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Measuring Word-of-Mouth Effects Using Spatial Dimension of Sales Data
1. BSI Measuring Word-of-Mouth Effects Using Spatial Dimension of Sales Data
Marketing 2.0 Conference, Hamburg 2005
2. BSI
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MARKETING 2.0 CONFERENCE
Paris, France 28/29 March 2011
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3. Christian-Albrechts-University at Kiel
Chair of Innovation, New Media and Marketing
Institute of Innovation Research
Measuring Word-of-Mouth Effects Using Spatial
Dimension of Sales Data
Christian Barrot
Christian-Albrechts-University at Kiel, Institute of Innovation Research
Chair of Innovation, New Media and Marketing, Kiel, Germany
Christian Barrot
Measuring Word-of-Mouth Effects Using Spatial Dimension of Sales Data
1st International WOM Marketing Conference 2005 Page 1
4. Christian-Albrechts-University at Kiel Early Prediction of New Product Success
Chair of Innovation, New Media and Marketing
Make a decision: Stop or Go?
Institute of Innovation Research
20000
Adopters (cumulated)
15000
10000
5000
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
Time periods (Quarters since launch 1997)
> We need better information than just sales figures.
Christian Barrot
Measuring Word-of-Mouth Effects Using Spatial Dimension of Sales Data
1st International WOM Marketing Conference 2005 Page 2
5. Christian-Albrechts-University at Kiel Agenda
Chair of Innovation, New Media and Marketing
1. Social Contagion
Institute of Innovation Research
2. Small World Networks
3. Transition to Marketing Practice
4. Case Study
5. Summary
Christian Barrot
Measuring Word-of-Mouth Effects Using Spatial Dimension of Sales Data
1st International WOM Marketing Conference 2005 Page 3
6. Christian-Albrechts-University at Kiel Introduction | What Determines the Diffusion of Innovations?
Chair of Innovation, New Media and Marketing
Diffusion model of Bass (1969):
Institute of Innovation Research
• Coefficient of innovation (P): Adoption of an innovation independently of
the decisions of other individuals (external influence).
• Coefficient of imitation (Q): Adoption of an innovation influenced by the
decisions of other adopters (internal influence).
Meta-analysis of Sultan / Farley / Lehmann (1990):
• Analysis of 213 applications of diffusion models from 15 studies
published between 1957 and 1987.
• Mean value for P: 0.03 (0.000021 to 0.03297)
• Mean value for Q: 0.38 (0.2013 to 1.67260)
> Effects represented by Q have a larger influence on the diffusion of
innovations than those represented by P.
Bass, F. (1969): A new product growth model for consumer durables, in: Management Science, 15(January), 215-227.
Sultan, F., Farley, J.U., and D. Lehmann (1990): A meta-analysis of application of diffusion models, in: Journal of
Marketing Research, 27(February), 70-77.
Christian Barrot
Measuring Word-of-Mouth Effects Using Spatial Dimension of Sales Data
1st International WOM Marketing Conference 2005 Page 4
7. Christian-Albrechts-University at Kiel Introduction | What is „Q“?
Chair of Innovation, New Media and Marketing
Van den Bulte / Lilien (2001): „Social Contagion“
Institute of Innovation Research
• Information transfer (i.e. Word-of-mouth).
• Normative pressure (Coleman / Katz / Menzel, 1966).
• Competition concern (Burt, 1987).
• Performance network effect (Katz / Shapiro, 1985).
> The individual strength of each effect remains unclear.
Van den Bulte, C. and G. Lilien (2001): Medical Innovation Revisited: Social Contagion versus Marketing Effort, in:
American Journal of Sociology, 106(5), 1409-1435.
Basis for processes like Word-of-Mouth or imitation
are relationships between humans („ties“).
These ties can hardly be observed empirically.
> Research is based on very small samples or simulations.
Christian Barrot
Measuring Word-of-Mouth Effects Using Spatial Dimension of Sales Data
1st International WOM Marketing Conference 2005 Page 5
8. Christian-Albrechts-University at Kiel Agenda
Chair of Innovation, New Media and Marketing
1. Social Contagion
Institute of Innovation Research
2. Small World Networks
3. Transition to Marketing Practice
4. Case Study
5. Summary
Christian Barrot
Measuring Word-of-Mouth Effects Using Spatial Dimension of Sales Data
1st International WOM Marketing Conference 2005 Page 6
9. Christian-Albrechts-University at Kiel Approach of Garber et al. (2004) | Simulation
Chair of Innovation, New Media and Marketing
Simulation of relationships through Small-World-Networks
Institute of Innovation Research
• Strong relationships among neighboring individuals and weak relation-
ships (max. 5%) among non-neighboring individuals are to be specified.
