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  • Brian Chapman – Database Research Analyst in Marketing. As my agenda bio indicates, I’ve been with State Farm for nearly 20 years. While my first 10 were heavily in the claims arena, the last 10 years have been spent in Corporate. In 1998 State Farm began investing heavily in new technology – specifically database research. I had the good fortune of coming on board shortly afterwards and have been deeply involved in the database research in the arena of marketing. When I was invited to speak, I was aware that the theme of the conference is Marketing Messages: Are you being heard? State Farm is a large company. There are so many areas where SF get’s their message out – Agency, Zones, the Web, Advertising, Corporate Marketing. I think one of the most interesting things SF has done is “Enhanced” their Marketing efforts trough technology. Right offer to the right person at the right time. It’s a cutting edge use of technology and with respect to this group, I thing you’ll find it rather interesting.. NEXT SLIDE
  • READ the Marketing definition. There are a lot of definitions out there. Here’s just one – from the American Marketing Association…. I think the key takeaway here is “creating, communicating, and delivering value to customers….and managing customer relationships. RELATIONSHIPS! Let’s talk about the past…specifically for State Farm, but I wouldn’t doubt many of you in this room who have been involved in “sales” know from the past that…. NEXT SLIDE
  • There is selling. State Farm just turned 85. For nearly 75 of those years, the company focused on selling. Specifically the Agents. Tell the story of Agent trying to sell me life insurance when I was fresh out of college, had no assets, and no family…. Agent’s “SOLD” Life to qualify for trips. Besides, it was PROFITABLE! Think about all the products sold, and how anyone would just walk to your door and try to sell you something, even if it was obvious to the sales person you’d have no use for the product. CLICK – Then there is Marketing. “Right Offer Right Person Right Time” - Makes so much more sense! Not only does it increase sales, it’s more cost effective, and it also solidifies the relationship with the customer because the customer general feels ‘someone is watching out for them’ NEXT SLIDE
  • BEFORE THE FIRST “CLICK” One of the latest and most innovative ways to get the “right offer, right person, right time” is through data mining. “How many are familiar with the specifics of data mining? How many have heard of it?” Before you get to the definition, give the disclaimer. I am going to talk in several ways. First, I will talk in generalities, simple laymen's terms. But, I will also talk very technically at some points. It will be brief, and wont last long enough to cause drowsiness. The reason I feel the need to do this is so the each of you can hopefully leave here with something to think about. Either through academic capacity, HR capacity and perhaps some of the skill sets that are becoming more and more in high demand by employers , or perhaps in the corporate capacity and getting some fresh ideas on how to leverage what you already know about your own customers. Now, I could talk in hypothetical's. But, I think it’s best to use State Farm as an ‘example’ on how the evolution of data mining took place at State Farm. We’re going to: Briefly talk about technology Look at some actual data, especially some of the ‘conventional wisdom’ examples and how they can be squashed by data mining Reach the payoff of an improved marketing technique at lower cost and with a higher return. Hopefully, you’ll take away a few ‘gee wiz’ points…. CLICK Process the slide….
  • 50,000 feet…..Before going in: Here are the keys: Industry? Doesn’t matter – widgets? Service? Academia? Customers – Who are your customers? Retail Consumers? Students? Alumni? (There’s a lot of data there!) Fellow employees? Know your data….we’re going to spend a lot of time on ways to do this Know your tools….tell the story about ‘tools’ and Paul Mitchell. Techniques…technical…briefly…
  • Here’s the payoff in the end: Data Mining at State Farm is being done in many areas. But here’s the payoff I’d like to specifically present in the end.…. Direct Mail. Many organizations use direct mail. I think direct mail was part of some of the discussions today. Check your mailbox. Do you know how much is spent on direct mail industry for very small (less than 1%) response rates? We’ll bring full circle the impact of data mining on Direct mail…. Lead Delivery to Agents…..This is the most interesting and the success has been huge….hopefully you’ll see the payoff as the discussion comes to a close…
  • Here’s the Generalities. But it’s a good flow to follow. REMEMBER: We will use State Farm as a real example…right from the beginning nearly 10 years ago. Go around the horn…
  • Obvious…
  • State Farm takes this very seriously and has databases regarding privacy and we are constantly monitoring the issue..
