A customer-first approach is a critical component of every successful retail operation, but supporting such an approach requires an awareness of which customer data sources matter — and which don’t.
Whether you’re an e-commerce-only operation or a brick-and-mortar stalwart, successfully navigating today’s hyper-competitive retail landscape requires a customer-first mentality—or what we like to call “customer-obsessed retail.”
“The most important single thing is to focus obsessively on the customer,” Amazon CEO Jeff Bezos attests. “Our goal is to be the earth’s most customer-centric company.”
Insofar as it provides each of its customers with a personalized experience reminiscent more of Netflix than of a traditional big box store, Amazon has already achieved this goal.
As illustrated by companies like Zara, personalizing customer experiences pays equal—if not greater—dividends in brick-and-mortar retail. The Spanish fast-fashion giant has made a concerted effort to place customer input at the very center of its production strategy.
“We speak to our product teams in Spain daily to discuss what is or isn’t selling well, as well as customer comments on what they do and don’t like,” says one store manager from Zara Singapore. “We’re always keeping a close eye on how our customers are responding to the current collections and the trends they may be asking for.”
In keeping with this philosophy, Zara only commits to between 50 and 60% of its inventory prior to the start of each season, reserving 85% of its production capacity for in-season adjustments. This enables the brand to transform real-time customer feedback into new product lines in the span of just three weeks—the epitome of customer-obsessed retail.
Giving customers what they want is the crux of both Amazon’s and Zara’s business models, and while it’s a simple principle, its execution requires knowing what to do with all of that customer data you have siloed across departments, locations, and channels.
Don’t Undervalue the Data at Your Fingertips
Many retailers already have access to the data they need to transition to a customer-obsessed approach. Traditional retailers in particular may harbor the belief that data that’s collected as a matter of course, such as purchasing data and email engagement data, isn’t enough to paint a comprehensive picture of a specific customer segment. This simply isn’t true.
Purchase history—i.e., when money changes hands—is the single strongest indicator of what a customer actually wants or likes.
Data points like online browsing behavior and demographic information add detail to a customer portrait, but there’s an immense amount of meaningful information embedded within purchasing data alone—not only what was purchased, but which marketing channel drove the purchase, which price point and/or discount level the purchase occupied, and more.
When aggregated and analyzed properly, this information provides retailers with critical insights into how they should develop specific customer relationships.
What’s more, this first-party data is usually more complete and more reliable than the third-party data that retailers often purchase to flesh out their databases.
Third-party data providers are something of a black box, meaning retailers never know how the data they’re purchasing was collected. Which sources were queried? What methodology was used? When was the data collected? The timeliness issue is especially important, as data that is a year or two old isn’t going to be particularly helpful in determining current customer preferences.
As such, most retailers are typically better off relying primarily on first-party data that is both free and—given the right data collection protocols—accurate and timely.
Once a retailer establishes a relationship with a customer, the absence of a purchase becomes just as informative as a shopping spree.
Prioritizing the Right Customer Data Sources
First-party data exists in a number of forms, but certain kinds of data—namely, transactional and CRM data—tend to have an outsized impact on retailers’ day-to-day operations.
As previously discussed, purchasing data (which comprises the bulk of transactional data) is the strongest indicator of what a customer wants. But once a retailer establishes a relationship with a customer, the absence of a purchase becomes just as informative as a shopping spree.
Digging into this data to determine the cadence with which a customer shops helps a retailer understand how to most effectively market to that customer. Are they a weekly shopper? A quarterly shopper? A shopper who makes a purchase anytime there’s a sale? Teasing out these nuances is the first step in delivering a personalized experience at every juncture along a customer’s purchasing journey.
Further, integrating transactional data with CRM data pertaining to customers’ demographics and historical preferences allows retailers to optimize their operations around more sophisticated (and more consequential) metrics like customer lifetime value. Retailers can capture this CRM data through any number of mechanisms, including loyalty programs, customer surveys, and brand credit cards.
While transactional and CRM data are table stakes, savvy retailers can also derive value from things like email engagement and website behavior data. The former helps refine a retailer’s understanding of which customers prefer to be engaged over which marketing channels, while the latter helps a retailer improve the digital experience it delivers and identify product lines that are a tweak or two away from being a big hit.
Using Data to Set a Brand Apart
The good news is that the lion’s share of this critical customer data is already housed somewhere within most retailers’ IT infrastructures. The challenge is figuring out how to leverage this data in a way that contributes to a customer-obsessed approach.
For retail marketers, bringing numerous data sources together—and acting on the insights they generate—requires forming robust partnerships with their brands’ IT teams. Different data sources often tag customers in different ways, so matching specific customers’ identities across sources demands a baseline competency in data management tools like SQL.
Amazon and Zara have figured out how to gather, aggregate, and act upon their data-driven customer insights at scale, and retailers who are able to follow suit will be well-positioned to cement an enduring competitive advantage as the retail industry continues to pivot to a customer-centric status quo.
To learn more about how the data housed within and siloed across you organization can be gathered to reveal customer-centric insights that can guide your retail marketing efforts, watch our webinar Jumping the 3 Big Hurdles to Predictive Modeling.