• A matrix representing adoption on the part of individuals.
• Transition rules of the probabilities of adoption between periods.
Garber, T., Goldenberg, J., Libai, B., and E. Muller (2004): From density to destiny: Using spatial dimension of
sales data for early prediction of new product success, in: Marketing Science, 23(3), 419-428.
Christian Barrot
Measuring Word-of-Mouth Effects Using Spatial Dimension of Sales Data
1st International WOM Marketing Conference 2005 Page 7
10. Christian-Albrechts-University at Kiel Approach of Garber et al. (2004) | Results
Chair of Innovation, New Media and Marketing
Results of 100 simulations with 2,5000 potential adopters each
Institute of Innovation Research
successful products (large Q) failed products (small Q)
> Successful innovations show a distinct pattern of clusters of adopters.
Garber, T., Goldenberg, J., Libai, B., and E. Muller (2004): From density to destiny: Using spatial dimension of
sales data for early prediction of new product success, in: Marketing Science, 23(3), 419-428.
Christian Barrot
Measuring Word-of-Mouth Effects Using Spatial Dimension of Sales Data
1st International WOM Marketing Conference 2005 Page 8
11. Christian-Albrechts-University at Kiel Approach of Garber et al. (2004) | Results
Chair of Innovation, New Media and Marketing
Cross-Entropy (CE) as indicator for successful new products
Institute of Innovation Research
• High CE-values signal strong divergence to expected distribution
> Successful innovations show a U-shaped curve of CE-values over time.
Garber, T., Goldenberg, J., Libai, B., and E. Muller (2004): From density to destiny: Using spatial dimension of
sales data for early prediction of new product success, in: Marketing Science, 23(3), 419-428.
Christian Barrot
Measuring Word-of-Mouth Effects Using Spatial Dimension of Sales Data
1st International WOM Marketing Conference 2005 Page 9
12. Christian-Albrechts-University at Kiel Agenda
Chair of Innovation, New Media and Marketing
1. Social Contagion
Institute of Innovation Research
2. Small World Networks
3. Transition to Marketing Practice
4. Case Study
5. Summary
Christian Barrot
Measuring Word-of-Mouth Effects Using Spatial Dimension of Sales Data
1st International WOM Marketing Conference 2005 Page 10
13. Christian-Albrechts-University at Kiel Step 1 | Spatial Data as Proxy
Chair of Innovation, New Media and Marketing
Geographic distance is used as proxy for social distance
Institute of Innovation Research
• The likelihood of contacts between individuals decreases with
increasing geographical distance.
Number of
contacts
Hägerstrand, T. (transl. by A. Pred) (1967):
Innovation diffusion as spatial process, Chicago [u.a.].
Distance
Mean information field
> Contrary to social distance, companies often do have information on the
geographical distance between their customers.
Christian Barrot
Measuring Word-of-Mouth Effects Using Spatial Dimension of Sales Data
1st International WOM Marketing Conference 2005 Page 11
14. Christian-Albrechts-University at Kiel Step 2 | GIS Analysis
Chair of Innovation, New Media and Marketing
Generate a map of your customers
Institute of Innovation Research
1. Collect required customer data (date of adoption, geographic
location, both at the most detailed level available).
2. Geo-code the address data of your customers
(e.g. using Microsoft MapPoint).
3. Check the obtained results for mismatches or typing errors.
4. Plot the geographical locations of your customers on a map
(e.g. using ArcView GIS software).
5. Compare the plot with the basic distribution of your customers
(e.g. proportional to population density).
> If you observe a divergence to the expected distribution, this can be
an early indicator for a successful diffusion.
> Limitation: Difficult to incorporate non-uniform basic distributions.
Christian Barrot
Measuring Word-of-Mouth Effects Using Spatial Dimension of Sales Data
1st International WOM Marketing Conference 2005 Page 12
15. Christian-Albrechts-University at Kiel Step 3 | Cross-Entropy Measure
Chair of Innovation, New Media and Marketing
Calculate the divergence
Institute of Innovation Research
1. Collect required customer data (date of adoption, geographic
location, both at the most detailed level available).
2. Aggregate data over time (e.g. months, quarters) and space (e.g.
postal code areas, states).
3. Define a basic distribution reflecting the market potential of each
geographic unit (e.g. total population, car owners, cell phone users).
