Domain-specific behavior-based. based customer segmentation. Mitja Pirc, Universitat Pompeu Fabra SAS Forum International 2005 Lisbon, July 23rd 2005

Transcription

1 Domain-specific behavior-based based customer segmentation Mitja Pirc, Universitat Pompeu Fabra SAS Forum International 2005 Lisbon, July 23rd 2005

2 Research Project The research project presented was done during the year 2004 in collaboration with TeliaSonera Sweden R&D, Stockholm School of Economics, and SAS Sweden. Focus of this research was to uncover usage behavioral strategies in mobile telecommunications services and how different segmentation levels are linked.

3 Segmentation bases* Level of variable Objective Subjective General Domain-specific Income Age Education Behavioral patterns Situation Frequency of use Substitution behavior General values Lifestyle Personality Opinions Perception Attitude Domain-specific values Brand-specific Brand loyalty (behavior) Frequency of use actions Brand loyalty (attitude) Preference Evaluation Purchase intention * van Raaij, W. Fred in Verhallen, Theo M.M Domain-specific market segmentation. European Journal of Marketing, Vol. 28, No. 10, pp

4 Research focus Uncover consistent mobile services usage strategies (behavioral patterns). Compare and elaborate the connections between the general, domain-specific and brand-specific segmentation level. Demonstrate the usage of this concept on one of the brands.

5 Analyzed brands and price plans Cost of service* Telia Mobil 25 Refill Halebop Start cost (SEK) Fixed cost per month (SEK) Calls weekdays 8-18h (SEK) (25min free) Calls otherwise (SEK) SMS (SEK) * On date , 1 is approx. 9 SEK

6 General Level Segmentation* Youth: ages between YE - Youth Explorers: explorers and driving YF - Youth Followers: following the explorers. Households WithOut kids: WS - White Collars Spontaneous: highly educated, at least high school. They are spontaneous. WP - White Collars Planning: they are highly educated as well, their actions are planned. BS - Blue Collars Spontaneous: lower education, workers, housewives, jobless. Spontaneous decisions. BP - Blue Collars Planning: lower education, workers, housewives, jobless. Planned decisions. *already done within TeliaSonera

7 General Level Segmentation* cont. Households With kids: aged: with children aged MF - Modern Families: these are explorers TF - Traditional Families: These have traditional/stable habits Seniors: aged SE - Seniors Early Adopters/Followers: These are explorers or are at least neutral towards new technologies. SL - Seniors Laggards: These are traditional and old fashioned people who prefer not to use new technologies. *already done in TeliaSonera

8 Data sources A random sample of customer records was made from threet internal secondary data sources: transaction data for outgoing services transaction data for incoming services marketing research data The joining variable subscription number.

9 A sample of variables analyzed Some of the usage variables that were used in Analysis (per given customer in a given month): Average price of phone calls Average duration of phone calls Number of calls to customer s Voic box Number of calls made Number of SMSs sent Sum of total time of calls to Voic box Sum of total time of calls made Number of different networks called Number of different networks SMSs s were sent Total incoming revenue from within the same network.

10 Analysis* Missing Values Removing Outliers Seeds found by k-means method. Ward Clustering method. Cubic clustering criterion stopping rule. Log transformation *using Enterprise Miner TM software

11 Segmentation variables Variable name Importance log(nrsms) 1.00 log(calls2answ) 0.94 log(nrcalls) 0.78 log(incomingrevenue) 0.62 AveDuratnCall 0.23 AvePriceCall 0.00

12 Exploring clusters Cluster descriptions can be made and usage strategies defined.

13 Segments Segment % Segment characteristics 1. Heavy All 16% Services: Calls, SMS, Voic box. Other: price conscious, high incoming revenue from TSS customers 2. Inactive 20% Services: Voic box. Other: these users are inactive, which might indicate churn, their incoming revenue is also low. 3. SMSers 17% Services: Calls, SMS Other: their incoming revenue is second highest. 4.Average Callers 5. Callers &Voice 26% 21% Services: Calls Other: these are average callers, their incoming revenue is low and they have the highest average price per call and average duration of calls. Services: Calls, Voic box Other: their incoming revenue is above average

14 Behavioral x General Segment Youth Families No Kids Heavy All (calls, SMS, voic box) Inactive (voic box) Seniors All 33 % 15 % 18 % 2 % 16 % 23 % 19 % 17 % 22 % 20 % SMSers 24 % 20 % 17 % 3 % 17 % Average Callers 8 % 23 % 24 % 53 % 26 % Callers and Voice (calls and voic box 12 % 23 % 24 % 20 % 21 % Sum 100 % 100 % 100 % 100 % 100 %

15 Segments & price plans There are significant differences of structure of behavioral segments across price plans.

16 Brand level Halebop tariff plan and its brand was chosen as it is different from the other two The clustering analysis followed same logic When running the clustering 6 segments were chosen, which are approximately the same size.

17 Brand Exploring clusters

18 Brand segments We can observe similar segments as before, with a difference that we have two Inactive segments, namely cluster 1 and cluster 4. Users in cluster 1 are really inactive. Users in cluster 4 are not making any outbound communication,, howeverh they check their voic box frequently and they have rather high incoming revenue.

19 Brand level cont. Low out/incoming trafic and low voic box usage Average incoming trafic and high voic box usage Two Inactive segments: difference in incomming revenue and voic box calls

20 Conclusion There are different behavioral patterns when using mobile services (5) The domain-specific level proved to be a good mediator between general level segmentation and brands Once again was shown the power of bringing together various data sources in thus increasing the number of perspective.

21 Thank you!