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Discounting Isn’t The Way Through COVID-19 — And Other Retail Truths

The global pandemic has impacted every person, brand and industry. As businesses, in particular retailers, adjust to this new normal, they should look to data to be a source for answers to how this crisis has impacted customers. With the right analysis of your existing data, it can be possible to maintain relationships, and — if you’re lucky — even build new ones.

Here are my recommendations on how to use customer data to increase online engagement and sales to weather the storm during COVID-19:

1. Analyze specific impact on customer behavior. Not all brands will feel the pinch in the same way. I recommend comparing buying data for Q1 2020 vs. Q4 2019 and Q1 2019 to understand where you feel it most acutely. Ask yourself questions such as: What percentage of your customers are still shopping, and how? Is their purchasing behavior different — that is, are they buying items at different price points? Is their average cart total more or less than previously? How are they getting to the site? The answers to these questions could all be very different from pre-COVID. It’s impossible to make effective changes without understanding these behavioral patterns first.

2. Create all new personas. By now you may have found that your buyers — and their purchasing behaviors — have changed, and different types of customers won’t map to the old personas (or clusters of buyers) you previously created. Some retailers will see more drastic changes than others, but everyone needs to start from scratch to examine their customers right now. That means looking at the channels with which they interact, the messages to which they respond, and their average shopping cart totals. The previous luxury spender could be found perusing lower-cost labels when they never would have before, for example.

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3. Find new correlations to identify customers and understand their mindset and needs. One tactic many brands are using is prioritizing geographical data, looking at total online sales per state and comparing sales to the growing number of confirmed COVID-19 cases. When sales, customer and product data are correlated with outbreaks by state, brands can adjust promotions and inventory as they plan for a comeback. Purchasing patterns and behavior will also differ by demographics within geographies. Understanding these elements will be critical to optimizing customer experience. I’ve also seen companies personalize emails and brand messages, as well as even change up delivery companies, based on zip codes. 

4. Use discounts wisely. It might seem optimal to offer excessive promotions or creative discounting to increase sales, particularly on goods already stuck in the supply chain, but that could actually be a very ineffective tactic. As I’ve said, this is a moment where understanding your buyers is critical to defining your discount strategy, and testing is required. Discounting is a bit like drugs; it’s both addictive and bad for you, creating unwanted and long-lasting negative impact on a brand. Instead of mass discounting, I’d generally recommend using discounting to solve specific problems for specific small audiences. A retailer operating with 50% gross margin will make up a 20% discount by selling 40% more, but most discounts of 20% won’t increase revenue by 40%, thus resulting in a loss. The exception is seasonal items that need to be cleared from the supply chain quickly. In this case, brands should offer discounts to reactivate, or increase, shopping frequency, rather than making them available to every customer.

5. Keep a close eye on the volume and value of new orders. It’s likely some of the routines we’ve developed over these past few weeks will stick and influence shopping behaviors for months to come. Brands must understand changes in channel and category levels by region, developing “before” and “after” benchmarks, and in doing so, be careful in testing hypotheses and purchasing new inventory. My suspicion is that luxury, travel and food categories will permanently lag as a result of the pandemic, while sporting goods and cooking equipment will get a permanent lift. The action item is for brands to brainstorm what the “new normal” looks like for them. Looking at customer data across different variables, retailers can better predict product categories that will come back, and to what level. From there, they can model and create a sensitivity analysis to optimize future manufacturing, design and supply chain costs.

This global pandemic has already profoundly impacted businesses everywhere. As brands adjust to new ways of working and find moments to build relationships with customers, continuing to creatively analyze data and learn from it will be essential.


Omer Artun is chief science officer at Acquia. He was previously the founder and CEO of customer data platform company AgilOne, acquired by Acquia in 2019. Artun holds a Ph.D. in Computational Neuroscience and Physics from Brown University, where he studied with Nobel Laureate Physicist Leon Cooper on pattern recognition, data mining and complex systems modeling at the Institute for Brain and Neural Systems. Artun was an Adjunct Professor of Marketing at NYU Stern School of Business, teaching graduate-level relationship and analytical marketing courses.

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