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There are two types of pricing research – direct questioning and choice experiments. Direct questioning as used in the Van Westendorp or Gabor Granger tools is simple but often leads to price estimates that are inaccurate or biased because of two reasons: (1) The lack of competitive environment in the test and (2) the transparency of the questioning purpose leading to tactical responses. On the other hand conjoint based choice experiments require the respondent to choose repeatedly between alternatives including the competitive environment. The price estimates are more accurate and less prone to bias. A major disadvantage however is that they often feel artificial, lengthy and boring, a significant problem with complex products or a large number of alternatives – like most fmcg-categories. This problem is solved by virtual shelves that make choice experiments very lively and less artificial. More alternatives can be presented in a less tedious way which increases naturalness of the choice task and validity of the results. The webinar gives answers to the following questions: • How does the task look and feel to the respondent? • How many products and price levels can be tested? • What is the output of the method? • How does the model accommodate for differences in awareness and distribution of the tested products? • How well does the preference model predict actual market shares? • How are the results interpreted? The shelf-based conjoint analysis might be the most powerful market research tool developed so far for pricing research. But as with any complex method, it requires expertise and diligence in planning and analysis. The webinar will enable participants to assess and maximize the usefulness of conjoint-based pricing models.
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Modern pricing research for FMCG –More validity, less boredom
Dr. Thomas RodenhausenHarris Interactive AG
1© Harris Interactive 04/09/2023
Pricing research for FMCG – Challenges
Large number of product variants and alternatives
Complex competitive environment
Recommended retail price vs. price promotions
Poor price knowledge
Low involvement
© Harris Interactive 2
Traditional pricing research tools – „monadic“ approach
© Harris Interactive 3
Direct question
Priced purchase intention
Van Westendorp PSM
Gabor-Granger approach
Traditional pricing research tools – „monadic“ approach
© Harris Interactive 4
Direct question
Priced purchase intention
Van Westendorp PSM
Gabor-Granger approach
• Efficiency?• Motivation of
participants?• Validity?
Traditional pricing research tools – Efficiency?
• Direct question• Priced purchase intention
One question per SKU
• Van Westendorp PSM
Four questions per SKU
• Gabor Granger Approach• Van Westendorp PSM with NMS
More than four questions per SKU
© Harris Interactive 5
Traditional pricing research tools – Motivation?
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1 SKU 3 SKU 9 SKU 81 SKU
Direct question /priced purchase intention 1 3 9 81
Van Westendorp PSM 4 12 36 324
Gabor Granger approach(5 price levels) 5 15 45 405
Van Westendorp PSM NMS 6 18 54 486
Traditional pricing research tools – Validity?
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ProductEstimated
normal price (E)
Actualretail price
(A)Abs[(A-E)/A] (A-E)/A
Persil(Washing powder) €5.35 €5.45 33% 0%
Lenor(Fabric softener) €2.80 €1.45 95% -93%
Pril(Dishwashing liquid) €1.76 €1.35 39% -28%
Schauma Shampoo €1.81 €1.65 30% -10%Drei-Wetter-Taft(Hairspray) €2.33 €1.75 40% -33%
Palmolive(Dishwashing liquid) €2.05 €1.25 66% -64%
Eberhardt, T., Kenning, P., Schneider, H. Kennt der Kunde Ihre Preise? Projektbericht. Friedrichshafen 2009.
Traditional pricing research tools – Validity?
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€0.00 €0.50 €1.00 €1.50 €2.00 €2.50 €3.00 €3.50 €4.00 €4.50 €5.00 €5.50 €6.00 €6.50 €7.00 €7.500%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%Average retail price: €3.75
„Optimal price“: €3.02
„Normal price“: €3.28
Traditional pricing research tools – Validity?
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What is your willingness to pay 0.39 € for Kinder Riegel …?
… if a bar of Mars would cost the same?
