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Analytical CRM
- 1. Customer Relationship Management Analytical Aspectsof CRM Presented by Rahul Upadhyay 1
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- 3. Analytical Aspects ofCRM Data as Infrastructure 3 Sourced from Diverse sources Centralised Used by the entire Organisation Requires Quality Control
- 4. Analytical Aspects ofCRM Managing Data Quality- Issues • Current , Complete, Correct, Unique
- 5. Managing Data Quality ImprovingData Quality • Current , Complete, Correct, Unique
- 6. Biometrics & CRM FacialRecognition in action
- 7. Expanding the Database Sources Source Internal Online Phone Coupons/ DM Referrals ExternalLists & Databases Test for Commercial Value
- 8. Database Types Relational DatabaseNon Relational database Structured Tables Uses Structured Query Language Simple , Accurate Easy to access Flexible Performance issues Used by Content Management systems like Youtube etc. Handling unstructured data Fast Open source Readability Limited functionality
- 9. Developing a CustomerProfile Key Characteristics Used Segment Transactions Satisfaction score Identity & Lifestyle Channel Pref. Products Customer value Includes Hard & Soft Elements
- 10. Customer Profiling Measurement ofTransaction Data
- 11. Customer Data Maintenance InternalSources Contact Database Warranty registration Transaction Database Sales Information Systems Marketing Databases
- 12. Customer Data Maintenance ExternalSources Database sellers Government Search Engines & SM Market Research Technology Databases
- 13. Using External DataSources Considerations Legal issues Intrinsic Quality Relevance Compatibility Costs
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- 15. Data Analyses &Data Mining
- 16. Data Analysis Background Trends 16 TransactionVs Relationship Orgns IT Driven Explosive Growth Develops Segments & Profiles
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- 18. Data Analysis Process 18 Problem Definition DataIdentification Data Cleaning Processing Patterns & Visualisation Implementation & Response
- 19. Data Analysis Types ofQuestions & Objectives 19 Segmentation & Selections • Create homogenous addressable groups • Creation of Lists for Marketing Campaigns Acquisition & Selections • Channels of interaction and onboarding • Data Quality of Customer list Customer Analyses & selections • Retention • Relationship Development • Cross Selling Opp • Deep Selling Opp. • Upselling Opp. Channel & Comms. Analyses • Campaign effectiveness • Channels impact • Lists quality
- 20. Data Analysis Preparing forAnalysis 20 Selection of Variables Random Sample Analysis Training set to develop the model Validation set to Test Reliability
- 21. Data Analysis A/ BTesting 21 Two treatments and one acts as the control for the other
- 22. Final Data Analysis DataMining Techniques 22 Neural Networks • Computing systems with interconnected nodes like neurons in the human brain. • Using algorithms, they can recognize hidden patterns and correlations in raw data, cluster and classify it, and – over time – continuously learn and improve Evolutionary Computation • A Collection of Algorithms based on evolutionary Biology Processes • Can also be defined as Optimisation Program routines for a defined Population, eliminating members basis factors till the fittest is arrived at. Association Rules • Conclusions based on the relationship between characteristics of a defined group and aspects of their behavior. Case Based Reasoning • A current problem is addressed via precedents or historical data.
- 23. Final Data Analysis DataMining & AI in Action- Segmentation 23
- 24. Segmentation Basis 24 B2C Geographical Demographics Socio Economic B2B Demographics Operating Factors PurchasePractice Psychographics Employee Characteristics
- 25. Segments Traits 25 Size , PurchasingPower & Characteristics of the segment Measurability Large enough in size to be Profitable Substantial Can be reached and Serviced effectively Accessibility Allows effective Marketing Plans to be created for it . Actionable Unique and respond to stimuli differently Differentiated
- 26. CRM Analytics Tools 26 • Clusteranalysis • Discriminant Analysis • Qualitative Data • Market Research
- 27. Segmentation Techniques Cluster Analysis& K Means Cluster Analysis 27 k-means cluster analysis is an algorithm that groups similar objects into groups called clusters. The endpoint of cluster analysis is a set of clusters, where each cluster is distinct from each other cluster, and the objects within each cluster are broadly similar to each other.
