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Subscription Services

Get more customers

Get more business from existing customers

Make your marketing more efficient and effective

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Improve Acquisitions and Renewals…3 Response Modeling….4

Profiles and Financial Analysis…5Test Case Example…6

Advanced Strategies…12 Optimizing Your Mail and Price Strategy….13Improving Response Model Effectiveness…14

Improved Response through Research and Segmentation…15

Contents

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Improve Acquisitions and Renewals

Response ModelingProfiles & Financial Analysis

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Response Modeling

DART builds sophisticated “custom” models that generate superior results.

Purpose:To make your direct mail and telemarketing more efficient and effective.

Method:Our highly experienced modelers predict responders with remarkable precision. They achieve this by scientifically determining the degree to which all of the data contribute to the likelihood of response. Models can also be built with other objectives in mind, such as maximizing revenue or predicting price sensitivity. Using which ever form of regression proves most effective, variables are reduced to include only those contributing to the solution. Data quality checks are performed and any problems or questions are reported before the model is complete.

The standard write-up includes a description of the model and a gains chart.

The expanded write-up adds: 1) Descriptive statistics - a basic report summarizing what data was used and what the mail file data distributions were. 2) Descriptive profiles - shows responders versus non-responders. 3) Decile Analysis - compares the top three deciles to the bottom three for more distinctive profiles. 4) A one-page model evaluation and validation summary. 5) A two-page financial analysis showing expected performance by decile and in summary.

Results:An elegant model that makes sense, will be easy to implement, and will greatly reduce mailing costs.

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Expanded Write-up: Profiles and Financial Analysis

We provide a detailed analysis and an expanded write-up, which makes sense out of the numbers. Each of these can be provided a la carte as well.

Descriptive Statistics: A basic report summarizing what data was used and what the mail file data distributions were.

Descriptive Profiles: This analysis describes the attributes of responders. For example, “Responders are 36% more likely to live in large cities than those in the mail file.” This also reveals targets for your creative.

Decile Analysis: This analysis profiles the attributes of those the model identifies as likely to respond. It compares subscribers in the top three deciles to those in the bottom three deciles.

Model Evaluation and Validation: This is the final step in the analysis process. We compare actual responders to modeled responders to validate the model and screen for inconsistencies.

Financial Decile Analysis: This analysis shows the expected financial performance of each decile. The analysis is thorough, considering everything from response rates to the residual revenue from expected future sales.

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Subscription Response Modeling

Test Case Example

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Decile Table of Model Performance (Gains Chart)

Test Case: This is an acquisitions response model example (we also build pricing, revenue and renewal models). Those mailed in the top three deciles were more than five times as likely to subscribe as those in the bottom three deciles. To provide added precision, we can also divide these into verntiles (20 groups) or simply use a raw score to allow selection of the desired number of names.

Response ModelDecile Table

Response CumeDecile Rate Index Index

10 12.4 264 264

9 8.7 9.0 184 224

8 6.1 129 193

7 4.7 100 169

6 4.2 90 153

5 3.2 68 139

4 2.8 59 128

3 2.4 52 118

2 1.5 1.7 31 109

1 1.0 22 100

Total 4.7 100

Top 3 vs Bottom 3 Ratio: 5.5

184

129

100

9068 59 52

31 22

264

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50

100

150

200

250

300

10 9 8 7 6 5 4 3 2 1

Decile

Ind

ex

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Descriptive Profiles, Decile Analysis and Model Validation

Description:This table compare profiles of actual responders to the modeled responders.

The Descriptive Profiles are on the left, the Decile Analysis is on the right.

The full version of this write-up includes charts. The Decile Analysis also includes brief descriptions identifying statistically significant findings.

Model Validation of the Test Case:The similarity in the indexes shows that the model is doing an excellent job of assigning responders to the top three deciles for each variable.

Mail Response Bot 3 Top 3 % % Index Deciles Deciles Index

Number of Times RenewedNone 72.1 39.4 55 89.5 42.0 47 Once 18.2 22.2 122 10.1 19.7 195 Two or more 9.7 38.4 396 0.4 38.3 999+

100.0 100.0 100.0 100.0 Census Regions

Northeast 33.2 23.6 71 33.2 21.2 64 Midwest 28.2 16.9 60 28.2 14.4 51 South 22.2 28.1 126 22.2 30.6 138 West 16.4 31.5 192 16.4 33.8 207

100.0 100.0 100.0 100.0 Term

One Year 82.2 87.1 106 62.0 76.4 123 Two+ Years 17.8 12.9 72 38.0 23.6 62

100.0 100.0 100.0 100.0 Promotion type

Agent 40.7 22.4 55 68.1 16.6 24 Publisher 59.3 77.6 131 31.9 83.4 261

100.0 100.0 100.0 100.0

Decile Analysisfrom Model Predictions

Descriptive Profilesfrom Actual Mail File

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Financial Decile Analysis

Decile 10 Decile 9 Decile 8 Decile 7 Decile 6 Decile 5 Decile 4 Decile 3 Decile 2 Decile 1

