The term ‘retail experience’ is often centered in the physical realm — namely, as the combination of in-store phenomena and shopping ‘Easter eggs’ that make the experience exciting. What this approach leaves out is the more internal, organic experience laid out by the data driving every interaction a customer has with a brand or retailer. As our world began to require digital interactions — with each other as well as with companies — at the start of the COVID-19 pandemic, that latter version of “experience” has become much more important, as retailers try to bring their digital offerings into line with the physical. We’ve seen a number of them leave out a critical piece of the puzzle, however. This article will examine that missing piece. First, we’ll look at the fragmented approach historically taken on the road to ‘customer experience,’ what this model leaves out, and a few examples. Then we’ll will dive into the shifting mindset in corporate leadership, addressing new modes of data analysis toward creating a new standard for the customer journey — and, further, why we actually need to expand our understanding of ‘experience’ beyond just that of the customer.
The weak links are low-hanging fruit.
Why should any company with retail operations expend energy and funds in upgrading their customer experience? The measure of the potential impact speaks for itself. According to one report, by further personalizing the customer experience, especially in terms of digital interaction, several industry players have increased the lifetime value of a customer by 20 to 30 percent across high-priority segments. It’s easier than one might think to get this wrong — even a small misstep can cause outsized losses. The weak-link theory explains that one bad experience could make an otherwise loyal customer walk away forever.
Looking at a common approach to customer service reveals a number of potential weak links. Take, for example, a recent experience a friend described to me while trying to get his car serviced. Appointments for this particular brand have to be made using their app, but the app was down. My friend found a number to call, but no one answered at the other end. Now, even though the car only needs servicing a few times a year, technical glitches in accessing something as basic as making a service appointment reveal a massive missed opportunity. This is a fractured, fragmented approach to customer service: one destination for one kind of need (servicing), and another destination thrown in (now the app needs servicing, too). The end result, and final insult, is that there is no one at the other end communicating with the customer.
Instead, there could be an integration of past customer data with current interactions: The app knows when your car needs servicing — based on when you bought it, when you last brought it in, and how much you drive — and it reminds you to make an appointment. It may offer you a coupon to the on-site café while you wait; it may suggest a number of weather-appropriate add-ons to servicing your car based on the time of year and the climate.
How one small tweak revolutionized Amazon’s shopping experience.
Without such insight deployed in current day-to-day interactions, a retail operation will disappoint its customers with a weak link in experience, but this “miss” also represents a major point of friction. Amazon is the standalone example of just how effective tweaking the system to remove friction can be. The single biggest move the company made toward that end was the one-click buy button. When that patent expired in 2017, it was, by one account, valued at $2.4 billion. It had single-handedly increased company sales by five percent, owing to its ability to decrease cart abandonment, which generally rests at a rate of about 70 percent. However, through data analysis, Amazon figured out that the place in the journey where customers most often abandoned their carts was when they had to fill out all of their information. The one-click button was Amazon’s tweak, allowing people to bypass repetitive data entry. It found that customers would rather have that ease of functioning than buy from independent retailers or other chains — even despite unfavorable price comparisons. In other words, they’d rather spend more at Amazon, just to reduce the friction.
Personalizing the retail experience means getting to know your customers in new ways.
A successful approach to experience takes into account every interaction a customer has with the company — from store, to website and app, to transactions, phone calls, and emails. These interactions are both analog and digital. And all of the data gleaned from them need to go toward making the experience not simply satisfying, but delightful.
We know that the more personalized an interaction, the more emotional the level of engagement. Companies have a range of tools they can use to capture and keep the customer’s attention, including human-centric design, visual design, and other psychological principles. However, what provokes delight is not the same for every customer with every brand. One take on personalization for Amazon is a delivery tracker that tells you exactly how many stops away your package is. Does this cost the e-tailer many millions of dollars for a service that changes virtually nothing? Absolutely. But does it make you feel special? You bet it does.
After a tough pandemic year, demand for luxury goods isn’t expected to bounce back to pre-pandemic levels until the end of 2022 — however, a November 2020 report shows online luxury shopping to have doubled, from 12 percent of total purchases in 2019 to 23 percent in 2020, and predicts that e-commerce will be the dominant channel for luxury by 2025. Industry leader Hermès was the first luxury brand to launch an e-commerce channel, way back in 2001, and has continued to expand and improve its online experience, to the point where, by July 2020, the company reported, even in the face of a massive revenue drop due to COVID-19, that 75 percent of its e-commerce customers were new. Even if people couldn’t afford it, Hermès listened, and made them feel special.
As the luxury sector — indeed, all of retail — eventually returns to normal, share of online shopping will only continue to grow. It will be critical for companies to design online experiences that are the digital twins of the personalized attention, recommendations, and structural control that a customer service rep/concierge may offer to an in-store customer. Emerging techniques to achieve this, such as machine learning (AI)-driven “concierge bots” and curated online journey managers, all rely on past purchase history, consumer demo- and psychographics, purchase power, and more. All of these algorithms are hungry for data.
