Is Your Retail Data Ready for Customers’ Real-Time Buying Decisions?

Today’s consumers are armed with a powerful decision-making tool when shopping for the best deals: real-time data. With a single click, they can search for daily deals and code words that offer valuable discounts. While they might find exactly what they're looking for on one merchant's site, finding something similar for a lot cheaper somewhere else has never been quicker or easier. This economy of choice means retailers without a solid data strategy are left in a lurch.

Fortunately, modern retailers also hold massive amounts of data about their customers’ habits. Unfortunately, turning this data into results is hardly as easy as a single click. Part of the struggle is a simple numbers game. Retail engineering teams face the unique task of unifying data from brick-and-mortar, online and mobile sources, in addition to nonpurchase portions of the buying journey like customer service and fulfillment.

But these challenges go deeper than just bringing data together. Poor communication between teams that collect data and teams that analyze data can lead to questions of data accuracy or relevance. What’s more, even if all data is organized and clean, it can be overwhelming to know where to start when it comes to profiling customer data.

From in-store point-of-sale (POS) systems to e-commerce platforms, and online advertising to marketing activation data, the sheer number of channels and technologies that are creating new data can easily hinder actionable decisions about the customer-level buying experience. Prioritizing and retargeting marketing tactics with this much data can be nearly impossible when customers fit infinite microsegments, campaigns and personas, and retailers risk overwhelming buyers with deals and conflicting calls to action.

Keys to Better Understanding Your Customers Through Data

As daunting as it may seem to make decisions based on huge datasets, it’s equally as rewarding once retailers are at the point where all data is in a central location. A unified, independent data layer — i.e., a behind-the-scenes structure that provides consistent, updated data on any given query — gathered from the entire retail information ecosystem allows retailers to truly hone their competitive edge. POS data, ad data and customer e-commerce journeys, coupled with store inventory information, supply chain analytics and call-center data, provide leadership with unprecedented customer profiles that fuel real-time retail adaptation.

Leaders in charge of a retail operation’s data infrastructure have hopefully accepted how critical it is to break down silos and create a scalable, sustainable data foundation. It’s also likely they've imagined the light at the end of the tunnel — a personalized customer snapshot that informs retailers in offering exactly what shoppers need, where and when they're ready to buy. But the question remains: What’s the right strategy for an individual company to reach that goal?

The following principles will help ensure a successful strategy — no matter a retailer’s point in this journey:

• A strong data layer relies on good data. “Garbage in, garbage out” is a common mantra in data management, but it’s particularly important in retail. Because of the diverse ways customer information is gathered — whether it be collecting an email address in-store or tracking a user’s path through an e-commerce site — opportunities for inconsistency abound. Mobile apps and emerging Internet of Things systems create even more collection environments. Even the most advanced analytics software will struggle to produce meaningful insights when the inputs are inaccurate or inconsistent. To combat this issue, leadership should target any opportunity to standardize existing data sources.
• Data readiness requires coordination and communication. We often think of standardizing and cleaning data as an analytics team’s concern. However, especially in retail, this process requires a group effort. With multiple teams and agencies creating campaigns across a number of channels, it’s critical for everyone to be on the same page. A single missing or incorrect character on a campaignID can render reports useless. If ads are tagged inconsistently, data is impossible to track and activate. Leaders must encourage the entire organization to take data standardization efforts seriously and ensure all teams communicate regularly and strategically. The bottom line: Everyone should expect good data because everyone is focused on capturing data correctly.
• Remember what you’re up against. Retail companies may be more data savvy than ever before, but consumers are also becoming more informed about their purchasing decisions. Browser extensions that search for discount codes, websites that compare prices, and a growing number of media outlets dedicated to deals are making customers a lot more likely to look elsewhere for their next purchase. If a company lacks the agility to pivot its efforts and match customers’ behavior in real time, it will almost certainly be left behind.

Building a better data layer will pay dividends for retailers that commit to getting it right. As customers gain more retail decision-making skills, it’s critical to see this investment through. Once a unified mechanism for data collection and transformation is in place, retailers can capitalize on a newfound customer view — and enjoy a future where no data goes to waste.

Sav Khetan is vice president of product at Tealium, the company that first pioneered customer data orchestration.