Making Sense of Big Data To Impact Enrollment Goals
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2018 UPCEA Marketing and Enrollment Management Seminar presentation on continuing education digital marketing and enrollment.
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Making Sense of Big Data To Impact Enrollment Goals
- 2. Nice to Meet you! © 2017 Primacy / Proprietary & Confidential2 Rosie Walker EVP, Marketing Strategy rosie.walker@mediate.ly 860-404-3373
- 3. Datais our Business. But,we must understandyours,first. Mediate.ly is an omni-channel programmatic advertising company, powered by AI and smart marketers.
- 5. Nice to Meet you! © 2017 Primacy / Proprietary & Confidential5 Jon Hinderliter Director of Marketing & Communications University College | Washington University in St. Louis
- 7. Challenges • Little brand awareness, plenty of brand confusion • No digital marketing efforts • Decline in enrollments over several years • Data across several system but not talking
- 8. Making Sense of Big Our Data to PositivelyImpact Enrollment Goals
- 9. BIG DATA OPPORTUNITY • Mine untapped audiences • Make informed decisions • Prioritize opportunity based on ROI BIG DATA CHALLENGE • Multiple data sources • Limited budget and resources • Pressure to show results – quickly!
- 10. Defining the Starting Point 1. Refocus our efforts on Request for Information 2. But a lot of tactics into play around one goal: build>measure>learn 3. Not just raise brand awareness but capture it – evidenced by quality leads and ultimately, enrollments
- 11. Providing a path for the non-traditional student is vital The good news: More students are attending college, particularly in the low/mid wealth groups. The bad news: They are dropping out, especially in the low-mid wealth groups Source: https://www.nytimes.com/2018/03/25/opinion/college-graduation- gap.html?rref=collection%2Fsectioncollection%2Fopinion- columnists&mtrref=www.nytimes.com&gwh=DD269FCD073EC41B57AA22EB47CD7666&gwt=pay&assetType=opinion
- 12. Degrees Matter
- 13. Competition is Fierce $1.6 billion Source: https://emgonline.com
- 14. Disparate Data Sources • Organizational, paid and unpaid data sources are challenging to reconcile • Wheels of higher ed are slow to move to smooth our measurement opportunities
- 15. Universal Question How do I know what I am doing is working, or not. And, how do I use what I can know to deliver results for my organization. “ “
- 17. Wade into the data, don’t swim in it. Or, you will drown 1. Set foundation with data 2. Map student journey and assign metrics at each point 3. Practice agility, every day
- 18. Define target audiences with data Inform/validate target audiences to find pockets of opportunity. Syndicated data can help prioritize audiences and determine size of addressable audiences. Undergrad Target 776,877 31% Graduate Target 48,138 2% DMA Adults 18+ 1,664,934… 2.5M 18+ Adults in St. Louis DMA Undergrad Target Graduate Target DMA Adults 18+ Set foundation
- 19. Build personas with data Combine first and second party data to understand targets and refine your assumptions Millennial Focus: Median Age is 27.5 years 75% of prospects are between 21-34 years old 18% are African American Gender Balanced: 50% M/ 50% F Balancing Work and Life: 29% have young kids (0-5 years) in the household 75% of prospects are employed with 14% (index 132) are working moms. Interrupted Education: Some college and in the market to return to school One-in-four are currently taking a class (index 269) Attended a job fair (index 181) Active Life Changes: 34% are looking for a new job (in the next 12 months). Index 215. 35% are planning to marry (in next 12 months) Affinity for concerts and sports: 2x as likely to play football or soccer in past 12 months Saw a show at The Pageant (Index 192) Nielsen Scarborough data gives us a head start on segmenting Set foundation
- 20. First-party data serves to validate - or revise reality Source: Mediate.ly Audience Intelligence – University College Programs & Degrees subdirectory pages Strategies: • Look at site visitor and admission/crm data. • Compare cookie data across different areas of your site. Customize creative to segments. • Compare data based on visitors vs. converters. • Focus media segments on the converters. Set foundation First PartyData Validated Age Target – when pulling out internal audiences!
- 22. Build audience segments and creative around the research data Segments • Industry • Profession • Gender / Age • Geos • Interests Cross with high indexing media channels Plan around these findings with initial hypothesis. Be open to change and fluid movement based on results. Document changes. Set foundation Campaign segments map to personas
- 23. Map the marketing journey and points of data collection Assign Metrics
- 24. Map the marketing journey and points of data collection Assign Metrics
- 25. Assign Metrics Impression pools for brand awareness channels allow us to capture and retarget with lead gen creative.
