It boils down to choice. Price is a matter of choice. More choice tends to make consumers more sensitive to price. The jargon is elastic, meaning that if the price goes up a little then the demand will go down a lot. This is due to the value that consumers perceive - the maximum price they are willing to pay.
One of the core drivers of any brand's profitability is the price it can set for what it sells. Marketers want to know how much volume they can sell at different price points, so they can figure out what price point is best for their product. There are levers that can be pulled to get more revenue for the same volume at a higher price. Discerning these levers, quantifying how much pull results in how much incremental revenue increase, and then optimizing the cost of pulling those levers against the incremental revenue gained, constitutes the complexity of optimizing profits in the realm of price.
Choice is not uniform across consumers and depends on a lot of factors, like the availability of comparable substitutes, the cost relative to a consumer's income, how much the consumer identifies with the brand, and how much time he or she has to make a decision.
Many marketers assume that their supply is good, and plan price relative to demand. They use a simple price elasticity ratio - the change in volume over the change in price across a time period. Since choice is consumer-centric, marketers spend time dissecting how different consumers react to different prices under different conditions. Of course, this is tricky business.
How can you associate specific conditions to specific consumers and know their behaviors? Scanner panels are, of course, handy. They let you track households by assorted characteristics like income, size, owned or rented, and urbanicity, against behaviors in different shopping conditions - store type, size of basket, perceived deal and price paid. Hence, convenience store prices differ from those of club stores, and both of these vary by neighborhood for the same goods.
This helps with promotions, but not trade or advertising. What features and displays did the consumer see? The scanner has no clue. What advertisements did the consumer get exposed to? The scanner has no clue.
Good information about what trade was supposed to run in which stores and when (along with a whole lot of hard work) allows results from trade to be guessed at. Like advertising, not all trade runs according to plan. Yet knowing when the trade was supposed to run in specific stores at times when you know particular consumers bought your product at specific prices at least allows this guesstimate.
But what source tracks advertising exposures and specific product purchasing of the same consumers? Fortunately, working on Project Apollo, I am sitting on just such a single source dataset that allows me to peel this onion one layer further.
We put this data into the hands of smart people and asked them to investigate the relationship between advertising and product pricing. They found that price elasticity declined with increased advertising for specific targeting groups. The effects of this type of branding are short-term since consumers' identification with the brand fades, so recency comes into play here as well.
We framed three hypotheses to investigate: Exposure to advertising decreases consumers' tendency to react to price changes. In aggregate, advertising and price elasticity are inversely related. And consumer groups whose price sensitivity is affected differently by media exposure can be identified.
We used the Project Apollo data, which tracks media exposures and purchase dynamics, to test these hypotheses. Using logistic regression techniques and statistical tests on various consumer groups under various purchasing conditions and levels of advertising exposures, we found significant correlations that identified how advertising exposures affect different consumer groups continuing to purchase products at non-discounted prices.
We looked at purchasers of a particular category where approximately one-half were exposed to Brand X's TV advertising. The households exposed to the ads were then divided into groups, the Low Group with one to three advertising exposures and the High Group with over four exposures.
We found that the price paid by like consumers became less elastic as their exposures to the brand advertising increased. The hypothesis that exposure to advertising decreases consumers' tendency to react to price changes is true. As the graph indicates, pricing sensitivity is reduced even at low TV-advertising exposure levels compared to the audience with no exposures. Additional TV exposure further reduces pricing sensitivity.
In this work, we studied mature brands with good equity and proven messaging. It is possible that this hypothesis and the others are false for new categories and new brands, and it is likely false for brands with poor messaging and equity.
We also found that in aggregate, advertising and price elasticity are inversely related. An inverse relationship was observed across various levels of TV exposure. Purchasers of Brand X who had not been exposed to TV advertising tended to buy more product as prices were discounted. Those exposed to one to three TV ads were less sensitive to pricing. Consumers with four or more exposures showed a further decline in sensitivity, with behavior changes tapering off at between seven and eight exposures.
Finally, not all consumer segments behave the same way. We found that various consumer segments had differing price sensitivities to media exposures. In one category, we discovered that TV exposure had a substantial impact on the price sensitivity of households that frequently shop the category. As an example, for category purchasers of six or more units within a fixed time period, media exposures had little impact on price elasticity for Brand X purchases.
The fact that advertising affects price elasticity means advertising has direct short-term impact on profitability. However, since not all exposure levels are equally effective and not all consumers are affected the same way, getting your targeting right is imperative. In the case of Brand X, we clearly see that price elasticity for light- to medium-category purchasers can be influenced with media exposures. If the primary goal of our advertising is to keep price up, then we want to target these purchasers. Of course this is an example, and there is plenty of room for refinement.
Notice that price is deeply related to choice, which is all about identity, purchasing habits and purchasing conditions. Translation: If you want to use advertising to manage profitability, then demographic targeting is history.
Mark Green is senior vice president for strategic measurement initiatives at the Nielsen Company. (email@example.com)