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What we talk about when we talk about personalization in Higher Ed

By Keith P. O’Brien

7 minutes estimated time to read

Part one of a two-part series

If you spend any time with enrollment leaders at colleges and universities, you get a sense of what the phrase “canary in a coalmine” means. Enrollment management sits at the forefront of the struggle to maintain a rapidly crumbling business model. One perceptive take on this business stated: “The entire multi-billion-dollar, 2,000-campus American college system depends overwhelmingly for its very existence on one resource: an ever-renewing supply of fee-paying undergraduates.”  

The “perfect storm” has arrived where the diminishing supply of high school grads intersects with the contracting international student pool (a mainstay of tuition revenue). In fact, the higher ed sector operates in a “buyer’s market” where supply exceeds demand for college seats and most institutions must compete harder for undergraduates.

I use the term buyers’ market deliberately because higher ed operates as a marketplace where prospects and students—both traditional and nontraditional—are the customers.  Students choose an institution and a course of study based on personal need and preferences, price and brand in return for a tuition expense. No other voluntary purchase by a person comes close to the life changing effect of choosing a college. How the university interacts with the student across the enrollment cycle (a uniquely long sales cycle) greatly influences the ultimate enrollment outcome. 

Consumers inherently expect universities to understand them as individuals and provide the personalization, convenience and service they experience daily through Spotify, Netflix, Amazon Prime, WhatsApp, and Uber. Enrollment leaders struggling to seat a class that meets academic profile, tuition and diversity targets must treat their prospects and families as consumers. To influence consumers’ decision making institutions must embrace personalization: “delivering tailored, meaningful and relevant customer communications.” Personalization is no longer optional but rather a competitive necessity for business-to-consumer firms. And, higher ed institutions that ignore this market reality do so at their peril. Institutional leadership striving to spur enrollment quickly would do better to invest in personalization rather than other common (and much imitated) strategies such as tuition resets, going test optional, or launching market- orientated degree programs (cybersecurity, data analytics or health sciences).

Personalization and Nudging

Personalization influences a target audience’s behaviors by providing the right message at the right time. True personalization is neither easy to design nor implement: every prospect and admitted student have unique academic, financial, and wellness needs. And, “Dear Jane” emails no longer clear the bar. Data, however, are the lifeblood of personalization. Universities possess unequaled demographic and behavioral data across the student lifecycle. Implementing true personalization at scale–right message, right time, right channel—requires leveraging insights about human decision making.

A new approach to personalization lies in the application of behavioral economics. This discipline’s concept of “nudging” provides new insight on how people think, make decisions, and behave. A nudge involves “changing the presentation of choices for people so they’re more likely to choose one option rather than the other” — Michael Hallsworth, Behavioral Insights Team.

Nudging is not new to higher ed. Recently two studies found large-scale nudging had marginal impact on FAFSA completion and college enrollment, respectively: Nudging at a National Scale: Experimental Evidence from FAFSA Completion Campaigns and College Board’s Realize Your College Potential Campaign for Postsecondary Enrollment. The studies’ findings, however, miss the mark somewhat by not assessing the level of personalization employed. A comment from the report on nudging FAFSA completion is illuminating: “Our results suggest a more effective path to scale may depend on using advances in data science and technology to develop more targeted, personalized nudges.” 

Reimagining Personalization 

Personalization has traditionally been based on demographic attributes: age, gender, location, socio-economic status, and ethnicity. Two people with exactly the same socio-economic and demographic characteristics can choose products in very different ways: one may focus on value and cost, the other on quality and durability. Traditional “personalization” approaches would message them the same way. But this approach will likely fail because the quality-conscious person won’t respond positively to a price-focused message. It is the individual’s attitude (value versus quality) towards the decision that motivates purchasing behaviors.

Human attitudes explain the “why” of human behavior. And personalization based on a person’s attitudes will generate more favorable responses than other approaches. For higher ed institutions, accordingly, the mindsets of prospects, students and alumni play a critical role in how they make decisions.

Identifying each student’s mindset and tailoring communications to individual attitudes may sound complicated, time consuming, and costly. But attitudinal personalization is viable as:

• The necessary student data already exist on every campus in the CRM, LMS and SIS.
• Artificial intelligence can translate existing behavioral data into a profile of the student’s attitudes. (A process that does not use any Personally Identifiable Information.)

