introduction

The COVID-19 pandemic has changed virtually every aspect of the lives of American consumers, from the way we engage socially and how we work, to the way we buy and gamble. Not surprisingly, consumer spending behaviors have also changed significantly. Understanding these changes is essential for developing effective strategies in the future. To this end, we studied the dynamics of spending across the consumer wallet via a new dataset. Our first questions asked how consumer spending changed during the pandemic in three main dimensions:

  • Level – How much has the amount spent by consumers changed?
  • Type – What did they spend it on?
  • Instrument – What did they use to spend?

In addition to these questions about past performance, we also explored expense recovery. Concretely, has the recovery already started? If so, when did it start and how is it progressing? If expense recovery is not yet complete, when can we expect it to be? Of course, a study of this magnitude and complexity requires a very special set of data.

Analytical dataset

Analyzing spending at the individual consumer level is a challenge. The main difficulty is that no institution has access to all channels of consumer spending. Consider that credit card issuers can see every transaction on their own cards but cannot see those of their competitors. It is the same for the networks of processors. Beyond issuers and network processors, transaction volumes are increasing through alternative channels (e.g. Venmo, Zelle, Cash App, PayPal, etc.), which is a universal gap for issuers, networks and traditional data aggregators. Of course, it remains impossible to track pure cash transactions.

To overcome these challenges, Verisk Financial created a dataset of 20 million anonymized consumers selected through stratified random sampling among consumers for whom Verisk Financial has both credit and debit transaction data. These 20 million consumers are the common breakdown of all credit card users in the United States by credit risk level, age, geography, and overall credit card balance as of Q4 2018.

In total, our analytical data set includes 12.4 billion credit and debit card transactions. While not the last word, analysis of this dataset offers perhaps the most comprehensive exploration of spending during the pandemic available today.

Results

Expense level

Figure 1 shows the year-over-year trend in overall levels of consumer spending for 2020. This approach allows us to control for seasonality. There are three important aspects of this graph. The first is the trend: a significant drop in spending in March, when businesses closed across the country, followed by an even more severe drop in April. However, spending immediately started to recover in May 2020. This recovery has been relatively stable and consistent since its inception. The rapid onset of spending recovery in this pandemic is a marked departure from the experience of recovering from the 2008 recession, where declining spending and changes in payment priorities persisted for months.

Pandemic figure1

Point two: This tendency towards an initially severe but short-lived shock, immediately followed by an onset of recovery, was remarkably consistent across all views of the data. As an example, Figure 2 shows the same trend for consumers aged 35 to 39 in the northeastern region of the country. In no other analysis have we seen such a consistent response dynamic.

Pandemic figure2

We believe that there are several explanations for this consistency. First, the national shutdown in March and April 2020 was likely a driver of the initial uniform decline in spending. Also, the attention of the national media on the pandemic probably contributed to this initial reaction. When it comes to speed of retrieval, we believe robust online and digital shopping options have allowed consumers to quickly adapt to remote and contactless transactions (think Instacart, UberEats, Grubhub, etc.) . Taking into account the speed at which people adapt to remote working and the availability of government stimulus programs, we believe the data speaks to an environment that has fostered remarkable resilience among consumers.

The third notable feature of these charts is the counterintuitive ranking of spending decline by level of credit risk. The conventional wisdom is that low risk consumers generally have higher disposable income and therefore should have more means to recover quickly. In fact, the opposite was true. More information on this result later in this article.

Type of expenses: essential vs non-essential

Figure 3 shows the year-over-year trends in essential spending, that is, things considered fundamental to continued health, safety and well-being. Note the rapid recovery across the risk spectrum. By the end of 2020, almost all risk levels had fully recovered their essential expenses or were almost fully recovered.

Pandemic figure 3

Figure 4 shows the same basic pattern for non-core spending, although the decline in March and April was more severe and the recovery slower.

Pandemic figure4

There are several possible drivers of this behavior. The first is that the nature of non-essential expenses differs according to the level of risk. For example, travel accounted for 24% of non-essential spending before the pandemic for low-risk consumers, but only 17% for higher-risk consumers. So while consumers may have felt more comfortable in the second half of 2020 while eating in an outdoor restaurant, they may not yet have felt comfortable on board. a plane. Consumers’ comfort with various non-essential activities is not uniform, and their spending will reflect this fact.

Another potential factor for a slower recovery in non-essential spending among low-risk consumers is that the recovery in this group requires more transactions and more spending. For example, consider consumers aged 30 to 34 living in the Midwest. Those with credit scores between 620 and 639 had on average only five non-essential transactions less in 2020 compared to 2019, for an average drop of just $ 68. This amount could be offset by an additional meal at the restaurant. In contrast, those with credit scores between 720 and 759 had 12 fewer non-essential transactions, but with an average drop of $ 1,285. The point is clear: low-risk consumers had more non-essential spending to catch up to fully recover, and may still be uncomfortable with some of the non-essential spending categories they favored before the pandemic. These results imply that full expense recovery will not take place until non-essential expenses are recovered from the least risky consumers.

Instrument of expenditure: credit or debit

The initial drop in the percentage of credit spending in March and April 2020 (Figure 5) was more pronounced for high-risk consumers. This dynamic is due in part to the fact that low-risk consumers have much more credit, so they are under less pressure to hold onto their credit in uncertain times. Another driver is the revival of the government; higher risk consumers were more likely to turn to debit spending to access these funds. In any case, this effect was short-lived; credit spending as a percentage of overall spending had more or less fully recovered by December 2020.

Pandemic figure5

Recovery analysis

The recovery began in May 2020 with remarkable consistency. Core expenses had more or less fully recovered by the end of 2020, as had the ratio of credit usage to debit. The remaining factor in achieving a full recovery of expenses is therefore the recovery of non-essential expenses.

To estimate when the non-essential expenses will be recovered (and therefore when the overall expenses will be recovered), we built simple linear and logarithmic models (respectively Figures 6 and 7) based on the non-essential expenses by risk level from May 2020 to May 2021.

Pandemic figure6

Pandemic figure7

We tend to lean towards the more conservative logarithmic model, expecting a full recovery in spending in late 2022 or early 2023. Our outlook is partly informed by the fact that the pandemic is far from being completed; Delta and other variations continue to create uncertainty in the market.

The decline in spending during the pandemic suggests that analyzing spending “on us / off us” would be a particularly useful exercise in the short term. As consumers return to pre-pandemic spending levels, they may choose to do so with different cards than the ones they used before. Transition periods are also good times to evaluate marketing strategies; what might have been effective with certain market segments before the pandemic may have been less effective now, and vice versa. This may be particularly salient given the many counterintuitive behaviors we found through this analysis.

Summary

Analyzing spending at the consumer level is a challenge. This study provides several important insights into consumer behavior during the pandemic. While some of these behaviors confirm received ideas, others force us to reconsider our notions of the consumer’s state of mind. We also discussed how strong technologies and merchant innovations can influence spending behaviors. Beyond the significant value of this information, the results suggest several strategies for issuers to explore. Those who understand not only the changing spending preferences of consumers, but also the right approach to engagement during this transition period, will be in a much stronger position to gain market share and build relationships. long term with their respective customers.


Author: Linda Turnbull, Managing Director at Argus Information and Advisory Services



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