The effects of discount levels on purchase intention and quality perceptions of different product categories and brand characteristics

Paper appears in the 2004 Proceedings of the Northeast Business & Economics Assn. Conference.

Authors: Marlene Jensen & Ronald Drozdenko

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INTRODUCTION

The best way to determine the optimal discount is to systematically test alternative levels in the marketplace. Direct marketers routinely test pricing and discount levels by extracting random samples from their databases and using experimental designs to determine the optimal pricing level.

For traditional on-ground distribution channels, marketers use matched geographic test markets or simulated test markets to set prices. A few “off-price” retailers use progressive discounts dependent on the time the stock is on the shelf. Discounts increase as the time on the shelf increases. This progressive discount method, while it has limitations (e.g., consumer confusion, time needed to move some products), minimizes profit losses by using the market response to specific products to determine the real value to consumers.

However, with time pressures to increase sales, marketers often use less systematic methods to determine discounts. Discounts are set based on industry convention, competitive response or historical precedence.

Using unsystematic methods of setting discounts may result in at least two potential negative outcomes. First, a specific discount may achieve a managerial objective (e.g., to sell X units, increase sales by X%, etc.). However, the marketer would not know if a smaller discount would achieve a similar sales response but with a higher profitability.

Another issue relates to consumer perceptions of the product. Deeper discounts may be associated with a lower perception of quality. This lowered perception of quality may affect brand loyalty. Many national marketers who used coupons aggressively in past decades move to “everyday low pricing” policies because they believed discount coupons eroded brand loyalty. Similarly, the lowered perception of product quality that is associated with higher discounts may discourage some consumers from purchases.

A number of studies have looked at consumer responses to discounts, with, unfortunately, contradictory results. For example Mobley, Bearden & Teel (1988) found a positive, increasing response to 25% and 50% discounts. Marshall & Leng (2002) concurred for product sales, but disagreed for services, finding in those instances that 40-70% discounts were no more positive than 20% discounts, while 30% garnered the most intent to buy.

Studies compared fixed prices vs. discounts – to see the impact of each on perceived quality, thus on perceived value. Madan & Suri (2001) found no differences in quality perceptions at the 15% level, but higher perceived quality in fixed prices over discounted prices at the 20% and 45% levels.

Other researchers studied consumer perceptions of higher discounts, including Kahneman & Tversky (1979), Sherif & Hovland (1964) and Ong & Jensen (1996). Each found that the higher the discount the more likely it would be discounted in determining an internal reference price.

Yet, Urbany, Bearden & Weilbaker (1988) found that even exaggerated, implausible external reference prices were still perceived positively by consumers.

To evaluate why there is such discrepancy, we looked at the individual studies. Many used different products at different overall price points. Many looked at only one discount level.

Some used fictitious brands and others used name brands. This is significant, because Moore & Olshavsky (1989) found discounts positive at all levels for name brands, but negative at the highest levels (75%) for unknown brands.

For that reason, this study pulls together the quality vs. discount questions, and controls for four discount levels (0, 15%, 30% and 45%), five product categories and price points (toothpaste, vodka, yogurt, performance tires, and HD plasma TVs), and whether the brand is recognized or is fictitious.

The objective of this study was to determine how discounts affect purchase intention and quality perceptions in the different product categories. Additionally, we were interested in determining if consumers responded differently to discounted known brands versus unknown (fictitious) brands.

Methods

Respondents

A convenience sample of 634 consumers (328 men and 306 women) primarily from Northern Fairfield county Connecticut participated in the study. Ages ranged from the twenties to the seventies. Surveys were distributed and collected by students in a junior-level Consumer Behavior course.

Survey Instrument

Respondents were asked to rate the quality and purchase intent of 10 products on a 1-10 scale. The products were in 5 product categories, vodka, HD TV, toothpaste, yogurt, and tires. For each product category respondents evaluated a well-known and fictitious brand. Each respondent was assign to one of four discount groups. The groups were constructed so that no respondent would get the same discount for both the known and unknown brands.

Prior to rating purchase intention and quality, respondents viewed mocked-up ads for the products. Each ad included a description of the product, a logo, a picture of the product and a clearly stated discounted price. The original price was crossed-out and the discount (either 15%, 30% or 40%) was stated as “Now X% off – just $X.XX” ($X.XX being the discounted price). Within a product category, “actual” prices were the same regardless of discount level; the manipulation occurred with the list prices. Also ads were matched for content, only the name of the product was changed.

SUMMARY OF RESULTS

Figure 1.

H1: Perception of quality and purchase intention are positively correlated.

There was a statistically significant positive correlation between the combined means (see Figure 1.) of the purchase intention and quality perception measures at the 4 discount levels (R=.98, P<.016). The correlation coefficient was also positive and statistically significant (P<.05) for all product categories tested individually.

