2 Realizing the Power of Big Data via Segmentation Robust Platform for Integrating Marketing Functions and Data Sources Noel Dunivant, PhD Vice President MaPS (919)
3 Workshop Content What we re going to cover 1. How to build a segmentation that provides a conceptual framework to underpin all marketing functions platform to link and leverage Big Data system that generates golden insights 2. Identifying and actioning target segments 3. Examples of segmentation/targeting applications leveraging Big Data and driving insights
4 1. How to build a segmentation that provides a conceptual framework to underpin all marketing functions platform to link and leverage Big Data system that generates golden insights
5 But first, an introductory note Soumen discussed branding as a conceptual framework and platform for Marketing Research Departments to deal with the challenges of fragmentation and proliferation of data and insights functions. Now we re going to consider segmentation as an alternative conceptual framework and platform. We should keep in mind that. Branding and segmentation go hand-in-glove. Either start with current brand positioning, and then find (target) segments that are attracted to that positioning. Or start with (target) segments the company wants to own, and then find a brand positioning that will attract them.
6 Segmentation is a conceptual framework powerful enough to underpin all of marketing As a comprehensive platform, it provides consistent focus across strategic and tactical marketing functions and generates synergistic insights. Cross Selling Programs Market Mix Customer Value Models Loyalty and Retention Programs Product Optimization Segmentation Market Sizing and Forecasting Pricing New Product Development Brand Extensions Brand Positioning Promotions Advertising
7 Segmentation can be the platform that integrates, aggregates and synthesizes multiple data sources Marketing Research Departments can use segmentation to overcome problems of fragmentation and proliferation to create a holistic and strategic basis for its consumer knowledge base. Online Communities/ Panels IRI/Nielsen HH Panels Online Shopper Database Credit Reporting Bureaus Catalog Shopper Database Quantitative Research Segmentation Subscriber Lists Qualitative Research CRM Data Warehouses Demographic Databases Website Visits Segmentation affords MR Departments the opportunity to capture and leverage Big Data (and little data too). Segmentation platform with multiple data feeds produces unique synthetic and synergistic insights.
8 But a special kind of segmentation is required Not all segmentations are able to provide the requisite conceptual framework and platform. In the past, most segmentations have failed to fulfill even more limited goals. Future segmentations must not only be actionable, they must also provide ways to leverage Big Data and connect various insights functions.
9 Criteria for Actionable and Leveragable Segmentation To be actionable and leveragable, segments must 1. Be based on drivers of purchase-related behavior and usage. 2. Be real and reliable, i.e., not be just statistical artifacts. 3. Be highly differentiated be based on product category drivers that are homogeneous within segments and heterogeneous between segments. 4. Include target segments for client brand, i.e., high response potential. 5. Make marketing sense segments must be intuitively meaningful. 6. Be accurately identifiable in customer and prospect databases, media audiences, shoppers, or sales prospects.
10 Building an actionable and leveragable segmentation platform requires multiple data sources Illustrative approach for consumer segmentation using online research Household Demographics Compiler Applications Strategy Online Panel Third Party Processor Online Survey Analytics Classification Modeling Programs Marcomm Pricing Distribution Research Databases Client's Customer Data Warehouse Classification modeling is equally, if not more important than analytics.
11 Utilizing Multiple Data Sources Data sources important for assessing needs, profiling, and classification Self-report data from surveys Needs, motivations, benefits sought Attitudes Perceptions Intentions and behaviors Demographics Psychographics and lifestyles Firmographics and corporate culture Shopping and purchase data Customer (CRM) databases (transaction history) Panels (IRI, Nielsen household panels) Specialty compiled databases (Polk, catalogers) Credit bureaus (Transunion, Equifax)
12 Utilizing Multiple Data Sources Data sources important for assessing needs, profiling, and classification (cont) Demographic or firmographic data Demographic data (Acxiom, Polk) Firmographic data (D&B, InfoUSA) Lifestyles and AIO Magazine subscription files Web usage Contributor lists Will be necessary to devise rules to resolve discrepancies in data from different sources, including Self-reported purchases and transactions in data warehouse Self-reported demographics and 3rd party demographics
13 Low Demo/Behavioral Reachability High Max Relevance The Segmentation Analytics Challenge In segmenting the market to identify actionable segments, usually must make tradeoffs between marketing relevance (needs/drivers) and reachability. Low Demographic/ Behavioral Segmentation Undesirable Maximize Reachability Balanced Segmentation Marketing Relevance Best Case: Demographic/Behavioral and Wants & Needs Alignment Drivers Segmentation High Optimal solution when database demographic and behavioral data align with selfreported drivers and correlates (needs, attitudes, preferences, and intentions) Segments are constructed rather than discovered.
