Customer Experience in the New World of Retail: Context is Critical
by Ray Grady
Personalization is paramount in the retail world of 2015. If you read our piece on Banana Republic’s failed attempt at personalization, then you know the kind of powerful impact that can have on a previously devout, brand loyal shopper. Today’s consumers have developed savvy sensibilities and a distinct BS meter to weed out the retailers who do it right, and those who don’t. The difference between delivering a personalized, contextual experience with relevant recommendations vs. one that is manufactured and mis-timed is a matter of lost sales, lost customers, and a poor brand image. If retailers aren’t ready to dive in and get their hands dirty using hard data to inform their recommendation algorithms, then this could be the year that they start to really fall behind.
After recently noticing an updated Amazon.com homepage, a few of us at Acquia tested out what appeared to be an amped up recommendation engine. I received some relevant book recommendations, and was pleased to see a few new-to-me author and title suggestions. The experience encouraged me to engage more than I intended. It did precisely the job that good content should do.
My colleague, who had recently purchased a set of green curtains, was delivered a recommendation to buy the same curtains, only this time in blue. Had Amazon recommended a set of curtain rods or another household accessory, they might’ve made another sale, but instead they delivered redundant content that did more to dissuade a purchase than to encourage another one. What worked in the case of the books -- recommending different titles within the same product category -- did not work with the curtains. The algorithm needs to be smart enough not just to differentiate between product types, but product categories, colors, titles, authors, and any other variables that exist both in the catalogue of SKUs but also in the shopper’s mind.
The same issue apples in a multi-channel environment, where a shopper might buy curtains online, and matching pillows in store. So what recommendations does this person get next? The solution is complicated, because there isn’t just one right answer. Perhaps your algorithm would recommend a rug, or a side table, or a lamp for the side table -- context is absolutely imperative here, to be sure that the shopper is continually being delivered relevant recommendations that are smart. Unifying the online and offline customer data you have collected is imperative here, or your recommendations could completely miss the mark. One single, synchronized view of the customer is the only way forwards.
This, in a nutshell, is the difficulty and challenge of digital personalization and recommendations: they must be made in the correct context, and they must be delivered by sophisticated algorithms. Today’s shopper is savvy -- they know when they’re being genuinely catered to, and they also know when they’re dealing with a less-than-perceptive piece of bad artificial intelligence. Think of it as the modern version of Alan Turing’s “imitation game”: if the shopper sees poor recommendations, then the Wizard behind the curtain gets exposed. In the case of those blue curtains, Amazon took the purchase at face value instead of looking at it within the context that the customer did, and that is where they fell short. In the words of Acquia’s Senior Vice President of Technology, Chris Stone: “All too often, technology meant to power digital marketing only ends up exposing the marketer’s ignorance.”
In the last few years, there has been a major uptick in the number of companies with some level of personalization on their websites. What started first as targeted search results -- ‘if you like X then you might like Y’ -- geo-targeted offers that sussed out one’s rough location through IP address lookup, or simply a site remembering to welcome you back by name with a cookie -- has grown into something much more advanced, intuitive, and contextualized. Today’s best personalizers are all about delivering the right content at the right time in the right context - not just personalizing an experience, but predicting behaviors and crafting recommendations based off of those. The stakes of the new Imitation Game are more than proving that if machines can pass as people, it’s the difference between being a brand that gets its customers as people, or one that treats them like just another number.
In the case of those curtains, this shift in thinking and capabilities means understanding that a curtain purchase isn’t followed by another nearly identical purchase, but perhaps by the purchase of matching decorative pillows, or a cozy throw blanket. The customer bought the curtains in the context of addressing a need in his home -- that need was filled, so Amazon needs to be smart enough to anticipate the next need, and deliver on that instead.
Personalization takes careful planning, research, and implementation. One prominent CEO in the high tech industry once told us -- in reflecting on his days running the website for an extremely well known global tech company -- that the biggest miss in his career was the botched delivery of an ambitious personalization project. Achieving true personalization is getting harder and harder as people expect a higher touch digital experience. Personalization has been a part of the broader retail and e-commerce conversation for many, many years, but today’s version of personalization -- contextualization -- means catering towards a consumer that is far more particular, discerning, and smart. In order to properly personalize for today’s shoppers, a retailer must know their customers inside and out -- their pain points, their primary concerns, their wants and needs, their preferred device(s), and what kinds of content and offers will get them to click and convert. For many companies, this is a major initiative this year. Walmart is launching their own personalization algorithm in Q1 of 2015, with many more brands sure to follow.
Consumers aren’t stupid. We all understand how this game works now, and for retailers to get it right, they need to deliver smart and tactful recommendations based off of data that informs next purchasing decisions, not that replicates behavior patterns from the past. They also need to be able to maintain a cohesive experience across channels, so that a mobile experience is no different from a desktop or tablet experience. Too often recommended products miss the mark (think retargeted ads that follow you around the internet), and instead of having the intended positive effect on the consumer, they leave a sour taste in our mouths and a frustration with the brand that doesn’t lead to good things.
In order to adequately personalize, the engine or algorithm working behind the scenes to drive the front window of the website needs to be sophisticated, and the information being fed into it needs to be the right information. It all comes back to knowing the customer -- first their basic demographics, then their browsing and buying habits, their most commonly searched terms, their most frequented areas of the site. Without more in-depth data and a robust, integrated system for implementation, brands will continue to fall short as Amazon did in this instance, and brand loyalty cannot be built out of a place of irritation.