OPTIMIZING YOUR MARKETING STRATEGY THROUGH MODELED TARGETING

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1 OPTIMIZING YOUR MARKETING STRATEGY THROUGH MODELED TARGETING 1

2 Introductions An insights-driven customer engagement firm Analytics-driven Marketing ROI focus Direct mail optimization 1.5 Billion 1:1 pieces in 2014 Joe Holleman Manager, Marketing Strategies P M

3 Precision Dialogue 2012 Customer Engagement Agency Strong Performer Rated as a strong performer in the 2012 Customer Engagement Agency Wave with high rankings received for: Business strategy capabilities Analytics Technology integration Customer satisfaction CEA Wave Report: 4Q, 2012 Capabilities Snapshot Business Strategy Analytics Technology Integration Customer Satisfaction Precision Dialogue Competitor Average

4 Topics of Discussion Customer Lifecycle Analytics Targeting Continuum Data Profile Analysis Acquisition Modeling Retention Modeling Modeling Approach to Campaigns Getting Started Research & Customer Experience User Experience Questions and Answers Additional Information

5 Customer Lifecycle

6 Analytics What it means to marketers Knowing who your audience is, identifying their purchase triggers and reaching them at the right time by the right channel are keys to creating relevance and increasing response rates. "66% of marketers believe that data-driven marketing drives positive value to their company today and 93% believe it will in the future. "More than two-thirds say acting on data to improve marketing performance is one of their most important objectives". Source: DMA Statistical Fact Book 2014

7 Analytics What it means to consumers When asked, When you do receive hard copy direct mail, which type do you prefer? 63% 22% 15% Highly personalized with messages and offers that are unique to my needs/interests Only personalized with your name and address but not much that is unique to your needs/ interests Non-personalized messages and offers that everyone receives regardless of individual needs/ interest Source: InfoTrends

8 Analytics Common Engagements Predictive modeling for customer acquisition, retention, & cross-sell initiatives Campaign test design & measurement Omni-channel attribution analyses CRM / contact strategy optimization Customer valuation & segmentation Credit risk analysis Web analytics Custom analytical databases Analytical workbench

9 Targeting Ability Targeting Continuum Baseline Profiles Customized to client partner Not predictive Syndicated Segments Off-the-shelf segments Based on geodemographics Some DM response Forecast Model Based on look-alike responders (walk-ins) Not based on DM campaign Better than profiling at capturing multiple variable relationships Campaign Response Model Based on responders to mail campaign Captures natural response + marketing effect Able to calculate marketing effect to drive better ROI marketing decisions Tool

10 Demographic Profile Analysis Objective: Better understand the customer profile across numerous demographic elements. Paint a picture of the Dots customer as to grow foundational customer understanding for the business. Methodology: Assemble analysis data-set o Customer database with categories (if applicable) Dots o National consumer household file Metrics Exploratory data analysis Append demographic data Develop Customer Profiles Document and present findings Deliverable: 1. Demographic customer profile(s) by pre-defined customer category(ies)

11 Acquisition Modeling Objective: Identify new potential customers based on a look-a-like (based on composition of existing customers) or response (response to an acquisition campaign) methodology. The criteria will primarily be made up of demographic and spatial data, with a two-stage approach: 1) Response, and 2) Value. Implementation results in prioritized lists of prospective customers in Dots trade areas for purchase in support of targeted marketing campaigns. Methodology: Define modeling objectives and identify scope of data needed Aggregate analysis files o Customer database o Transaction database Perform exploratory data analysis Variable creation and reduction based on predictive correlation to response/value Model estimation and validation Model scoring and application Deliverable: 1. Model findings documentation and scoring algorithm for implementation 2. Implementation recommendation

12 Retention Modeling Objective: Knowing 48% of customers shop every 2 weeks, identify customers who are likely to lapse or attrite given recent behavioral trends and purchase behavior. Quantify the degree to which the customers will cease purchasing. Methodology: Define modeling objectives and identify scope of data needed Aggregate analysis files Perform exploratory data analysis Variable creation and reduction based on predictive correlation Model estimation and validation Model scoring and application Deliverable: 1. Model findings documentation and scoring algorithm for implementation 2. Implementation recommendation

13 Modeling Approach to Campaigns

14 Modeling Approach to Campaigns Decile Mailed Conversions Interval Statistics Conversion Pct Margin Mail Cost Return Margin Mail Cost Net Return 1 9, % $15,876 $5,988 $9,888 $15,876 $5,988 $9, % 2 9, % $8,904 $5,989 $2,915 $24,780 $11,977 $12, % 3 9, % $6,237 $5,988 $249 $31,017 $17,965 $13, % 4 9, % $4,641 $5,989 -$1,348 $35,658 $23,953 $11, % 5 9, % $2,709 $5,988 -$3,279 $38,367 $29,941 $8, % 6 9, % $1,491 $5,989 -$4,498 $39,858 $35,930 $3, % 7 9, % $924 $5,989 -$5,065 $40,782 $41,918 -$1, % 8 9, % $735 $5,988 -$5,253 $41,517 $47,906 -$6, % 9 9, % $609 $5,988 -$5,379 $42,126 $53,894 -$11, % 10 9, % $336 $5,988 -$5,652 $42,462 $59,882 -$17, % Total 99,804 2, % $42,462 $59,882 -$17,420 Cumulative Statistics ROI Sample Strategy 50% volume reduction Mailed Conversions Conversion Pct Margin Mail Cost ROI Total 99,804 2, % $42,462 $59, % Our client can reduce mail costs by 50% and only sacrifice 10% of sales and conversions Top 50% 49,902 1, % $38,367 $29, %

