Webinar 3 Steps Model Customer

8/3/2019 Webinar 3 Steps Model Customer

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Sponsored by:

Presented by:Dr. Kate Webster | Chief Statistician

AccuData Integrated Marketing

Three Steps to Finding Your Model Customer:Defining, Segmenting and Scoring
http://www.accudata.com/index.aspx


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About DIRECT

Learn more and subscribe at

www.directmag.com


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Introductions

Dr. Kate Webster

Chief Statistician

AccuData Integrated Marketing


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Webinar Sponsor

AccuData Integrated Marketing drivesclient-specific results by providing data, predictiveanalytics, and database marketing solutions todeliver the most complete, independent, andinsightful view of customers and prospects.

AccuData Integrated Marketing Headquarters


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Marketing and Statistical Modeling

Marketinghelps us to reach stated businessobjectives, most notably:

Grow Sales and Revenue

Increase Profitability

Gain Market Penetration

Modeling and analytics, enhances the efficiency &effectivenessof marketing endeavors, particularly:

New Customer Acquisition

Current Customer Retention

Up-Sell

Demo and BehavioralPredictors

Offer Segmentation

Response Analysis

Ongoing Customer Optimization

Getting the most out of data analytical effortsrequires a sound Analytical method using a

Learn-do-Loop Process:


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3 Steps to Finding Your Model Customer
http://www.accudata.com/index.aspxhttp://www.accudata.com/index.aspx


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Marketing Analytic Process

New CustomerAcquisition

Current CustomerRetention

Single-BuyerConversion

Score andTarget

Define

ResponseAnalysis

Custom Algorithms

Market Penetration Profiling Product Profile

Best Customer/ProspectLook-alike models

Responder models

At risk models: Retention

At risk models: Optimization Repurchase/growth model

Loyalty analysis/scoring

Promotion, price elasticity

Customer categorization

Promotion, price elasticity

Custom Scores Promotion, price elasticity

Product Sequencing

In-market experimentation

Promotional baseline

Response survey



Response survey



Response survey

BehavioralPre

dictors

CurrentThinking

ForwardThinking

Optimizing Current& Future Thinking

K-2 Cluster Analysis

Source Segmentation

Loyalty Segmentation Buyer Value Segmentation

RFMT Segmentation RFMT SegmentationSegment

Denotes methodologically-shared applications


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What do I need to know?

Who are my customers? What is my market penetration?

Who are my best Customers? How do I find prospects that look-like" my

best customers?

Who is most likely to respond to mycampaign?

Who is most likely to convert to a customer?

Which customers have the highest probability

to stop buying (attrite)? Which customers are most likely to buy a

certain product and how do I maximize mycurrent customer relationships?

How can I make this an ongoing process?

Which solutions provide the answer?

Descriptive ProfilingSolution: Define overall customers and compare withUniverse of consumers or businesses.

Descriptive Modeling + SegmentationSolution: Segment customers and rank them to identifyyour BEST customers.

Predictive ModelingSolution: Score your customer database or prospect listusing a predictive algorithm to predict those customersor prospects with the highest statistical likelihood ofmigrating toward or newly becoming one of your BESTcustomers

Response AnalysisSolution: Compare mail file(s) to responders and currentCustomers. Analyze ROI as it relates to offer and call toaction (learn-do-loop)

Step 1: Define Your Marketing Objectives


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Descriptive ProfilingDefining what your current customers look like across multiple indicators

Market Penetration IndexProfile PLUS a penetration index that compares your customersrepresentation divided by a random sample Universe representation (*100)Index of 100 indicate no difference (36%/36%) * 100 = 100100 Small Effects we begin to tell the story of the customer profile150 Large Effects capturing more of the story

AccuData Intelligent Profile

Web based, real-time match and append service resulting in standardizedmarket penetration index scores that rank prospects from highest to lowestin their resemblance to customers.

Step 1: Define Your Current Customers




Define Market Penetration Profiling Product ProfileBehavioral

Predictors

CurrentThinking

Defining Your Current Customers Story...


