Crises can happen when people least expect them, and as the current COVID-19 pandemic illustrates, they may have global impacts rather than just regional ones. No matter the specifics of such catastrophes, brands are often better able to weather them if they have insights about how consumer behaviour changed during past emergencies, and data science can aid substantially in making such discoveries.
1. Crises give brands opportunities to prove their relevance
People experiencing dire circumstances often take the time to refocus on the things that truly matter. A person who survives a natural disaster, for example, may feel extremely grateful to be alive and shift their priorities to spend more time and money on genuinely rewarding things.
Brands can use crisis periods to deepen their relationships with customers, thereby proving their worthiness. Predictive analytics helps brands succeed in the often-daunting task of giving consumers what they need and want.
A 2019 survey found that people who received valuable assistance messages when shopping were 20% more likely to bring the brand commercial benefits, such as increased cart sizes and repurchases. Tuning into customers can allow companies to reap the rewards.
Businesses could also analyse past social media messages and comments received through email to get an idea of how much people will take advantage of a new offering. When crises cause people to restrict their spending and possibly cast aside brands that no longer meet their expectations, enterprises need to rise to the challenges faced.
2. Data science reveals the link between purchases and past associations
When people go through an economic crisis or similarly disastrous circumstances, groceries represent a continual need. Something that differs, though, is which items they want or need most. Moreover, how consumers feel about certain products typically influences their likelihood of continuing to buy them after a situation improves.
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A joint research effort from the Food Marketing Institute (FMI) and insights specialist IRI recently examined data collected during Hurricanes Harvey and Irma. The goal was to get some predictive behaviour indicators about how grocery shopping may change due to the more recent crisis caused by the COVID-19 pandemic.
Researchers said drops in demand differ across particular categories. Some items, like canned meats and soups or items for quick meal preparation, are also likely to sell briskly during difficult times, but see demand slump during recoveries. People associate those consumables with recent struggles, and they want to move past the new spending habits adopted when things look better.
However, the historical data showed that indulgent treats have growth opportunities during periods that challenge households and afterward. People like to buy these things during tough times to reward themselves. After the crises pass, such buying patterns are likely to continue since consumers associate those products with positivity.
These conclusions show valuable predictive behaviour trends, plus emphasise how crucial it is for brands to position their products so that consumers see them as relevant and desirable during a crisis or outside of it. For example, if a marketing team for a product that people snatched off the shelves during an economic downturn positions the item as convenient for people with busy schedules, they may continue to feel they need it.
3. Data science shows how people may spend differently to cope
Outside of grocery shopping, in particular, a different study examined 2009 spending patterns to see how people purchased as they emerged from the recession that ended a year earlier. The results showed that 54% avoided tempting stores and websites, and 64% questioned whether a purchase was a smart use of money.
The company asked the same questions more recently and found that more than half of those polled are already wondering if an item is a smart use of money, while a third reported steering clear of websites or stores that may tempt them to spend.
Those results strongly suggest that companies should start drawing consumers’ attention to things about their products that make them wise purchases. For example, a product’s durability, versatility and real-life applications are things that can encourage people to buy merchandise as they closely monitor their bank accounts.
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4. Historical statistics can pinpoint the link between buying patterns and waste
Even as many people clamp down on their spending, some still buy things in a panic, particularly if they perceive supply chain shortages. If companies become more reliant on data science tools, they may develop a clearer understanding of what happens when people buy differently. For example, the toilet paper-buying rush associated with the COVID-19 shouldn’t lead to much waste since it involves a non-perishable product that everyone will need at some point.
However, in August 1971, President Nixon implemented years’ worth of price controls to mitigate inflation. The beef market suffered as a result due to prices kept so artificially low that producers could not make money. They often kept their animals instead of selling them for slaughter, which caused a beef shortage.
The lower-quality cuts of beef were the most readily available, but research showed that people often threw it away — presumably because they didn’t like the taste or were not sure how to cook it. If marketers see something similar happening in modern times, consumer education — such as offering recipe tips — could be the key to waste reduction.
5. Data helps brands see the similarity in past downturns
An emergency can seem wholly foreign immediately after it occurs. That’s not always the case, no matter how things feel, though. Researchers investigated economic crisis data from the 2001 and 2008 recessions to find clues for the future.
One of the tidbits uncovered within the report was that 80% of Americans experienced only small gains in discretionary income and thereby changed their spending habits after each recession. One of the shifts made was to show a preference for discount and off-price retailers. For example, during the Great Recession from 2007 to 2017, the relative strength of discount brands grew by 6% annually, while the retail industry as a whole declined by 5%.
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The research team also determined that the reinvestment rate — the ratio of capital expenditure to depreciation — was one of the things that set top-performing brands apart from the pack as they recovered from recessions. It showed how much the companies continually replaced their assets while keeping an eye on the future.
The authors recommended that companies start anticipating the next recession even during times when the economy seems stable. Looking at consumer behaviour data from past recessions can make brands feel better equipped to understand what people will prioritise when they tighten their budgets and face uncertainty. Companies may initially think that each recession is entirely different from another, but that’s not true. Finding patterns aids preparedness.
Predictive behaviour data boosts brand resilience
It’s impossible to know precisely how consumers will buy differently in a crisis. As these examples show, however, getting a glimpse into the past with help from data can guide company decision-making and keep brands strong.
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