Financial SummaryMail File 74,674 74,674 74,674 74,674 74,674 74,674 74,674 74,674 74,674 74,674 Cumulative Mail 74,674 149,348 224,022 298,696 373,370 448,043 522,717 597,391 672,065 746,739 Response Rate 30.6% 18.0% 14.2% 9.3% 6.0% 4.5% 4.0% 3.5% 2.1% 1.5%Responders 22,864 13,469 10,597 6,931 4,473 3,390 2,955 2,586 1,569 1,115 Cumulative Responders 22,864 36,333 46,930 53,861 58,334 61,724 64,679 67,265 68,833 69,948 % of Responders 32.7% 19.3% 15.1% 9.9% 6.4% 4.8% 4.2% 3.7% 2.2% 1.6%Cume % of Responders 32.7% 51.9% 67.1% 77.0% 83.4% 88.2% 92.5% 96.2% 98.4% 100.0%

Cost per Order ($0.50) (1.63)$ (2.77)$ (3.52)$ (5.39)$ (8.35)$ (11.01)$ (12.64)$ (14.44)$ (23.80)$ (33.48)$ Average Net Amount 11.89$ 10.28$ 9.55$ 9.12$ 8.95$ 8.89$ 8.74$ 8.27$ 6.58$ 8.53$

First YearRevenue 271,857$ 138,461$ 101,203$ 63,209$ 40,033$ 30,138$ 25,824$ 21,382$ 10,322$ 9,512$ Mail Cost ($0.50) (37,337)$ (37,337)$ (37,337)$ (37,337)$ (37,337)$ (37,337)$ (37,337)$ (37,337)$ (37,337)$ (37,337)$ Net Profit 234,520$ 101,124$ 63,866$ 25,872$ 2,696$ (7,199)$ (11,513)$ (15,955)$ (27,015)$ (27,825)$ Cumulative Net Profit 234,520$ 335,644$ 399,510$ 425,382$ 428,079$ 420,879$ 409,367$ 393,412$ 366,397$ 338,572$

Cume Gain Due to Model 200,663$ 267,930$ 297,938$ 289,953$ 258,793$ 217,736$ 172,366$ 122,554$ 61,682$ -$ Mail Cost Savings/Wave 336,033$ 298,696$ 261,359$ 224,022$ 186,685$ 149,348$ 112,011$ 74,674$ 37,337$ -$

Lifetime ValueResidual Revenue 119,974$ 30,470$ 16,737$ 6,467$ 2,551$ 1,433$ 1,064$ 767$ 221$ 144$ Net Profit + Residual 354,494$ 131,595$ 80,603$ 32,339$ 5,247$ (5,766)$ (10,449)$ (15,188)$ (26,793)$ (27,681)$ Cumulative Net Profit 354,494$ 486,089$ 566,692$ 599,031$ 604,278$ 598,512$ 588,063$ 572,875$ 546,082$ 518,401$ Cume Gain Due to Model 302,654$ 382,409$ 411,171$ 391,670$ 345,077$ 287,471$ 225,183$ 158,155$ 79,521$ -$

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Financial Summary

Description:This table shows the expected results from one mail effort.

Key Findings:Mailing only the top half of the list generates:- 83% of the responders, - 86% of all revenue, and - 98% of residual revenue.

Given a mail file of 747k names, mailing only the top half saves $187k per mail effort.

Top Middle Bottom Mail Mail3 Deciles 4 Deciles 3 Deciles Top Half Everyone

Mail File 224,022 298,696 224,022 373,370 746,739 Response Rate 20.9% 5.9% 2.4% 15.6% 9.4%Responders 46,930 17,749 5,269 58,334 69,948 % of Responders 67.1% 25.4% 7.5% 83.4% 100.0%

Cost per Order ($0.50) (2.39)$ (8.41)$ (21.26)$ (3.20)$ (5.34)$ Average Net Amount 10.57$ 8.93$ 7.79$ 10.54$ 10.18$

First YearRevenue 496,212$ 158,406$ 41,066$ 614,763$ 711,941$ Mail Cost ($0.50) (112,011)$ (149,348)$ (112,011)$ (186,685)$ (373,370)$ Net Profit 399,510$ 9,857$ (70,795)$ 428,079$ 338,572$

Cume Gain Due to Model 297,938$ 172,366$ -$ 258,793$ Mail Cost Savings/Wave 261,359$ 112,011$ -$ 186,685$

Lifetime ValueResidual Revenue 167,182$ 11,515$ 1,133$ 176,199$ 179,829$ Net Profit + Residual 566,692$ 21,372$ (69,662)$ 604,278$ 518,401$ Cume Gain Due to Model 411,171$ 225,183$ -$ 345,077$

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Advanced StrategiesOptimizing your mail strategy

Increasing prices without reducing sales Improving response model effectiveness

Improving response through research and segmentation

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Where do we go from here?

