeForest →

The best theme!

“The best theme! I have 3 licenses myself and I always urge my clients to start their site with Udesign. “
– stevepol84 Nov. 2017
ThemeForest →

Gain the inside advantage with NLG and become a Writesof Beta Partner.

BECOME A BETA PARTNER

I am text block. Click edit button to change this text. Lorem ipsum dolor sit amet, consectetur adipiscing elit. Ut elit tellus, luctus nec ullamcorper mattis, pulvinar dapibus leo.

NATURAL LANGUAGE GENERATION

With Writesof’s launch of Automated Copywriting— “AC”, online retailers and product marketers can now harness the power of natural language generation (NLG) with e-com tailored natural language processing (NLP) platforms that understand your highly competitive and costly content creation workflows.

A PARTNERSHIP THAT WRITES YOUR TICKET

Your top and bottom line heavily depend on your writing department’s creative ability, adaptability, and productivity. We want to partner with you to look deeply into your content, to analyze the crap out of it, and to present you with natural language generation examples that will prove substantial value-ROI, over benchmarked performance data.
BECOME A PARTNER
BUY U-DESIGN THEME!
LEARN MORE