In my next blog, I’ll show how one university used just 18 data points on admitted students to deliver attitudinal nudges that reduced melt.

Two free resources to learn more about:

1. Personalization in higher ed, attitudinal nudging and AI’s game-changing potential: download our free eBook, Artificial Intelligence and Higher Ed
2. How attitudes, “think-alike mindsets”, and nudging drive human decision making: get our free whitepaper, Why Attitudes Drive Decision Making: Reimagining Personalization

Want to learn more or schedule a product demo? [email protected] 

Effective Personalization: Student Mindsets Matter Most

By Keith P. O’Brien

8 minutes estimated time to read

Part two  of a two-part series

Summer melt remains a persistent issue for heads of enrollment at colleges and universities. Even a few admitted students failing to turn up or dropping out can undermine headcount, academic profile, diversity, and tuition revenue objectives. Admitted students choose one college over another for an array of reasons; institutions that demonstrate they “get” the student on some personal level tend to win out. Personalizing outreach and interactions builds affinity and mitigates melt.  My previous blog proposes that the most effective personalization aligns to a student’s attitudes. The following case study shows how one university engaged student attitudes to cut FAFSA verification melt..

What is Verification Melt?

“Each year, approximately one in three aid applicants gets another chore. The U.S. Department of Education requires millions of new and returning students to submit additional information to colleges, which then must verify the accuracy of each FAFSA flagged for review. Students must comply to get their money. If you haven’t been through the time-consuming procedure, then you’re probably not poor.”   — Eric Hoover, Chronicle of Higher Ed

The federal government requires every institution to verify selected student’s FAFSA information. Verification melt occurs when students fail to initiate or complete this (laborious) process and don’t qualify for Title IV grants and loans. Underrepresented and first-generation students are disproportionately affected

Case Study: Identifying Student Mindsets    

At the university, 150 students from the Fall 2019 cohort were selected for FAFSA verification. The VP for Financial Aid partnered with Discourse Analytics (DA) to help these students—the target population—complete the process and enroll.  DA used a four-step process to profile the attitudes of these students towards verification and develop nudges targeting these attitudes.)

1. Use existing student data as indicators for attitudes: for each student in the target population, DA’s artificial intelligence (AI) platform ingested 18 data fields from the CRM (Slate) and financial aid software (PowerFAIDS). These data included the number of AP classes; household debt to income ratio; and social club participation. No Personally Identifiable Information (PII) was used.
2. Pinpoint each student’s attitudes: the AI-platform evaluated 12 non-cognitive attributes (capacity, growth mindset, proactivity, resiliency, self-awareness, self-confidence, self-discipline, social intelligence, comfort with technology, etc.) to create an attitudinal profile of every student. The profiles pinpointed a student’s attitudes that drive their behavior regarding FAFSA verification.
3. Cluster like-minded students: DA’s “Think-alike” engine identified three clusters of students with shared mindsets. Each mindset cluster predicts how its students will behave and prescribes nudges towards the desired action. Clustering enables personalization at scale by tailoring interventions to shared mindsets. (See Exhibit for details of one mindset cluster.
4. Nudge student mindsets: DA and Financial Aid created nudges for each mindset cluster. The nudges help students make better decisions for themselves by changing the context in which they make choices. The nudges were sent via the university’s email platform to streamline access to students and maintain confidentiality. DA’s platform uses machine learning to assess student responses and continuously refine the mindset clusters; each nudge shapes attitudes so re-calibration is essential. 

Motivating Student Attitudes Boosts FAFSA Verification

DA created a randomized control group of students going through verification to benchmark the results. The target group students receiving attitudinal nudges were much more likely to complete verification: 25% increase versus the control group.  

The Personalization Imperative

Consumer habits shape a student’s approach to decision making: how to parse information sources, consider options, and make choices. This consumer orientation and its expectations around responsiveness, convenience and personalization will rarely, if ever, cease at the campus gate, or on the college website. And students’ needs are increasingly distinct as societal and demographic trends blur the distinctions between traditional and nontraditional students. Institutions serve cohorts with highly disparate characteristics and needs: using personalization has become a non-negotiable for every institution, from the most selective to open access.

