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Finding the Right Prospects Online
- 1. Finding the right prospects online A Sallie Mae case study
- 2. Rick Erwin President, Targeting, Experian Marketing Services Joyce D’Addio Digital Marketing Director Sallie Mae, Upromise Jennifer Astle Sr. Director, Channel Mktg Sallie Mae, Upromise Your presenters Bob Baer Director of Online Acquisition Sallie Mae, Upromise
- 3. Agenda • What’s old is new • Who is Upromise? • Upromise’s business challenges • Why Experian? • Modeling and optimization • Display campaigns • Campaign KPIs • Results
- 4. Then Now List Audience Service Bureau DMP/Onboarding Partner Printing & Letter shop DSP Direct mail Digital Media Impressions What’s old is new
- 5. Marketers: People: Delivering • the right offers • at the right time • to the right person Searching • what they want • when they want it • where ever it may be Direct Marketing Digital Marketing
- 6. Who is Upromise by Sallie Mae?
- 7. Upromise by Sallie Mae • Upromise is a simple, yet powerful cash back rewards program where members can turn everyday spending into money for college. • 15MM+ members; $700MM+ in member savings to date • Mission: “To help people save, plan and pay for college” • Member composition: • Parents of high school students • Parents of young children • Students • Free to join
- 8. How members can save • Earn 5% cash back on online purchases with 900+ retailers & travel agencies • Earn cash back when dining, getting groceries, or filling up your tank at specified retailers • Earn 1% on every purchase using a Upromise credit card What members can do with their earnings • Keep in Upromise account • Transfer Upromise savings into a 529 plan • Use Upromise savings to pay down a Sallie Mae student loan • Transfer Upromise savings into Sallie Mae high-yield savings account Upromise Products
- 9. Upromise Business Model How does Upromise make money? • When a member transacts with a Upromise partner, the partner funds the member’s savings and Upromise revenue Who is Upromise’s ideal customer? • Joins and stays engaged with the rewards program • Adopts 1 or more financial products
- 11. GoalCurrent status Find the right customers @ the right time who will transact consistently and engage with 1 or more Financial Products Many people signing up for free service, but not transacting regularly Revenue increased by 150% Year over year Key Challenge
- 12. From casting a wide net… To honing in on the ideal target Low Engaged Members Grocery Affiliates Prospect Email Key Question • How do we find our ideal target and increase the odds that the prospect will become an engaged member? • “Ideal” Upromise member - Transacts frequently with our partners - Eligible for financial products - Skews toward higher credit quality with above average levels of disposable income
- 13. Database Marketing Concepts • Apply database marketing concepts and strategies to online display • Powerful targeting capabilities available by combining traditional marketing strategies and new technology • Advertise to your ideal prospect where they are, not where you think they might be Traditional Offline Marketing Goes Digital
- 14. New, active Customers Join Upromise Deliver Display Ads Cookie Targeting Model Best customer Summary Key question: How does Upromise find customers who will engage in the Upromise service?
- 15. Why Experian? • Confidence in data quality and analytical capabilities • Breadth of data sources • Reach of ad network • Similar privacy and compliance standard
- 16. Modeling
- 17. Define High Value Customer within Sallie Mae CRM1 Apply to ConsumerView and identify 200-300 variables that best define audience2 Create model with these variables. Rank entire US population3 Target top deciles online 4 Display Modeling Process with Experian
- 18. Best customer Find more of them Identify and profile your best customer Getting Started • Use key criteria for defining “best customer” • Create model using Upromise’s existing customer base • Build lookalike models to find other people who look like Upromise’s best customers
- 19. Model Objectives Target individuals with the following attributes: • Prospects with a high potential to be active Online Mall purchasers • Prospects with a high potential to develop into Financial Product consumers • Customers who have been enrolled since 2010 and have had online mall purchase since 2011 Max (score 1, score 2) Sallie Mae loan holder look-alike model score Credit Card look-alike model score Online look-alike model
- 20. Online Mall Purchasers – Model 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% PercentofOLMCustomers Percent of Prospects Model Lift: Online Mall customers vs ConsumerView Baseline Online Mall Customer model 38% 34% 72% of likely best customers are found in top 30% of ranked data
- 22. Define best customer, create models, define audiences 1 Campaign Execution and optimization 4Set Segment Objectives: What does success look like? 2 Campaign design: creative, test plans, media plans 3 Display Campaign Process
- 23. Define best customer, create models, define audiences 1 Defining Your Universe 1. Define your best customers – Revenue – Secondary product adoption – Method and timing of acquisition 2. Create the Model – What attributes separate your best customers from the rest? – What behaviors should the model predict? 3. Define your Audiences – Life stage – Product adoption – Self-selected (how they use your product or service)
- 24. Set Segment Objectives: What does success look like? 2 Segmentation and Defining Objectives 1. Segment definitions should be aligned with goals & products 2. Segment definitions need to consider size – the larger the segment, the better yield optimization opportunities 3. Multiple segment campaigns generally outperform single segment campaigns 4. CPA & CPC objectives are preferred over CTR and eCPM
- 25. Campaign design: creative, test plans, media plans 3 Campaign Design 1. Creative – Align creative with segments – Reinforce messaging and creative in organic channels 2. Select Ad Networks – Consider reach and traffic volume – NAI/DAA standards 3. Impression Frequency and Lookback Windows – Test # of repetitions required for conversion – Determine appropriate look back window for counting clicks and view-thrus
- 26. Home Value Online ShopperActive Credit Cards Presence of Children Geo-locationAge & Income Data Selects Data Models Execution and Optimization Now that we know who else in ConsumerView would be a good Upromise member, we use Experian’s display capabilities to find them Campaign Execution and optimization 4
- 27. Results
- 28. New, active Customers Join Upromise Deliver Display Ads Cookie Targeting Model Best customer Summary Key question: How does Upromise find customers who will engage in the Upromise service?