- 28. Segmentation Techniques Cluster Analysis& Segment Profiling 28
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- 30. Segmentation Research Compiling theList – Calculating RFM 30
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- 32. Segmentation Research Tactical Marketing– RFM based Campaign 32
- 33. 33 Segmentation Research Chaid- ChiSquare Automated Detection • Variable chosen by Marketer • Creates Multiple sub- segments • More accurate than RFM Used to build a predictive model to outline a specific customer group or segment
- 34. 34 Segmentation Research CART Analysis •Two groups/ variable • Yields Only Two segments • Works on a Ratio Scale • The best split point of each input is obtained. • Based on the best split points of each input in Step 1, the new “best” split point is identified. • Split the chosen input according to the “best” split point. • Continue splitting until a stopping rule is satisfied or no further desirable splitting is available.
- 35. 35 Segmentation Research Response GainChart • Based on historical data and test models • Enables choosing the most responsive sections • Relationship Phase a key variable
- 36. Retention & CrossSell Analyses 36
- 37. 37 Retention & CrossSell Analyses The Importance of Customer Retention • Customer – Definition ?
- 38. 38 Customer Retention Analyses Choosingthe Variables Stayers Vs. Quitters Which Variables? Revenue Response to Comms. Time Period External Factors Cust. Experience Cust. Satisfaction Destination? Internal or External
- 39. 39 Customer Retention Analyses Developingthe Model Data set: 50:50 ratio of current & former customers Training & Validation set Correct Classification of Observations Combination of Classification for best results
- 40. 40 Cross Selling What isit Opportunities Product Centric Pdts from different cat. Expanded Product Over Time More of the same Two different Products Purchased later
- 41. 41 Cross Selling Exploratory Analysis Economicimpact of Heavy users Time Progression of Sales Sequence of Purchase Cross Tables for commonly grouped Product categories.
- 42. 42 Cross Selling Variables forAnalysis. Ex. Customer Profile Demographics Lifestyle indicators Client Supplier Relationship Account Manager Frequency of Contact Product Consumption History Current Purchases Expected Purchases Qty. purchased , Value, Recency
- 43. 43 Cross Selling E.g. PurchaseHistogram
- 44. 44 Cross Selling Probabilities Interpretation–Other Factors Time Lapse between Purchases Macro Economic changes Changes in Supplier Offerings Changes in Customer Situation
- 45. 45 Cross Selling Analysis inAction- Factors During Customer contact Based on Probabilities Context dependent Outreach Based on Probabilities Segment Rankings Costs & Margins Testing the Probabilities
- 46. Evaluating Impact ofMarketing Activities on Customer Relationship 46
- 47. 47 Impact of MarketingActivities Evaluating The Sales Process • Helps in Targeting • Generate Cross Sell Calendar • Align with Organisation’s Objective • Enable Faster closing • Reduce Costs • Contribute to LTV
- 48. 48 Marketing Activities Learning withExperiments Experiment conditions Internal Sanctity of environment Cause & Effect Measurement External Mirror Real world Representation of sample, stimuli & Response Variables • Experimental Vs Control Group • Null measurements • Application of Stimuli • No. of Experimental Groups used
- 49. 49 The Learning Organisation CreatesKnowledge from Experience Team Learning - One to Many Helps develop mental models Shared vision Links Learnings to Operations Feeds in to Strategy Cross functional Knowledge Sharing & Mgmt
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- 51. 51 Evaluating Results Methodologies Life TimeValue Non Economic Value Balanced Scorecard
- 52. 52 Evaluating Results Lifetime Value •Avg Transactions x # of Transactions x Retention Period- Costs • Past Data used to Project future expected earnings • Determines the Profitability of the Customer • Determines the effectiveness of Strategy & Marketing Plans • Enables forecasting & Strategy formation • Applicable for a limited time period in the future
- 53. 53 Evaluating Results Lifetime Value– Calculation & Usage
- 54. 54 Evaluating Results Non EconomicValue Goes Beyond immediate transactions Dependant on Value attributed by Buyer & Supplier Deduced from Relationship Patterns/Lifestyles etc. Examples - Complaints - Departing Customers - Receivables