Transcript CRM level
Information Systems: Customer Relationship Management Source Régis Meissonier 1 Economics of customer retention “Winning back a lost customer can cost up to 50-100 times as much as keeping a current one satisfied.” Rob Yanker, Partner, McKinsey & Company Understanding your customer is key to retention….. 2 CRM: towards the « one to one » Marketing One to One Offer Personalization level Offer segmentation Mass market XXth century XXIth century 3 CRM level Collaborative level : CRM spread to the suppliers, CRM Development level distributors, etc. Operational level : Interactions with the customer by all the fitting media to this effect Analytical level : Collect, archiving of the data customers in order to establish a segmentation, typologies, informers, etc. Relational developed level 4 The CRM market Leaders: Siebel, Peoplesoft, SAP, Oracle Sales in France (Source 01Net.com, Professional internet) 2001 = 1,115 B€ 2002 = 1,224 B€ 2005 = 1,990 Billion Euros Development costs = from hundreds thousands to several hundreds million of Euros. Duration = from some months to several years The main question: which return on investment? 5 CRM architecture Customer Data collection web, telephone, meeting, mail, etc. Data aggregation datawarehouse or SGBD Customer’s knowledge Relationship management segmentation, predictibility of the models, datamining recommendation, campaign management 6 Data collection CTI (Coupling Telephony Computer): call centers forms, logs and stats files, clikstream, received messages, etc. 7 Data aggregation Supply Chain Stocks management outflow of the products storage cost Supplying management Income cost Historic of the estimates, orders, Anticipations of the sales Costs of purchases Datawarehouse Evaluation of the risk customer Allocation of the indirect loads Check management Customer Worked hours for the customer Human ressources 8 Customer knowledge: datamining Prediction level Neurones networks Genetic algorithms Bayésians networks Results legibility level Scores Regression Cluster Decision shaft Association analyze Reasoning on cases 9 Data Mining The non-trivial extraction of novel, implicit, and actionable knowledge from large database Extremely large datasets Discovery of the non-obvious Useful knowledge that can improve processes Can not be done manually Technology to enable data exploration, data analysis, and data visualization of very large databases at a high level of abstraction, without a specific hypothesis in mind. Sophisticated data search capability that uses statistical algorithms to discover patterns and correlations in data. Source : Seyyed Jamaleddin Pishvayi 10 Customer knowledge : segmentation Reconquest policy Fidelisation policy Customer value Relationship intensity Abandonment policy Rationalisation policy 11 Data Mining in CRM (cont.) Data Warehouse Customer Profile Data Mining Customer Life Cycle Info. Marketing Campaign Source : Seyyed Jamaleddin Pishvayi 12 An other way to predict customer purchases: the recommendation technique Information given back to the customer according to his profile Description user centered Customer photofit picture Collaborative Filtrage 13 In the facts… 65-69% of corporations questioned consider that CRM involved no improvement (Deloitte & Touches ; Meta Groups) Firms tend to integrate functions that do not correspond to business objectives Information system oversized compared to the effective needs Source : Mc Kinsey, 2002, How to rescue CRM? 14 Conclusion Define clear objectives: what wants to do the company? To increase its customer portfolio? To increase its incomes with the current customers? To increase the number of customers representing large value? To reduce the number of customers representing small value? Etc. Which strategy to use? To improve the quality of the distribution chanels? To improve customer satisfaction ? To increase crossed sales? Etc. 15