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Data Mining
 

Data Mining

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    Data Mining Data Mining Presentation Transcript

    • Who Should Receive What? Steve Piantanida, Associate Director Missionary Association of Mary Immaculate Liz Dixon, National Sales Manager, Target Analytics
    • Exploring Donor Data Steve Piantanida Missionary Association of Mary Immaculate
    • What is data mining
      • Data mining is a process that involves 4 steps:
          • Studying the data
          • Analyzing the data
          • Interpreting the data
          • Researching the data
    • What data should be included
      • Basic Donor Demographics
        • Address, age or birth date, personal interests, marital status, children/no children, types of campaigns the Donor responds to, how the Donor came onto your file
      • Enhanced Data
        • Cooperative data that provides propensity scores that identify the Donors
          • Likeliness to respond to your appeal by
          • association of like appeals of other organizations
          • Free census data
          • Purchased data (such as wealth information)
    • What is the goal of data mining?
      • Your goal should be to identify Donors that “look like” the Donors who respond to:
        • Your mail campaigns
        • Telefunding campaigns
        • Internet site
        • Other fund-raising initiatives
    • How Do I Identify These Donors?
      • Develop classifications of Donors
        • Based on decisions you’ll have to make by classifying your historical direct mail campaigns
      • For example, at The Missionary Association a few of the subjects of our campaigns include : Liturgical, Marian Devotion, Hallmark and Mission
    • How Do I Identify These Donors?
      • Let’s examine a few of the Missionary Associations campaigns to see how they might be classified….
        • Mother’s day, Father’s day, Valentine’s day are appeals we classify as Hallmark
        • Christmas and Easter are appeals that would be classified as Liturgical
        • Appeals written to poverty stricken areas such as Tijuana, Brazil and Haiti are classified as Mission
        • Feast of the Immaculate Conception, Birth of Mary, Novena to Our Lady of the Snows are appeals that we classify as Marian Devotion
      • Data Mining of one-time givers is a little different - your database will only allow you to know how the Donor came on the file
    • How Do I Identify These Donors?
      • Following the classification of campaigns conduct data research involving the process of identifying the Donor audience that responded to campaigns that fall within the class
      • Utilize a consistent selection process that your organization defines – the most popular approach among non-profits is to use the Recency, Frequency and Monetary (RFM) model
      • If your organization has budget available to develop a custom model utilizing statistical methods, your selection processes can be improved to include propensity scores developed with the model
    • Analysis…Analysis…Analysis
      • Analysis involves proper interpretation of data – it is critical that you understand the data in order to assemble useful information
        • Determine the break-even point of selected donors
        • Don’t mail to donors that don’t respond or donors that return a negative ROI
        • Analyze the selections available versus selections you choose to test
          • If initial results are marginal, analyze the offer to determine an optimal continuation strategy
    • Analysis…Analysis…Analysis
      • When determining the selections to mail you must consider how the Donor responded to the offer versus how the Donor was brought onto the file
        • Consider how Donors respond to competitive offers through cooperative data
          • For example, Target Analysis Group offers propensity scores of Donors in a Co-Op…these propensity scores can be used for a variety of strategies…for example, lapsed tags give the propensity of your Donors that have given to similar offers of other similar organizations in the Co-Op
    • LTV
      • Keep focused on LTV when analyzing the “cost to raise a dollar”
        • Consider testing different offers and Creative to the same list as the control can impact your results
          • Track responses as accurately as possible to allow successful back-end analysis
    • List Performance
      • Evaluate list performance of other lists within the same category
        • Stack-rank the performance of lists
        • Select highest ranking lists
        • Continue to monitor lists to ensure consistent performance
          • Analysis has to be an on-going process
          • Adjustments are crucial to a successful strategy
    • List Performance
      • Utilize matching reports to analyze and identify lists that are dropping a substantial volume of names versus those within the same classification
        • Protect your investment by adjusting the ranking of lists within your merge-purge process
    • Keep Focused on What You Mail
      • Retailers have traditionally sent the same offer or Creative to expand “ brand ” recognition
      • Your non-profit campaigns should utilize a similar approach to avoid confusing your Donor
    • BenchMarks
      • Establish benchmarks to measure performance
      • In tougher economic times it becomes increasingly important to stay focused