14 2. Identifying and actioning target segments
15 Targeting making segmentation actionable Segmentation per se has NO value, unless it is turned into action through targeting. Not all engines are equal Segmentation is like an engine without a car body and driver. Targeting provides the vehicle, fuel, driver and map to destination.
16 Capturing the Full Power of Targeting Targeting involves 3 processes Selecting target segments (must be part of segmentation analytics) Aligning brand values and customizing offers and communications to meet targets' unique needs even more closely Focusing resources on the target (high potential) segments Target Segment Needs Targeting Match Brand Promise/ Product Benefit Delivery Segment 1 Segment 3 Segment 6 Segment 9 Target Priority Primary Secondary Tertiary
17 Selecting Target Segments TargetFinder maps help select and communicate target segments. Assets excl. home Shop for rates/products Financial Resources High 11 Conservative/traditional values Service Quality Importance Insensitive to bank price Low 6 0 High Key Prefer personal Segments are numbered. Circle size 10 Price/return important service indicates relative segment size. Proud to be bank customer Primary Target Segments 6 1 Secondary Target Segments Nontarget Segments 1.0 Low -1.0 HH income HH financial condition Perceived bank differences Accept investment risk Self-direct investments 4 Bank Primary Targets Dislike using credit Segmentation of the market for retail banking services
18 The Classification Models Challenge Number No matter how good the analytics to identify (construct) the segments, targeting success depends on number and accuracy of classification models. Input for Model Building Models to Score CRM Databases Self-Report Demographic Databases Website Visitors Online Communities Customer Database Demographic Database Nielsen/IRI HH Panels Survey Samples Qualitative Recruits Batch Long-form Short-form Real-Time EM CATI/Excel CART CART Gearbox Q2 Q1 Q3
19 The Classification Models Challenge Accuracy Actual Group Membership Basic Personal Callers Basic Personal Callers Mobile Managers Constrained Cost-Sensitive Social Callers Connectors Power Personal Callers Affluent Convenience Callers Active Business Callers Safety- Minded Seniors Power Business Road Warriors Callers Mobile Managers Constrained Social Callers Cost Sensitivity Connectors Power Personal Callers Affluent Convenient Callers Active Business Callers Actual Group Membership Safety Minded Seniors Accuracy Criteria Overall 70% Target Segments 65% Customers 70% Prospects 55% Power Business Callers Road Warriors
20 3. Examples of segmentation/targeting applications leveraging Big Data and driving insights
21 Utilizing Multiple Data Sources Multiple data feeds Included in following segmentation and targeting examples. Customer Data Warehouse Credit Bureau Data Segmentation Knowledgebase Household Demographics Compiler Nielsen/IRI HH Panel Survey Data
22 Multi-source dataset used to create segmentation and build classification models; foundation knowledgebase Customer Purchase Behavior Full 3 year history Trip frequency. Cross-shopping. Channel interactions (Brick & mortar, Online, Catalog). Formats patronage. Market basket (Categories, Divisions, Brands). Promotional affinity. Method of Payment. Best Customers. Attrition. Lapse. Profitability (Gross Margins) Illustrative from Retail Case Customer Data Warehouse Online Consumer Segmentation Survey Acxiom s Infobase Needs and benefits sought Attitudes toward shopping S-R shopping frequency, spending Retailer perceptions and preferences Lifestyles and demographics Demographics Append 350+ attributes - Household data, Real Property data, Purchase Behavior data, Wealth Indicators, InfoBase Trends, Vehicle data, Premier Life Traits data, Lifestyle data, Psychographic segments data, Geographical data, Personal data. Prospect List National list million households, 170 million economically active individuals. List rebuilt every quarter starting with 300+ million names.
