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Creating Retail Agility Requires a New Way to Think about Data

Disruptions to the supply chain, shifts in customer behavior, and even unexpected weather events are impacting retail sales across ecommerce and brick-and-mortar — changing where, when, how and why customers purchase. It’s those retailers that can most quickly and accurately react to these turbulent market forces and behaviors that are creating the most innovative and positive customer experiences. Creating Retail Agility Requires a New Way to Think about Data. The rest are at risk of losing customer loyalty, brand affinity and revenue.

A recent study found that 83% of retailers say they cannot leverage customer data to its full potential. This is problematic because customer data should guide a majority of business decisions including marketing, inventory management, merchandising and more. Relying on pre-pandemic data only leaves retailers guessing, and most analyze data infrequently or use basic dashboards that don’t provide detailed, actionable insights into changes in customer behavior. Their ability to quickly recognize and adapt to changing conditions and customer behaviors is nearly impossible.

So how do retailers regain the power of their data? Human analysis alone is no longer the answer. There is just too much data to analyze, and much of it changes too frequently. It’s time to adopt AI-driven automated data analysis tools that can handle the massive scale of today’s data. AI-powered analysis can sift through all of a retailer’s data daily, no matter how many data sources there are, to uncover unexpected changes and bring the most important ones to their immediate attention. This allows human analysts and business leaders to quickly and easily uncover the risks and opportunities hidden in millions of retail data points.

Decluttering the AI Data Landscape

There are a multitude of AI data analysis tools that claim to have the answer. When choosing the right tools for the job, it’s important to look for a few key features and functions that will add value for the analyst team as well as other organizational leaders that need access to the insights highlighted by AI.


1. Minimal implementation requirements. Adding another platform to your tech stack can require months of setup, continuous maintenance that can often limit its flexibility, and can take far longer than expected to deliver useful insights. Instead, look for a SaaS solution that layers on top of existing data and reporting platforms and that doesn’t necessitate a lengthy implementation or custom integrations built just to access existing data stores. A free trial is also always a bonus.

2. The right integrations with key data sets. AI works best when it plays well with data from your key business data sources. Identify a solution that complements existing analytics and BI tools and leverages data from leading platforms including Google Analytics, Facebook and other social channels, Adobe Analytics, Snowflake, SAP/HANA, MySQL and others. Ideally, the platform offers zero-effort integration, meaning that new sources can be connected in minutes, not days, and you can add new data connections as needed.

3. Daily reporting on just the actionable changes in your data. A common misconception about traditional BI dashboards is that they uncover changes in data and behaviors that quickly lead to action. But because they are built to answer questions or scenarios you program into the platform, BI tools neglect to surface changes that you didn’t know to ask about, are unexpected or unknown. Ideally, an AI platform continuously monitors all data to highlight changes that brands and analysts aren’t looking for. Instead of just building more dashboards, look for an AI platform that automates its discoveries and proactively alerts teams to changes each day on the platform via email, to ensure more immediate and focused actions.

4. Reports for every team member. Most data reporting tools on the market offer customizable dashboards, but what you really want to look for is a platform designed with both business leaders and data analysts in mind. The solution should provide data stories that are simple enough for a non-technical business user to immediately understand, but also enable analysts to drill down into the details for root-cause analysis and comparisons as needed.

Identifying the Right Data Use Cases

Through automated intelligence tools, retailers can leverage all of their customer data to uncover emerging customer experience issues or new growth opportunities. From store layout and merchandising to the digital experience and social media, retailers can utilize changes in customer behavior data to learn what translates into increased revenue and brand loyalty while uncovering new trends, areas of opportunity and hidden relationships as they’re happening.

So where to start? One approach is to identify emerging issues in the business that have gone unaddressed or changes in customer behavior that have no obvious root cause. Another is to look at use cases from other retailers for examples of how AI has brought unknown issues to light, leading to improved revenue or customer experiences.

In one example, marketers at a leading bath and beauty brand were alerted to an unexpected rise in sales of a product category when overall revenue was generally falling. With an automated business analysis platform in place, the bath and beauty marketing team was automatically notified when candle sales exceeded the expected sales volume.

The team wasn’t analyzing each of their thousands of SKUs against their expected sales performance because there was far too much data for any analyst team to regularly analyze manually. But the AI tool automatically found this insight, and in doing so, helped direct the marketing team toward a specific product trend so they could bring in additional revenue. This “green shoot” of opportunity is a great example of how the next great marketing strategy can be hiding in business data in plain sight, but impossible to find without help.

As a result, the bath and beauty brand was able to quickly launch marketing campaigns to promote candles and leverage this positive change in customer buying behavior. This unexpected insight also helped the team ensure that inventory levels could align with the new expected sales. By simply uncovering a trend, the brand was able to capture more sales by capitalizing on an otherwise unseen potential revenue stream.

In another example, a CPG company was managing warehouses with hundreds of employees receiving and shipping perishable food products. Using automated business analysis, they discovered a quarterly low metric for the amount of time it was taking to start and complete a task in a particular warehouse queue. The work time for this stage was significantly shorter than average, and the CPG company wanted to figure out how to replicate this improved process across other queues to increase workflow and operational efficiencies.

By quickly integrating existing data into its AI tool, the brand identified positive queue activity among specific workers. The company then identified what these workers were doing differently and used these learnings and practices throughout the warehouse. As a result, the brand was able to drive up overall output and sales, which would have otherwise been overlooked — missing an opportunity to improve overall retail operations.

Turning Actionable Insights Into a Reality

To stay competitive and keep pace with changes in customer behavior, more than half of all companies are looking to apply AI to their digital strategies, according to PwC. By identifying how AI can benefit the business, and utilizing tools that can be deployed and integrated quickly, retailers can take charge of their business data to ensure they keep their customers happy and have the upper hand over their competition.    

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