Herein lie some of the keys to a successful ‘retail experience.’ Conversion alone is not enough. Retention and resurrection — adopting the product, then coming back for more — are also critical to a fully successful experience. In order to get there, much needs to evolve from our traditional approach, including our models of data management and analysis, corporate culture and leadership.
Keeping track of your data.
Here’s a scenario you may find familiar: You try to return a pair of pants you bought online via a store’s mobile app, which gives you a number to call; the representative on the other end of the line can’t find your order number and you don’t know it, either; it’s a time-consuming and frustrating experience. On the flip side, we’ve already looked at a number of examples in which brands use the data they’ve gathered about their customers to make the experience — everything from shopping to purchasing to owning — more seamless, frictionless, and fun. Here we’ll focus on how to get there: how to approach your data, how it can be put to use, and the necessary accompanying shift in mindset, leadership and culture.
We all know that there is a wealth of possibility in data. But keeping the totality of customer data in one place — in one massive, unwieldy platform — is a pipe dream. The current popular system of housing certain bits of data in different departments, depending on perceived relevance, leads to missing pieces for some and repetition for others… a similar failure. Instead, data needs to live in multiple locations that are able to run on their own but can communicate with one another in real time. The front-end website can be fed enough information on the back end to monitor the customer’s journey without having to hold every piece of that customer’s data itself.
A modern approach to housing, managing, and feeding data is important because you can only use what you can access. A couple of examples come to mind. One company we work with built a tool that can predict when a customer is about to abandon a cart, based on past purchase data as well as on the behaviors and habits of individuals who are demographically similar. We also discussed the example of Amazon’s one-click button — the e-commerce giant’s answer to cart abandonment as a major source of retail loss and a prime area for improvement. My client’s solution to that problem was to come up with a fully automated tool that can identify the critical moment of cart abandonment and create a personalized discount coupon for the customer, keeping the customer on track.
Another company I work with tracks sentiment through data, to find the precise point when a consumer’s sentiment shifts. The idea is to solve for weak-link theory, which, as we discussed earlier, explains that just one unpleasant interaction, even among overall positive experiences, is enough to lose a customer. The solution this company offers uses data from a range of customer interactions to determine if and when sentiment turns negative, and if so, to proactively reach out and re-engage the customer. The common thread connecting these tools is that they analyze the whole journey. How did the consumer get to making that purchase, in that particular position? What did they look at and abandon along the way? These data points represent key intelligence and areas for machine learning and pattern recognition to work behind the scenes.
Looking at data through the user experience.
The holistic integration of data, however, is only made possible by a corresponding unification of the ingredients that together comprise whatever it means when we use the term ‘experience.’ These include digital experience, analog or in-store experience, customer-company touchpoints, as well as where else the customer was looking before they even reached the company, the marketing they saw on Instagram, and the advertisement in the podcast they listened to; all of these ingredients are intertwined. There is a massive disconnect when corporate structure fails to realize that essentially unified nature. The result is that it remains segmented within itself. What were once silos of different information, controlled by different people — purchasing, merchandising, Chief of Digital Operations, Chief Information Officer (CIO) — need to come together. Just as the data must talk to each other, so, too, must the leadership.
How management can help harness data.
The CIO especially is now being charged with moving from function-centric applications to user-centric ones. That bears repeating – it’s the CIO’s job not to think in terms of the functionality of a given application, but to how the user engages with it. And by “user,” we could be referring either to the external user (i.e., the consumer), or the internal user (for example, the operations team). This thought is the backbone of an article in the Harvard Business Review, “Why Every Company Needs a CXO,” which describes the integral part that employee experience (EX) plays in customer experience (CX), and advocates for the creation of a new position, the Chief Experience Officer (CXO), to oversee a more holistic approach. Denise Lee Yohn, the article’s author, explains how so many companies focus only on the customer experience — ironically, to the detriment of customer experience: “If an organization separates leadership CX from EX, disconnects between CX and EX are likely to arise, even if those roles are part of the executive team. The fact is, employees can and will only deliver experiences to customers that they experience themselves.” Similar thinking and foundations underpin this approach to experience, both customer and employee, and their data-driven upgrades. Companies must rethink their internal operations, otherwise their insides won’t match their shiny new outsides, and that gap will become clear.
Bringing retail into the digital age requires more than just a website. Rather, precision data tools championed by the right leadership is critical in fueling brand success. Of course, digitization became a more pressing issue as more and more people started to shop from home during the pandemic, and we can anticipate that for some, those habits will remain, even as pandemic-era stay-at-home orders are lifted. One important lesson from the past year or so is that none of this will work without flexibility. Machine learning tools can analyze vast amounts of data, recognize patterns, and then implement tools to boost profits based on that knowledge — but any of those patterns can change at the drop of a hat. It’s imperative that our algorithms are constantly ready to experiment and learn alongside the corporate leadership that harnesses them. It’s only with their dual dynamism that retail can reach its potential for a seamlessly integrated, personal, and delightful experience.