H2: Known brands will have higher quality and purchase intention ratings relative to unknown brands.

For both quality and purchase intention ratings, the known brand in each of the five product categories had statistically significant (P<.01) higher ratings than the unknown brand. There was no overlap between known and unknown brands for either quality or purchase intention ratings for any of the product categories. All known brands were rated higher for both quality and purchase intention than all unknown brands.

H3: Higher discounts will decrease the perception of product quality and purchase intention for some product categories.

Figure 1. shows that for all product categories combined the perception of quality and purchase intention was lowest at the 40% discount level. For individual products, purchase probability decreased from the 30% to the 40% discount. for seven of the ten products – Crest and Advanguard toothpastes, Dannon and Wellgin yogurts, Michelin and Zurgin tires, and Elkskat vodka. The discount level was statistically significant for all of these products at the P<.05 level except for Michelin which was significant at the P<.08 level. For Absolute vodka, the discount level did not have a significant effect on purchase probability. For Chromateen, the unknown HDTV brand, the highest discount had the highest purchase intention. For the Sony HD TV, the no discount group had the highest purchase intention ratings.

For the perception of product quality, decreases from the 30% to the 40% discount were found for all products except Absolut and Chromateen. The discount level was statistically significant for all of these products at the P<.05 level expect for Advanguard which was significant at the P<.06 level. For Absolut and Chromateen the highest quality ratings were found in the no discount group.

H4: Known brands can be discounted more with less of a decrement in quality and purchase ratings relative to unknown brands.

Figure 2. shows that the known brands peaked at the 30% discount for both the quality and purchase intention rating. However, the unknown brand peaked at 15% on those measures. For both known and unknown brands the highest discount produced the lowest ratings of quality and purchase intention. The findings displayed in Figure 2. can only be considered descriptive since the P<.05 level of statistical significance was not reached.

Figure 2.

Discussion

Deep discounts may not only impact consumer perceptions of product quality, but may also decrease purchase probability. For all brands combined and when known and unknown brands were partitioned out, the highest discount produced the lowest quality and purchase ratings.

The highest purchase intention was found at the highest discount for only one brand, the unknown HD TV. Possibly for a relatively expensive ($3000) unknown brand, a large discount is necessary to elicit a greater relative probability of purchase. In contrast, Sony, which received the highest overall quality ratings, had the highest purchase probability with no discount

For lower priced unknown brands (toothpaste and yogurt) the highest discount was not the most effective in increasing purchase intention. For the unknown tire brand, Zurgin, the lowest discount (15%) was the most effective in increasing purchase intention. This level was statistically more effective than no discount, 30% or 40% discount. Possibly using deeper discounts for products that have safety implications such as tires may signal quality concerns for consumers when the brand is unknown. The quality ratings for Zurgin corresponded to the purchase ratings.

Further research could determine if consumer groups (e.g., males/females, age categories, brand loyal, etc.) vary in the response to discounts in general and in specific product and brand categories.

REFERENCES

Kahneman, D. and Tversky, A. (1979), “Prospect theory: an analysis of decision under risk,” Econometrica, Vol. 47, pp. 263-291.

Madan, Vibhas, and Suri, Rajneesh, 2001, “Quality perception and monetary sacrifice: a comparative analysis of discount and fixed prices.” Journal of Product & Brand Management, Vol. 10, No. 3, pp. 170-182.

Marshall, Roger, and Leng, Seow Bee (2002), “Price threshold and discount saturation point in Singapore .” Journal of Product & Brand Management, Vol. 11, No. 3, pp. 147-159.

Mobley, Mary F., Bearden, William O., & Teel, Jesse E. (1988), “An investigation of individual responses to tensile price claims,” Journal of Consumer Research, Vol. 15, No. 9, pp. 273-279.

Moore, David J., and Olshavsky, Richard W. (1989), “Brand choice and deep price discounts.” Psychology & Marketing, Vol. 6, No. 3, pp. 181-196.

Ong, Beng Soo and Jensen, Thomas D. (1996), “Reference price-quality claims effects on purchase evaluations.” Pricing Strategy & Practice, Vol. 4, No. 4, pp. 25-34.

Sherif, C. and Hovland, C.E. (1964), Social Judgement, Yale University Press, New Haven , CT.

Urbany, Joel E., Bearden, William O., and Weilbaker, Dan C. (1988), “”The effect of plausible and exaggerated reference prices on consumer perceptions and price search,” Journal of Consumer Research, Vol. 15, June, pp. 95-110.

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Ronald Drozdenko is the author of Optimal Database Marketing: Strategy, Development and Data Mining.

Marlene Jensen is the author of 46 Ways to Raise Prices -- Without Losing Sales.