  • Straight forward… A LTTLE TECHIE BUT NECESSARY….to paint the picture….This is today’s technology and more and more organizations are participating in this. Are the students of today prepared for this? Actual hardware! Old days called a mainframe. Not really. Individual servers connected by nodes. DB2 – IBM’s product Now up to 3000 attributes, give the vision of a glorified spreadsheet….individual customers on each row…thousands to millions and each column is an attribute – age, income, presence of children, marital status….we’ll get in to more… Now up to nearly 100 terabits Marketing unit: FUNNY story …New concept… is a husband and wife, each with their own car really to separate customers, or are they part of a household? Give example of some of the problems. NEXT SLIDE
  • Talk about quality….Big databases are not necessarily the best….garbage in garbage out….a few 1000 year olds….I’m married….what if the only product is a life policy on a 2 year old bought by a grandmother who has no relationship with SF? You have a 2 year old head of household. Entire thesis have been done on data quality…another day another time…
  • Back to privacy, no bias, no unfair analysis….
  • Take a moment to talk briefly about these tools and methods….I don’t know if undergrads are seeing these tools in their upper level classes, but a minimum of SQL and perhaps SAS programming would go a long way. This is the wave of the future. My area of expertise is SQL as I query the mart every which way. I also have expertise in SAS coding. Of course Excel… Database size can be small. Start out with Microsoft Access. It’s loaded on most people’s Microsoft Business packages. It writes SQL in the background as does Cognos. Simple Excel go reveal things in your data you’d otherwise not notice….
  • Here are the tools that actually do predictive modeling…looking a large amounts of data across multitudes of fields to ‘predict’ the greatest ‘needs’ of households…. We have a few really sharp modelers. I’ve dabbled in logistic regression and IBM’s Intelligent Miner for Data…but I am not a knowledge expert….
  • Each bullet is self explanatory…elaborate… Talk about how the third bullet “The factors that go into models are statistically complex….” and how grasping this is extremely important…. Talk about fourth bullet and how it is iterative and subject to change…
  • In model creation…it’s a complex process, but essentially, a set of individuals that currently OWN the product targeted are used to develop the model based on their attributes vs. those who do NOT have the product. CLICK Then those who do not have the targeted product are given a score based on the scientific variable interactions with those that do have the product. X-SELL IN BOOK EXAMPLE: Suppose a model is built to predict the propensity for a household to purchase a SF Annuity. Once the model is built, SF Texas households that do not have an annuity (over 99%) are scored. Then using MapInfo, perhaps these concentration of high scores can be mapped by county: NEXT SLIDE
  • Build Models for as many products as possible. We currently have over 25 models in production. We score all 27 million households once a month for their propensity to buy every product they currently don’t own. When direct mail initiatives are drawn up for certain products…having a limited number resources (you can sent 11 million pieces of mail to every SF homeowner that doesn’t have a homeowner’s policy), mail is ‘targeted’ for the highest scores. Every time there is a campaign for direct mail CLICK We are always testing….not only sales of products, defection, mail impact….Explain Mail vs Control. CLICK 17,000 agents ‘selling’ every day…help them to ‘Market’ by delivering all of their current customers right to their office via the secured web… CLICK
  • Read the success!
  • Models don’t last forever….
  • Remember one of my original bullets regarding modeling? It applies right here…..
  • The modification may involve new data, and the other aspects of data mining as the process has come full circle.

Transcript

  • 1. Selling vs. Marketing Predicting Your Customer’s Needs Through Data Mining Brian K. Chapman Marketing Database Analyst State Farm ® Insurance Companies
  • 2. Marketing Definition Marketing is an organizational function and a set of processes for creating, communicating, and delivering value to customers and for managing customer relationships in ways that benefit the organization and its stakeholders. Source: American Marketing Association   
  • 3. Enhanced Marketing at State Farm ®
    • There is selling .
    Persuading the customer to buy a product or service Making the right offer to the right person at the right time.
    • There is marketing .
  • 4.
    • Google Hits for “Data Mining” – 49,400,000
    • 20 Definitions at First Glance
    • Definitions Vary
      • “ The ability of users of a system to integrate a database ad hoc”
      • “ The analysis of relationships that have not been previously discovered”
      • “ Searching large volumes of data looking for patterns that accurately predict behaviors in customers and prospects”
      • “ A ‘hot buzzword’ for a class of database applications that look for hidden patterns in a group of data”
      • “ Using advanced statistical tools to identify commercially useful patterns in databases”
    Data Mining Definition
  • 5. Keys to Successful Data Mining
    • Know your industry
    • Know your customers
    • Know your data
    • Know your tools and techniques
  • 6. Data Mining for Marketing at State Farm ®
    • Direct mail
      • Right person
      • Right time
      • Right message
    • Lead delivery to agents
      • Direct mail to in-book
      • Individual proactive personal contact
      • Pivoting
  • 7. Database Marketing Data Collection Understanding the Data Model Creation Model Implementation Modify Data Mining
  • 8. Privacy
    • Privacy is a concern to most consumers
    • Public opinion can influence the legislative process
      • Keep customer concerns in mind during all aspects of data mining.
