Mining customer loyalty card programs
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Mining customer loyalty card programs

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Vera Lúcia Miguéis, Ana Santos Camanho, and João Falcão Cunha

Vera Lúcia Miguéis, Ana Santos Camanho, and João Falcão Cunha

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Mining customer loyalty card programs Mining customer loyalty card programs Presentation Transcript

  • Google Earth
  • Porto – PortugalView of Porto riverside
  • The School of Engineering
  • Vera Miguéis vera.migueis@fe.up.pt Ana CamanhoJoão Falcão e Cunha acamanho@fe.up.pt jfcunha@fe.up.pt +351-91-254 1104
  • A service system is a configuration oftechnology and organizational networksdesigned to deliver services that satisfy the needs, wants, or aspirations of customers.Firms, as service systems, need, want and aspire to survive, prosper, grow (sometimes also making profits ), relying on customers for that.
  • How can we use SSME Research in order to help the firm and its customers? We are still in the way of finding theanswers…and also the right questions!
  • This work proposes a new method for promotions design, informed by product associations observed in homogeneous groups of customers. The method is based on clustering techniques to segment customers, and decision trees to characterize the segments profile. This analysis is followed by the identification of the products usually purchased together by customers from each segment. This enables regular customization of promotions to specificgroups of customers, having in mind improved satisfaction of their needs, wants, and aspirations.
  • •  Research motivation•  Literature review –  Segmentation –  Market basket analysis•  Methodology•  Case study –  Contextual setting –  Data –  Segmentation results –  Market basket analysis results –  Customer centered strategies•  Conclusions and future research Contents   Contents   Mo5va5on          Literature   Methodology            Case  Study                        Conclusion   13
  • •  Evolution of marketing efforts in retailing companies Few concerns about consumersCompetitorsproliferation Need to keep customers Time Product centered strategies Lifestyle changes Need to satisfy customer needs Customer centered strategies Contents   Contents   Mo5va5on          Literature   Methodology            Case  Study                        Conclusion   14
  • Contents  Contents   Mo5va5on   Literature          Literature   Methodology            Case  Study                        Conclusion   15
  • Classification ClusteringAssociation Forecasting Visualization Regression [Ngai et al (2009)] Sequence Discovery Contents   Contents   Mo5va5on   Literature          Literature   Methodology            Case  Study                        Conclusion   16
  • •  Market segmentation [Smith (1956)] –  Segmentation criteria: •  Geographic (initially) •  Demographic •  Volume of sales •  Perceived value for customers •  Lifestyle •  Psycographic •  Customer behaviour – inferred from transaction records available in large databases, or surveys [e.g. Kiang et al. (2006), Min and Han(2005), Helsen and Green (1991), Liu and Shih(2005)] –  In particular: Recency (date of the last purchase), Frequency and Monetary (“RFM” model, [Bult and Wansbeek (1995)]) –  Techniques for segmenting customers: Data mining clustering Contents   Contents   Mo5va5on   Literature          Literature   Methodology            Case  Study                        Conclusion   17
  • •  Market Basket Analysis –  Applied to large databases (transactional) –  Application domains: •  Banking [e.g. Peacock (1998)] •  Telecommunication [e.g. Klenettinen (1999)] •  Web analysis [e.g. Tan and Kumar (2002)] •  Retailing [e.g. Chen et al. (2004)] –  Objectives: •  Cross-sales [e.g. Poel et al. (2004)] •  Product assortment [e.g. Brijs et al. (2004)] Contents   Contents   Mo5va5on   Literature          Literature   Methodology            Case  Study                        Conclusion   18
  • Customers segmentation K-means algorithm Characterization of customers’ profile Decision tree Market basket analysis (*) Apriori algorithm (Agrawal and Srikant, 1994) Design of customized promotions Improvement of service levels(*) market basket analysis within segments is very rare in the literature Contents   Contents   Mo5va5on   Literature          Literature   Methodology            Case  Study                        Conclusion   19
  • 14th February: Valentine’s Day ... Enjoy Fine French Cuisine Alongside Classic Opera with a Starter and Main Course for Two People, plus a Glass of Prossecco each at Le Bel Canto Restaurant 20 / 29
  • •  Chain of hypermarkets, supermarkets and small supermarkets;•  Two loyalty cards: approximately 80% of the purchases are done using such cards.•  Two ways of segmentation: –  “Frequency and Monetary value” segmentation; –  Lifestyle segmentation;•  Customer segments are not used to differentiate customers in strategic policies to promote loyalty: –  Discounts for specific products advertised in the store shelves and leaflets, that are applicable to all customer with a loyalty card; –  Discounts on purchases done on selected days (percentual discount or absolute discount on total value of purchases). These are applicable to customers that present at the cash-point the discount coupon sent by mail; –  Discounts for specific products on selected days. Contents   Contents   Mo5va5on   Literature          Literature   Methodology            Case  Study                        Conclusion   Case   21