Alternative – Discrete choice modeling in a virtual shelf
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SSI Manual,Sawtooth Software,p. 401
Virtual shelf DCM – Sawtooth‘s limitations
Quality of visualization
Inclusion of PoS material
Design flexibility
Correction for distribution
Automated analysis
Analysis of advanced KPI
© Harris Interactive 11
Virtual shelf DCM – Realistic, Efficient and Powerful Analytics
© Harris Interactive 12
Our approach combinesComplex designsautomated shelf
generationrealistic look-and-feel, individual part-worth-
utilitieshighly flexible simulation
toolautomated analysis of
thousands of scenarios
What we do – We generate complex conjoint-analytical designs …
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What we do – … and thousands of virtual shelves …
© Harris Interactive 14
What we do – … with a highly realistic look-and-feel
© Harris Interactive 15
What we do – We estimate individual part worth utilities …
© Harris Interactive 16
What we do – … using Sawtooth‘s renowned HB estimation
© Harris Interactive 17
What we do – But we discard Sawtooth‘s austere simulator …
© Harris Interactive 18
What we do – … and program an excel-based simulator …
© Harris Interactive 19
What we do – … offering a large variety of KPI and graphs …
© Harris Interactive 20
What we do – … and automated simulation capabilities
© Harris Interactive 21
Conjont-analytical prediction of market shares – Challenges
• Representative for market?
Sample
• Representative for purchase situation?
Task
• Aware of products and their characteristics?
Consumer
• Differences in terms of awareness, distribution, period in product life cycle?
Products
© Harris Interactive 22
Prediction of actual market shares far from being trivial!
Conjont-analytical prediction of market shares – Example
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Product 1Product 2Product 3Product 4Product 5Product 6Product 7Product 8Product 9
Product 10Product 11Product 12Product 13Product 14Product 15Product 16
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Actual revenue share (in %)
Conjont-analytical prediction of market shares – Example
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Product 1Product 2Product 3Product 4Product 5Product 6Product 7Product 8Product 9
Product 10Product 11Product 12Product 13Product 14Product 15Product 16
