Getting pricing right was never an easy discipline for consumer product companies. Well, now, it’s going to get harder, and the only effective planning solution will be advanced automation, enabled by artificial intelligence (AI).
It is all too easy to read the current retail landscape and make a case to suit your personal circumstances. Clearly, many companies are doing just that because responses to the Covid-19 crisis have varied so wildly from country to country, business to business, and person to person. Consistency and predictability have been the first hostages of this crisis, and even while we expect nothing to settle soon, they may never return if consumers change their buying behaviours more long term.
Early signs are that the anticipated sales bonanza may not arrive. In the second week of stores reopening, footfall climbed 7.7% according to ShopperTrak. But they and the British Retail Consortium (BRC) also found footfall was down over 53% on the same period last year. This led Helen Dickinson, chief executive of the BRC to comment: “Reopening is no magic bullet. Low consumer confidence and social distancing mean footfall is unlikely to return to pre-crisis levels any time soon.”
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The same can be seen in other markets, such as Germany, when retail reopened, with a short-lived rise and then decline in footfall reported in Berlin. This caused Nils Busch-Petersen, head of the Berlin-Brandenburg Trade Association, to warn of the long-term economic damage caused by the pandemic: “We are entering a long, lean period which could end up being even worse than the shutdown,” he said.
In grocery, the figures have remained steadier right through the lockdown, along with other essential retail, but research by Brand Nursery among shoppers found a number of interesting insights into how shoppers might buy in the future.
Grocery shoppers are using the same stores and staying loyal to their favourite ‘comfort’ brands, but they are upping their spend on wellbeing-related products, specifically fresh food. They are also exploring new brands particularly if their usual brand is out of stock, a much more common occurrence during the early days of lockdown when the UK and other EU markets experienced wide-spread panic-buying among shoppers.
Whether this will lead to a demand shift remains to be seen, but wider exposure to new and unfamiliar brands is certain to have some effect. Substitute brands will, in turn, put more pressure on mainstream brands leading, inevitably, to some downward pressure on price.
Other main findings are that shoppers had become more mindful in relation to things like waste, portion sizing, and ingredients.
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These changed behaviours are certain to lead to a reset by some consumer product companies in an attempt to protect market share, putting profitability into second place, at least in the short to medium term. But this reset will be more like a series of continuous adjustments rather than one single action.
Consumer product businesses will consider whether lowering prices will increase their competitiveness; whether cutting prices will engender higher loyalty; and what impact lower priced products will have on their relationships with retailers as they also try to recover.
A completely new balancing act will have to be managed – one where consumer product businesses try to maximise profits while maintaining share of shelf. The risk otherwise is that profitability, growth, and consumer relevancy will be lost if higher prices lead to lower volumes.
The calculations and the multiple data inputs required to determine the actions that will get the balance right are complex, and will therefore require artificial intelligence in support of pricing teams. By removing the heavy manual processing, teams are free to collaborate more widely across the business, and will be on a more equal footing with retailer partners that bring so much valuable data to the party.
We envisage a new strategic pricing framework where consumer product companies can:
- Segment products based on attributes, price, and consumer shopping behaviour to get a clear view of the competition as well as new competitors.
- Understand how substitution might cause demand transference and the relationship between price, attributes, and demand shifts.
- Invest in market share models that predict shelf share shifts as a function of comparing their own price to their competitors’.
- Balance profitability with the impact of market share to optimise revenue, margin, and share of shelf by taking multiple measures into account – input costs, business constraints like production run limitations, and marketing costs for customer acquisition.
By emphasising market share rather than profitability, AI enables consumer product businesses to understand every percentage change in price, the percentage change in market share, and, most importantly, who wins or loses share in the market as collective demand shifts.
These models are the key to understanding the full impact of pricing strategies on all dimensions of business outcomes. Leveraging this framework, along with sound pricing architecture and product innovation, will ensure consumer relevancy, differentiation, and value that will sustain consumer product companies during uncertain times and give them a competitive advantage in years to come.