15 Getting Started Data: Contact History Channel Data Transactional Data Customer Data Cluster Data Common Investment Scenarios: One Time Model Build Fees: $10,000 - $50,000 Data Enhancement Fees: $CPM Scoring Fees: $2,500 - $4,000 Resources: Partners/ Internally Software Commitment to the Approach Creativity

16 Customer Research 16

17 Customer Research Eye Tracking Not only learn what people DO, listen to what they SAY, and observe how they REACT, but eye tracking allows you to learn what they actually SEE Learn: What draws attention and why What content is being read or scanned Your customer s decision-making process Glean more detailed insights, such as: Exhaustive review Selective Disregard Obtain key visual measurements: Sequence, dwell time and hit rate Confidential Precision Dialogue 17

18 Customer Research Direct Mail Your creative has 3 seconds to gain attention 12 additional seconds for recognition 18

19 Customer Research Website 3 seconds for users to decide if your communication is credible or not 8 seconds for users to decide if they re on the right path or not Less than 1 minute to complete desired task 58% of visitors who experience problems NEVER return All Rights Reserved 2014 Precision Dialogue 19

20 User Experience A Heat Mapping Evaluation or Audit is a detailed review of a direct mail piece concentrating on user focal points within the printed material to identify key information based on visual impact. Precision Dialogue Heat Map Raffle ACTIVITY Research and Customer Experience experts perform a heat mapping evaluation Up to three-(3) direct mail pieces Postcard, Letter or combination PURPOSE Identify high ranking focal points on the direct mail piece Provide feedback on findings Deliverables Customized heat maps for each of the three pieces reviewed 30 Minute findings review with winner Action plan for next steps Redemption Rules: Heat mapping is valued at $4,000 Offer valid 10/13/15 12/1/15

21 Necessary Data Assets & Investment Scenarios Questions and Answers

22 ADDITIONAL INFORMATION 22

23 Precision Dialogue Perspectives

24 Analytical Solutions Quick Reference Guide Model / Analysis Type Definition Examples Classification / Propensity Used to predict an event or the likelihood of a response to a message (conversion) Responder Value Identify the value of a positive response Value models Acquisition models Response models Uplift models (determines response with vs. without offer) Lapse/defection models Clustering Association / Affinity Sequence Used to analyze patterns and identify groupings of consumers. Used to determine the best cross-sell and upsell opportunities Used to identify the most likely next action for each consumer Segmentation Demographic profile analysis Behavioral segmentation model Cross-Sell model Pattern Detection / Marketing Trigger analysis Market Basket Analysis Product sequence models

25 Market Basket Analysis Objective: Identify and quantify products with strong cross-sell associations, with a particular emphasis placed on high-margin products and high-volume products. Additional insight into purchase sequence results in targeting based on customers who like this also like X. During the initial stage of this analysis, document current marketing strategies and identify the preliminary list of customer behaviors for analysis (Closet and Purchases). Methodology: Acquire a dataset including both Customer and Transaction data Document current transaction-level strategies and constraints Identify the list of customer behaviors for analysis Determine optimal window for market basket definition (e.g., 7 days) Create analytic data sets and develop/apply rules to determine opportunity Apply Association Analysis to establish statistically meaningful product relationship patterns Develop financial pro-forma to forecast business impact from leveraging Deliverable: 1. Cross-sell trigger findings report and assessment analysis 2. Business impact pro-forma 3. Implementation recommendations including in-store and on-line merchandising opportunities

26 New Customer On-boarding Strategy Objective: Knowing 12% of transactions are associated with New customers, test into a contact strategy for new customer on-boarding communications, leveraging findings from the Marketing Basket and Purchase Sequence Analysis. Methodology: Document current strategies and constraints Incorporate findings from Market Basket Analysis Design channel and cadence tests to analytically derive the most impactful on-boarding strategy Execute tests scenarios, report findings, and establish a champion on-boarding communication strategy Deliverable: 1. On-boarding test performance report and analysis 2. Implementation recommendation

27 Rules-Based Segmentation Objective: Identify meaningfully different audiences within the customer base. The rules-based segmentation strategy will be defined by as yet unknown analytical findings, Metrics expects to evaluate a number of variables including, but not limited to current value/margin, demographic cohort, tenure, etc. Methodology: Confirm data set includes customer information, transaction information, demographic profile output, acquisition modeling output, market basket output, retention modeling output Leverage findings from the prior analyses to drive the candidate variables for inclusion in the segmentation strategy Aggregate transaction information to the customer level Merge various datasets for a comprehensive view of information Produce iterative cuts of variable combinations Determine variable combinations with most discriminatory power and favorable implementation characteristics Deliverable: 1. An agreed upon approach to segmenting the customer base 2. A means to drive differentiated direct marketing content, messages, and offers

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