Forecasting Approaches for Reshaped Consumer Demand

Today, most consumers have moved past the early panic-driven pandemic stages of stockpiling household and food staples, although suppliers of consumer goods are still struggling to predict what consumers want to buy—and where and when they prefer to purchase. As a result, manufacturers and retailers are dealing with lopsided inventory positions, being out of stock in some items and excessive inventory of other items.

Companies are also struggling to understand pent-up demand: how much pent-up demand exists, and when it will materialize in purchases of goods and services. Various stages of the pandemic recovery will look different across the different cohorts of consumers, and suppliers will continue to struggle to accurately predict how and when the pent-up demand will materialize in purchases.

To adapt to consumers’ evolving preferences, manufacturers, suppliers, and retailers should focus on key aspects of forecasting consumer demand by (1) understanding how the attitudes and behaviors of their consumers have been changed by the pandemic, (2) examining what the pandemic recovery stages will look like for their customers and how this will impact their demand curve, and (3) revisiting the historical data sets and models currently used to predict demand and adapt existing processes to accommodate the evolving expectations of consumers. Here are some considerations for any CPG company seeking to improve its forecasting efforts:

Embrace a continuously evolving consumer profile

The most challenging aspect of forecasting consumer demand will be understanding how the attitudes and behaviors of consumers have changed as a result of the COVID-19 pandemic. Not only have consumer preferences changed, but consumers have changed where they want to make their purchases, how they want to make their purchases, and how much value they place on social responsibility. The continuous shift from brick and mortar to e-commerce should not come as a surprise. According to a retail report by the US Census Bureau, e-commerce sales have been climbing steadily; in the first quarter of 2021, e-commerce accounted for 13.6% of total sales, up from 11.8% in Q1 of 2020 and only 7.3% in 2015. What did come as a surprise was the shift to e-commerce across all age and income cohorts, not just younger or traditionally tech-savvy consumer brackets. Companies must now understand how much of the shift to e-commerce will become permanent and how they can use this information to position products to best meet the current needs of consumers.

Examine the roads to demand recovery, knowing the path and pace may vary

The shift to e-commerce was nearly universal across cohorts, but the recovery in US consumer spending is likely to be uneven across cohorts of income, age, and geographical location. Savings in the United States are at an all-time high, partly due to the stimulus packages, but what consumers chose to do with their savings —consume, hold, invest, or repay debt— will have an impact on the consumption recovery. As a result of the pent-up savings, CPG companies are anticipating a shift in consumption toward older and wealthier consumers, due to a growing share of the population over age 65. By contrast, companies are anticipating a slower post-pandemic recovery for low-income cohorts.

Rethink the data

While it may seem intuitive that companies should revisit their historical demand assumptions and data sources, this has proven to be a challenge for most organizations. Pre-pandemic, suppliers of consumer goods relied on historical sales data and overall category growth assumptions to predict future demand. Although this approach worked well in pre-pandemic days when demand was fairly constant, suppliers are now finding that their demand curve for certain products has drastically changed. Since suppliers cannot rely on historical data, they must find alternative data sets to predict demand. Alternative data sets may include retail foot traffic, overall consumer sentiment, vehicle traffic, and local knowledge from sales reps, customers, and others “on-the-ground.”

Adding to the uncertainty of demand planning is the uncertainty of the overall supply chain. From commodities to freight to labor, manufacturers are struggling to not only predict consumer demand, but also the availability and cost of the inputs required to meet the demand. To determine leading indicators as to which products to produce and sell, organizations will more heavily rely on non-traditional data sources such as retail foot traffic and consumer data from locations with more lax pandemic restrictions.

From ketchup packets to furniture to beer cans, CPG manufacturers and retailers are still dealing with unpredictable demand resulting in stockouts across the entire portfolio of goods. Companies can address reshaped consumer expectations by creatively rethinking which traditional and new data sets will best inform and validate today’s consumer profile and purchase preferences. An updated approach to forecasting will help CPG organizations ensure better supply timing for any delayed purchases and uncover any expectations that might prevail as a new standard pattern of demand.