“Successful and Unsuccessful Price Increases” Considered Based on Consumer Price Acceptability

Since 2022, there has been a continued rush to raise prices in Japan due to the sharp depreciation of the yen and the rise in raw material costs resulting from higher crude oil prices. As described in the article “Ongoing consumer good price hikes and how consumers are guarding their household purse strings” (5), a wide range of product categories are subject to price increases. As of September 2023, the average price of canola oil rose to 173% of what it was before the price increase, flour to 128%, and regular coffee to 124%, showing that prices are rising in retail stores, especially for food items. The price hikes have led to changes in consumer behavior, including an increase in purchasing alternative products, cutting costs by buying larger quantities, and making use of bargains and coupons.

On the other hand, the price hikes were driven by the unavoidable decision of companies to secure profits through trial and error. High raw material and shipping costs put pressure on profits and forced pricing to reflect these higher costs. However, while companies are prepared for a certain amount of customer churn as they implement price hikes, choosing the wrong degree of price increase can substantially reduce consumers’ interest in purchasing, resulting in a loss of competitiveness and a rapid decline in market share. In this way, raising prices to secure profits may in fact result in a decline in profits. Therefore, in making price adjustments, it is necessary to search for “where the right price is” that can maintain competitiveness and improve earnings, rather than simply stacking the higher costs onto the purchase price. As the environment in which consumers live constantly changes, there is a need to search for appropriate prices that capture such consumer changes.

Deriving “Appropriate Prices” from Data

The ideal form of price change is to increase profits in such a way that sales volume does not decline even though there has been a price hike. Of course, it is difficult to completely eliminate customer churn due to price hikes, but price hikes implemented with an understanding of consumers’ “price acceptability” can reduce customer churn. Price acceptability is the price range that consumers can accept when making a purchase. There is a receptive area where raising prices does not have a significant impact on the volume that consumers purchase, and conversely, there is a risk that raising prices beyond the receptive area will cause a sudden decrease in purchasing volume. Price changes implemented with an awareness of price acceptability can prevent a decline in sales volume and guarantee profits.

The selling price of a product is not always uniform. Some stores sell them at standard prices, while others routinely sell them at a discount. Analyzing POS data containing information on “When (date), how many people (number of customers), what (product name), how much (selling price), and how many pieces (quantity) were sold,” which represents actuals sales conditions, can clarify the relationship between the selling price and sales volume of each product. This enables companies to ascertain consumers’ price acceptability and use it to search for the appropriate price.

The Importance of Understanding Price Acceptability in Test Cases

This article will take a look at an analysis to find the appropriate price for Processed Food A, on which a price increase was implemented in 2022. Processed Food A, the subject of the analysis, saw its market share plunge after a price hike of about 30 yen in 2022. Since then, the product’s market share has been recovering in part due to price increases on competing products, but the market share has not returned to the level it was before the price increase (Fig 1). We used daily data for the three months preceding the price hike of Processed Food A within the supermarket sector using INTAGE SRI+.

Fig 1

First, let us examine whether the price increase of Processed Food A was appropriate from the viewpoint of consumer price acceptability by looking at the selling price and sales volume graph in Fig 2. The x-axis shows the actual selling price range. The bar graph shows the proportion of days of selling price (％), which represents the distribution of how many units were sold at which price range. The line graph represents the number of units sold per store per day (*Footnote), showing the sales by price range.

Fig 2

The price range with the highest bar in the bar graph was the modal (standard) price, and the number of units sold at that time was 11.1. The price of Processed Food A was raised by about 30 yen, so assuming that the modal price were raised by 30 yen, the number of units sold could decrease by about 41% to 6.6. In other words, the data suggests that a 30 yen increase would lead to a substantial reduction in sales volume. On the other hand, the data indicate that if the price increases by up to 10 yen above the modal price, the number of units sold will not decrease significantly.

Next, let us look at how the price increase for Processed Food A has impacted its relationship with its competitor, Processed Food B. Competitors’ prices are important in pricing, and the likelihood of consumers switching to competitive products increases if a price hike widens the price gap between a company’s product and that of its competitors. If the sales volume of a company’s product decreases and the inventory turnover rate slows down, display shelves may even be stocked with competing products instead.

Fig 3 shows the price gap and relative market share between Processed Food A and rival Processed Food B before the price hike. The x-axis shows the price gap between the two products (price of Processed Food A – price of Processed Food B). In the same way as in Fig 2, the bar graph is the proportion of the days of selling price (%), which shows the distribution of what price gap resulted in the most sales.

Fig 3

The bar graph distribution reveals that Processed Food A is generally sold at a higher price, but the most frequently set price gap is 0 to 4 yen, indicating that before the price hike, it was most often sold at approximately the same price range as is competitor. The line graph shows the market share by quantity (unit share) (%) of Processed Food A. When the price gap between Processed Food A and B was 0 to 4 yen, the unit share of Processed Food A was 83%.

If Processed Food A is raised by 30 yen, the price gap between Processed Food A and Processed Food B is assumed to widen to 30 to 34 yen on average. In that event, Process Food A’s unit share would be 67%, with about 16% of its market share being lost to the rival Processed Food B. The results also suggest that a 30 yen price increase for Processed Food A will lead to a reduction in sales volume.

If the price gap between Processed Food A and Processed Food B is less than 20 yen, the unit share does not decrease significantly, but the data indicate that the unit share decreases substantially when the price gap reaches around 20 yen or more. The results also indicate that a price hike for Processed Food A of 10 yen was appropriate in order to minimize the loss of sales volume.

In fact, the month after Processed Food A’s price was raised, its competitor Processed Food B also raised prices, but as shown in Figure 1, the share of Processed Food A did not immediately return to its original level. Once a certain price gap is reached even momentarily and market share is lost, it seems to be no easy task to regain that lost market share. Consequently, it was foreseen from the data that the strategy of hiking the price about 30 yen risked losing a large piece of market share due to consumer price acceptability and the distribution of price gaps with competitors. In this case, a 10 yen increase would have been a price range that consumers would accept. Furthermore, the price gap with competitors would not have widened too much, and the company could have secured profits while maintaining its market share.

Search for the Appropriate Price and Aim for the Best for Consumers and Businesses

It is difficult to determine the appropriate price in a highly uncertain market environment. However, it can be estimated from current data. The above analysis and simulation can be carried out using the variance in selling price of the products currently displayed in stores. In this case, data prior to the price change suggested there were risks in a price hike. If the data had been analyzed beforehand, the strategy could have been revised based on that suggestion.

As shown in Fig 4, our findings reveal that there are effective types of price hikes and reductions for products for the purpose of maximizing profits. For example, if the company sells milk with a cost of 80 yen for 120 yen, and the data shows that the sales volume remains the same even at 130 yen, the company can raise the price without fear of failure. On the other hand, if it is found that the sales volume of milk sold for 120 yen can be greatly expanded by selling it for 115 yen, there is a way to maximize profit by reducing the price intentionally. There are various patterns that maximize profits based on category characteristics and brand strength, so it is important to draw hints from current data to find the appropriate price for consumers.

Fig 4

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This article is a reimagining of a MarkeZine contribution (Exploring “Successful and Unsuccessful Price Increases” and “Where the Appropriate Price is” Based on Consumer Price Acceptability).

*Note:
The daily sales quantity per store in the line graph in Figure 2 represents the number of units sold per store per day. It is standardized to be per 1 million yen sold per week in the food category to account for store size. In order to show the threshold value of the price effect clearly, the data has been processed so that when the price falls, the number of units purchased remains constant or rises.

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