As optimism grows globally that the end of the COVID pandemic may be in sight, manufacturers are increasingly grappling with a new challenge: inflationary pressures. Demand has surged after the decline caused by the pandemic. Unlike some past episodes of inflation, which were more driven by monetary factors, current inflation is also being driven by supply chain issues and raw material shortages.
This has resulted in a multifaceted dilemma for manufacturers. Firstly, margin pressures on individual SKUs increase as raw material prices increase. At the same time, shortages and supply chain issues mean that manufacturers must decide which parts of their product assortment should have priority in distribution. Many manufacturers have been forced to trim their product portfolio due to production and supply chain issues or to reduce pack sizes or counts. Therefore, marketers are under huge pressure to make complex decisions to shift pricing and product assortment decisions all at once.
The default response – namely, to let a combination of old consumer data and logistics make the decision – is fraught with risk. The pandemic has shifted consumer behaviour, with economic uncertainty impacting consumer wallets and a shift to digital channels from offline channels. Money can be left on the table with hasty pricing and portfolio decisions, while also giving old (and new) competitors an opening to take long-term shares. Reducing pack sizes and counts can also be risky, sometimes permanently reducing volume for the brand and category moving forward as substitutes come into the market.
In this complex environment, what is the role of research and the insights industry? The best way to make strategic (pricing) decisions is to take all relevant data sources into account. A holistic view is crucial, as all data sources have their strengths. In an inflationary situation, relying on past time series or marketing mix models becomes less useful, as extrapolating from past price steps when price changes were quite small is no longer theoretically sound. The past few decades of stable prices or even deflation around the world cannot accurately inform the new normal of inflation.
Consumer-based methodologies like conjoint modelling and virtual simulations – which can consider higher price variances, new pack sizes, promotional mechanics, and above all, current consumer mindsets – become more powerful and accurate compared to other methods.
FIGURE 1 SUMMARISES THE KEY STRENGTHS OF CONJOINT AND TIME SERIES DATA
One of the core strengths of conjoint and virtual stores is to look forward, predicting the future with prices that are not on the market yet. Dealing with price steps of 20% or more is therefore not an issue.
Sales data is often used to look over time (including informing on seasonality) and is therefore used to predict trends at an SKU or category level. Sales data can also predict share moves by extrapolating historic price movements, but they’re not designed to do this.
CONTEXT IS KEY TO EFFECTIVE PRICING AND PORTFOLIO DECISIONS DURING PERIODS OF HIGH INFLATION
Competitive context is one area that typically gets neglected in very basic pricing research solutions, but decades of pricing strategy experience have shown us that context is key. Especially since the onset of the pandemic, manufacturers have needed to take into account channel shifts and new competitors: the marketplace isn’t a vacuum. Modern conjoint and virtual stores e-stores can recreate the consumer context very accurately and hence provide a realistic model of consumer behaviour.
UNDERSTANDING ALL THE KEY INTERACTIONS ENABLES SMARTER DECISIONS
One of the core strengths of conjoint is that it enables users to see which brands and SKUs are interacting with each other. This way the manufacturer can optimise a full portfolio, instead of one brand or SKU only. For instance, if you find out Brand A users go to Brand B and Brand B users go to Competitor C, it would be smarter to increase Brand A prices, since you have a safety net in Brand B. This could even be the case when Brand A is more price elastic than Brand B, something which cannot be understood from a time series analysis.
\”It was a great pleasure to work with you on all the Light price conjoint studies. It created lots of traction across markets, and you are our preferred supplier for these pricing studies in APAC. The speed, flexibility, and simple, easy-to-digest report marked SKIM above any other agency I have worked with to date.\”
-Head of Revenue Growth Management, leading consumer goods company, APAC-
UNDERSTANDING PROMOTIONAL MECHANICS FROM SCRATCH
Inflation can ‘push’ consumers even further towards promo buying, as consumers who were traditionally brand loyal can start to drift towards more cost-effective options. Instead of running promotions from previous years on ‘autopilot’, manufacturers thus need to take a blank-slate approach and understand which promotion mecanics work and which don’t work, and which consumers are attracted towards promotions in the new normal.
CONSIDER DATA FUSION ANALYTICS AND CALIBRATED PREDICTIVE MODELS TO INCREASE SHELF LIFE OF INSIGHTS
Inflation impacts macro-economic trends and consumers’ spending power. In general, these effects can be measured better using longitudinal data or forecasting methodologies, but these can then be included in conjoint studies in the form of a calibration of existing market models.
INVEST IN COMPREHENSIVE INSIGHTS
Pricing and portfolio decisions in the current environment are more crucial than ever before. Manufacturers should get a fresh and comprehensive view of their market pricing, assortment, and promotional mechanics to make these decisions. Given the present speed of change in the consumer’s and marketer’s environment, such investments will give a high ROI in the near future.
This article was first published in the Q1 2022 edition of Asia Research Media