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Ata 2010 Steve Brubaker Data Analytics
- 2. Intelligent Interactions Improve Response Rates by Getting to Know Your Customers Through Data Analytics Steve Brubaker Chief of Staff InfoCision Management Corp. www.infocision.com
- 4. Top trends in the contact center industry 10. Cell phones – erosion of landlines 9. Trend back to the phone call – technology is driving down call center costs while paper and postal costs are increasing direct mail costs. 8. VOIP – Voice Over Internet Protocol
- 5. Top trends in the contact center industry 7. Salaried contact center agents 6. Highly/Specially trained agents with ability to free flow conversations and not always work off a script 5. Skill based inbound customer service – impacts up-selling and cross-selling. Inbound doesn’t make $. By up-selling and cross-selling you can make $.
- 6. Top trends in the contact center industry 4. Social Media monitoring in the call center 3. Work at Home Agents/Virtual Contact Centers 2. Multimedia communication channels – blending email, chat, phone. Agents are expected to interact at different levels.
- 7. Top trends in the contact center industry 1. The use of data analytics to develop a multichannel approach to reach out to a wide variety of consumers in the most personalized and effective way. Tweet questions or comments with hashtag #ATAdata
- 9. The Implementation and Impact of Predictive Modeling on Telemarketing Acquisition Case Study
- 15. The Implementation and Impact of Business Intelligence on Telemarketing Acquisition
- 19. Superior Lead Generation through Real Time Scoring and Targeted Routing Online Application Study
- 20. Tweet questions or comments with hashtag #ATAdata
- 21. Here’s how R3 works: Fast Response A request comes in from your website Quick Routing An InfoCision communicator promptly contacts the lead Intelligent Transfer Calls are transferred to agents or counselors if needed
- 24. Market Applications: Education Student requests information about specific campus or educational program Financial Prospect requests more information about a specific type of loan or offer Commercial Customer expresses interest in a specific product line or service Calls are routed to Agents or Counselors who are trained and knowledgeable on those specific products and markets
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- Of the lists that we rent, we only see that 20% perform to the degree that we can roll them out. Using a model, we can score this universe and determine which records are the best to call thus improving the number of lists that we can roll out while at the same time improving their subsequent performance. This has the double impact of increasing the callable universe and increasing results.
- We first overlay the data we wish to model and do a profile. This allows us to better understand the audience we are modeling and allows us to better understand the model results. We then will model the records using regression analysis to determine which attributes contribute most heavily to performance. These attributes are then scored so that when the model is applied the records we are applying it too can be scored. When a new list is brought in, we score it using the model and rank order the scores into deciles.
- These are the different types of data that we can use in the model. Transactional is the one that is built based on prior results. We will look at transactional with a focus on our behavior of interest, i.e.. Recency, frequency, monetary. This will depend on the type of model we are building.
- These are examples of attributes in the database that may be used in the model. There are no certain ones that will be used in each model as it will depend on their relationship to the behavior of interest that we are modeling. Below are the main attributes that we look at for a model: OVERLAY GROUPS Group A Individual Information 1. Age Range 202 2. Gender 205 3. Married 220 4. Estimated HH Income 213 5. Census Education Level 170 6. Race 206 7. Family Position Code 207 8. Image Children Present 236 9. Number of Children 230 10. Voter Party 512 11. Net Worth Indicator 514 12. Homeowner 211 13. Religion Code 311 14. Donor 515 15. Donor Index 516 16. Occupation Code (Group) 237 17. Voter Indicator 513 18. CBSA Code 135 19. DMA Code 131 20. Household Composition 523 21. ZIP Level Household Income Decile V1.9 286 22. Census Income Percentile 182 Group B Housing Information 1. Length of Residence 209 2. Dwelling Type 212 3. Census Median Home Value 186 4. Own/Rent 317 5. Nielsen County Size 140 6. Number of Persons in HH 224 7. Online HH Access 241 Group C Mail Response Information 1. Mail Order Responder 215 2. Mail Order Buyer 216 3. Mail Order Books 370 4. Mail Order Books/Magazines 371 5. Mail Order Children’s 372 6. Mail Order Gifts 376 Group D Credit Information 1. Credit Active 217 2. Bank Card 218 3. Retail Card 219 4. Credit Cards: Premium AMEX 331 5. Credit Cards: Premium DISC 332 6. Credit Cards: Premium OTHE 333 7. Credit Cards: Premium STR 334 8. Credit Cards: Premium V/MC 335 9. Credit Cards: Regular AMEX 336 10. Credit Cards: Regular DISC 337 11. Credit Cards: Regular OTHE 338 12. Credit Cards: Regular STR 339 13. Credit Cards: Regular V/MC 340 Group E Donor Information 1. Donor: Animal 424 2. Donor: Arts/Cultural 425 3. Donor: Children’s 426 4. Donor: Environment 427 5. Donor: Health 428 6. Donor: Political Conservative 430 7. Donor: Political Liberal 431 8. Donor: Religious 432 9. Donor: Veterans 433 Group F Transaction Information 1. Internet Shopper 591 2. Continuity Shopper 592 3. Internet: Purchase Online 369 Group G Interest Information 1. Veteran in HH 342 2. Hobby: Self Improvement 358 3. Music Pref: Christian/Gospel 389 4. Music: Country 391 5. Reading: Bible 404 6. Reading: Children’s 407 7. Reading: Computer 409 8. Reading: Country 410 9. Reading: Medical 414 10. Reading: Military 415 11. Reading: Natural Health 417 12. Reading: Sports 422 13. Reading: World News 423 14. Sporting: NASCAR 445 15. Sporting: Hunting 444 Group H Cluster Information 1. Health/Insurance Responder 779 2. Mindbase Groups 634 3. Mindbase Segments 636 4. Mature Data Profiles 595
- As a second step, we studied various economic indicators to determine if they had an impact on giving. We found that Household Income showed the strongest correlation to giving. We used this information to derive variable gift asks. Other indicators we looked at were net worth, home values, education, and zip level income percent. It is my belief that income is the best indicator due to the nature of our business. We are asking for a monetary response in a matter of 5 seconds. People will quickly think about how much money they have readily available to give. It is also my belief that net worth is a better indicator for direct mail as people do not have to make a split second decision and as such think about what they can afford in a broader scope.