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Segmentation Models and RankingIdentify significant differences or similarities across subgroups of customer types

Cluster Analysis: Custom clusters group customers based on similarities inproduct purchase and demographics.

RFMT: (Recency, Frequency, Monetary Value and Tenure) Transactional Values

Customer Loyalty Analyses (Tenure): essential to understanding lifetime valueand retention across customer base

Step 2: Segment




CurrentThinking

K-2 Cluster Analysis

Source Segmentation

Loyalty Segmentation Buyer Value Segmentation

RFMT Segmentation RFMT Segmentation

SegmentBehavioral

Predictors

Defining Your Best Customers Story...

Protect the PlatinumGo for the GoldSlice the SilverBag or Boost the Bronze


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Focus on mathematical overlap (look-alikes)

Logistic Regression or Discriminate Function Models

Mathematical algorithm/equation that predicts the likelihood (probability)of some event occurring

The equation is applied to every prospect or customer record

Cross Validation of the model is essential to validity and reliability

Scores are ranked into deciles and list selection occurs through a topdown fashion or sampling for testing

Step 3: Score and Target




Behavioral

Predictors

Score andTarget

Best Customer/ProspectLook-alike models

Responder models

At risk models: Retention

At risk models: Optimization Repurchase/growth model

Loyalty analysis/scoring

ForwardThinking

Finding the Best Buyers of Your Story...


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Track and Evaluate Model Performance




Behavioral

Predictors

ResponseAnalysis



Response survey



Response survey



Response survey

Optimizing Current& Future Thinking

Quantify gains attributable to specific marketing treatments

Quantifying lift

Rolling 24-month marketing ROI tracking

Response by Campaign CodeResponse by Channel

Response by Vender

Response by Offer

Capture Trends

Refine Targeting/Algorithm

Re-Deploy Model The Learn-Do-Loop

Tracking Your Recent Buyers Story...


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Expected Key Learning's and Deliverables

Model Debrief Collaborative exchange of process and findings

Match report of customer data

Definition of customer segmentation

Summary statistics on transactional and product data

Comparative graphical analysis on all relevant characteristics that best define the target group Recommendations for list selection, offer and creative and proceed to scoring

Validation Scoring Reports An interpretive narrative that details the scoring and validation distributions

Gains tables or charts that depict the scoring of the universe of prospects as compared to thedefined customer group and validation file

Counts of prospects or best customers by score and decile

A scored universe file with mail information and logistic regression score and decile for allprospects appended to the output file for list selection


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Expected Timeline

Customer File Receiptand Preparation

Modeling and OutputCreation

Scoring Universeand Data Pulls

~ 20-25 Business Days

1-10 Business Days(Based Upon)

3-7 Business Days(Based Upon Output)

5-7 Business Days

Number of Files

Completeness of Files Level of File Hygiene Complexity of File

Offer Segmentation Response Analysis Ongoing Optimization

5-7 Business Days 3-5 Business Days Scoring 2 Business Days Data Pull


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How to get started?

A collaborative and experienced data and analytic partner

Clearly established goals and objectives

Current customer file (or a random sample across somespecified period of time)

Mail file

Responders to those mail files

Flags for campaigns (creative and offer)

Any transactional data (if available and applicable) or dataelements to help derive tenure, recency, frequency, andmonetary attributes of the customer if not readily present indata

Data must be drawn as random samples or full files

Data should be able to be sent to client in multiple formatsthrough secure FTP sites


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Model Application

Business-to-Consumer

XYZ Bank Customer Acquisition


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ProblemA well established community bank (New England) wasexperiencing an increase in attrition. XYZ Bank wasinterested in understanding if their recent attrition was

related to an even more recent expansion of their DTA orwas it a combination of rate sensitivity, customer channelpreference, geography including distance to competitors

bank and nearest XYZ BANK from customers home.

Solution300 demographic and life-stage indicators were used toenhance the clients transaction/product file. Built a

predictive cross-sell model using Customer transactionaldata and demographic/ behavioral data elements toidentify those with the highest propensity to attrite andtheir relationship to product(s).

