Disruptive Retail Business Intelligence: The Shift to Self-Service and Modern Analytics

Technology

As of Thursday, April 19, 2018

For most retailers, gone are the days when data was stored in silos with multiple versions of Excel. Today, sources of data are centralized and can be pulled for reporting across channels and organizational areas. These include stores, online, mobile apps, email, loyalty, customer support, social media, reviews and third-party sources. Overwhelming, to be sure!

Data sources have increased, decisions are being made quicker than ever before, and information technology (IT) can no longer support the full business organization with reporting. Having common key performance indicators (KPIs) across the organization helps keep everyone sane, but there are still multiple ways to slice and dice the data. There is not a one-size-fits-all answer as it depends on the particular retail business and volume of its data.

One thing we know for sure—IT no longer owns business intelligence (BI). Sure, it is responsible for creating the centralized data from multiple sources in a data warehouse, so everyone has access, but the new data users are the business leaders within the organization, whether in finance, merchandising or at a bricks-and-mortar store. There is also a new chief in town—the chief data officer. With all the data now available, data governance, quality and security have become important and must be up to par to support the business where data sources are used instantly.

Retail BI disruption started with the addition of Visualization, providing interactive data analysis for every member of an organization down to a desktop or mobile device. Then came Machine Learning, adding the automated assistant to interpret the data against those KPIs for a “self-service” model. The newest games in town are search capability, Natural Language Programming (NLP) and Voice technology, turning data analysis into Alexa- and Siri-like analytics where all you need to know is how to ask the right question.

IDC, a leading industry marketing-analysis firm in Massachusetts, observes that 75 percent of workers whose daily tasks involve the use of enterprise applications will have access to intelligent personal assistants to augment skills and expertise. Gartner, a premier industry-analyst firm out of Connecticut, predicts that by 2020, 50 percent of analytical queries will be generated via search, NLP or Voice. Gartner actually created a new segment for analytics—Modern Analytics. All of these new tools are sitting on top of the traditional data warehouse for a modern approach accessible to all workers for instant, actionable data.

The power of data for retailers is plentiful for understanding consumer shopper behavior, acquisition of customers and predicting merchandising trends. Imagine the CEO driving to work and asking a mobile device what sales occurred, which were the best products and what customer segment made which purchase. This is not the future; it’s the present—interactive, actionable data that tells a story through digitally assisted analytics that can answer questions by churning through data in minutes, if not seconds.

Traditional and modern BI companies now fall into two segments, according to Gartner, and many retailers will use more than one, depending on the size and type of retailer.

Let’s look at a few retailers that have seen benefits leveraging their data in the new, modern world of analytics.

Sephora, a leading retail cosmetics brand based in San Francisco, is a data-driven company that delivers analytics to store professionals with SAP Roambi, an analytics and business intelligence mobile app. It saved employees 150,000 hours a year by alleviating the back office from analyzing data. Sephora has also leveraged customer and product data to drive personalization across marketing campaigns with two leading customer-focused analytics solutions from Adobe and Microsoft PowerBI.

IBM Watson was used to create the Under Armour UA Record app. The cognitive coaching app provides users with real-time, data-based coaching. It takes customer input data on sleep, fitness, activity and nutrition and combines it with external data to determine factors such as weather and environment on a personalized geo basis. The data provides Under Armour, a manufacturer of athletic apparel in Maryland, with customer insights and the ability to do micro-segmentation.

Peter Glenn, a specialty outdoor retail chain based in Vermont, uses AgilOne Analytics to view data from online and offline channels to drive segmentation of its customer base. Advanced segmentation and more personalization based on customer lifestyle data combined with shopping behaviors provided a 30 percent increase in AOV (average order value).

Why move into the modern world of analytics? In a fast-paced retail world where consumers are connected all the time and decisions must be made in the moment, the power of data needs to be in the hands of decision-makers within an organization.

Get on the data journey and use your data to tell a story that will benefit your company, employees and customers. l

Paula Levy is the Chief Strategy Officer for Demand Worldwide. She is a business technology strategist whose focus today is assisting retailers and brands in transforming their marketing and customer-engagement strategies with adoption of new technologies and business practices.