- 29. MES = Median Credit Score For acquired members not exposed to Experian campaign: Lower credit scores For acquired members exposed to Experian campaign: Higher credit scores Results
- 30. $0 $100 $200 $300 $400 $500 $600 01 02 03 2012 2013 Results 2013: Shifted to quality over quantity Spend per Shopper is up 55% vs. 2012!
- 31. Next Steps 1. Engage our customers through the channel(s) they prefer and/or transact on 2. Implement a mobile strategy 3. Drive for cross-channel optimization
- 32. ? Thank you…
Hinweis der Redaktion
- Rick to present panelIn order of presenting
- Rick presents
- Rick presentsAudience is the new ListDMP is the new Service BureauDSP is the new Printing and Lettershop (this is a little nuanced and needs some explaining… in essence its where the data gets matched to the creative and readied for delivery to consumer) Ad Networks are the new Delivery System (I’m hesitant to say Postal Service but this is where the ad gets delivered to the consumer) There is also the potential to point out that the role that DMPs play in marketing audiences for use in the ecosystem is similar to the role that List Managers have played in the offline model
- Jen to present
- Jen to present
- Jen to present
- Jen to present
- Jen to presentAs I said at the beginning, many people would probably to be very happy to acquire new customers for a free service. But, as we went through our business model, you saw that people need to spend money in order to save money. With a free service, we have problems finding people who will transact consistently with Upromise partners. Upromise case study:Successful at acquiring a high volume of low-cost customers but they were not engaging in the service – ROI-negativeWilling to acquire higher-cost, quality customers but struggled to find enough of themExperian’s modeling and display solutions helped solve this problemWho should Upromise target?How can Upromise find them?Current statusLots of people signing up for free service, but not transacting regularlyNeedFind customers who will engage consistently in the Upromise service (customers with high disposable income)
- Jen to present
- Bob to presentApplying Database Marketing Concepts and Strategies to Online DisplayPowerful targeting capabilities Advertise where your prospects are, not where you think they might beApplying Database Marketing Concepts and Strategies to Online Display Direct Mail has been using these concepts for decades, cookie matching, addressable media have provide the tools to do this onlinePowerful targeting capabilities We can take the lookalike model, identify the cookies that we want to target, and show them Upromise display banners as they browse the webTarget people, not sites Significantly broader reach than searchAdvertise where your prospects are, not where you think they might be Pure demographic targeting can be unreliable, or at the least, unpredictable Advanced targeting allows you to increase both reliability and predictability
- Bob to presentNotes from our conversationPreviously had to wait months to get/see engagement (campaign success)Upromise now knows right away by seeing activity and engagementProvides proof that we are targeting the right people
- Jen to presentConfidence in data quality and analytical capabilitiesUse their data and analytical services for internal custom modelsKnew what to expect from a data quality and service level standpointBreadth of data sources Subsidiaries, segmentations, summarized credit statisticsReach of ad networkTop 2 Display Advertiser 2011Over 140M cookies linked to our US Consumer database of 120m householdsWe currently partner with 4 of the top 5 publishers and dozens of digital partnersSimilar privacy and compliance standardsWe all know how fun legal and compliance can be in pushing through new initiatives
- Joyce to present
- Joyce to presentUse key criteria for defining “best customer”Create model using Upromise’s existing customer baseBuild lookalike models to find other people who look like Upromise’s best customersConsumerView has thousands of variables per person235 million AmericansDeciles – split evenly in groups of 10. or 10%
- Joyce to present
- Joyce to presentOn the last bullet – mention the reason for 2010 & 2011 was due to acquisition strategy changes. Customer booked prior to that time look different than after. It also gives us recency of data and customer performance within the same economic cycle
- Joyce to presentThe model is a powerful tool to find Upromise Online Mall Prospects among 116 million ConsumerView households72% of likely active Upromise online mall customers are found in top 30% of ranked dataChart demonstrates the lift performance of the Online Mall shopper model. These data are from the “holdout” data set...it’s a completely separate sample from the data the model was built on, but it’s drawn from the same population. This is a standard method of validating model results to make sure that the results are reliable. The curve represents the cumulative percentage of customers (vertical axis) found within the cumulative percent of the ConsumerView population (horizontal axis). The straight diagonal line is the baseline—if we didn’t use any models and made random selections from the population, we would expect to find 50% percent of customers at 50% percent of the population, 10% of customers at 10% of pop., etc. It’s used as a null hypothesis ---how the customers would be distributed if the model added no predictive power at all. Visually, then, the area between the curved line and the straight, diagonal baseline represents the lift performance of the model. If the model were not effective, we would see little or no separation between the curve and the baseline, or we would see the curve cross back and forth across the baseline.