Data are indispensable for personalization and institutions have ever-growing data sets for the entire student lifecycle. AI and data science grant universities the opportunity to turn student data into attitudinal profiles and deliver personalization at scale—the right mindset message on the right channel at the right time. Attitudinal personalization helps an institution rise above today’s buyers’ market by boosting student and institutional outcomes. 

Two free resources to learn more about:

1. Personalization, mindsets, the FASA Verification project, and AI’s game-changing potential:  download Discourse Analytics’  free eBook, Artificial Intelligence and Higher Ed
2. How attitudes, “think-alike” mindsets and nudging drive human decision making: get our free whitepaper,  Why Attitudes Drive Decision Making: Reimagining Personalization 

Your campus and AI: 6 Questions to Prepare for Success

By Keith P. O’Brien

8 minutes estimated time to read

 Why Artificial Intelligence is the New Electricity

“Just as electricity transformed almost everything 100 years ago, today I actually have a hard time thinking of an industry that I don’t think AI will transform in the next several years.”

— Andrew Ng, co-founder of Coursera and Adjunct Professor of Computer Science, Stanford University  

About a century ago, we replaced steam powered machines with those using electricity, transforming everything from transportation to healthcare.  Equating Artificial Intelligence (AI) with electricity is apt given its impact on industry and markets. I’m not trying to up the ante on AI’s importance but I was taken by the assessment of Google CEO, Sundar Pichai, “AI is one of the most important things humanity is working on. It’s more profound than electricity or fire.”  The reference to fire resonated because I’d read where a professor used fire as an analogy with AI: fire became life changing for humans not for our understanding of its chemical properties but because humans discovered remarkable ways to use fire. Leaders across all industries including higher ed have to determine how to apply AI in the most consequential ways without getting distracted by technological minutiae.  

AI presents colleges and universities with the opportunity to deliver personalized experiences across the student lifecycle. But many administrators are wary of the hype and perceived complexity of AI. Such concerns are overstated—only AI can translate the terabytes of student data on campuses into experiences that students value and benefit from. The time to act is now; the rewards of adopting AI far outweigh the risks from procrastination given today’s enrollment and tuition revenue challenges. So how does a college get started? 

Where and How Should We Use AI? Six Questions to Get Started

AI can do many things on campus from automating routine administrative tasks to identifying the mindsets of students that drive their behaviors. Use the following six questions to shape how AI is deployed strategically and operationally on campus:

1. What are we trying to do here?  The starting point for any AI project is pinpointing the business problem the technology can address. You must consider the university’s strategic priorities, the nature and urgency of the problems, and the organizational and cultural implications. Problem identification and prioritization are critical because deployment of AI solutions often raises unrealistic expectations. TIP: Campus leaders can start by addressing these five basic decisions: forge consensus on the problem, identify the project owner, pinpoint the operations and campus units  involved, clarify the role of the IT department upfront, and define the performance metrics. 
2. Do we have leadership buy-in?  The AI project needs sponsors who clearly and publicly commit to the initiative. The project sponsor(s) must have the authority to allocate resources to the project and the backing of campus leaders to drive implementation across campus units. TIP: Communicate regularly to foster support and dispel misinformation. At a minimum, communicate the project’s strategic importance, operational impact (faster, smarter, cheaper), and benefits for students and the institution.  
3. What’s the timeframe for expected returns?  Some administrators expect substantial returns quickly given the aura around AI. The timeline and ROI are a function of the problem being tackled first and foremost. For example, deploying AI to help more students complete FAFSA verification targets a well-defined issue, a set timeframe and a clear performance metric. The campus units involved and the performance metrics are critical factors too.  TIP: Groundbreaking technologies don’t fit neatly with existing ways of operating. Remember the advent of the Internet? At first it was very disruptive but now is a competitive necessity. Don’t let false precision over timing or outcomes let your institution get blindsided: early adopters of AI gain a market advantage while skeptics must play catch up. 
4. Have we addressed data readiness and governance issues? AI generates vast new troves of information. Data readiness means that data can be connected across systems and campus units throughout the institution. Matching data using student id represents one such opportunity. Admissions systems don’t employ a student id and so alternate match keys are required. Policy readiness requires that data governance, identity resolution, privacy and other policies authorize the university to use specific data and systems for specific purposes, including informing student communications. 
5. How receptive is the Institution to change?  Every AI project operates in the context of an institution’s unique culture, governance models, and campus unit processes. AI project sponsors must gauge the institution’s cultural receptivity to change, and plan accordingly. TIP: Here’s two criteria help assess campus receptivity:

• • Has a data-driven culture—the ability to test new data driven initiatives in a controlled environment indicates organizational readiness for AI. Taking a data-driven approach helps test, validate and implement process improvements. Data-driven Institutions tend to generate greater returns from AI.
  • Embraces continuous improvement—a track record of embracing change is a key marker of process readiness. Process driven self-evaluation indicates leadership has fostered a “growth-mindset” campus climate that embraces new technologies and decision-making approaches.