- 26. Assign Metrics
- 27. Assign metrics and markers for each point in the marketing journey Estimate CTR by channel to determine media weight Typically 70% drop off b/t click and LP Visit Estimate Visit to Lead Form Conversion What %age of leads convert to starts/finish. By channel? What are the business expectations? Assign Metrics Enrollments Applications Leads Landing Page Visits Impressions
- 28. Assign metrics and markers for each point in the marketing journey Embrace low tech tools! Google Sheets Lead Form mirrors journey Happy to send you this lead model google sheet! Email me rosie.walker@mediate.ly!Assign Metrics
- 29. Simplify to visualize KPIs • Dashboards help to stay on point. • GDS enables real-time data. • Campaign detail uploaded weekly to a shared space on Basecamp. • Uploaded Monday to discuss every Wednesday. Assign Metrics
- 30. Use data to revise important metrics Focus on leads shifting to appts with advisors Assign Metrics
- 31. Focus on creative strategy Video strategy: data story • Chart on how the videos are performing • Multiple videos | multiple video channels | multiple audiences • Mapping effort from brand awareness into retargeting poolsAssign Metrics
- 32. Kaizen. Constant Improvement. • Allow machine learning to do its thing, but don’t let it take over. • Re-evaluate initial assumptions and hypothesis • Be an integrated, agile team Practice Agility
- 33. In Conclusion • Small Teams Big Trust - Every person counts and needs to be dependable to accomplish your goals. • Data has a voice - Let the data be an invisible member of the team, providing insight and keeping everyone focused on the goal • OODA Loop - Observe, Orient, Decide, Act: focus on solving problems and testing new ideas, fail forward fast
- 34. Questions? Thank you. Jon Hinderliter Rosie Walker hinderliterj@wustl.edu rosie.Walker@mediate.ly
Editor's Notes
- ROSIE:: Start the intro….
- ROSIE: We buy and optimize media based on how consumers consume it.
- ROSIE: We work with a variety of higher ed clients – all of these clients have campaigns directed at adult learners and non-traditional students.
- JON: Start the intro….
- JON
- JON: Our path forward, out of brand confusion, 5 years of decline, and an challenging environment was to ground our business in data, make sense of it, and make decisions with it – all with the focus on positive enrollment growth.
- JON We knew we had a big opportunity to use data to mine untapped audiences, to inform, prioritize and make decisions – particularly with a limited budget, and to create an agile relationship across our team to support quick, incremental improvements in our enrollment marketing efforts. ROSIE: With that opportunity, comes a challenge – and that’s were our teamwork, and an agile approach to using data to support a focused effort would work.
- Refocus our efforts on request for information (previous digital campaigns spread the budget across too many goals) But a lot of tactics into play around one goal, build>measure>learn Not just raise brand awareness but capture it
- Rosie: What are seeing is that the opportunity is great. And important. More students are going to college, but the drop out rate – particularly among low/mid wealth groups – is rising. Providing a path for degree completion is filling a real need.
- ROSIE: and, degrees matter.
- ROSIE: What we are seeing across clients in higher ed, particularly those serving the non-traditional adult learner. External forces: Competition is only increasing; Differentiation is nearly impossible
- ROSIE: There are internal challenges too. Data sources (across both organization and media channels) are disparate and the wheels of higher ed are slow to move to smooth out the pulled-thru metrics of the paid and non-paid efforts
- Rosie: Every client wants to know…… To take it even further, end-to-end attribution is nirvana. But, getting there can be a challenge – as we all know.
- ROSIE: So, let’s get started on how to approach.
- Rosie:
- ROSIE: Do not assume. Use what you know as a starting point, but not the ed point. Inform/validate audiences Example: UC assumed it’s audience was 35-45, in age, but when we removed WashU employees it dropped to 25-35 Data sources examples: 1st and 2nd party data. [example: UC persona(s) with multiple data inputs that both inform and validate. Admissions data validated site 1st party data and syndicated opportunity data.]
- ROSIE: Inform/validate audiences Example: UC assumed it’s audience was 35-45, in age, but when we removed WashU employees it dropped to 25-35 Data sources examples: 1st and 2nd party data. [example: UC persona(s) with multiple data inputs that both inform and validate. Admissions data validated site 1st party data and syndicated opportunity data.]
- ROSIE: Inform/validate audiences Example: UC assumed it’s audience was 35-45, in age, but when we removed WashU employees it dropped to 25-35 Data sources examples: 1st and 2nd party data. [example: UC persona(s) with multiple data inputs that both inform and validate. Admissions data validated site 1st party data and syndicated opportunity data.] Example: UC assumed it’s audience was 35-45, in age, but when we removed WashU employees it dropped to 25-35
- Rosie:
- Agree on goal, and what metrics feed into that goal. Walk the track backwards… show how to use key data points to get from enrolled student back to marketing efforts in market.
- Agree on goal, and what metrics feed into that goal. Walk the track backwards… show how to use key data points to get from enrolled student back to marketing efforts in market.
- Agree on goal, and what metrics feed into that goal. Walk the track backwards… show how to use key data points to get from enrolled student back to marketing efforts in market.
- Agree on goal, and what metrics feed into that goal. Walk the track backwards… show how to use key data points to get from enrolled student back to marketing efforts in market.
- ROSIE: Assign goals by channels with contingency plans for exceeding or failing to meet goals by stage. Agree on goal, and what metrics feed into that goal. Walk the track backwards… show how to use key data points to get from enrolled student back to marketing efforts in market.
- Agree on goal, and what metrics feed into that goal. Walk the track backwards… show how to use key data points to get from enrolled student back to marketing efforts in market.
- ROSIE this is how we stay on pt. Add campaign upload>>>>> Refreshed and uploaded to BC every Monday. Discuss every Wednesday.
- Fail fast…. Example: Industry focused campaign and BSIS program.