Modern pricing research for FMCG –
More validity, less boredom
Dr. Thomas Rodenhausen
Harris Interactive AG
1© Harris Inte...
Pricing research for FMCG – Challenges
Large number of product variants and alternatives
Complex competitive environment
R...
Traditional pricing research tools – „monadic“ approach
© Harris Interactive 3
Direct question
Priced purchase intention
V...
Traditional pricing research tools – „monadic“ approach
© Harris Interactive 4
Direct question
Priced purchase intention
V...
Traditional pricing research tools – Efficiency?
• Direct question
• Priced purchase intention
One question per SKU
• Van ...
Traditional pricing research tools – Motivation?
© Harris Interactive 6
1 SKU 3 SKU 9 SKU 81 SKU
Direct question /
priced ...
Traditional pricing research tools – Validity?
© Harris Interactive 7
Product
Estimated
normal price
(E)
Actual
retail pri...
Traditional pricing research tools – Validity?
© Harris Interactive 8
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
€0.00 €0...
Traditional pricing research tools – Validity?
© Harris Interactive 9
What is your willingness to pay
0.39 € for Kinder Ri...
Alternative – Discrete choice modeling in a virtual shelf
© Harris Interactive 10
SSI Manual,
Sawtooth Software,
p. 401
Virtual shelf DCM – Sawtooth‘s limitations
Quality of visualization
Inclusion of PoS material
Design flexibility
Correctio...
Virtual shelf DCM – Realistic, Efficient and Powerful Analytics
© Harris Interactive 12
Our approach combines
Complex des...
What we do – We generate complex conjoint-analytical designs …
© Harris Interactive 13
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 purch...
Conjont-analytical prediction of market shares – Example
© Harris Interactive 23
27
14
10
6
6
5
5
4
4
4
4
3
3
3
1
1
Produc...
Conjont-analytical prediction of market shares – Example
© Harris Interactive 24
27
14
10
6
6
5
5
4
4
4
4
3
3
3
1
1
13
8
7...
Conjont-analytical prediction of market shares – Example
© Harris Interactive 25
27
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4
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1...
Conjont-analytical prediction of market shares – Example
© Harris Interactive 26
27
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10
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1
27
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Results – Price sensitivity analysis for a single product …
© Harris Interactive 27
Results – Cross price sensitivity analysis for two products …
© Harris Interactive 28
Product 1 Product 1 Product 1 Produc...
Conclusion – Advantages of a virtual shelf DCM
© Harris Interactive 29
Efficiency
• Up to 100 SKU per DCM with up to 7 pri...
Harris Interactive AG
Dr. Thomas Rodenhausen, President
Harris Interactive AG
Beim Strohhause 31
20097 Hamburg
30© Harris ...
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Pricing Research Webinar

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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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  • Big portfolios / line-ups. Quick paced innovation. Products can often serve multiple purposes and therefore have multiple exchange relations to different categories. Example – chocolate because I am hungry for sweets (competitions sweets), because I want to indulge myself (could also be a luxury bathing soap) or because I want to make a present (perfume, book, other sweets or flowers).

    The manufacturer only loosely controls retail price.

    Consumers often have limited price awareness or knowledge.
  • Monadic means that only the price for one product is tested in an isolated way. But relating to the complexities just explained the price perception and awareness can extend largely on the context. If I compare a premium chocolate bar price to a generic chocolate it is high, if i compare it to a bouquet of flowers it is not expensive at all.
  • Monadic questioning means – only one price estimate per observation
    Multiple tests are boring for respondents
    The validity is questionable – particularly for a complex environment with multiple subjectie contexts, where this



  • Effizienz – mit jedem Produkt, das getestet wird, vervielfachen sich die Abfragen
  • Effizienz – mit jedem Produkt, das getestet wird, vervielfachen sich die Abfragen
  • Validität – Befragte sind nur schlecht in der Lage, Preise von bestehenden Produkten einzuschätzen  Wie sieht es dann erst mit Preisbewertungen für neue Konzepte aus?
  • Beispiel – „Normalpreis“ für Beck‘s wird in PSM massiv unterschätzt
  • Bei „monadischen“ Preisangaben bleibt unklar, ob und wenn ja wie der Wettbewerb einbezogen wird durch die Befragten
  • Sawtooth als Standard für DCM, aber mit Schwächen im Detail
  • Complex conjoint-analytical designs TO ADAPT TO A VARIETY OF BUSINESS PROBLEMS
    Automated generation of thousands of virtual shelves TO EXTEND THIS ADAPTIVITY TO THE VISUAL SET-UP OF THE RESEARCH DESIGN
    POWERFUL AND EFFICIENT ANALYSIS
  • Idee – Diese und die nächsten Slides als Darstellung unseres Vorgehens in schneller Abfolge (Überschriften beziehen sich aufeinander)