6. How do we educate the campus on AI?  While all technology projects and integrations pose challenges on campus, AI-led  projects can generate unease among some staff: a common misconception is that AI serves to automate work and erode job security.  However, as one university CTO observed about the reality of AI: “Nothing will ever replace the human touch aspect of an educational professional interacting directly with a student.”  Higher ed has many federal- and state-mandated reporting processes that can be automated by AI, freeing up campus staff to focus on the more value-adding aspects of their roles. Project leaders must communicate proactively to set and manage expectations by sharing the goals, expected benefits, and the exact role the AI performs across campus. 

AI can streamline red tape, reduce or eliminate data siloes, and improve staff capacity to engage students one-on-one. It turns disparate student data sets into unparalleled insight on individual behaviors and needs. Just as it’s impossible to imagine a campus without electricity,  AI has to be part of how colleges and universities engage students. In my next blog, I’ll look at how institutions can approach working with vendors on AI-led initiatives. 

This blog is based on our free eBook about AI’s game-changing potential, Artificial Intelligence and Higher Ed: Cutting Through the Noise

To learn more contact: [email protected]

Three Questions to Vet AI Vendors

By Keith P. O’Brien

4 minutes estimated time to read

One of the striking things about Artificial Intelligence (AI) is its seemingly unlimited potential–it can be applied to an array of challenges. There is no shortage of higher ed challenges that AI could help tackle given the competitive pressures, funding issues and vast data repositories on campuses.  My earlier blog suggested 6 foundational questions to help campus leaders establish their approach to AI. However, for administrators, identifying the right problem for AI to target and picking an AI platform inevitably involves working closely with vendors; AI doesn’t lend itself to a DIY strategy!

As with any disruptive technology in the marketplace, a thriving AI vendor community or ecosystem (to use business speak) exists. Crunchbase (tracks technology startups and companies) tracks 9,986 companies in the AI space. Undoubtedly more and more consulting companies and technology firms are promoting their AI platforms and expertise to two- and four-year institutions.  Here are three key questions to  help administrators find an appropriate AI partner. 

THREE QUESTIONS TO VET AN AI VENDOR

1. What Does Your Technology Do?   Given the specific challenge(s) our institution seeks to address, how does your AI platform/model pinpoint the student, processes, or systems that specifically influence the outcomes we want to achieve? If you have never tackled this issue with a college or university, do you have examples from analogous consumer-focused industries like financial services or health care? 
2. How Do We Use It and How Difficult Is It to Use?  How does the AI translate its insights into recommended actions for campus units and students? How are these recommendations communicated to staff and students? How does the platform access and integrate the data within our CRM, SIS, LMS and communication channels?  Institutions should also consider:
   • Colleges and universities that adopt an AI-enabled initiative are, de facto, empowering an algorithm to shape decision making. Therefore, understanding how the algorithm works and decisions will be operationalized on campus are important
   • If implementing vendor recommendations requires introducing new channels (for example, conducting campus wide texting) then investigating the cost, time and privacy implications are crucial 
3. How Do You Manage Data Privacy?  As every AI algorithm requires massive data sets which specific data sources do you need from our systems? For all student data do you need any personally identifiable information (PII)? And how do you maintain FERPA and GDPR compliance? How do you share the new data sets generated by your algorithms with us?

These questions are start starters–raising some of the core issues that apply irrespective of the type of institution or challenge. To gain a bit more insight on vendor offerings and selection, take a look at these articles:

• Classifying the AI Vendor Landscape: Making Sense of the Market: a 2019 Forbes piece that helps demystify the world of AI vendors
• 6 Tips for working with AI vendors: a set of tips for CIOs that are applicable for higher ed and non-CIOs!