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Observed revenue share (in %) Actual revenue share (in %)
Correlation between actual and observed revenue shares 0.68
Conjont-analytical prediction of market shares – Example
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Product 1Product 2Product 3Product 4Product 5Product 6Product 7Product 8Product 9
Product 10Product 11Product 12Product 13Product 14Product 15Product 16
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Observed revenue share corrected for distribution (in %)Actual revenue share (in %)
Correlation between actual and observed revenue shares after correction for distribution 0.78
Conjont-analytical prediction of market shares – Example
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Product 1Product 2Product 3Product 4Product 5Product 6Product 7Product 8Product 9
Product 10Product 11Product 12Product 13Product 14Product 15Product 16
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Observed revenue share after calibration (in %) Actual revenue share (in %)
An iterative calibration of individual results can enforce a nearly perfect reproduction of actual market shares as sound base for further analysis!
Results – Price sensitivity analysis for a single product …
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Results – Cross price sensitivity analysis for two products …
© Harris Interactive 28
Product 1 Product 1 Product 1 Product 1 Product 1 Product 2 Product 2 Product 2 Product 2 Product 2 Product 3 Product 3 Product 3 Product 3 Product 3 Product 4 Product 4 Product 4 Product 4 Product 4 Product 5 Product 5 Product 5 Product 5 Product 5 Product 6 Product 6 Product 6 Product 6 Product 6 Product 7 Product 7 Product 7 Product 7 Product 7150 155 160 165 170 70 75 79 85 89 140 150 160 170 180 120 130 140 150 160 120 130 140 150 160 190 200 210 220 230 169 179 189 199 209
SKU 1 150 2.0 2.7 0.0 2.1 2.2 0.4 0.6 0.8 0.0 0.9 3.0 3.8 0.0 2.9 9.9 0.7 0.7 0.0 0.7 0.8 1.1 0.9 0.8 0.0 1.7 3.9 4.5 6.3 0.0 3.1SKU 1 155 1.6 2.5 0.0 1.8 1.9 0.3 0.6 0.6 0.0 0.8 1.2 1.7 0.0 1.5 4.7 0.5 0.6 0.0 0.7 0.7 0.7 0.8 0.7 0.0 1.5 3.5 4.2 6.1 0.0 3.0SKU 1 160 28.2 18.7 0.0 -8.0 -15.2 1.1 1.5 0.0 1.4 1.6 0.3 0.4 0.6 0.0 0.7 0.7 1.0 0.0 1.0 2.9 0.4 0.4 0.0 0.6 0.6 0.6 0.6 0.6 0.0 1.4 2.1 2.3 3.7 0.0 2.8SKU 1 165 0.8 1.1 