Customer Experience Data That Generates Content

The customer experience (CX) is a complex, granular, and fast-moving process.  Companies work very hard to keep customers positively engaged with their respective brands, products, and content, yet, the reality-perception gap between internal CX bias and consumer-perceived CX is surprisingly large.  Most companies are simply out of touch with customers because machines are taking over, quite literally.  Today, a company’s ability to understand its customers equates to its ability to extract the right data and interpret this data in an optimal and exacting manner.

Investments in marketing automation technologies will continue to accelerate, with much of this investment going toward customer analytics and personalization automation solutions.  Writesof has adopted certain aspects of such analytics and personalization methodologies to be implemented in our proprietar audience-driven its natural language software solutions.  Audience-driven natural language generation is similar to other forms of NLG, but instead of primarily relying on human-training (decisions) that produce machine-generated content, it “learns” from consumer data and other market data signals.  This data is transmitted in near-real-time to each of our “NLG personas” (virtual author machines) and serves as a training mechanism to aid each machine in forming decisions based on predicted outcomes.

Writesof uses proprietary data sets to assist in this machine learning process, making our decision algorithms far more efficient and accurate than other natural language generation that would attempt to do the same.  With these proprietary data sets, that are growing in volume every day, the machine learning process is adequately shortened, and adaptable to a variety of product categories and brands.  If we did not have this data, our machine learning would require, at minimum, tens of thousands of costly “experiments” before it could achieve the same results.  This puts Writesof at an advantage in the natural language generation software industry, especially with consumer product brands and online merchants with long product assortments.   As a result of investment in big data, Writesof’s NLG decision algorithms can reliably predict progressively favorable outcomes of each narrative that it generates.

Natural language generation closes the gap between what writers can analyze and create and what marketing teams should be doing to engage new and returning customers. Our technology helps retailers improve their sales performance by boosting customer engagement and providing a sense of satisfaction from consumers before, during, and after checkout.

Keeping up with advancements in online retail means implementing the latest technological innovations that promise to reorganize nearly every aspect of a merchant’s business strategy.  From progressive web apps that enable consumers to have a fast-loading mobile site experience, to robots that work in tandem with humans, e-commerce technology and automation solutions are rapidly evolving.  Increasing numbers of online merchants are planning to boost e-com tech spending, seeking improvements in customer acquisition, conversion rate, customer experience and personalization.

Personalized Shopping Experience, On Message

Consumers seeking the best deal, whether it’s based on price or quality, want to be “convinced” with visual and text information that appeals to their practical and emotional needs. Writesof has built systems that “learns” and “understands” what convinces shoppers to buy, then generates content that complements their predicted behavior upon delivery. With the right data sets, our system can even help predict message generation by buyer mood – yes I just said that our system can predict mood, as a function of behavior insights, and craft messages that will complement the customer’s mood, accounting for message medium and delivery timing. And since the language generation, in most cases, can produce millions of versions of a single message, it can gradually test new language with user types. It’s a machine that can write copy, perform multivariate analysis, A/B test, and model language that, through machine learning becomes comprehensively more appealing to every customer that visits your web properties and online sales channels. Customers often want to feel a sense of reward for making a purchase. Product communications, throughout the sales cycle and particularly with landing page copy, is an essential function of attracting and keeping customers. For your company, landing page copy is where the bulk of your brand image is developed with customers. The copy is consumed, by user, along a timeline. If you are surviving, customers are probably returning which elongates their copy consumption data as a function of user behavior and profile analysis.
Stand out amid an increasingly competitive e-commerce market