Your product communications footprint – ads, product pages, emails, and social – is the strength of your brand. Every well-written word should be viewed as a dependent variable in your aggregate brand value. Customer relationships begin and are sustained with communications. Especially for large online retailers, product pages represent the greatest number of communication variables for a brand. Together, product titles, bullets, descriptions, details, Q&A, and reviews, comprise the customer-facing text that influences engagement, conversion, and opportunities for post-conversion customer relationships.
High quality personalized product descriptions resonate better with unique customers, creating a sense of familial brand awareness. This has the potential to create loyal customers that return to buy more, open and read promotional emails, leave positive product reviews, and add user-generated content via social media. It all starts with the product page.
With sophisticated language processing tools that were designed for online retail, Writesof can show you how each language chunk correlates with user data. These data relationships are only somewhat useful to human writers, but it can reveal both generalized and specific examples of each data relationships, helping writers fine tune their message targeting. Writesof utilizes the data to interpet dynamic relational values between language chunks and users. Our system is constantly lookig for changes in the values, using scoring data (engagement and conversion driven) to decide on what language to edit and how to edit it. Predictive values are driven by volume and variety, by user interaction history. The system is designed to detect and predict patterns in how different chunks of language, when combined to form a message, will create user resonance, engagement, and conversion.
A single word can be classified by as many as hundreds of different user variables and language-variables. Each variable is dependently weighted by the word’s position in the content, use in context, inherent linguistic properties, and user engagement / actions. cumulatively deter hundreds of user-interaction values, each weighted and scored independently based on the type of language used – it’s linguistic characteristics, its position in the section and on the page, its contextual and objective relationship to other words on the page, and the history of user-interactions with the language.