0.0 0.8 1.1 0.2 0.4 0.4 0.0 0.7 0.6 0.8 0.0 0.8 2.5 0.3 0.4 0.0 0.4 0.5 0.4 0.5 0.4 0.0 1.3 1.2 1.3 1.5 0.0 2.7SKU 1 170 0.7 0.9 0.0 0.7 0.9 0.2 0.4 0.4 0.0 0.4 0.3 0.4 0.0 0.5 1.0 0.3 0.3 0.0 0.3 0.4 0.3 0.4 0.3 0.0 1.0 1.1 1.2 1.5 0.0 2.2SKU 2 70 5.0 4.2 0.0 5.8 5.3 5.1 6.4 4.7 0.0 1.5 6.9 6.7 0.0 9.5 6.0 7.1 7.9 0.0 5.0 6.3 4.3 4.4 5.2 0.0 2.1 7.9 8.2 7.1 0.0 5.1SKU 2 75 5.2 4.1 0.0 5.5 5.1 4.2 5.1 3.8 0.0 1.5 5.8 6.5 0.0 8.5 5.4 6.1 7.1 0.0 4.7 4.6 4.0 4.2 4.9 0.0 2.1 7.5 8.0 6.8 0.0 5.0SKU 2 79 4.0 2.6 0.0 5.0 4.7 15.5 6.7 0.0 -7.0 -11.3 3.1 2.6 3.4 0.0 1.3 5.1 5.1 0.0 8.8 5.6 4.2 4.3 0.0 4.1 4.0 3.4 3.4 4.7 0.0 2.1 6.1 6.5 6.0 0.0 4.8SKU 2 85 3.7 2.4 0.0 4.0 4.1 2.8 2.2 2.5 0.0 1.3 4.7 5.0 0.0 7.8 5.3 3.6 3.8 0.0 2.8 3.1 2.8 2.8 3.8 0.0 2.0 4.2 4.6 4.1 0.0 4.5SKU 2 89 3.6 2.3 0.0 3.8 3.8 2.8 2.2 2.4 0.0 1.1 4.4 4.7 0.0 7.3 4.9 3.5 3.7 0.0 2.6 2.8 2.7 2.7 3.6 0.0 1.7 4.0 4.4 3.9 0.0 3.9SKU 3 70 22.9 23.1 0.0 28.8 22.6 1.0 1.4 3.3 0.0 1.7 1.4 1.9 0.0 4.9 3.8 2.9 3.1 0.0 7.3 5.0 11.2 14.2 26.5 0.0 3.7 18.2 16.3 21.8 0.0 8.6SKU 3 75 20.7 21.1 0.0 27.9 21.7 0.9 1.3 2.9 0.0 1.6 1.3 1.8 0.0 4.6 3.6 2.7 2.9 0.0 6.8 4.8 10.7 13.8 25.5 0.0 3.7 18.0 16.3 21.5 0.0 8.6SKU 3 79 11.9 9.3 0.0 23.2 16.8 14.8 7.5 0.0 -15.7 -19.6 0.8 1.2 2.7 0.0 1.6 1.0 1.2 0.0 4.3 3.3 2.1 2.1 0.0 6.2 4.5 9.5 12.0 23.3 0.0 3.6 15.5 13.7 19.6 0.0 8.3SKU 3 85 10.2 7.6 0.0 19.4 14.9 0.7 1.0 2.1 0.0 1.5 0.8 1.0 0.0 2.7 2.6 1.6 1.6 0.0 3.7 3.0 4.2 4.2 7.2 0.0 2.4 10.3 8.5 11.3 0.0 7.7SKU 3 89 9.1 6.5 0.0 18.6 13.4 0.7 1.0 2.0 0.0 1.1 0.8 0.9 0.0 2.5 1.6 1.5 1.6 0.0 3.5 2.8 3.7 3.7 6.1 0.0 1.8 9.9 8.1 10.8 0.0 6.5SKU 4 70 3.1 3.5 0.0 3.1 2.5 2.7 2.8 4.4 0.0 1.5 2.1 2.5 0.0 3.0 2.0 2.7 2.9 0.0 1.9 1.8 2.5 2.7 2.7 0.0 1.2 4.8 4.8 4.4 0.0 4.4SKU 4 75 2.9 3.4 0.0 2.9 2.4 2.5 2.5 3.7 0.0 1.5 2.0 2.4 0.0 2.4 1.7 2.5 2.8 0.0 1.8 1.8 2.4 2.6 2.6 0.0 1.2 4.5 4.6 4.2 0.0 4.3SKU 4 79 2.0 2.2 0.0 2.5 2.1 19.2 7.9 0.0 -7.5 -9.7 2.3 2.1 3.4 0.0 1.4 1.6 1.6 0.0 2.2 1.6 1.9 1.9 0.0 1.6 1.6 2.0 2.0 2.4 0.0 1.1 3.7 3.7 3.8 0.0 4.1SKU 4 85 1.7 1.9 0.0 1.9 1.8 2.1 1.9 2.6 0.0 1.4 1.5 1.5 0.0 1.7 1.3 1.6 1.6 0.0 1.0 1.2 1.7 1.8 2.1 0.0 1.1 2.9 2.9 2.8 0.0 4.0SKU 4 89 1.6 1.8 0.0 1.8 1.6 2.0 1.9 2.6 0.0 1.4 1.4 1.4 0.0 1.6 1.2 1.5 1.6 0.0 1.0 1.2 1.6 1.7 2.0 0.0 0.9 2.8 2.9 2.7 0.0 3.7SKU 5 70 4.9 4.7 0.0 4.7 5.9 7.5 5.5 11.8 0.0 9.0 6.6 7.8 0.0 8.8 6.3 7.3 