        • RESEARCH, ANALYZE and INTERPRET
    • DIKAR
      • Embrace DIKAR
      • Data
        • Information
        • Knowledge
        • Action
        • Results = $$$
    • Celebrate!!!!!!!
      • You succeeded in getting these donors/members on file……………….
      • ……… ..NOW, WHAT DO YOU DO???
    • All in a day’s work……...
      • Strategy ….. Focus… Messaging…
      • … .Affinity….. OBJECTIVES ..... LOYALTY….
      • ..Sustainers… Net Revenue…. Response
      • … . Cost to Raise a $... VALUE…. Appeals….
      • Devotional... Gross Revenue..gifts
      • … Campaigns. . . Foundation…board meeting…
      • RELIEF…. membership…. acknowledgement…
    • FOCUS
      • Focus – You are the steward of your organization’s data
      • Focus – Who are these donors?
      • Focus – How did they come to file and on which medium?
      • Focus – Respect your donor
      • Focus – Retain your donor
      • Focus – Strongly build your relationship
    • Assessing your Donor Database
      • Examine your internal processes
      • Is your database providing you the adequate reports you need to understand who these donors are?
      • Can you easily extract the information you require to successfully accomplish your objective
      • Invest in your software
      • Invest in your staff
      • All relevant data is golden and the knowledge data provides if paramount
    • Modeling Techniques Utilizing Organization’s RFM and Outside Data Sources
      • Datamining Lapsed Modeling
      • Sustainers Capital Campaign
      • Planned Giving Prospect Management
      • Annual Fund Major Giving
      • Warm Conversion
    • Retention Rates:Q2 Target Analytic’s Quarterly Update National Index                  
    • House-File Modeling
      • Data from a coop is combined with your own RFM data on a donor-by-donor basis
      • Donors are scored and ranked by their likelihood to respond
      • The models work by “spreading” response rate (and/or revenue per piece) from high to low
      Modeling How can lapsed donors be modeled?
    • Example: Lapsed Donor Modeling
      • In the example above, mailing to 40% of the donors in an un-modeled data set results in about 40% of the potential response, compared to about 70% of the total response.
      • Unmodeled (deciles 1 – 4): 314 responses, 1.57% RR, & $93.53 AG
      • Modeled (deciles A – D): 516 responses, 2.58% RR, & $98.12 AG
      Modeling How can lapsed donors be modeled?
    • Example: Usage Strategies Modeling How can lapsed donors be modeled? Decile Contact Frequency A High Appeal as frequently as possible without annoying donors B C Medium High Appeal frequently D E Medium Determine optimal frequency based on response rates F G Medium Low Reduce the number of appeals H I Low Appeal infrequently or not at all for an entire year Decile Quantity Jan. Feb. Mar. Apr. May Jun. Jul. Aug. Sep. Oct. Nov. Dec. A 25,000 X X X X X X X X X X X X B 25,000 X X X X X X X X X X X X C 25,000 X X X X X X X X X X X X D 25,000 X X X X X X X X X E 25,000 X X X X X X F 25,000 X X X X X G 25,000 X X X X H 25,000 X X X I 25,000 X X J 25,000
    • Warm Prospect Challenge Facing Fundraisers Lack of data! For most donors, you have plenty of information to describe their giving history with you: Name and address Gift dates Gift amounts Gift types Premium? Non-premium? But with warm prospects, it’s likely that you only have: Name and address …and not much else… So what can you do? Get outside data There are lots of sources of outside data that can help your file: Predictive model scores Mail Responsiveness Age Income Wealth Home Value Length of Residence “Psychographics” The data can then be integrated into tests to see what might be correlated to increased response.
    • Warm Prospect Conversion Modeling The basic premise behind a warm prospect model is to determine who are the most likely warm-prospect candidates to convert to traditional donors. Who would you score with a warm prospect model? Basically, anyone on your file for whom you have relatively little information, and particularly those who have never donated to your organization Examples: - Individuals expressing interest in your efforts - Visitors to your web site - Event participants - Exhibit attendees - Survey respondents etc… “ Non-traditional” donors are also good targets for a warm prospect model: - Emergency appeal one-time donors - Memorial donors - Single gift donors
    • Tips:
      • Don’t overlook your own house file! Always match your warm prospects against your house file. It’s surprising just how many so-called warm prospects are already donors.
      • Always NCOA. Depending on their source, warm prospect files can be some of the “dirtiest” files in terms of delivery – even when the data has been collected relatively recently. Using NCOA, DPV, etc… ahead of time can save your organization lots of money.
      • One of the most important variables that separates responders from non-responders in a warm prospect population can be direct marketing responsiveness. Make sure whoever builds your model has access to some sort of variable measuring recency or “signs of life”.
      • Warm prospects have a limited shelf life. Mail them quickly, and don’t let your models get stale.
      • Performance often drops off quickly in warm prospect populations. Expect to only be able to mail the top 20-50% effectively – sometimes less.
    • Example of Warm Prospect Model Performance
    • Questions and Answers