This blog is based on our free eBook about AI’s game-changing potential, Artificial Intelligence and Higher Ed: Cutting Through the Noise

To learn more contact: [email protected]

Financial Services’ Missing link: Personalization at Scale and Mindsets

By Keith P. O’Brien

6 minutes estimated time to read

My father has always had a “bank manager,” his main point of contact for financial services—mortgages, pensions, personal and corporate bank accounts, investments. And, by way of comparison, his 26-year old niece in Austin does not have a bank manager—she uses Marcus by Goldman Sachs as her savings account and orders a customized Starbucks halfway through her commute to work that she pays for through her Starbucks App when she picks her coffee up in her building’s lobby. These relationship-based banking approaches may appear either anachronistic or radical depending on your perspective, but they are actually identical. I am not contrasting 20^th Century banking with our digitally focused financial services but rather analogizing them.

My father’s customer experience encapsulates what personalization should deliver—interactions, advice and services tailored to individual needs and priorities. Banks fund significant IT and analytics budgets to replicate the one-on-one personalization my father expects but delivered via the digital platforms that my cousin takes for granted. Personalization at scale—to use the marketing term of art—is a priority for every financial institution.

A recent BCG (Boston Consulting Group) analysis captured the personalization relearning that the banking sector is undergoing:

“In many ways, personalization at scale is a 21st-century approach to delivering what the banking industry lost many years ago: the ability to truly know customers, anticipate their needs, engage in a rich dialogue about their financial lives, and, as a consequence, foster loyalty that can last a lifetime.”          

What Does Personalization in Banking Really Mean? March 12, 2019, Boston Consulting Group

The onus on personalization has nothing to do with nostalgia and all to do with escalating competition among both traditional institutions and new entrants (as forecast by The Economist for 2020). Consider Goldman Sachs, a capital markets powerhouse, prioritizing Marcus, it’s online consumer banking platform or the US launch of Monzo, a UK-based “bank” that operates solely as a mobile app. Such in Monzo’s ability to deliver services innovatively that 18 new customers join Mono for every one that leaves—the highest rate for any UK bank.

“Almost everywhere, competition will intensify with the rise of online banking. Fintech companies and payment services will extend their reach.”                                                                                                                                 Financial services industry outlook, The World in 2020, November 5, 2019, The Economist

A more competitive banking marketplace is not a recent phenomenon. What has fundamentally changed is consumer expectations. Personalization has been redefined for all banks because the standards set by digital innovators like Netflix, Amazon, Spotify for customized experiences, convenience and access have reset consumers’ service expectations of all consumer industries. 37% of consumers said they’d like their bank to be more like Amazon in a BCG survey.

Banks, given the centrality of money in our lives, are subject to especially demanding expectations. Meeting consumer anticipations creates an array of operational issues from navigating fragmented customer databases to coordinating across sales channels. Important as implementation issues are, almost every bank overlooks how personalization must align with human behaviors.

Attitudes are the “why” of human behavior

The conventional wisdom around banking personalization underscores demographics, psychographics and behaviors as the ways to segment and personalize interactions with prospects and customers. The point of personalization is influencing behaviors of course. But humans make decisions based on attitudes or mindsets towards the issue at hand; everything else is largely irrelevant. Think about this way: if Jane is a procrastinator this mindset affects how she views choices and acts. Say a bank wants to sell her a one-year CD: Jane will get personalized messages shaped by Jane’s lifestyle, zip code, age and so forth. This personalized approach is highly unlikely to get a response because they’re ignorant of how procrastination stalls Jane’s economic choices. Behaviors can be seen; attitudes must be uncovered.

Choices are mostly based on attitudes, and once we understand attitudes, we can determine consumers’ likely choices with a higher degree of reliability. Personalization that influences an individual’s choices must engage that individual’s mindset. Communications must be compatible with Jane’s procrastinator mindset by pairing a time-sensitive offer with customer support that simplifies and expedites purchase.

To deliver an effective personalized engagement strategy requires financial institutions to adopt a paradigm shift towards a customer’s mindset rather than informational or marketing segmentation models. Artificial intelligence (AI) has the ability to translate customer data into insight on an individual customer’s mindset. We partnered with DXC Technology, the global IT services firm,  and American Banker to show how financial services providers can leverage AI to transform the customer experience,  reduce customer turnover, and boost share of wallet in an on-demand webinar. Watch Using AI to Personalize Customer Engagement.

Contact us: [email protected]