6.9 0.0 6.8 5.0 2.9 3.1 3.0 0.0 3.1 5.4 5.3 4.7 0.0 8.8SKU 5 75 5.1 4.5 0.0 4.5 5.7 8.2 5.1 9.9 0.0 8.9 5.7 7.1 0.0 7.2 5.4 7.0 6.7 0.0 6.9 5.2 2.8 3.0 3.0 0.0 3.1 5.0 5.0 4.4 0.0 8.5SKU 5 79 4.1 3.3 0.0 3.9 5.0 20.4 8.9 0.0 -7.6 -10.9 7.7 4.4 9.2 0.0 8.4 4.4 4.7 0.0 6.3 4.8 5.4 4.7 0.0 6.3 4.8 2.3 2.3 2.8 0.0 2.9 4.3 4.3 4.0 0.0 8.1SKU 5 85 3.6 2.8 0.0 3.0 4.4 7.0 3.8 6.9 0.0 8.3 3.6 4.1 0.0 3.9 3.7 3.6 3.7 0.0 2.7 2.7 1.9 2.0 2.4 0.0 2.8 3.2 3.4 2.7 0.0 7.8SKU 5 89 3.4 2.6 0.0 2.9 4.0 6.8 3.6 6.6 0.0 6.3 3.4 3.9 0.0 3.8 3.2 3.5 3.6 0.0 2.6 2.5 1.8 1.9 2.3 0.0 2.1 3.1 3.3 2.6 0.0 5.7SKU 6 140 1.1 0.9 0.0 0.7 1.1 4.2 1.7 0.0 1.3 1.6 10.9 10.5 0.0 5.4 5.8 15.2 18.4 0.0 1.6 5.7 1.6 1.4 0.6 0.0 1.8 1.9 1.6 1.3 0.0 6.2SKU 6 150 0.9 0.8 0.0 0.6 1.0 1.6 1.4 0.0 0.9 1.2 15.6 9.2 0.0 2.8 4.2 13.9 18.1 0.0 1.3 4.8 1.4 1.3 0.5 0.0 1.8 1.5 1.4 1.1 0.0 6.0SKU 6 160 0.8 0.6 0.0 0.6 0.9 1.4 1.2 0.0 0.8 1.1 12.9 3.2 0.0 2.1 3.1 9.3 11.6 0.0 1.0 3.3 1.1 1.0 0.5 0.0 1.6 1.2 1.1 0.9 0.0 5.6SKU 6 170 0.7 0.6 0.0 0.5 0.8 1.3 1.1 0.0 0.4 0.8 36.6 13.7 3.1 0.0 -5.1 12.8 3.1 0.0 1.5 2.8 9.2 11.5 0.0 0.8 3.1 1.0 0.9 0.4 0.0 1.6 1.0 0.9 0.7 0.0 5.5SKU 6 180 0.6 0.5 0.0 0.4 0.6 1.2 1.0 0.0 0.4 0.6 12.5 2.6 0.0 1.4 2.3 8.6 10.6 0.0 0.7 2.6 1.0 0.8 0.3 0.0 1.0 0.9 0.8 0.7 0.0 3.4SKU 7 120 2.1 0.8 0.0 0.8 1.1 1.9 1.5 0.0 1.4 1.5 6.5 4.4 3.5 0.0 3.6 8.5 9.5 0.0 1.8 12.1 3.1 1.7 1.1 0.0 19.2 1.2 1.2 1.3 0.0 2.5SKU 7 130 4.5 1.5 0.0 1.0 1.7 1.1 1.2 0.0 1.0 1.3 2.8 3.9 2.1 0.0 3.5 6.3 8.1 0.0 1.6 8.0 4.1 2.1 1.2 0.0 19.8 1.2 1.2 1.3 0.0 2.5SKU 7 140 6.8 2.2 0.0 1.2 2.4 0.8 0.7 0.0 0.8 1.1 2.6 3.3 1.9 0.0 3.3 28.3 11.4 0.0 -5.1 -13.6 3.7 4.1 0.0 1.5 7.0 4.9 2.3 1.3 0.0 20.2 1.0 1.0 1.2 0.0 2.4SKU 7 150 7.4 2.4 0.0 1.2 2.6 0.6 0.5 0.0 0.5 0.8 2.7 3.2 1.5 0.0 3.3 4.1 4.1 0.0 1.3 6.7 5.1 2.3 1.2 0.0 20.3 0.9 0.8 0.8 0.0 2.4SKU 7 160 10.6 3.8 0.0 1.8 4.1 0.6 0.5 0.0 0.5 0.6 2.5 2.6 1.5 0.0 2.6 3.4 2.9 0.0 1.2 3.8 6.0 3.1 1.5 0.0 10.2 1.0 0.8 0.8 0.0 1.8SKU 8 120 0.4 0.4 0.0 0.7 0.8 1.3 1.7 0.0 1.1 1.0 7.1 2.6 2.9 0.0 2.4 2.7 3.3 0.0 1.4 1.2 0.5 0.5 0.4 0.0 0.7 0.8 1.0 0.8 0.0 0.9SKU 8 130 0.4 0.4 0.0 0.6 0.8 1.0 1.5 0.0 0.8 0.8 1.9 1.9 1.6 0.0 2.2 2.3 3.0 0.0 1.0 1.1 0.4 0.5 0.4 0.0 0.6 0.7 0.9 0.7 0.0 0.9SKU 8 140 0.3 0.3 0.0 0.5 0.7 0.6 0.9 0.0 0.7 0.7 1.7 1.6 1.6 0.0 2.0 29.3 13.3 0.0 -6.8 -11.9 1.2 1.5 0.0 0.8 0.9 0.4 0.5 0.3 0.0 0.6 0.5 