23 Mobile Telephone Segmentation Analysis and Classification Modeling using Multiple Data Feeds We performed segmentation and created classification models to score the customer database at high accuracy (70%+) as well as survey respondents using a short set of questions. Attitudes, Perceptions, Self-reported Behaviors, Demographics Usage, Spend, Other Behaviors, Demographics Survey Data warehouse Customers Only Both Customers and Prospects Unified Market Segmentation Classification questions and algorithm A short set of questions that immediately indicates the respondent s segment. Allows segmentation to be replicated in other research studies. Even though the two classification algorithms used completely different sets of data, 70% to 85% accuracy was achieved in both Segment classification algorithm The algorithm assigns each customer in the CRM database to a segment enabling post-hoc analysis and proactive marketing actions.
24 Innovative Segmentation Analytics Required The challenge was to segment an incomplete database constructed from multiple data sources in one step rather than the customary approach of developing a solution based on just one set of data and retrofitting it to another. Type of Information Available Survey Data Customer Database Client Customers (~5K) (surveyed customers) Comp. Customers (~5K) (surveyed consumers) Needs, Attitudes, Self-Reported Behaviors, Demographics Actual Behaviors, Demographics X Client Customers (~80M) (from customer database) X Actual Behaviors, Demographics We deployed an innovative solution based on a sophisticated latent class model explicitly designed to work with missing blocks of data.
25 Forecasting segment growth and revenue Multi-source data was used to forecast the growth of each mobile telephone segment in terms of number of consumers (buyers), units purchased, and dollar volume. Projected churn quantified expected customer loss. Net growth is a forward-looking measure of the potential value of the segment. Total Price Sensitive Price Sensitive Total Connectors Connectors Total Cellular Customers 14,660, ,799 Growth Potential (3 month) Customer Growth 694, ,943 Competitor Cellular Customers 9,734, ,211 Customer Growth % 14.11% 24.80% Competitor Cellular Share 66% 85% Client Customers 4,925,958 73,588 Customer Growth Revenue $29,926,213 $5,550,884 Client Share 34% 15% Revenue Growth % 12.2% 163.0% Segment Shares 100% 3.3% Customer Churn (3 month) Competitors 100% 4.2% Customer Churn 368,591 3,475 Client 100% 1.5% Customer Churn % 7.48% 4.72% Customer Churn Revenue ($18,222,606) ($160,821) Monthly Revenues Net Growth Average Total Spending $50 $46 Customer Growth Net 326, ,468 Total Cellular Revenue $728,168,781 $22,389,921 Customer Growth Net % 6.62% % Competitor Cellular Revenue $483,504,066 $18,984,316 Customer Growth Revenue $11,703,607 $5,390,063 Client Revenues $244,664,715 $3,405,604 Revenue Growth % 4.8% 158.3%
26 Estimating Discrete Choice Models using disaggregate data from multiple sources Disaggregate DCM "bridge" models can leverage primary survey data and secondary sources such as IRI, ACNielsen, or store/customer databases. Primary research captures responses to new products and tests new attributes or new levels for existing attributes (e.g., broader price ranges). Secondary data sources are based on actual in-market behavior, allow us to estimate price or attribute elasticities in a more complete competitive context, and are not subject to any survey biases. Using both sets of data simultaneously exploits the advantages of each while minimizing their limitations. MaPS joint model is operationalized by estimating "scale" and "bias" parameters that allow for statistical comparability between the variables that are unique to each of the two datasets. Illustrative from CPG Case New Concepts Nutritional Additives Fat Content Claims Survey Data Price Flavor Brand Fat Content Size Display Feature Promotions More Brands and Flavors IRI Household Panel merged with Store Data By Week In one of MaPS studies using this approach, we estimated models for two categories salty snacks, and salad dressings. Data was from a consumer choice survey and IRI Household Panel.
