Drowning in Data? How automation and AI are eliminating retail data fatigue
Consider all the data a retailer has on its customers, including name, address, email, phone, buying preferences, past purchase history, birthday, gender, etc. It’s a lot of data. A typical large national or multi-national retailer will generally have hundreds of thousands of customer records, each with all those details. That’s millions of data points.
So, what’s buried in all that data?
More than you think. Aside from the usual POS data, retail databases include customer demographics, Web traffic behavior, buying segments, location, purchased or viewed product SKUs, in-store behavior versus online behavior and length of time as a customer, to just to name a few.
Once you add this all up, a retailer might have four to five million dimensions of consumer data leading to millions of customer segments. It’s clear to see that we cannot possibly analyze all that data on an ongoing basis and have any measurable impact on sales. It’s simply too much to analyze without sophisticated technology that dives in and delivers actionable insights.
Unfortunately, very few technologies available help retailers do this in a meaningful way. Sure, there are lots of reporting and business intelligence dashboards available to help track metrics. But they produce the same charts, each and every day. For example, you can see sales by store and how that compares to last month or last year.
But, this doesn’t provide meaningful context. How do managers know when to really pay attention? The key piece that’s currently missing is the change analysis. It’s that piece of insight that helps retailers understand when customers are performing as expected and when they’re not.
The idea that we can automatically analyze and alert on unexpected data behavior is called automated business analysis. It’s a strategy of using ongoing curated data analysis that elevates and proactively reports on the unexpected. This is critical for retailers that want to get ahead of changes in consumer behavior, competitive tactics and other business trends.
An automated business analysis approach offers personalized insights each morning to leaders and managers based on data from the last 24 hours, highlighting exactly where they should be looking and what they should be looking for in the data.
This approach delivers a couple of important benefits. First, it adds context and timeliness to insights by delivering, proactively, only the reports that indicate some level of unpredicted change. It points to something specific that needs further review. Second, the reports drive the day’s action items in a way that allows managers to address activity and adjust quickly, shortening the time-to-action.
One great example of this approach is Jack Rogers, a digital-first footwear and accessories retailer with select brick-and-mortar stores. While the nearly 60-year old brand knows its customers’ general demand for different product categories, managers also wanted to have better insight into changing preferences in order to optimize marketing spend. To do this, they needed to see how behavior changed in real-time and looked year-over-year for opportunities to accelerate sales. It turns out they only had to look at one campaign at just the right time to make a difference.
According to the director of e-commerce for Jack Rogers, the company has a small digital analytics team, but a lot of data. Jack Rogers was using Google and Facebook for paid promotions, so they applied Outlier’s automated business analysis platform to analyze and alert on the consolidated paid advertising data. Outlier was able to automatically analyze the Google Analytics and Facebook Ads data to indicate when something unexpected happened within the data.
Simply using this source data, Outlier identified that product interest had unexpectedly increased for a segment of footwear. While the increase would have been spotted eventually in historical data reports, having Outlier flag it in real-time enabled the Jack Rogers her team to modify marketing activities and spend to promote the category that was trending upward.
As a result of this action, Jack Rogers was able to identify an unexpected jump in interest in a particular product category, which was much earlier in the season than they anticipated. Because of Outlier, the team was able to adjust email marketing campaigns in real-time to take advantage of the interest, resulting in a 30% sales increase for the company year-over-year.
For Jack Rogers, this automated business analysis campaign gave the retailer immediate visibility into unexpected changes in customer buying patterns, helped them correct potentially business-damaging operational issues and allowed them to identify and optimize high performing marketing programs (on and offline) in order to drive sales.
As retailers continue to find their footing in an omnichannel world, it’s important to understand behavior changes as they happen. While there are lots of tools available for historical reporting and general dashboards, only automated business analysis platforms have the power to direct real-time operational adjustments, meaning brands can understand, evaluate and change behaviors just as quickly as consumers.
Sean Byrnes is CEO of Outlier, which delivers automated business analysis for some of the consumer businesses.