Advanced CRM Analytics

Recently, I did some research (web mining) on CRM analytics. To my surprise, I found that almost 95% of the websites are talking about Info cubes, OLAP cubes, CRM reports, C-SAT scores, sales reports, sales dashboards, marketing dashboards, sales charts blah blah blah…. Well, I feel CRM analytics is not all about creating glittery BI dashboards.

What else can we do apart from regular BI reporting and basic averages tracking?

1.   Market basket analysis/Affinity analysis/Association rules

• Use historical data & relevant statistical measures to quantify association between products and build association rules accordingly. These rules are use for Up-Selling and Cross-Selling.
• Eg rule : A + B + C -->E + F (This rule states that if products A, B, and C are chosen, products E and F are proposed. Products E and F are only proposed if all three products are selected)
• Statistical techniques
• Cross tabulation , Multiple Response tables
• Correlation coefficient, regression, odds ratio etc.,
• Chi-square test of independence, concordance and discordance tables
• Sequence Analysis, Link Analysis etc.,
• Bayes and Conditional probabilities 

2.    Customer segmentation & profiling:

Customer segmentation will help us in Target marketing, loyalty & retention programs, Up selling/Cross-Selling etc.,

• Identifying homogeneous customer segments that are similar in specific ways relevant to marketing such as
• Demographics(Age, Gender, Education, Income, Home ownership, etc.)
• Psychographics(Lifestyle, Attitude, Beliefs, Personality, Buying motives, etc.)
• Brand Loyalty(Geography, State, ZIP, City size, Rural vs. Urban, etc.) 
• Statistical methods used:
• Cluster Analysis(K-means clustering, Hierarchical clustering),
• CHAID, CART etc.,
• Logistical Regression & Discriminant Analysis

3.   Customer lifetime value analysis (Customer scorecard building)

Build predictive models based on customer profile data and historical behaviors to assess how likely a customer is to exhibit a specific behavior in the future in order to improve sales

Score card takes customer profile variables (like Age, Gender, Education, Income, Home ownership, Lifestyle, Attitude, Beliefs, Personality, Buying motives, Brand Loyalty, Geography, State, ZIP, City size, Rural vs. Urban, spending patterns etc.) as input and gives a simple score that indicates customer value to company.

• Statistical methods used:
• Logistic & liner regression model building
• Weight of evidence & information value
• Trees and segmentation

4.   Customer Satisfaction analysis & drivers of customer satisfaction

• Survey Analysis: Analysis of customer response data to find the overall C-SAT and satisfaction by various cuts
• Analysis of C-SAT time series data, calculation of control limits, seasonality & trends etc.,
• Calculation of net promoter score & C-SAT peer comparison
• Identification of  most impacting factors on customer satisfaction

5.   Text mining

• Analysis of customer verbatim data to define overall customer satisfaction
• Effective algorithms to identify positive, negative & neutral comments
• Summarization of overall comments into main themes (most frequent topics) and their positive & negative frequencies

Conclusion: There are several other advanced analytics techniques. Above five are most generic ones & they can be applied in any business.

I wish to thank my CRM guru Mukul Biswas