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How Consumer Behavior Changed Throughout the First Coronavirus Wave

As many feel the second wave has already hit, see what you can learn from past data to better prepare this time around

Back in April, during the first wave of coronavirus, we analyzed new versus existing customer behaviors and trends across various retail verticals. It was one of many #MarketingAmidCorona items we published, which readers are greatly engaged with.

That April study focused specifically on consumers’ behavioral patterns at the start of the coronavirus outbreak and went well into the first wave.

Then, in June, many felt the Coronavirus health crisis was behind us. On this blog, we, too, reduced the frequency of publishing #MarketingAmidCorona content, and our readers were significantly less engaged with it.

Many felt confident we know what’s coming.

But now, two-thirds of the way into July, it seems we are in the midst of what many call “a second wave.” And since we now can look at the entire first wave and see how customers’ behavior changed throughout these past few months, we thought it would be a good idea to give you the full perspective of the first wave. So, here we go.

The migration Exodus to online shopping

For starters, we split customers into three groups:

  • Retail Customers
  • E-commerce Customers
  • Net New Customers.

For those, it measured the average number of online transactions per day. One of the most predominant trends that we saw between February and May was the migration to online shopping:

What’s interesting here is that Net New customers purchased nearly 4X more per day by May compared to new customers in February. Predominant retail customers, on the other hand, remained loyal to the brands and discovered their online presence.

Then as we often do, we wanted to ask: Are your newly COVID-19 acquired customers different from your existing customers, though?

When identifying differences in consumer behavior between newly acquired customers during the pandemic and existing customers who purchased during the crisis, we chose to focus on three key differentiators:

  • Average Order Values (AOV)
  • Discount Affinity & Brand Loyalty
  • Product Preferences

Note that we followed the progression of their average order values for each group of customers compared to the average order values in the same period in 2019. We split March into two as the pandemic dramatically impacted our lives in the second half of the month, when most of the world shut down, and quarantine orders kicked in.

Average Order Values (AOV)

As you can see in the chart below, the AOVs of new customers acquired in the first half of March 2020 were 13% lower than the AOVs of new customers who were acquired in the same period last year.

For existing customers, the lower AOV is consistent in March and April compared to 2019. Still, it’s very apparent that Net New customers’ average order value dropped more steeply since WFH orders were put in place.

A Takeaway

Thanks to a strong Memorial Day Weekend in May, average order values were close to last year’s AOV rates. We can also conclude that brands acquired lower value customers during the shutdown in the past few months.

Discount Affinity & Brand Loyalty

For each of the New and Existing groups, we looked at the percentage of orders that contained a discount during the month of April, and compared 2019 to 2020:

  • 62% of orders made by Net New customers contained a discount this year, up 44% from last year
  • Existing Customers saw an increase of 26% in discounted orders, from 42% of orders in 2019 to 53% of orders in 2020

A Takeaway

The pandemic has created a new type of “holiday season” in terms of discounting, very similar to Cyber Weekend and Black Friday, for instance. This will definitely affect customers’ loyalty, survivability, and future lifetime value. Find a wealth of Customer-in-Holidays knowledge here.

Product Preferences

The graph below was taken from the underwear and loungewear vertical. It displays the percentage of items purchased in each category out of the total number of items. On the left-hand side of the dotted line, you can see the distribution of New Customers who were acquired during each period and the products they purchased:

A Takeaway

It’s clear that underwear was the dominant category pre corona times for both New and Existing Customers. But when WFH kicked in, the acquisition patterns shifted towards bottoms – makes sense right, fancy top and sweatpants bottom Zoom calls?!

How do you like their discounts?

The discounted environment has shifted the purchasing balance towards new customers YoY. For this study, we looked at two retail verticals, Skincare & Cosmetics — an industry that traditionally benefited from in-store sales, and the Fashion industry — which has gained traction in recent years in terms of online penetration.

In each period, we compared the customer revenue mix YoY to identify interesting patterns. The first thing that pops up is the flip in Skincare & Cosmetics since the shutdown began, (the 9% in the orange bar in the middle), with customers adapting to the new environment and seeking solutions online.

The Fashion industry joined shortly after in April, probably due to the discounted environment that emerged. It’s safe to say that brands have entered a new phase of balancing between new and existing customers from April onwards.

What now?

So, if we are now in the midst of a second wave, look at these trends and see if they can help you anticipate what is going to happen in your industry.

As the reality is that for most businesses, investment in acquiring new customers comes at the expense of nurturing relationships with existing ones, and vice versa. And when marketing budgets are smaller, it’s a decision many must take.

Companies in different phases have a different “sweet spot” for the optimal balance. We hope the data above would help you plan to find yours.

For more information, contact us.

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Yoni Barzilay

Yoni Barzilay is Optimove’s Director of Data Science, North America. He has a knack for finding creative solutions for extreme data challenges, and has led some of Optimove's biggest e-commerce onboarding projects. Yoni holds a BSc in Industrial Engineering and Management, specializing in Information Systems.