Win the Experience Game — How to Bridge the Gap Between What Customers Say and What They Do

Win the Experience Game — How to Bridge the Gap Between What Customers Say and What They Do
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My experience in my previous role at Xbox taught me that our Halo customers looked forward to purchasing any associated product as soon as it hit the market. I knew these avid collectors would pay premium prices for a collectible statue game piece, so we went with it. We designed it based on our product knowledge and what we thought our fans would want, and then put it out on the market a year later, thinking it couldn’t fail. Then it didn’t sell as we had thought it would. We thought we had done everything right — we knew our customers and had done some research to understand their preferences even before designing the product. So, what went wrong?

What we experienced was a gap between our concept of how customers would act — in this case, based on prior purchasing behavior and an internal focus group — and what they actually did. The troubleshooting results showed that prior behavior and focus groups can reveal false positives for many reasons — including peer pressure and group thinking — and because these research methods aren’t set up to provide real-time customer intelligence as the market and customer preferences change over time.

The problem was that, in the past, this model always worked to create brand wins. But what had worked previously was no longer effective. Our entire market-research model had to be reassessed to succeed in a new landscape — the fast-paced Experience Era in which customers expect real-time relevance from brands.

When what’s always worked doesn’t work anymore.

In 2017 and beyond, 89 percent of brands expect to compete mostly on customer experience. This means brands must be always on point with both offerings and marketing messages. Jordan Kretchmer, senior director and GM of LiveFyre, explains how in the Experience Era, “data artists won’t collect data just to take measurements. Instead, they will collect data to inform the creations of new experiences.” If your research concerning products or marketing campaigns misses the mark when used to craft customer experiences, it’s time to expand your customer intelligence mix. When used on a continual basis, a variety of research methods — both quantitative and qualitative — give brands a complete view of customers’ past, present, and most probable future behaviors that are necessary to deliver real-time, hyper-relevant, and competitive customer experiences.

Here are three steps that leading Experience Era marketers use to collect the real-time and holistic customer data needed to produce the experiences today’s customers expect.

First, adapt to stay competitive.

Like Xbox did, many brands still use the familiar, traditional marketing research models that worked in the past. But, the Experience Era is different than bygone marketing eras. As brands focus on customer experience offerings, they must do so in a fast-changing landscape, among ever-rising customer expectations, and as new industry innovations influence consumer perceptions, wants, and needs — sometimes daily. The fact that customer experience trumps product offerings means that customers want brands to know their in-the-moment needs and meet them. In this environment, traditional research — such as focus groups and surveys — expire quickly. Their qualitative value solidifies their place in Experience Era research, but real-time research must complement them. Thus, an experience era research model could look like a sandwich, or toast and a topping.

The sandwich. In this model, quantitative research is sandwiched between qualitative research, like surveys and focus groups. Using qualitative data as a research starting-point, brands learn who customers are. Then, they dig deeper into real-time analysis via data sets and predictive analytics to determine in-the-moment perceptions and needs.

For example, the qualitative research of a gaming brand may find that one game character would be welcomed by players. Then, the brand may dig deeper via quantitative data, testing hundreds of possible character powers to find which would be most popular in light of large-audience demands and popular competitor offers.

Lastly, throughout the product-design and marketing phases, brands using this model circle back to qualitative data to ensure more granular quantitative insights ring true among real-world customers. If customers say one thing on a survey, but behave differently in real-time, circling back to qualitative research with more refined questions helps brands identify where the gap exists.

Toast and a topping. This model has two ingredients — one round of qualitative research, and one of quantitative research. Brands that use this model already know their customers’ general preferences, so they don’t need the initial qualitative analysis to narrow down the most relevant questions to test quantitatively.

A gaming brand may know customer demand for a specific character, so it could skip to testing what character capabilities customers and the market demand. Then, to bridge any customer-intent gaps, it would again conduct qualitative analysis to confirm results with real-life target customers and dig deeper with more refined questions.

Second, get creative to build complete customer intelligence.

Every type of market research has limitations. Even social media posts don’t always reflect customer intent, but are influenced by fleeting peer behavior or trending topics. Experimenting with new ways of gathering customer intelligence is valuable, as brands use each viewpoint to confirm findings and stitch together an ever-more holistic and, therefore, accurate customer view.

Earlier, in my former role at Walmart, while seeking previously-unexplored customer viewpoints, we tested a new customer-engagement approach. On a small budget, we ran short YouTube commercials. The data showed that customers loved them, so we ran longer commercials. When those performed well, we developed a YouTube channel. By trying something new and using real-time analytics to gauge performance, we moved from advertisements customers were more apt to ignore to valuable content — like recipe ideas and wine-chilling best practices — creating a delightful customer experience, while transmitting brand messaging.

Even more significant, Walmart can use that channel as a new testing avenue to create a more complete customer view. Tools like Adobe Audience Manager, Adobe Analytics, and Adobe Target, when paired with existing data sets and myriad ways of communicating brand messages, help brands stitch together a more complete understanding of customer preferences, and continually test them to stay relevant as perceptions change. In Walmart’s case, if wine chilling tips resonate particularly well with viewers, the brand may consider expanding its offerings of wine-chilling products, resources, and expert tips via brand messaging.

Third, stockpile real-time data to stay relevant.

Whether you use the sandwich or toast research model, real-time data bridges the gap between what customers say and what they do. Both qualitative and quantitative data provide different customer viewpoints, and together help create a complete and accurate customer view. However, specifically with qualitative data, you’re often working against a shelf-life. The trick is to collect data that is as close to real-time as possible for best results.  Continually circling back — quarterly or, better, monthly — keeps it relevant.

When researching changing customer perceptions around a video game, for example, we provided a 30-second snippet of the current game, or a competitor game, to the same respondents quarter after quarter. Then, we asked for their updated perceptions, using the same quarterly survey each time. The answers revealed changing perceptions, and how we could adapt to stay relevant. This works equally well for commercials or other marketing content.

Brands can dig deeper via real-time data collection and analysis tools to gain even more updated quantitative insights as customers engage with marketing content or use products. The goal is to go along with customers in their brand journey and deliver based on what you know and have heard along the way. For instance, at Xbox, we used analytics to determine which products would be most welcomed by players at different video-game stages and then offered them in game at relevant moments.

Mix it up — think beyond focus groups.

As I researched why the once-beloved Halo statue didn’t fly off the shelves, I discovered that today’s brands must turn data into accurate insights quickly and at scale to deliver delightful customer experiences to large audiences in real-time. Qualitative research serves as a valuable starting point, and plays a confirmation role in the experience era research model, but brands have an opportunity to surface more real-time insights by streamlining their quantitative research methods.

So, in the experience era, here’s my challenge to you — think beyond surveys and focus groups by adding data analysis to your product and marketing research mix. Doing so creates wins for brands and their customers as organizations deliver competitive experiences that will keep customers coming back for more.

Read more about how company leaders can prepare their brands to compete in the Experience Era, and read more in our #KnowYourCustomers series.

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