Price Decision Based on Consumer Value Perception
Conjoint Series — Scene 6
Conjoint Series — Scene 1 — How to Understand Consumer Preference?
Conjoint Series — Scene 2 — Which Product are Most Important?
Conjoint Series — Scene 3 — Designing Orthogonal Survey?
Conjoint Series — Scene 4 — Regression Analysis for All Respondent
Conjoint Series — Scene 5 — Market Segmentation using Conjoint Analysis
“Hi Dess, I just wondering. On the case of spot remover, we understand that price is one of important decision factor when they want to buy the end product. The result from one respondent even show that price is the second decision factor to purchase the product,” Ray is the fact before he ask questions.
“Is there a way to actually decide the best price based on this result?”
“That is another good observation Ray, let me explain.”
Traditional way many companies price their product is based on cost plus pricing. For example, a conditioner manufacturing companies might price the product based on cost to produce the product plus 50%. Let say that the increment 50% is the price to retailer. Lets not debate if the initial production cost will include the cost of human capital, marketing, logistics or any other associated fixed cost. For simplicity, let say that the company want to get 50% margin of its product.
This cost based pricing is prevalent to use because of several reasons. The first reason is, it is simple and easy to implement. If the manufacturing company produce various product with different package, deciding price like this will definitely reduce many pricing complexities.
Second reason is, the manufacturing company can maintain the margin similar to the past. The last reason is, it is easy to justify of the price increase when the production cost increase.
However, this pricing mechanism is forgetting one important thing, the voice of customers. Conjoint analysis can help the manufacturing the price based the product based on the feature attached to the product it self. Lets take a imaginary case. Imagine that the production cost for A and C is exactly the same, should we priced design C similar to design A, even though based on our data, design C is more preferred and has higher ranking when it is compared to design A?
Then, how is the best price will be decided? Lets comeback to the regression result from the spot remover case.
Call:
lm(formula = inverse_rank ~ package_design + brand_name + price +
good_housekeeping_seal_y_n + money_back_guarantee_y_n, data = spot_remover)
Residuals:
Min 1Q Median 3Q Max
-1.3333 -0.5000 0.1667 0.4583 1.1667
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 8.0000 0.6351 12.597 5.09e-07 ***
package_designB 8.0000 0.5500 14.546 1.47e-07 ***
package_designC 4.5000 0.5500 8.182 1.85e-05 ***
brand_nameGlory -2.0000 0.5500 -3.637 0.005430 **
brand_nameK2R -1.5000 0.5500 -2.727 0.023323 *
price$1.39 -2.8333 0.5500 -5.152 0.000602 ***
price$1.59 -7.6667 0.5500 -13.940 2.13e-07 ***
good_housekeeping_seal_y_nYes 1.5000 0.4763 3.149 0.011750 *
money_back_guarantee_y_nYes 4.5000 0.4763 9.448 5.73e-06 ***
---We can predict the rank based on the coefficients estimate:
Predicted Inverse Rank = 8 + 8*(design B) + 4.5*(design C) — 2*(brand Glory) — 1.5*(brand K2R) — 2.833*(price$1.39) — 7.67*(price$1.59)+1.5Approval + 4.5(Money Back Guarantee)
The equation implied that price $1.59 reduce the score by 7.67, price $1.39 reduce the score by 2.83 and price 1.19 do not reduce the overall score (inverse rank). Thus increasing the product price in the range $1.39 to $1.59 can cost the ranking to drop by (7.6667–2.8333)/20 equal to 4.83 or 0.24 score drop per cents.
Now, look at another angle. Offering money back guarantee can increase the score by 4.5 points. Because we know that increasing the price in range $1.39 to $1.59 could drop the rank by 0.24 points per cent, adding money back guarantee you should increase the price by 0.24 *price increase = 4.5 or price increase = 4.5/0.24 = 19 cents.
What does that mean? Increasing your price from 1.39 to 1.59 should not change the perception of your product when you add money back guarantee.
“Thanks Dess. That’s quite an explanation.” Ray added
“And remember Ray, Ran and Abe, that there is a catch. This method does not ensure profit maximization. There is other analysis method where you can incorporate consumer preferences into profit maximization. We can discuss on our next weekly meeting.”
Source:
Winston, W. L. (2014, January 8). Marketing Analytics: Data-Driven Techniques with Microsoft Excel.