27 Multi-source DCM simulators can be used to design optimal offering for each target segment Cross-elasticities, cannibalization, attrition, etc. estimated for each segment. Disutilities Associated with Transaction/Flat Fees Fee Levels Segment 3 Segment 2 Segment 1 Segment 4 Segment 5 The simulator interface permits the user to request results for individual segments or all segments combined in this debit card example. -13 Segment 6-15
28 Feeds from credit bureaus and demographic data compilers to support new product research Concept test identified segments with the greatest market potential for a new credit card concept. Online panelists were classified into segments by appending demographic data from Acxiom and credit bureau data from Experian, and then applying a classification model that had been built in previous research. On introduction, the new credit was targeted to high-potential segments using direct mail marketing targeted to prospects (Acxiom) and customers (scored CRM database). The package and offer to each target was customized based on its top drivers and demographic characteristics. Response rates from target segments exceeded nontargets by 425%. Market Potential by Segment (%) Segment (sample size) (300) (206) (238) (251) (199) (119) (265) (184) (158) (425) Stated Convergence Calibrated
29 Leveraging Nielsen Household Panel insights with segment scoring Segment profiling Retailer Segment Profile: Segment 6 Seg 8, 13% % of HH Seg 1, 7% % of HH Seg 2, 5% Seg 8, 10% % of $ % of $ Seg 1, 7% Seg 2, 7% All Outlet Loyalty to Retailer 17% 16% Seg 7, 16% Seg 3, 17% Seg 7, 19% Seg 3, 14% 5% Seg 6, 15% $ per Trip Trips per HH 5 5 Seg 5, 6% Seg 4, 22% Purchase Dynamics $9.80 $12.92 $15.97 Seg 6, 13% Seg 5, 10% Source: Custom Homescan 52 Weeks ending dd/mm/yy 12 Total OTC Beauty $ per HH $45 $62 Seg 4, 20% Total OTC Beauty $197 Segment 6 Total UPC OTC Beauty % $ on Deal Segment 6 Total UPC OTC Beauty
30 Leveraging Nielsen Household Panel insights with segment scoring Segment tracking Classifying members of the Nielsen Homescan HH Panel for segment membership enabled the retailer to track YOY changes in segment composition and shopping/purchase behavior. Target segments showed different patterns of net YOY change 21.2% 21.4% 21.1% 21.1% % 13.9% 14.5% 13.5% 13.2% 13.4% 7.0% 5.8% 4.7% 4.7% 5.1% 6.3% Seg 1 Seg 2 Seg 3 Seg 4 Seg 5 Seg 6 Seg 7 Seg 8
31 Attrition Rate Targeting impact illustrated Leveraging scored small business CRM file to introduce loyalty program to target segments. Decrease in Attrition Among Target Small Business Segments Following Loyalty/Retention Program Before After Loyalty Program 20.0% 15.0% -20% -20% -25% -17% 10.0% 5.0% 0% 0.0% Target Segments
32 Targeting impact illustrated Dimensions having greatest influence on segmentation solution. Drivers (Needs/Attitudes) Impression of client brand Factors important in choosing a plan Receptivity to CDHC Price Sensitivity Current Health Plan Design Individual Deductible % FT employees covered % Premium paid by employer Currently offer CDHC Behavior Chose the lowest cost plan at last renewal Firmographics Number of Employees (D&B) Industry (D&B) Company Tenure (D&B) Multi-site or single site (D&B) Metropolitan Statistical Area (D&B) Age and Gender composition of workforce Employee tenure Segmentation of the small and middle market for employer health insurance plans
33 Targeting impact illustrated Comparative attractiveness of target and nontarget segments Impressions of Client Brand Attractiveness High Mod Low Segment A Segment B Segment C Segment D Segment E Segment F Segment G Segment H Segmentation of the small and middle market for employer health insurance plans Value Interest in CDHC Deductible Price Sensitivity Overall Attractiveness Customizing direct mail based on segment attractiveness, drivers, and firmographics produced 4- to 8-fold increase in response rates
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