    Complexity often arises out of the need to build in restrictions i.e. certain SKUs should not appear together on the shelf, certain price patterns across a line-up are restricted and so forth.
  • Whereas the visual appearance of most off-the-shelf-solutions is quite restricted, we do use (and like) Sawtooth‘s Hierarchical Bayes Estimation to compute individual part utilities. This enables us for instance to provide benefit segmentations for almost all conjoint designs.
  • However we prefer tailor-made simulators to the off-the-shelf-simulation provided for by Sawtooth.
  • ...an excel-based simulator runs on virtually every client computer and has an open programming interface.
  • ….enabling us to offer a wide variety of key performance indicators and graphs.
  • …also enabling us to provide for automated simulation runs.
  • Herausforderung Abschätzung von Marktanteilen – angesichts der Einflussfaktoren nicht trivial
  • Beispiel – Umsatzanteile im Markt
  • Beispiel – Umsatzanteile im Markt im Vergleich zur Simulation
  • Beispiel – Korrektur für Unterschiede in Distribution mit deutlicher Verbesserung  starker Validitätsbeleg für DCM-Ergebnisse
  • Beispiel – mit Kalibrierung kann die exakte reale Wahlanteilsverteilung hergestellt werden als Basis für eigentlich interessierende Preisanalysen
  • Beispiel für eigentlich interessierende Analyse: Preissensitivität für einzelnes Produkt
  • Beispiel für eigentlich interessierende Analyse: Kreuzsensitivitäten von zwei Produkten
  • Pricing Research Webinar