  • •  Data available: –  Transactions for the last trimester of 2009 –  Demographic information for each customer: residence postcode, city, date of birth, gender, number of persons in the household•  Data analysed: –  Customers whose average amount of money spent per purchase was up to 500€ –  Customers whose average number of purchases per month is up to the mean plus three standard deviations (11.7 visits per month) »  2.142.439 customers »  16.341.068 shopping baskets Contents   Contents   Mo5va5on   Literature          Literature   Methodology            Case  Study                        Conclusion   Case   22
  • •  Segmentation variables: –  Average number of purchases made per month –  Average amount of money spent per purchase•  5 clusters defined according to DB index and elbow curve 1.2 0.54 Davies Bouldin Elbow Curve 1 0.538 SumOfSquares/k 0.536DB index 0.8 0.534 0.532 0.6 0.53 0.528 0.4 0.526 0.2 0.524 0.522 0 -1 1 3 5 7 9 11 0 2 4 6 8 10 12 Number of clusters (k) Number of clusters (k) Contents   Contents   Mo5va5on   Literature          Literature   Methodology            Case  Study                        Conclusion   Case   23
  • #Customers (%) 37% 27% 20% 8% 8%Contents  Contents   Mo5va5on   Literature          Literature   Methodology            Case  Study                        Conclusion   Case   24
  • •  Clusters’ profile: Avg.#  purchases  per  month   ≤3.2 >3.2 >6.2 Avg.  Amount  money  spent  per   purchase   ≤135.9 >135.9Avg.#  purchases  per  month   ≤1.5 >1.5 Contents   Contents   Mo5va5on   Literature          Literature   Methodology            Case  Study                        Conclusion   Case   25
  • Contents  Contents   Mo5va5on   Literature          Literature   Methodology            Case  Study                        Conclusion   Case   26
  • •  Transactions were aggregated by customer•  The products were aggregated by subcategory –  Examples of rules obtained: Cluster 4 Antecedent   Consequent   Hair Conditioner Shampoo Tomatoes Vegetables for salad Sliced ham Flemish cheese Cabbage Vegetables for soup Pears Apples Contents   Contents   Mo5va5on   Literature          Literature   Methodology            Case  Study                        Conclusion   Case   27
  • •  Customer development: –  The company may issue a discount voucher at the PoS that advertises a consequent product of the association rule, which was not recently bought by the customer who bought the corresponding antecedent product. •  Examples: –  In Cluster 4: »  Discount shampoo to customers that have bought conditioner but did not buy shampoo. »  Discount vegetables for salad to customers that have bought tomatoes but did not buy vegetables for salad. Contents   Contents   Mo5va5on   Literature          Literature   Methodology            Case  Study                        Conclusion   Case   28
  • This work proposes a new method for promotions design,informed by product associations observed in homogeneous groups of customers. The method is based on clustering techniques to segment customers, and decision trees to characterize the segments profile. This analysis is followed by the identification of the productsusually purchased together by customers from each segment.This enables regular customization of promotions to specificgroups of customers, aiming at improved satisfaction of their needs, wants, and aspirations.
  • •  Data mining allows to find natural clusters of clients on large retailing databases, by means of customer behaviour segmentation.•  Decision trees enable discovering the rules characterizing customer segments.•  Market basket analysis within segments seems to show good potential to support the design of customized promotions and consequently the provision of better service to customers.•  In the future, we intend to interview panel customers belonging to each cluster, in order to see if they consider that the service levels are improving or can be improved.•  We also intend to monitor the evolution of the results of the satisfaction surveys. Contents   Contents   Mo5va5on   Literature          Literature   Methodology            Case  Study                    Conclusion   Case      Conclusion   30
  • •  What are the adequate promotions to improve service levels?•  Are derived association rules more relevant than creativity to design promotions?•  What “level” of segmentation should be used? No segmentation? The one proposed here? Individual segmentation?•  How important is it to listen to customers, in each segment, and individually?•  …? Contents   Contents   Mo5va5on   Literature          Literature   Methodology            Case  Study                    Conclusion   Case      Conclusion   31
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