      • Good Neighbor policy = Good business policy
    • Understand the Legal Environment
      • Federal
      • State (SB1 – California)
  • 9. Privacy
    • “ Do-Not-Share”
      • Gramm-Leach-Bliley-Act: Financial Services Modernization Act of 1999
      • Health Insurance Portability Act of 2005 (HIPPA)
      • Fair and Accurate Credit Transaction Act (FACT Act)
    • “ Do-Not-Solicit”
      • Controlling the Assault of Non-Solicited Pornography and Marketing Act of 2003 (CAN-SPAM)
      • Telemarketing and the Telephone Consumer Protection Act (TCPA)
      • Junk Fax Prevention Act of 2005 (Junk Fax Act)
  • 10. Data Collection Data Mining
  • 11. Data Collection Marketing Database (MD)
    • Developed in 1998
    • Built by IBM on Unix platform
    • DB2 “Relational Database”
    • Currently contains over 3000 data attributes
    • Updated monthly - 24 month history
    • Federated Data Warehouse
  • 12. Data Collection Understanding the Data Data Mining
  • 13. Understanding the Data What data does State Farm ® have in MD?
    • Demographics (age, gender, marital status, children, etc.)
    • Geographics (city, state, zip)
    • Policy information (qty, type of policy, when purchased)
    • Item insured (vehicle, home, classic car, paintings)
    • U.S. Census (neighborhood)
    • Life Events and Change (marriage, birth, recent move)
  • 14. Understanding the Data Information State Farm ® does not have in IMD…
    • Name
    • Street address
    • Phone numbers
    • SSN
    • Ethnicity
    • Psychographics – motivations, preferences
  • 15. Understanding the Data Methods and tools of Data Mining (ad hoc)
    • Structured Query Language (SQL)
    • SAS
    • SAS Enterprise Guide
    • SPSS
    • Cognos
    • MapInfo
    • Microsoft Access
    • Microsoft Excel
  • 16. Data Collection Understanding the Data Model Creation Data Mining
  • 17. Model Creation Methods and tools of Predictive Modeling
    • SAS Enterprise Miner
    • SPSS
    • IBM’s Intelligent Miner for Data
    Techniques of Predictive Modeling
    • Logistic Regression
    • Decision Trees
    • Neural Networks
  • 18. Model Creation
    • Predictive models consider complex combinations of factors when looking at a group of customers.
    • Those factors can include any factor that relates to both the customer’s and the primary market area.
    • The factors that go into models are statistically complex and any particular factor’s importance can change depending on other factors present at the same time.
    • Model creation is an iterative process, updated regularly, and subject to change.
  • 19. Model Creation Demographics Recent Life Events Agent’s Info U.S. Census Data Policy Info Probability Score
  • 20. Data Collection Understanding the Data Model Creation Model Implementation Data Mining
  • 21. Model Implementation
    • Direct mail
      • Right person
      • Right time
      • Right message
    • Lead delivery to agents (LM):
      • Direct mail to in-book
      • Individual proactive personal contact
      • Pivoting
    ALWAYS TESTING!
  • 22. Model Implementation Success stories “… My staff used (the model) to target high propensity renters…sending out 100 postcards to the high propensity customers. Of the 100 prospects, we quoted 12 policies and wrote 6 of them!...” “ My staff sent out 60 high propensity Auto postcards last Wednesday and in one week we wrote THREE autos! This is the best return on investment…!” “ I became an instant believer in (modeling) when Darwyn contacted our high propensity Life prospects in order to set up IFR appointments. As a result, we reached our Life sales goal by October 31!”
  • 23. Data Collection Understanding the Data Model Creation Model Implementation Modify Data Mining
  • 24.
    • Changing environment
    • Changing customer base
    • Changing data availability
    Modify “ Model creation is an iterative process, updated regularly, and subject to change.”
  • 25. Database Marketing Data Collection Understanding the Data Model Creation Model Implementation Modify Data Mining