Amjad Zaim, PhD. Cognitro Analytics, Founder and CEO

Transcription

1 Amjad Zaim, PhD Cognitro Analytics, Founder and CEO

2 Cognitro Analy-cs Profile Overview of Marke-ng Segmenta-on Value Proposi-on Segmenta-on (VPS) Case Study United Global Holding Conclusion 2

3 COGNITRO ANALYTICS PROFILE Page 3

4 Empire State Bldg. 350 Fi4h Avenue 59th floor New York, NY 10118

5 Company Summary Cognitro AnalyFcs was founded in 2004 through the NSF (NaFonal Science FoundaFon) seed fund Researchers (StaFsFcians, MathemaFcians), Business Analysts, Management Consultants Clients range across wide spectrum of industries with a focus on banking, insurance, retail and transportafon Value ProposiFon Targeted solufons that deliver on the promise of Advanced AnalyFcs Bridging innovafve research with the business world Page 5

6 Overview of Marketing Segmentation Page 6

7 Marke-ng Segmenta-on Views a heterogeneous market as a number of smaller homogenous market segments Segments should be maximally homogeneous within each segment Market segments need to ac-onable (i.e. response to a par-cular marke-ng program or product posi-on, to be useful )

8 Classifica-on of Segmenta-on Methods Descrip-ve methods Do not dis-nguish between dependent and independent variables. Based mainly on customer demographics: heterogeneous market using a number of smaller homogeneous segments based on customer similarity Include K- means, hierarchical clustering and neural network methods Predic-ve methods Analyze the rela-onship between a set of independent variables and one or more dependent variables. Based mainly on customer behavior: predict exogenous response variables using segmenta-on bases. Include clusterwise regression model, clusterwise logit model and CHAID. 8

9 Common segmenta-on schemes are either based on demographics or responsiveness based on demographics Adv. Useful for finding homogenous segments of customers that exhibit similar profiles (e.g., age, gender and etc.) Dis. Resul-ng segments are not very useful when they are used to predict differences in customer response to a marke-ng promo-on. 9 based on responsiveness Adv: Ac-onable and useful for taking measures Dis. Does not have the desired segment homogeneity with regard to customer descrip-ve variables.

10 Generally Accepted Criteria for Effec-ve Segmenta-on solu-on Iden-fiability Responsiveness. Substan-ality Ac-onability Accessibility Stability

11 Generally Accepted Criteria for Effective Segmentation solution Iden-fiability Iden-fiability Responsiveness Substan-ality High Responsiveness Substan-ality Medium Ac-onability Accessibility Low Ac-onability Accessibility Stability Stability Segmenta-on Based on Product/Service Data (Purchase, Usage, etc..) Segmenta-on Based on Demographic Data (Age, Gender, Marital Status)

12 Common Problems with Traditional Segmentations Schemes Which variable is my target? OYen faced with ten objec-ves based on user experience How many segments? Op-mal representa-on of homogenous segments How to relate mul-ple segmenta-on findings Affects accessibility and iden-fiabilty

13 Desired Segmentation Characteristics Prac-cally Speaking Technically Speaking Based on customer behavioral similarity Accounts for mul-ple segmenta-on goals Reasonable balance between segmenta-ons Clear visibility of all segmenta-on tradeoffs Predic-ve segmenta-on approach Op-mized for mul-ple objec-ves simultaneously Generates a set of Pareto op-mal solu-ons HolisFc view of segmenta-ons

14 Multi-Criterion Unified Segmentation Framework Multiobjective problem definition Multiobjective optimization solutions Pareto optimal solution set analysis Optimization objectives Constraints Preferences Convergence and diversity control Optimization preferences Filtering (manual, automatic or mixed) Number of segments and the Suggested solutions 14 Source: Ying Liu, et al, Multi criterion Market Segmentation: A New Model, Implementation, and Evaluation, Journal of Marketing Science, May 28, 2008.

15 Segmentation Model Objectives 15

16 VPS (Value Proposition Segmentation) Page 16

17 Motivation Behind VPS Marke-ng manager have one common goal: maximize customer relafonship It entails op-mizing mul-ple marke-ng objec-ves (maximize revenues, maximize customer purchases, etc..) Balance between: Customer value to firm (Customer Value) Firm value to customer (Customer Benefits)

18 Customer Marketing Matrix

19 Customer Marketing Matrix

20 VPS Holistic View

21 Pareto-Optimal Solution

22 Case Study Page 22

23 Case Study United Global Holding (UGH) Publically traded company, established in 2008 the largest integrated logis-cs services and solu-ons provider in the middle east. Has a fleet of 1200 state- of- the- art trucks to serve the various industries in transpor-ng all types of specialized freight. Stores customer demographics and transac-onal data in the datawarehouse. 23

24 UGH Fright Data Sets Minimu m Maximum Mean Std. Deviation Customer Benefit Attributes Mean shipments units Mean shipment weights (kg) , Mean number of on-time deliveries Customer Value Attributes Mean monthly revenue (US$) 00 10,270 5, Mean surcharges 00 5, Mean months in service Random sample of 1000 cases from 8,497 cases 24

25 Building the VPS Model Segmenta-on Basis WCOS (Within Cluster Omega Square) = WCSS (Within Cluster Sum of Squares)/TSS (Total Sum of Squares Op-miza-on Dimensions Minimize WCOS of customer Value: Customer Revenues per year Total months in Service Minimize WCOS of customer benefits: Shipment units per year Shipments weight per year On- -me Deliveries per year (within one day or less) 25

26 Segmentation Results Customer Value WCOS segment 0 solutions 4-segment solutions Customer Benefits WCOS 2-segment solutions 3-segment solutions 26

27 Analysis of 4-Segment Solution Segment Number 1 Size Mean Std. Deviation 2 Size Mean Std. Deviation 3 Size Mean Std. Deviation 4 Size Mean Std. Deviation Total N Mean Std. Deviation Mean monthly revenue (USD) Length of Service (Months) Mean Shipment Units Mean Shipment Weight (KG) Number of On- Time Deliveries Satisfied Customers with high value, No need to take action Dissatisfied customers, low activity despite low delay, need investigation Impulsive Shipper, Not very loyal, but of high value, despite high delay Not loyal customers with low service quality and high delay, need attention 27

28 An important marke-ng tool that sa-sfy all generally accepted criteria for effec-ve segmenta-on solu-on A 360- Deg view of customer segments showing all tradeoffs between segments Produce ac-onable insights into customer behavior Enable marke-ng managers to take appropriate measures assess, maintain, and salvage (if necessary) customer rela-onships. 28