As part of my PhD (1+3), I had to complete another one year master. The master was an MA in Social Science Research. The last part of this master was completing a dissertation, on a topic of choice. I decided to do my dissertation on price recall. I will outline down below what the outcome of my dissertation was.
Theories of Price People’s awareness of what products cost has been facing a decline. De Chernatony and Knox (1992) showed that consumers were much more accurate in recalling prices in 1958 than consumers in 1984, by doing a meta-analysis of many other price recall studies.
My research aimed to explain this decrease in price awareness by looking at theories on pricing strategies. These strategies can roughly be divided into theories of exposure and rounding.
Rounding is an easy theory to dive into. Instead of remembering prices such as €14.99, we remember spending €15, and technically, that is wrong. It is the same for prices such as €23.95 (€24) and €6.01 (€6). Rounding is a process engaged in by short-term memory, to ensure a minimum capacity is needed. Short-term memory is an extremely limited capacity storage base (Baddeley, 1992), so it makes sense that we round prices to ensure we at least remember the most important part. Would this explain why we often wrongly recall prices?
The second set of price recall theories focussed on exposure. Coming back again to short-term memory, what we see more often (are exposed to) is what sticks in our minds (Monroe, 1973; Winer 1986). Repetition is great for remembering. What are we most frequently exposed to? Prices ending in .99. Many studies have confirmed that the digit 9 occurs amongst price endings far more often than chance would predict (Bergen et al, 2004; Schindler and Kirby, 1997; Twedt, 1965). Having 9 as a price ending could occurs as much as 64% (Harris and Bray, 2007). Research by Schindler and Chandrashekaran (2004) has found the highest recall accuracy for prices ending in the digit 9. They also found that when participants had no real memory of the price and turned to guessing, their guess would end in the digit 9 over half of the time.
I proposed to add a third theory to the mix: the theory of prominence. Numerical prominence was a topic proposed by Albers (2001). It assumes that the numbers 1,2,5, 10, 20, 50……. are prominent numbers. He did not empirically test his own theory, so this research did. It might be that we only focus on these numbers when engaging in remembering prices.
Now you might be thinking that the misremembering of one price, with the inaccuracy range of .05 to .95, does not lead a consumer into debt. However, the research I reviewed in my dissertation was done with the prices of products that were frequently purchased. Then, the high inaccuracy rate is worrisome. If consumers are unable to recall prices of products they buy every day, they might also be unable to correctly remember how much they have already spend, how much is left to spend and when they are getting into debt. We are bridging theories of decreased price awareness to the increase in (credit card) debt. US credit card debt totalled an all-time high of $1.023 trillion in November 2017, increasing 13.3 percent since the year before (CardRates, 2018). UK credit card debit also totalled an all-time high of £70.1 billion in December 2017, increasing 7.2% since the year before (Finder, 2018).
Research has shown that this increase in credit card debt can be explained by individuals very often failing to correctly estimate their credit card bill (Gross and Souleles, 2002). Srivastava and Raghubir (2002) also showed their participants’ inability to correctly recall expenditure, but argued that it could be improved using a decomposition strategy. They did warn, however, that this strategy improved recall to some extent, but still did not make the recalled numbers accurate.
Method To see how bad we have gotten at price and expenditure recall, we tested a sample of 3050 consumers of an on-campus grocery store. Consumer were asked to hand in their receipt and to recall their total spend when filling in a survey. As a result we could see whether customers were correct in estimating how much they spend, and if not, how wrong they were. We could also see from the receipt how many items were purchased, and if customers were recalling a price (1 item purchase) or an expenditure (multiple item purchase).
Results Without getting into heavy statistics and quoting p-values in this section, we found an indication of rounding and prominence being the main pricing theories driving the accuracy of recall.
The most accurately recalled price endings were .50, .00 and .75 respectively. Two out of three price endings are prominent numbers. The most accurately recalled expenditure endings were those of .00, .99 and .50 respectively. Again, two out of three endings are round and prominent. However, a second driver of recall accuracy, in line with short-term memory theory, is the number of items in the purchase set. We find a 84.2% accuracy rate within one item purchases, compared to an accuracy rate of only 51.1% within the multiple item purchases. When testing price (or expenditure) endings vs. number of items purchased, we find that the number of items purchased is a better variable for explaining recall accuracy.
Conclusion All in all, we concluded that complexity, both in numerical presentation but mainly as in number of items bought, drives accuracy of recall. But what does that leave us with exactly? Should we buy less in one go? Or should we just buy less to ensure we better remember for how much money we purchased? Although I don’t really see anything wrong with reduced consumption, that’s not really an argument we can make from this research.
What I can recommend is, if you are feeling like your expenses are spinning out of control, don’t go into a shop without a grocery list. Make the list and stick to the list. No extra “impulse” purchases. If you don’t need anything, don’t enter a store. And when you have spent your money, write it down somewhere, or track it via an app. It’s a great way to see where exactly your money is going, and where it should maybe stop going. Good luck!
References Albers, W. (2001). Prominence theory as a tool to model boundedly rational decisions.
Baddeley, A. (1992). Working memory. Science, 255(5044), 556-559.
Bergen, M., Kauffman, R. J., & Lee, D. (2004). Store Quality Image and the Rational Inattention Hypothesis: An Empirical Study of the Drivers of $9 and 9¢ Price Endings among Internet-Based Sellers. In Proceedings of the 9 th INFORMS Conference on IS and Technology (pp. 23-24).
CardRates (2018). 2018 Credit Card Debt Statistics: Average US Debt. CardRates.com. https://www.cardrates.com/advice/credit-card-debt- statistics/ Retrieved 16th of July, 2018.
De Chernatony, L., & Knox, S. (1992). Brand price recall: the implications for pricing research. Marketing Intelligence & Planning, 10(9), 17-20.
Finder (2018). Debt Statistics: How much debt is the UK in? Retrieved 16th of July, 2018. https://www.finder.com/uk/debt-statistics
Gross, D. B., & Souleles, N. S. (2002). Do liquidity constraints and interest rates matter for consumer behavior? Evidence from credit card data. The Quarterly Journal of Economics, 117(1), 149-185.
Monroe, K. B. (1973). Buyers' subjective perceptions of price. Journal of Marketing Research, 70-80.
Schindler, R. M., & Chandrashekaran, R. (2004). Influence of price endings on price recall: a by-digit analysis. Journal of Product & Brand Management, 13(7), 514-524.
Schindler, R. M., & Kirby, P. N. (1997). Patterns of rightmost digits used in advertised prices: implications for nine-ending effects. Journal of Consumer Research, 24(2), 192-201.
Srivastava, J., & Raghubir, P. (2002). Debiasing Using Decomposition: The Case of Memory‐Based Credit Card Expense Estimates. Journal of Consumer Psychology, 12(3), 253-264.
Twedt, D. W. (1965). Does the" 9 Fixation" in retail pricing really promote sales?. Journal of Marketing (pre-1986), 29(000004), 54.
Winer, R. S. (1986). A reference price model of brand choice for frequently purchased products. Journal of Consumer Research, 13(2), 250-256.
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