0.6 0.6 0.0 0.9SKU 8 150 0.2 0.3 0.0 0.4 0.6 0.5 0.8 0.0 0.4 0.4 1.7 1.6 1.4 0.0 1.9 1.0 1.4 0.0 0.4 0.7 0.4 0.5 0.3 0.0 0.5 0.4 0.5 0.4 0.0 0.8SKU 8 160 0.2 0.2 0.0 0.3 0.4 0.5 0.8 0.0 0.3 0.4 1.7 1.6 1.3 0.0 1.7 1.0 1.3 0.0 0.4 0.5 0.4 0.4 0.2 0.0 0.4 0.4 0.5 0.4 0.0 0.7SKU 9 120 1.1 1.1 0.0 1.5 1.6 3.9 5.3 0.0 3.2 3.1 15.3 27.5 4.9 0.0 12.0 10.2 15.7 0.0 9.4 9.1 2.1 2.1 1.1 0.0 1.2 3.9 4.2 4.3 0.0 5.4SKU 9 130 1.0 1.0 0.0 1.4 1.6 3.1 4.8 0.0 2.5 2.6 13.2 27.3 4.1 0.0 12.1 9.9 14.8 0.0 4.9 6.9 1.7 1.9 0.9 0.0 1.1 3.5 3.9 4.1 0.0 5.3SKU 9 140 0.7 0.7 0.0 1.2 1.4 2.0 2.7 0.0 2.1 2.1 11.4 23.2 3.2 0.0 10.8 11.3 16.5 0.0 4.0 8.1 17.7 10.2 0.0 -5.1 -9.7 1.1 1.2 0.7 0.0 1.0 2.8 3.2 3.7 0.0 5.1SKU 9 150 0.6 0.6 0.0 0.9 1.2 1.5 2.3 0.0 0.9 1.2 11.2 22.9 2.8 0.0 10.6 11.0 16.2 0.0 2.7 7.5 0.9 1.0 0.5 0.0 1.0 3.2 3.7 4.1 0.0 4.9SKU 9 160 0.6 0.6 0.0 0.9 1.1 1.6 2.2 0.0 0.9 1.1 10.9 21.5 2.4 0.0 9.2 14.1 16.9 0.0 2.8 10.7 0.9 0.9 0.5 0.0 0.8 3.1 3.6 4.1 0.0 4.5SKU 10 120 1.6 1.1 0.0 1.6 1.4 0.9 1.2 0.0 1.3 1.3 0.4 0.7 0.9 0.0 1.5 1.7 1.6 0.0 2.0 1.8 1.9 1.6 1.0 0.0 1.2 2.9 3.3 2.3 0.0 3.2SKU 10 130 1.0 1.0 0.0 1.4 1.3 0.7 1.0 0.0 1.1 1.1 0.3 0.7 0.8 0.0 1.4 0.9 1.2 0.0 1.7 1.5 1.2 1.2 0.9 0.0 1.1 2.6 3.0 2.0 0.0 3.0SKU 10 140 0.7 0.7 0.0 1.2 1.1 0.4 0.5 0.0 0.9 0.9 0.3 0.4 0.7 0.0 1.2 0.6 0.7 0.0 1.6 1.3 22.7 12.7 0.0 -9.9 -15.3 0.7 0.7 0.7 0.0 0.8 1.7 2.0 1.5 0.0 2.8SKU 10 150 0.6 0.6 0.0 0.6 0.8 0.3 0.3 0.0 0.4 0.5 0.2 0.4 0.5 0.0 1.2 0.5 0.7 0.0 1.1 1.0 0.5 0.5 0.3 0.0 0.7 1.3 1.5 0.8 0.0 2.7SKU 10 160 0.6 0.5 0.0 0.6 0.6 0.3 0.3 0.0 0.4 0.5 0.2 0.3 0.5 0.0 0.7 0.5 0.6 0.0 1.0 0.7 0.5 0.4 0.3 0.0 0.4 1.3 1.5 0.8 0.0 2.0SKU 11 190 4.4 2.5 0.0 2.3 4.8 2.6 3.0 0.0 3.8 3.7 2.7 2.7 1.8 0.0 3.7 13.4 13.7 0.0 9.8 32.4 3.8 3.3 0.0 1.4 2.0 14.5 14.5 8.8 0.0 9.2SKU 11 200 2.8 2.0 0.0 1.9 4.2 2.0 2.7 0.0 3.1 3.2 1.4 2.4 1.5 0.0 3.6 7.0 7.8 0.0 6.2 24.0 3.1 3.1 0.0 1.3 1.7 12.1 12.7 7.4 0.0 8.8SKU 11 210 1.9 1.4 0.0 1.6 3.8 1.4 1.5 0.0 2.8 2.9 1.0 1.7 1.3 0.0 3.2 4.7 4.6 0.0 4.5 18.9 2.0 1.8 0.0 0.9 1.5 6.1 6.0 6.1 0.0 7.9SKU 11 220 1.7 1.3 0.0 1.2 3.4 1.0 1.2 0.0 2.3 2.4 1.0 1.6 1.1 0.0 3.2 4.2 4.1 0.0 3.9 17.7 1.8 1.6 0.0 0.7 1.3 33.3 20.8 7.4 0.0 -10.5 4.6 4.7 4.5 0.0 7.5SKU 11 230 1.2 1.0 0.0 1.0 2.8 0.9 1.1 0.0 2.0 2.1 0.9 1.4 1.0 0.0 2.0 2.9 2.5 0.0 2.6 27.2 1.5 1.3 0.0 0.6 1.0 4.2 4.3 4.1 0.0 5.4SKU 12 190 1.3 