Convenience retailers need to rely on real-time, basket-level data for insight into what their customers want.

On January 20, 2020, the U.S. confirmed its first case of the novel coronavirus, COVID-19, in Washington state. The numbers are changing daily, but at the time we’re writing this blog, there are over 42,000 confirmed cases in the U.S. and more than 335,000 cases worldwide.

While we’re in the throes of the pandemic, economies around the world are screeching to a halt, the stock market has already wiped out years of historic gains, supply chains are being interrupted and governments are doing their best to mitigate the fallout.

Businesses are struggling to keep up, too. We’ve all seen the lines of people standing outside grocery stores for household essentials. As stock in some locations run low or becomes unavailable, many people are turning to convenience stores to fill the gap. But unlike natural disasters, seasonal fluctuations or event-based spikes that have predictable end dates, this crisis is unprecedented. The usual historical trend analysis, while helpful, isn’t sufficient to meet new customer demands.

Now more than ever, it’s important for convenience retailers and consumer packaged goods (CPG) brands to have accurate, real-time, basket-level data that delivers actionable insights into what their customers need right now.

So, what is the right data for retail success?

For many people reading this blog, the coronavirus has produced one of the most unpredictable supply and demand conundrums of the modern era. But in this case, uncertainty has an answer. Basket-level transaction data is one of the best sources of shopper and location data to meet localized demand, improve store-level sales, inventory, and category management. Here’s why:

  • Deeper data granularity: The average data platforms use modeled/theoretical data, collect basic sales information, are aggregated to a week ending number and are typically based on a limited sample of stores. As a result, they are not very reliable for much more than high level directional insights. In contrast, full store basket-level transaction data allows for complete store coverage and basket-level analyses like cross purchase correlations, basket size, seasonality, and dayparts to know when to run or target offers. Basket-level transaction data also analyzes non UPC items like foodservice. It’s critical for retailer profitability and is a category that could take a hit as more people eat at home or have food delivered.
  • Speed to insights: Systems that can process basket-level data on-demand can also handle real time data answers instead of being limited to measuring transaction data for a specified number of days
  • Type of insights: The key to navigating uncertain times and ensuring you’re maximizing margins by delivering the items your customers need and want lies in generating insights that are not simply descriptive or explanatory, but predictive and prescriptive. Basket-level data identifies the problem and enables recommendation and action plans to overcome it as well as anticipate it. With basket-level insights, retailers and brands can build profitable bundles and measure the ROI on promotions, displays and media based on full-store impact, not just sales of the item that was promoted.
  • Profitability: While meeting customer needs is important, convenience retailers must also remain profitable to keep their doors open. In addition to providing the items people require in times of crisis, basket-level data enables convenience retailers to analyze the profitability of bundles and promotions versus what is possible with only sales data. That level of detail goes further than some general data platforms that simply measure the estimated return on investment (ROI).
How Basket-Level Data is Helping C-Stores Navigate the Coronavirus Pandemic
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Another significant source to harness the right data is from loyalty programs. The outcome of shifting from a product- to customer-centric mindset is putting the customer at the heart of retail occupations and strategies, especially during extreme circumstances. That’s why understanding their purchase behavior, personal preferences, needs, and expectations by demographic type is so important.

Loyalty data is a subset of basket-data and can help identify what the most households purchase, their behavior over time, their loyalty to a brand and how they switch for price changes or life events like having a baby.  Combining loyalty to basket-level data is the pinnacle of establishing an optimal and efficient relationship with the customer, and therefore, growth.

Considering the current pace of change, the need for basket-level data is real. The overall effect of this is the changes occurring in the retailer-manufacturer dynamics, leading to a more challenging relationship: they must overinvest in power partnerships with each other to leverage advanced analytics and turn them into the Right Data.

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