4. Adjust for regional marketing effort (e.g. ads in local newspapers).
5. Calculate cross-entropy measure and plot results in diagram.
> If you observe high CE-values at the beginning followed by a sharp
decline, this can be an early indicator of successful diffusion.
Christian Barrot
Measuring Word-of-Mouth Effects Using Spatial Dimension of Sales Data
1st International WOM Marketing Conference 2005 Page 13
16. Christian-Albrechts-University at Kiel Agenda
Chair of Innovation, New Media and Marketing
1. Social Contagion
Institute of Innovation Research
2. Small World Networks
3. Transition to Marketing Practice
4. Case Study
5. Summary
Christian Barrot
Measuring Word-of-Mouth Effects Using Spatial Dimension of Sales Data
1st International WOM Marketing Conference 2005 Page 14
17. Christian-Albrechts-University at Kiel Case Study: Background
Chair of Innovation, New Media and Marketing
Data set
Institute of Innovation Research
• Customer data of a car-trading website (including exact date of adoption
and addresses on street-level).
• Approximately 40,000 potential customers (car dealers) in Germany
• Time period covered: 1997 – 2004.
Marketing strategy
• Online marketing and nationwide TV, national newspapers / magazines.
• No sales force, no localized advertising.
> Exposure of (potential) customers to the advertising efforts of the
company is not related to their geographic location.
Modeling
• Time periods: Quarters
• Window size: Postal code areas level of the first two digits
(e.g. 22529 and 22658 were part of the 22xxx area)
• Basic distribution of market potential: proportional to population density.
• No adjustments for localized marketing necessary.
Christian Barrot
Measuring Word-of-Mouth Effects Using Spatial Dimension of Sales Data
1st International WOM Marketing Conference 2005 Page 15
19. Christian-Albrechts-University at Kiel Case Study | Cross-Entropy Measure
Chair of Innovation, New Media and Marketing
Resulting cross-entropy measure (1997 – 2003)
Institute of Innovation Research
3
2,5
Cross-entropy
2
1,5
1
0,5
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
Time periods (Quarters since launch 1997)
> Strong indication of new product success through WOM at early stage.
Christian Barrot
Measuring Word-of-Mouth Effects Using Spatial Dimension of Sales Data
1st International WOM Marketing Conference 2005 Page 17
20. Christian-Albrechts-University at Kiel Early Prediction of New Product Success
Chair of Innovation, New Media and Marketing
They said “Go”…
Institute of Innovation Research
20000
Adopters (cumulated)
15000
10000
5000
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
Time periods (Quarters since launch 1997)
> … and got 120m € plus a nice finca on Majorca.
Christian Barrot
Measuring Word-of-Mouth Effects Using Spatial Dimension of Sales Data
1st International WOM Marketing Conference 2005 Page 18
21. Christian-Albrechts-University at Kiel Agenda
Chair of Innovation, New Media and Marketing
1. Social Contagion
Institute of Innovation Research
2. Small World Networks
3. Transition to Marketing Practice
4. Case Study
5. Summary
Christian Barrot
Measuring Word-of-Mouth Effects Using Spatial Dimension of Sales Data
1st International WOM Marketing Conference 2005 Page 19
22. Christian-Albrechts-University at Kiel Summary
Chair of Innovation, New Media and Marketing
Key takeaways
Institute of Innovation Research
> Social contagion effects like WOM or imitation are of eminent
importance for new product success.
> Strong social contagion effects lead to clusters of adopters that can be
observed already at the very early stage of diffusion.
> Although we have no information about the social proximity of our
(potential) customers, we can take geographical distance as a good
proxy.
> The spatial dimension of sales data is a valuable resource for
the early prediction of new product success .
Christian Barrot
Measuring Word-of-Mouth Effects Using Spatial Dimension of Sales Data
1st International WOM Marketing Conference 2005 Page 20
23. Christian-Albrechts-University at Kiel
Chair of Innovation, New Media and Marketing
Institute of Innovation Research
Christian Barrot
Christian-Albrechts-University at Kiel,
Institute of Innovation Research
Chair of Innovation, New Media and Marketing, Kiel, Germany
Christian Barrot
Measuring Word-of-Mouth Effects Using Spatial Dimension of Sales Data
1st International WOM Marketing Conference 2005 Page 21
24. Christian-Albrechts-University at Kiel
Chair of Innovation, New Media and Marketing
Institute of Innovation Research
Back-up
Christian Barrot
Measuring Word-of-Mouth Effects Using Spatial Dimension of Sales Data
1st International WOM Marketing Conference 2005 Page 22