XYZ Bank:Attrition and Cross-Sell Model

B2C Community Bank Attrition:Descriptive and Predictive Model


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Inputs:

11 Customer/Product files over last 15 Years

4.3 Million Records/Transactions

5 Global Products (70 specific)

Define: Summarizing the Current State Data Hygiene/Merge/Purge (Match and Append 300 Demographics)

Identify Attrite Group ($0 across specified time)

Derive Recency/Frequency/Monetary Values and Tenure

Calculate Distance (lat/long)

Define Original DTA and Plot Expansion

Identify Share of Wallet using consumer Universe

Segment Attrite vs. Active Customer: Diving Deep

To What extent is attrition occurring? Who is attriting?

Where do they live? What do they look like?

What is their tenure? What products do they start with/end with?

What are there interest rates? How far do they live to a competitor bank?

What channel do they prefer?

Method:Define and Segment XYZ BankCustomers


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Defining Attrite Characteristics

Young (mean/age 24)

Higher education

Upwardly mobile professionals

Predominately male

Living outside designated DTA areas Rely heavily on internet banking

Little branch use

Start product ATM

Highest interest rates with high variability

Shorter lengths of residence

Average 2 or less products

Most Attrite 12-18 months

This group of young urban professionals may indicate a trend in community banking where loyaltyand affinity is lowered as a result of internet banking and a clear lack of connection to branchlocations.

This group may be considered Jumpers based on offers from competitors. A targeted messaging

campaign that connects with this young urban segment may increase retention rates.

XYZ Bank: Step 2 Segment


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Recommendations: Predictive Model and Scoring

Develop Loyalty score to identify current customers with the highest likelihood of attriting The higher the score, the greater the risk of attriting Develop campaigns and messaging to interrupt loss or customer and revenue Predictive algorithm modified and applied to prospects universe within the XYZ footprint

Predictive Modeling Provided the Following Solutions

Predict the relationship between number of products and increased loyalty (sequencing) Continue DTA expansion and testing Predict which customers were most at risk for attrition over the next 6 to 12 months Predict which prospects within foot-print were most likely to become a customer Predict which product offer would be best as a start product offer for differing segments

Bottom Line

Significant Increased in ROMI and greater Growth Remains a Partner practicing in the Learn-Do-Loop for cross sell and acquisition

Results XYZ Bank: Step 3 Scoring andTracking


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ProblemLarge ad agency needed help identifying prospects withthe highest likelihood of purchasing a newly releasedcommercial vehicle for one of the Big Three

automakers..

SolutionBuilt a predictive cross-sell/acquisition model usingCustomer and Firmagraphics data to identify the bestB2B prospects for new purchase and up-sellopportunities.

Drive home prospecting B2B sales for Big Three Automaker

B2B Acquisition:Automobile Case Study


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Define

Profile existing B2B customers as compared to US businesses

Segment by Product

Calculate group different characteristics on geography, business size, industry, by product group Identify characteristics that most highly correlate with one product but not another

Dive DeeperCustom Cluster Segmentation Based on IndustryCluster development was mutually exclusive classifications where membership in one groupexcludes membership in another.

Predictive Model and ScoringLogistic Regression algorithm scoring Universe of business with the highest propensity topurchase a certain type of vehicle based on cluster formation of best customer

Results B2B Model:Define, Segment, Score


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Results

Modeling recommendations and report was presented tothe President of Internal Marketing Department inDetroit Division

Using predictive targeting, AIM model outperformedlong-term internal marketing strategies in several weeks

Within 30 days of model deployment the MM modelresulted in the sale of 23 automobiles while the controlgroup had sold one

ROMI Average Vehicles = $25,000*23 = $575,000

Initial modeling investment $25,000

Cost of the vehicles was not provided so a true ROI wasnot calculated. The agency is now a long-time partner ofAIM

Tracking Model Performance and ROMI


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Model Application

Additional Case Studies


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ProblemOnline Loan Aggregator wanted to increase responserates of click through customers (responders) anddecrease lead generation costs per mortgage application

Solution (Define, Segment and Score)Built a predictive acquisition model using client data andMulti-Source consumer demographic data thatincorporated capacity to purchase as well as higheConsumer Score indicators to find the best prospects

with the highest likelihood of completing and application

on line

Result (Tracking and Evaluation)Successfully accomplished a 50% reduction in leadgeneration costs while boosting response rates wellabove 60%

Increase lead generation sales for one of the LargestLoan Aggregators in U.S.