    1. 1. Modern pricing research for FMCG – More validity, less boredom Dr. Thomas Rodenhausen Harris Interactive AG 1© Harris Interactive 6/4/2014
    2. 2. 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
    3. 3. Traditional pricing research tools – „monadic“ approach © Harris Interactive 3 Direct question Priced purchase intention Van Westendorp PSM Gabor-Granger approach
    4. 4. 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?
    5. 5. 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
    6. 6. Traditional pricing research tools – Motivation? © Harris Interactive 6 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
    7. 7. Traditional pricing research tools – Validity? © Harris Interactive 7 Product Estimated normal price (E) Actual retail 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.
    8. 8. Traditional pricing research tools – Validity? © Harris Interactive 8 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% €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 €7.50 Average retail price: €3.75 „Optimal price“: €3.02 „Normal price“: €3.28
    9. 9. Traditional pricing research tools – Validity? © Harris Interactive 9 What is your willingness to pay 0.39 € for Kinder Riegel …? … if a bar of Mars would cost the same?
    10. 10. Alternative – Discrete choice modeling in a virtual shelf © Harris Interactive 10 SSI Manual, Sawtooth Software, p. 401
    11. 11. 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
    12. 12. Virtual shelf DCM – Realistic, Efficient and Powerful Analytics © Harris Interactive 12 Our approach combines Complex designs automated shelf generation realistic look-and-feel, individual part-worth- utilities highly flexible simulation tool automated analysis of thousands of scenarios
    13. 13. What we do – We generate complex conjoint-analytical designs … © Harris Interactive 13
    14. 14. What we do – … and thousands of virtual shelves … © Harris Interactive 14
    15. 15. What we do – … with a highly realistic look-and-feel © Harris Interactive 15
    16. 16. What we do – We estimate individual part worth utilities … © Harris Interactive 16
    17. 17. What we do – … using Sawtooth‘s renowned HB estimation © Harris Interactive 17
    18. 18. What we do – But we discard Sawtooth‘s austere simulator … © Harris Interactive 18
    19. 19. What we do – … and program an excel-based simulator … © Harris Interactive 19
    20. 20. What we do – … offering a large variety of KPI and graphs … © Harris Interactive 20
    21. 21. What we do – … and automated simulation capabilities © Harris Interactive 21
    22. 22. 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!
    23. 23. Conjont-analytical prediction of market shares – Example © Harris Interactive 23 27 14 10 6 6 5 5 4 4 4 4 3 3 3 1 1 Product 1 Product 2 Product 3 Product 4 Product 5 Product 6 Product 7 Product 8 Product 9 Product 10 Product 11 Product 12 Product 13 Product 14 Product 15 Product 16 Actual revenue share (in %)
    24. 24. Conjont-analytical prediction of market shares – Example © Harris Interactive 24 27 14 10 6 6 5 5 4 4 4 4 3 3 3 1 1 13 8 7 6 4 3 8 6 4 5 7 9 9 3 3 6 Product 1 Product 2 Product 3 Product 4 Product 5 Product 6 Product 7 Product 8 Product 9 Product 10 Product 11 Product 12 Product 13 Product 14 Product 15 Product 16 Actual revenue share (in %) Observed revenue share (in %) Correlation between actual and observed revenue shares 0.68
    25. 25. Conjont-analytical prediction of market shares – Example © Harris Interactive 25 27 14 10 6 6 5 5 4 4 4 4 3 3 3 1 1 21 4 11 6 4 4 11 4 5 6 7 7 5 2 1 3 Product 1 Product 2 Product 3 Product 4 Product 5 Product 6 Product 7 Product 8 Product 9 Product 10 Product 11 Product 12 Product 13 Product 14 Product 15 Product 16 Actual revenue share (in %) Observed revenue share corrected for distribution (in %) Correlation between actual and observed revenue shares after correction for distribution 0.78
    26. 26. Conjont-analytical prediction of market shares – Example © Harris Interactive 26 27 14 10 6 6 5 5 4 4 4 4 3 3 3 1 1 27 14 10 6 6 5 5 4 4 4 4 3 3 3 1 1 Product 1 Product 2 Product 3 Product 4 Product 5 Product 6 Product 7 Product 8 Product 9 Product 10 Product 11 Product 12 Product 13 Product 14 Product 15 Product 16 Actual revenue share (in %) Observed revenue share after calibration (in %) An iterative calibration of individual results can enforce a nearly perfect reproduction of actual market shares as sound base for further analysis!
    27. 27. Results – Price sensitivity analysis for a single product … © Harris Interactive 27
    28. 28. 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 7 150 155 160 165 170 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 3.1 SKU 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 3.0 SKU 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 2.8 SKU 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 2.7 SKU 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 2.2 SKU 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 5.1 SKU 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 5.0 SKU 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 4.8 SKU 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 4.5 SKU 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 3.9 SKU 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 8.6 SKU 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 8.6 SKU 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 8.3 SKU 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 7.7 SKU 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 6.5 SKU 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 4.4 SKU 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 4.3 SKU 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 4.1 SKU 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 4.0 SKU 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 3.7 SKU 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 8.8 SKU 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 8.5 SKU 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 8.1 SKU 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 7.8 SKU 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 5.7 SKU 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 6.2 SKU 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 6.0 SKU 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 5.6 SKU 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 5.5 SKU 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 3.4 SKU 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 2.5 SKU 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 2.5 SKU 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 2.4 SKU 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 2.4 SKU 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 1.8 SKU 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 0.9 SKU 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 0.9 SKU 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 0.9 SKU 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 0.8 SKU 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 0.7 SKU 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 5.4 SKU 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 5.3 SKU 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 5.1 SKU 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 4.9 SKU 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 4.5 SKU 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 3.2 SKU 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 3.0 SKU 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 2.8 SKU 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 2.7 SKU 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 2.0 SKU 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 9.2 SKU 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 8.8 SKU 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 7.9 SKU 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 7.5 SKU 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 5.4 SKU 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 6.3 SKU 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 6.1 SKU 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 5.7 SKU 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 5.5 SKU 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 4.0 SKU 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 1.3 SKU 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 1.2 SKU 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 1.1 SKU 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 -4.2 SKU 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 0.7 SKU 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 11.3 SKU 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 9.5 SKU 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
    29. 29. 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
    30. 30. Harris Interactive AG Dr. Thomas Rodenhausen, President Harris Interactive AG Beim Strohhause 31 20097 Hamburg 30© Harris Interactive 6/4/2014
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