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Retailers Need to Adapt Their Analytic Strategies to the New Norm

Since the Covid-19 pandemic began, there’s a term I’ve heard thrown around quite a bit: “The Before Times.” It’s a funny, almost lighthearted way of speaking of the era before so much of our way of living changed in an instant, and every time I walk into a grocery store or convenience shop these days, with my mask on my face and my hand sanitizer in my pocket, I find myself thinking about “The Before Times” quite a bit.

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 For retailers and the consumer packaged goods industry, it can feel like everything they knew before Covid-19 has shifted. And in many ways, that’s true. Supply chains and consumer demand feels unpredictable. Baselines have been transformed into high peaks and low valleys, and all predictive models that retailers once relied upon have been shown to be weak at best and useless at worst. Fashion and retail have seen declines of up to 30 percent, per Gartner; grocers on the other hand, have seen their foot traffic transform into a stampede. And they should feel that way: We’re living through the greatest economic disruption in more than half a century.

 But just like the face beneath my mask remains the same as it was before this pandemic, the basic truths about data analytics and their powers for intuitive insights and management still hold true. The old data strategies don’t work, but the power and potential of data itself is unchanged.

 Just a quick look at the numbers shows how far things have shifted: one study from Accenture tells us 56 percent of consumers are shopping locally now, and 84 percent plan to continue this behavior long-term.

 Here’s how retail and CPG can use this pandemic and its lessons to rethink how they operate and emerge better, stronger, and more insightful for the future.

 We’re not rebuilding the wheel

 When it comes to data analytics, the pandemic didn’t create new problems — it exposed issues that existed beforehand. Long before the first case of COVID-19, retailers were bogged down by data silos, a low adoption of AI, a download-to-desktop phenomenon that ate up productivity, exposed security risks and denied companies the multiple benefits available to them by transitioning to the cloud.

 But catalysts come in all shapes, sizes and events, and there’s no doubt that COVID-19 is a catalyst. In many ways, it will accelerate much-needed growth, change and adaptation, and for many retailers, those changes are long overdue.

 Adapt your analytics plan to key business drivers

 In the face of a pandemic that has now dragged on for more than half a year, it can feel difficult to organize priorities. But data-driven retailers need to stay focused, and understand that data offers a competitive edge in customer engagement – one that is impossible to replicate elsewhere. The three focus areas that are most important are: 

  1.  Working Capital. Improve cash flow by focusing analytic efforts on DPO. Secure your supply chain to make sure shelves stay stocked in the face of fresh lockdown, and create more dynamic inventory controls.  
  2. Gain better consumer insights. Demand changes rapidly in a time of crisis, from proactive (this is our deep hunter-gatherer behavior that saw us stockpiling food and paper goods) to prepping our pantries to living in a new normal. Anticipate where your consumers are living, and meet them there.
  3. Health & Safety. Protecting your staff and your customers is vital to survival. Predicting employee risks by store and role will allow you to maintain the quality of service your customers expect. Increase your sanitation metrics and data collection efforts to build confidence with consumers that it’s safe to shop in your store.  

Refresh your data strategy. This real-time new normal means you need to acknowledge that much of what you knew before March 2020 has now pivoted or changed entirely. Businesses need to understand that any data they gathered before Covid-19 is unlikely to accurately predict customer behavior in the future, and yet, McKinsey states that only 20 percent of companies have a data strategy in place that supports their AI efforts.

This is woefully shortsighted. As noted academic and AI expert Tom Davenport recently put it, “It’s a bad idea to wait until a full-fledged recession to begin considering how to lower costs and improve productivity with AI.” New data has emerged that didn’t exist just a few months before. Usefulness of other data has diminished. Be practical in adapting your data strategy and harness the power of AI to extend, enrich and add value to your existing data sources. If you’ve been hesitant to adopt machine learning, the time has arrived to give up those hesitations and jump in.

 Shift to the cloud, if you haven’t yet done so, is one of the easiest and most efficient ways to improve agility, and doing so will also unlock powerful tools for mobility and global teamwork. Gartner forecasts worldwide public cloud end-use spending to grow 18 percent in the next 12 months, so it’s time to jump on the bandwagon.

 Finally, upskill data literacy skills by taking advantage of these unprecedented times by offering classes and extra training for your staff who are working from home. There’s never been a better time for self-improvement, which benefits both the individual and the group. Harness it.

I’d love to hear how you have adapted your data and analytic strategy and what’s next for you and your analytics team. You can email me at rich.clayton@oracle.com or DM me on Linkedin. 

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