Approaches and Analytical Techniques for Customer Segmentation in B2B Market ScenarioTeam Members: Arjit Saran (GSEP14GLM27) Rahul Charkha (GSEP14GLM30) Shasanka Sahu (GSEP14GLM31)

Table of contentsIntroduction B2B Market Segmentation

2. Segmentation Approach and Basis for Segmentation

3. Source of Consumer Data

4. Customer Intelligence Analytical Methodology

5. IT Analytics and Survey Techniques

6. Final Thoughts

Introduction B2B Market SegmentationNeed for Segmentation:

Rising complexity in Business environment One size fits all no longer holds true

Customers have unique needs and expectations

Needs to be grouped based on similar buying preferences

Supply chain needs to be aligned with customers segments it caters to.

Segmentation ApproachesFactors of market segmentation 5 Broad categories on approaching segmentation Buying behavior factors Product risk and resiliency based, customer service needs , and market driven

Basis for segmentationBasis for segmentationSegmentation dimension Approach to segmentation has evolved from convenience , firmographics to based on behavior and needs

Segmentation dimensions for market segment based on buying behavior, attitudes, features, needs and preferences

Source of consumer Data

Customer Intelligence Analytical MethodologyAnalytical Techniques

Customer Life Time (CLV /LTV)Predictive ModelingVoice of Customer Analysis (VOC)MultiDimensional SegmentationCustomer Balance Scorecard

1. Customer Life time valueBuilding block of lifetime value over time frameCLV represents the net present value of past and future cash flow that customers generateCustomers generating same revenue can generate different profits for the companyCore areas for increasing customer value- cross selling, Up selling and retention Identifying customers with highest value helps in maximizing ROI

Evaluating customer Lifetime valueConceptual framework of CLV for segmentationSteps for evaluating customer lifetime value

Assembling the necessary historical data in analysis ready format

2. Understand cost at customer level

3. Apply predictive Analysis

2. Predictive modeling Identifying historical reasons for customer engagement in the past

Identifying customer target

Understanding and forecasting consumer behavior.

Return on Investment (ROI) Optimization

Measuring impact of marketing activities on consumer behavior

3.Voice of customer (VOC) analysis Voice of customer is part of six sigma process improvement program Information exchange of business and customers is known as VOC Ways through which customer communicates complaints , compliments, product return, customer referrals, closure rate of sales call

Translating VOC to CCRs VOC gets translated to understand critical customer requirements CCRs clearly defines the value proposition it has to offer to customers Useful for determining current defect level and helps to establish new target

4.Multi dimension segmentation Limitation having segmentation with one set of variables

Consumers consider multiple parameters for buying decision

Users of market segmentation have different needs

Each segmentation is independent

Powerful tool for analyzing complex datasets

Often leads to innovative marketing solutions.

5.Customer Balance Scorecard Developed by Robert Kaplan and David Norton in the 1990s.

Helps to understand risk and opportunities within the customer base

Helps to understand parameters that can create or destroy companys future value

Important indicators for strategic improvement

Evaluate companys strategy at all levels.

IT Analytics and survey techniques1. The CHAID Algorithm

2. TURF Analysis

3. RFM Segmentation

4. Partial Least Square Structural Equation Modeling

5. Cluster Analysis

Various IT Analytics techniques used to segment customers

The CHAID Algorithm CHAID (chi-squared Automatic Interaction Detection) developed by Gordon in 1980.

Primarily used in exploratory analysis

CHAID detects non-linearity in the data unlike linear regression

Can be depicted visually and easily interpreted

TURF AnalysisTURF Analysis output table TURF (Total Unduplicated Reach and Frequency) is used when choice combination is high and combinations that can be pursued is restricted.

The above result shows preference for a particular combination

A sweet spot or optimal trade off can be found

RFM segmentation RFM segmentation measures the Recency, Frequency, and Monetary data to segment customers

RFM segmentation is used by business to design promotion and sales strategies

Any low score among these factors have to be improved upon

Limitation of this technique is its inability to measure or segment customers other than these factors

Partial least squared equation modelingSmart PLS software screenshot It composed of two models - structural model - measurement model

Measurement model measures the relationship between observed data and latent variables

Structural model measures the relationship between latent variables

Cluster AnalysisHierarchical clusterK means clusteringCluster analysis helps to partition data into mutually exclusive and homogeneous groups.

Different types of cluster analysis - Hierarchical cluster - K means clustering - Density based clustering

Hierarchical clustering good for causal analysis

K means cluster representational of Voronoi Diagram

Density based clustering group variables based on density and their location close or away from density

Final Thoughts Best segmentation analysis is the one that that is most useful to particular user

No one method works best

Segmentation should be aligned to strategic goals and based on that particular techniques need to selected

Need for multi faceted understanding of consumers and target market

THANK YOU