The need for personalization in e-commerce came into sharp focus during the COVID-19 pandemic as in-person shopping gave way to online ordering and curbside pickup. This isn’t a new development — e-commerce consultants and marketing gurus have been touting the virtues of personalization for several years already — but the virus made it a more urgent priority for many vendors.
The Problem of Data and Personalization
Personalization is relatively straightforward for a chain like Starbucks. As long as the company’s app knows the customer’s name, the closest coffee shop to them and their favorite beverage, their needs have been met.
The situation is more complex for vendors with large, diverse product lines. Customer expectations are set by tech giants like Netflix and Spotify that can draw on a large volume of interactions to drive product discovery. It begins with simple “If you like a, you might also like b” algorithms, and evolves to fully personalized suggestions drawn from the individual user’s previous choices and by analyzing what they engage with digitally.
It works well for those companies despite the volume of content they offer because users visit them frequently. Netflix and Spotify users may spend hours each day with the service. It’s a much different scenario for most e-commerce vendors that typically must try to match that level of discoverability and personalization from a much smaller base of data that’s limited to age, gender and past purchases.
The Limits of Personalization
Clumsy, intrusive or poorly-conceived attempts at personalization can backfire, but customers want it and expect personalized experiences. In fact, Accenture’s 2019 Consumer Pulse Survey found that 73% percent of respondents were willing to share more information in exchange for that experience — as long as brands are transparent and ethical in how the information is used.
In spite of this willingness — and in spite of the growing volumes of data vendors can generate about their customers — many consumers also reported feeling that merchants’ personalization efforts missed the mark. They also reported that the personalization didn’t engage them.
In a recent Salesforce study, 84% of respondents said they want to be treated like a person, not a number. So what if you changed your point of view slightly from the need to keep up with personalization trends to asking yourself, “What can we do to better connect customers to what they want?”
Customers want a shopping experience that shows you know and understand who they are as a person in the same way a skilled staffer in a bricks-and-mortar store would. That requires an understanding of not simply purchasing patterns and basic demographics — which is relatively easy — but of their underlying psychology and why they purchase what they purchase, which is an entirely different challenge.
Psychology and Product Discovery
The first step toward that goal is the use of psychographic segmentation which has been embraced over the past few years by many e-commerce vendors. Instead of breaking consumers out by demographic markers, it carves out distinct personas across demographics. A college lecture hall filled with 20- year-olds might include science geeks, party animals and earnest savers of the world, for example. All of whom share a demographic profile but have markedly different motivations informing their purchase decisions.
Identifying those personas enables companies to craft marketing messages that are likelier to resonate and to surface product suggestions that are better tuned to the potential customer’s preferences. In 2018, market research firm Gartner Inc. predicted that artificial intelligence — and more specifically data-driven machine learning — would provide the tools necessary to take that last step and make truly individualized, psychologically-sound marketing practical. The company also speculated that this prospective breakthrough was still several years away from being realized. They were wrong.
Lily AI and Next-Level Personalization
Lily AI can already elevate your existing site from its current level to “next-level” in just a few weeks through our proprietary machine-learning algorithms. It’s a two-stage process: first our technology analyzes your products and identifies the wealth of crucial data you’re currently leaving untapped, then we leverage that data to create psychographically-sound profiles of every shopper on your site.
Most sites’ product taxonomy is limited and currently using only a few searchable tags to identify and quantify products. It’s a pragmatic constraint because applying tags takes time, costs money and needs to happen with every seasonal change in your inventory. Our software identifies all of the details that matter — up to dozens per garment — creating a dataset that’s richer by several orders of magnitude.
Armed with that level of data, our algorithms can drill down and find previously un-quantifiable relationships between products. Those relationships, in turn, help us identify the underlying psychological drivers of a given customer’s purchasing and browsing activity on your site. Surfacing the exact product a customer is looking for, exactly when they want it (whether they know they want it), is the ultimate goal of personalization.
The Future of Personalization — Now
Whether you’re an omnichannel retailer or a pure-play e-commerce vendor, the goal of personalization is the same: to replicate the feeling of shopping in a store where you’re a known and valued regular. The kind of experience where staff call you by name and are excited to show you things they just know you’ll like.
In 2019, consulting firm McKinsey correctly identified AI and machine learning as the tools needed to do that, or in McKinsey’s phrase “make empathy scale.” They predicted that personalization would be the prime driver of marketing success within five years. The COVID-19 crisis shortened that timeline, driving online shopping to unprecedented levels and (as a byproduct) making it critical for vendors to transition to next-level personalization as rapidly as possible.
That transition doesn’t need to take years. Contact Lily AI today to request a demo, and find out how we can transform your digital experience.
Accenture Interactive: See People, Not Patterns - Accenture Interactive’s 2019 Consumer Pulse Survey
Salesforce: Customer Expectations Hit All-Time Highs
Gartner Inc: How AI Will Drive Transformative Change in Marketing
McKinsey & Company: The Future of Personalization - and How to Get Ready For It