B2C Acquisition:Financial Model Case Study


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B2C Response: Analysis Case StudyProblem

Retailer wanted to increase response rates and increaseROI of existing customers while decreasing reliance onmanaged list rental

Solution (Define, Segment and Score)Built a predictive, current customer retention and valuemaximization model using client data andmulti-source consumer demographics, segmented bypurchasing behaviors scored a prospect and customerfile using logistic regression to target those with thehighest propensity to drive lapsed spenders back to the

store and increase traffic of best customer look-likesbased on responder profile. Identified those offers withthe highest ROI

Result (Tracking and Evaluation)Successfully accomplished a 50% reduction in datacosts and a 16% increase in ROI

Increase ROMI through Response Analysis with large Retail Chain

B2C Acquisition and Cross-Sell:Retail Model Case Study


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ProblemPrestigious universities desire improvement in existingdonor solicitations while decreasing lead generation

costs of new donors

Solution (Define, Segment and Score)Developed a predictive giving capacity (wealth) andpropensity (other giving behaviors) model identifyingbest givers based on wealth indicators and

combination of other consumer demographics

Result (Tracking and Evaluation)Successfully accomplished a 60% increase in leadgeneration to existing alumni for cross-sell opportunities

Alumni Donor Model Case Study

Increase cross-sell lead generation for potential Alumni Donorsto Top Universities


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Terminology


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Predictive ModelingEstimation of likely outcomes based on historical data. Results in the development of an algorithm that is applied tothe customer or prospect file.

Correlation Analysis(It all starts here) A statistical relation between two or more variables such that systematic changes in the value ofone variable are accompanied by systematic changes in the other (knowing something about tells us somethingabout the other)

Discriminant Function AnalysisPredicting membership/classification into a particular categorical outcome (e.g. responder vs. non responder, buyervs. non-buyer) by determining which continuous variables are the best discriminators between two or more naturallyoccurring groups (must have continuous predictors (IVs) and categorical DV)

Logistic Regression AnalysisPredicting the probability that an event will occur based on the predictive power of the indicators to discriminatebetween the groups. Must have categorical outcome (DV) and predictors (IV) can be both continuous or categorical.Due to the very nature of complied data, Logistic Regression is a popular method in marketing research.

Linear or Multiple Regression ModelsFinds the best fit for a set of independent variables (predictors) based on a single continuous dependent variable

you would like to predict

Multivariate AnalysisA generic term for any statistical technique used to analyze data for more than one dependent variable in the sameanalyses

Terminology


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Cluster AnalysisData reduction that sorts people, things, events, (rows in a database) into groups, orclusters, so that the degree of association is strong between members of the samecluster and weak between members of different clusters

Factor AnalysisAny of several methods for reducing (columns) continuous data into a smaller number ofdimensions or factors (e.g. buying patterns)

Structural Equation Modeling (SEM)A complex path analysis that combines the best of factor analysis with multipleregression

Custom SolutionsProduct Sequencing, Time Series, Automated Analytics and other custom solutions...

Independent VariableAlso know as Predictors or Grouping Variables

Dependent VariableOutcomes, what we want to replicate (can be continuous or binary

Cross ValidationThe practice of testing a model using a portion of the universe to test while holding theother portion constant

IndexA group of measures based on penetration that when combined produce a value meantto represent a more general characteristically

Terminology


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Follow Up Material / Support

White Paper: A Marketers Guide to DescriptiveVersus Predictive Modeling

Modeling Assessment and AIP Demo

Executive Summary: Challenges and Solutions

Dr. Kate Webster

Chief [email protected]


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Question and Answer Session


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Thank You for Attending!

Documents

Webinar 3 Steps Model Customer