So how do CMOs raise the bar?
To develop an effective personalization strategy, CMOs need to start with a solid foundation of traditional customer data. But that is not enough. The essential next step is building deeper levels of human data gained via preferences and attitudes.
Some brands already understand that.
“To achieve truly meaningful personalization and CX, we needed more than traditional purchase history and overlays of behavioral/inferred data. We needed to get customers to opt-in and tell us their individual preferences,” said Scott Emmons, head of the Neiman Marcus Innovation Lab. “But to earn that deeper level of information, we had to offer something meaningful. Our Memory Makeover smart mirrors are a high-value way for customers to share their individual preferences regarding cosmetic products. We make it clear that this information will be used to serve them in the stores and as part of ongoing email communications to reorder products or learn about new products that are uniquely relevant to them.”
Why Traditional Data Approaches No Longer Work
Findings from over 16,000 hours of Voice of Customer research conducted by our firm indicate that traditional customer experience, personalization, and personas are no longer effective. That’s because the B2B and B2C decision-making journey is neither linear nor simplistic, and customers are complex humans, not cohorts.
However, marketers must realize that they are not entitled to deep customer information. They have to earn it. A reciprocity of value is required, where customers opt in to provide deep preference data in exchange for smart, useful personalization.
But here’s the caveat: This data must be explicit data, meaning it is self-profiled preference information delivered via a site’s preference center or through dialogue boxes. Explicit data indicates deeper or longer-term preferences versus traditional implicit data, such as data-mined information or short-term consumer interests or needs.
“Too often, personalization relies on statistical inferences from a customer’s purchase and browsing history. This will likely be subject to error and spurious correlations, one reason why many customers are unimpressed with today’s attempts at personalization,” said Wayne Duan, director of merchandising and merchandising operations at Walgreens Digital Commerce. “The retailers who will win are those that successfully collect explicit customer input and harness those direct and intentional actions to improve the customer experience.”
Duan cited as an example Walgreens’ Beauty Enthusiast program, which asks customers their preferences, such as makeup style and skin needs. “We use this clearly expressed data to personalize the customer offering and experience within our beauty category,” he said.
Kevin Lindsay, director of product marketing at Adobe, built onto that with a point about context. “Historical customer data, such as purchases, is important but not predictive,” he said. “It must be enriched with contextual information to drive truly relevant personalization and CX. Contextual information provides the uniquely rich opportunity to understand the human dimension and situation of customers.”
Betabrand, a crowdsourced apparel company, is another brand that understands the fundamental shift in personalization. “Betabrand has a unique ability to measure and react to every click, vote, comment, purchase, etc., on our site,” said CMO Aaron Magness. “We use this rich data to provide a personalized shopping experience that goes way beyond the old-school segmentation mindset and truly serves you what’s relevant, not what’s relevant to people like you. Having data is one thing; understanding how to act on the important data is what matters.”
And while having the technology to analyze the data is important, equally important is not solely relying on it. “Companies tend to be lazy and arrogant by trying to mass-produce marketing or solve the problems by buying the latest martech tools,” said Silver Star Brands CMO Kathy Hecht. “One cannot achieve true personalization without deep human data from your customers.”