Get more subscriptions at higher prices for less money using services designed to pay for themselves through the value they return.

Test, monitor, and evaluate:We guide you through this process to ensure consistent improvement in direct marketing

results.

Optimize your pricing strategy:Statistically determine who is willing to pay what price. Once the results from the mailing

are in (about 3-4 weeks worth of responses are adequate), we can fine tune pricing strategy by decile. Requires structured prices tests during mailings.

Optimize your mail strategy:Financial modeling increases precision by measuring the affect of offers and mailing

different audiences the right number of times. Set your plan and test “what-if” scenarios.

Improve the effectiveness of the response model:Database Enhancement Research with Cost/Benefit Analysis

Other ways to improve response rates:Custom Segmentation – “I am not the median.” Pre Mail Research – Understanding purchase motivation and behavior, and desirable

product attributes leads to better targeted creative and better response.Post Mail Research – Understanding the “why” leads to more effective offers.

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How do we optimize our mail strategy and pricing strategy?

Financial Modeling

Purpose: Sell more magazines, at higher prices, for the least amount of money.

Method: Build a custom financial model based on the financial decile analysis, but further customized to consider list fatigue from multiple mail efforts, different packages and prices. Predict with relative certainty the results of the mailing.

Audiences and Packages - Allow for mailing only cherry picked audiences and packages within a decile, factoring in segmented performance. List Fatigue - Allow what-if scenarios by each mail effort, factoring in list fatigue. Multiple Prices - Evaluate the impact of changes in price on the fly. For best results, a structured price test or a long history of prices and response rates is required. The price test can be non-invasive and not significantly alter the results of any campaign. In absence of a price test, a linear price to response index is used in its place.

Results: Expect more responses at higher prices for less money.

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Are we missing data that will improve model performance cost

effectively?Database Enhancement Research with Cost/Benefit Analysis

Purpose: Examine the value of adding additional demographic and other variables to your database, and evaluate whether the benefits from increased model performance out weight the costs.

Method: Establish baseline - start with a model you already have (or build a new model) and include the variables currently available on your database.

Test additional data - append a large number of variables from a company like Experian or Donnelley to a small sample of your customer and mail files to minimize data costs.

Identify cost effective contributing variables - examine the new variables to see if they add efficiency to the model and include only those that make economic sense to add (either due to decreased mail cost or increased response).

Results: The value in this analysis is particularly evident long term. A residual benefit is any new information that leads to better targeting your creative.

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How can we improve response rates among those we finally

choose to mail?Custom Segmentation – “I am not the median.”

Purpose:Identify distinct target audiences within your subscriber base.

Method:Segmentation can be achieved either through obvious customer groups or through sophisticated statistical clustering techniques. Market to each segment individually with distinct creative, rather than treating everyone as if they were the median subscriber.

This technique is often coupled with pre mail market research to deliver highly informative creative and increase acceptance rates significantly within each target audience.

Results:Better targeting leads to better response rates.

Ask to see our white paper on segmentation!

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How can we improve response rates among those we finally

choose to mail?Pre Mail Research – Understanding purchase motivation and behavior, and desirable product attributes leads to better targeted creative and better response.

Purpose:Gain customer intelligence and understand purchase motivation by target audience. This leads to better targeted messages in your direct mail creative, increasing response rates.

Method:Through market research, find out what attributes of the magazine appeal most to the each target audience and which behavioral characteristics link to likelihood to buy. It may boil down to one or two key attributes that indicate whether someone is a good prospect and one or two key or behavioral characteristics that indicate why they buy.

Results:For example, Corvette buyers like to “drive fast” not to drive through winding scenic roads. It turns out that need for speed is different from need for performance. And, Corvette buyers are more likely to be middle aged men nostalgic for their childhood dream car, rather than young men dreaming of this car.

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How can we do better next time?

Post Mail Research - Understanding “why” leads to more effective future offers.

Purpose:Companies generally judge whether or not an offer worked solely by the response rate to the mailing. If the response was low they increase the offer, if it was high they do it again or reduce the offer. But, to learn “why” the offer did what it did, follow the mail with surveys designed to get additional detail about their thought process, purchase consideration, the value they placed on the offer, and most importantly why they buy/read/like the product.

Method:Implement three surveys as follows: 1) awareness and readership, 2) buyers - find out why bought, 3) non buyers - find out why didn’t buy. This could be condensed to remove the awareness and readership part, but experience indicates that it is worthwhile.

A database step often follows for the next mailing, where we assign who gets which message as determined by research.

Results:This will lead to a better offer next time, that will not be more expensive, just more effective.

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Getting Started…

Craig TomarkinDART Marketing, LLC

2333 Congress St.Fairfield, CT 06824

CTomarkin@dartm.net203-259-0676

Fax 419-858-8545

Contact us today!Learn more about how we can help you achieve better response rates for less money.