1.1 0.0 2.2 3.0 2.2 2.1 0.0 2.9 2.7 1.1 2.1 2.1 0.0 3.6 1.1 1.9 0.0 2.2 2.1 1.4 1.6 0.0 1.4 1.4 4.6 4.7 5.8 0.0 6.3SKU 12 200 1.0 1.1 0.0 2.0 2.8 1.3 1.9 0.0 2.4 2.3 0.9 2.0 1.9 0.0 3.5 0.9 1.7 0.0 2.0 2.0 1.1 1.4 0.0 1.2 1.3 3.8 4.1 5.0 0.0 6.1SKU 12 210 0.8 0.8 0.0 1.7 2.6 0.9 1.1 0.0 2.3 2.2 0.7 1.4 1.6 0.0 2.9 0.6 0.9 0.0 1.7 1.7 0.8 0.9 0.0 1.0 1.1 2.9 3.2 4.6 0.0 5.7SKU 12 220 0.6 0.7 0.0 1.3 2.3 0.7 0.9 0.0 2.0 1.9 0.6 1.2 1.3 0.0 2.8 0.5 0.8 0.0 1.1 1.4 0.6 0.7 0.0 0.6 0.9 26.6 18.6 10.9 0.0 -5.7 2.2 2.4 3.3 0.0 5.5SKU 12 230 0.5 0.6 0.0 1.2 2.0 0.6 0.8 0.0 1.8 1.6 0.5 1.1 1.2 0.0 1.6 0.4 0.7 0.0 1.0 1.0 0.6 0.7 0.0 0.6 0.7 2.0 2.2 2.9 0.0 4.0SKU 13 169 1.5 1.3 0.0 2.9 2.3 1.7 2.3 0.0 2.3 2.0 0.6 1.0 1.4 0.0 1.7 0.6 1.0 0.0 1.4 1.3 1.4 1.7 0.0 3.1 2.3 3.3 3.9 3.9 0.0 1.3SKU 13 179 1.3 1.3 0.0 2.7 2.2 1.3 2.0 0.0 1.9 1.7 0.5 0.9 1.2 0.0 1.7 0.5 0.9 0.0 1.2 1.0 1.2 1.6 0.0 2.8 2.1 2.9 3.6 3.5 0.0 1.2SKU 13 189 1.6 1.6 0.0 2.7 2.1 0.9 1.2 0.0 1.4 1.3 0.4 0.6 1.0 0.0 1.5 0.3 0.6 0.0 1.1 0.9 0.8 1.0 0.0 1.9 1.5 1.7 2.1 2.8 0.0 1.1SKU 13 199 2.1 2.4 0.0 3.0 2.2 0.6 0.8 0.0 0.7 0.8 0.3 0.5 0.8 0.0 1.5 0.3 0.5 0.0 0.7 0.7 0.6 0.9 0.0 1.0 1.0 1.0 1.1 1.0 0.0 0.9 34.0 24.9 10.3 0.0 -4.2SKU 13 209 2.0 2.4 0.0 2.9 2.1 0.6 0.8 0.0 0.7 0.7 0.3 0.5 0.7 0.0 0.9 0.3 0.5 0.0 0.7 0.5 0.6 0.8 0.0 1.0 0.9 0.9 1.0 0.9 0.0 0.7SKU 14 100 2.2 1.6 0.0 3.7 3.6 11.4 8.6 0.0 9.7 8.8 19.0 8.7 11.9 0.0 8.9 11.2 7.3 0.0 12.7 8.6 9.7 7.7 0.0 10.4 7.9 5.3 5.4 5.1 0.0 3.4 9.8 10.1 6.8 0.0 11.3SKU 15 100 4.6 4.3 0.0 7.6 6.4 5.1 6.8 0.0 11.2 9.4 1.7 3.2 2.6 0.0 4.9 3.4 4.4 0.0 4.4 4.2 5.6 6.3 0.0 4.6 4.2 9.5 11.3 10.7 0.0 6.8 14.3 15.5 9.5 0.0 9.5SKU 16 100 1.2 1.4 0.0 3.2 3.1 1.4 1.8 0.0 2.7 5.0 0.7 1.3 1.4 0.0 2.3 0.6 0.5 0.0 1.1 1.0 1.0 1.3 0.0 1.9 1.6 1.5 1.7 1.3 0.0 3.4 4.0 4.6 7.3 0.0 4.2
… identifying exchange relationships between products
Conclusion – Advantages of a virtual shelf DCM
© Harris Interactive 29
Efficiency• Up to 100 SKU per DCM with up to 7 price levels each• Automated analysis of cross price sensitivities
Motivation of participants• Easily understood task• Realistic look-and-feel
Validity• Naturalistic question and response formats• Inclusion of competitive environment
Harris Interactive AG
Dr. Thomas Rodenhausen, PresidentHarris Interactive AGBeim Strohhause 3120097 Hamburg
30© Harris Interactive 04/09/2023
