As Covid-19 continues to impact critical facets of our lives, and sheltering in place has become the norm, Marketers are rethinking their strategies about where consumer attention deficit has increased. Prospects are completely homebound and a shrinking pool of possible students are understandably crisis conscious and increasingly risk averse.
As we reconsider our media mix across websites, affiliates, mobile and desktop search, social media, and earned media, today’s Higher Education marketer has so many ways to reach and connect with these prospective students.
During these challenging times, it would be easy to abandon the science of marketing. It’s understandable why marketers might simply cut budgets or conservatively stick to proven methods of driving prospects and assigning last event, last click marketing credit.
If we combine these challenges with a renewed industry focus on 3rd party cookie deprecation, global privacy laws, tightening data compliance laws, and persistent pressures to limit cross channel browser tracking, then marketers may once again be forced to revisit data collection and attribution methods.
Marketing attribution answers arguably the most important question: “Which marketing effort, or touchpoint, drove this specific sale?” By organizing your data you can accurately measure the effectiveness of different marketing efforts. This is especially germane to Higher Education marketers because students rarely enroll after the first touchpoint. It takes considerable time to cultivate trust before such an important life decision as where to study. So, how do we measure effectiveness when digital attribution either undervalues or over estimates?
Consider the following:
How do we learn from historical performance in a world where digital behaviors have rapidly changed and our ability to track consumers has narrowed?
How do we properly assign credit to marketing’s effort when our primary digital identifiers for combining data have been or are being dissolved?
Should we simply surrender, and continue with last touch, reserved to the fact that we are under or overvaluing our channel marketing efforts while discounting the impact of branding and awareness efforts?
Perhaps we should reluctantly continue to use attribution services from third party ad serving platforms and trust Google to manage the marketing spend.
Is there still a strong business case to be made for maintaining — let alone expanding — your marketing efforts in an uncertain economy using approaches that are more rigorous than last touch, less spurious than digital multi-touch, butfaster than traditional marketing mix modeling?
Together, let’s review the marketing checklist and evaluate channel marketing effectiveness using Attribution Models to decide.Different models for different data
For the higher education marketer, you can never truly give credit to just one step in the student journey. As previously mentioned, a lot goes into a student’s enrollment decision. That said, you can get a good idea of what’s working and what’s not. Let’s examine a few ways a higher ed marketer can assign, calculate, and define marketing effectiveness using attribution.
Each of these models provide different insights but there are a lot of advantages.
Optimize customer journey: By studying the marketing funnel and the student journey, we can give you a detailed look into that journey and ways you can optimize your marketing for better results.
Optimize multi-channel media spends: The most important advantage of channel attribution analytics is its ability to identify the best conversion and sales; and the information needed to decide to invest more on those channels. Digital marketers can get a clearer picture of what works and what doesn’t, and then allocate their marketing dollars for the best options.
Credit where it’s due: Attribution analytics provides transparency around introductory, assisting, and converting channels. When you know where to assign credit, you can.
Cost per acquisition: Avoid waste in media impressions, invest in the right places, and keep CPA under control.
Data requirements are simple: The required taxonomies required to understand and ingest the data required for modeling are easily understood and often quite accessible, as an example ad serving data, or web analytics. Event level data is ideal, but even data aggregations can be used to help marketers quickly evaluate the effectiveness of channel mix, both traditional and digital.
Potential students take their time in researching a course or school. They seek advice from friends, family, and advisors. It’s only then will they interact with your marketing messages — and usually several times before enrolling. This is a complex journey that can be difficult to keep straight. Capturing and analyzing that journey is key to the development of an attribution model that can enhance your campaigns, spend, and conversions.