- Bob to present
- Bob to presentDefine segments (done by lookalike model)Set KPIs for each segment and campaignDesign the campaignLaunch the campaignSelf-optimizingOriginal slide:Define segmentsSet segment objectivesHigh valueNet new membersUpsell additional productsCampaign DesignMedia selectionCreative developmentSet KIPs, test plan and yield optimizationCampaign ExecutionFlexible flight datesSeasonally adjusted spendWeekly campaign reportingMeasurement reports
- Bob to presentDefine segments (done by lookalike model)Set KPIs for each segment and campaignDesign the campaignLaunch the campaignSelf-optimizingOriginal slide:Define segmentsSet segment objectivesHigh valueNet new membersUpsell additional productsCampaign DesignMedia selectionCreative developmentSet KIPs, test plan and yield optimizationCampaign ExecutionFlexible flight datesSeasonally adjusted spendWeekly campaign reportingMeasurement reports
- Bob to presentDefine segments (done by lookalike model)Set KPIs for each segment and campaignDesign the campaignLaunch the campaignSelf-optimizingOriginal slide:Define segmentsSet segment objectivesHigh valueNet new membersUpsell additional productsCampaign DesignMedia selectionCreative developmentSet KIPs, test plan and yield optimizationCampaign ExecutionFlexible flight datesSeasonally adjusted spendWeekly campaign reportingMeasurement reports
- Bob to presentDefine segments (done by lookalike model)Set KPIs for each segment and campaignDesign the campaignLaunch the campaignSelf-optimizingOriginal slide:Define segmentsSet segment objectivesHigh valueNet new membersUpsell additional productsCampaign DesignMedia selectionCreative developmentSet KIPs, test plan and yield optimizationCampaign ExecutionFlexible flight datesSeasonally adjusted spendWeekly campaign reportingMeasurement reports
- Bob to present
- Bob to present
- Bob to presentNotes from our conversationPreviously had to wait months to get/see engagement (campaign success)Upromise now knows right away by seeing activity and engagementProvides proof that we are targeting the right people
- Bob to presentThe model is generating members who are much more likely to be approved for the Upromise credit card.
- Bob to presentThe number of people engaging in the online shopping program is nearly double our prior rates so shopper volume has remained the same despite decreasing the overall acquisition volume.The number of shoppers in 2012 and 2013 is the same. However, the spend / shopper from the 2013 shoppers is 60% higher because the new members are modeled after our best online shoppers. Our best existing shoppers had high household income. The Experian lookalike model was able to find more people who had high household income (and other attributes similar to these best existing shoppers). Even though we’re spending more for high quality, this is ROI positive and we will be continuing with this strategy.
- Bob to present(Previous slide included) Apply models to other channels – email, mobile, direct mail, site placements. Expand to other products in the portfolio and among other business units Continue to optimize the model Increase lifetime value of customersIncrease frequency of transactionsDrive loyaltyIncrease additional product adoptionCompress purchase cycle time lineBob to presentNext Steps: Score ConsumerView households to make targeted group available for digital display advertising Consider use of scored prospects for other marketing channels (direct mail, e-mail, etc.) Consider use of model for targeted marketing efforts to existing Upromise customers Consider building additional models to select targets for other Sallie Mae products within different marketing channels and populations
- Rick