Excellent article from McKinsey – “Cracking the digital-shopper genome“. Companies have more data at their fingertips than ever, so why do online shoppers remain such a mystery? The solution begins with bringing all the information together to form a meaningful picture of the consumer.
While every e-commerce company wants a comprehensive view of its customers, few put in place a disciplined system for collecting and organizing those insights. In the same way that cracking the human genome requires decoding the DNA packages it comprises, companies should aspire to create a complete picture of the customer across a complete set of shopper characteristics:
Customer decision journey captures customer behavioral pathways and attitudes at each stage of a purchase journey. A customer may initially look for inspiration (ideas on what to buy) and then information (product descriptions, reviews, informational blogging content) before seeking the best way to buy a given service or product. Interactions can be tailored to this process: for example, one technology manufacturer seeks to identify shoppers on its website who are early in their journey and ensures they don’t see pricing promotions, which are instead offered to visitors who are closer to actually buying.
Digital-channel preference highlights how a shopper prefers to interact with a brand. These insights come from understanding how customers interact through various digital channels—such as apps, e-mail, social media, and video—and the ultimate value of that interaction. The most sophisticated approaches then map these channel preferences to phases of the customer decision journey to create a clear picture of the customer’s cross-channel experience.
Product affinity details what products and product attributes customers prefer across brands and categories. These insights are based on where customers spend their time while visiting a website and on their product-purchase history, analyzed for “key preference indicators” that help to create useful product taxonomies, such as whether the customer shows a preference for a certain designer or style. In structuring a taxonomy based on this behavior, retailer The Children’s Place, for example, uses Demandware’s CQuotient to analyze language in customer reviews and selects the most relevant words to inform a product’s metadata.
Response to offers details how customers respond to various offers and what incrementally results from those interactions. These responses track how coupon offers, discounts, and loyalty rewards, for instance, affect customer-shopping behavior at a level of detail that allows an e-commerce company to understand which offers yield the most cost-effective payoff by customer segment and, eventually, by individual customer.
Life moments and context looks at episodes in a customer’s life (such as having a child, getting a new job, or moving house) and behavior during seasonal events (such as at Christmas or on vacation). This analysis provides a better understanding of the consumer based on how much time typically elapses between purchases and whether the customer is buying consumables or durables (such as furnishing a new home or office).
Demographics, preferences, and needs provide insights about shoppers based on information beyond interactions with a specific e-commerce company. In recent years, there’s been impressive growth in the quantity and quality of data aggregated about customers at the “abstracted ID” level (that is, information that is not personally identifiable). Sophisticated data aggregators such as Acxiom and Nielsen’s eXelate are able to append not only demographic data such as age, gender, or zip code–based income level but also preferences and intent deciphered from browsing behavior across networks of hundreds of websites. For this to work over time, e-commerce companies need to adhere to strict standards about keeping data abstracted and respecting consumer privacy.
