How AI Enhances Advisors’ Ability to Support Students

It’s easy to feel that academic, financial, and career advisors in higher ed could be replaced by AI, especially with all the talk about ChatGPT and generative AI. As more colleges and universities adopt technology to help achieve desired student outcomes, this future seems more like an upcoming reality.   But the adoption of AI should … Read more

Why Behavioral Data is Better Than Demographic Data

Data is essential to understanding today’s learner. What type of data is used is equally important.  

 

Education has made the shift from anecdotal and descriptive data to a more predictive data focused approach over the past decade, but more needs to be done to embrace different types of data and analytics approaches that enable better understanding, support, and connections between instructors and students. That’s where behavioral data comes in.  

 

What is behavioral data?  

Behavioral data is data on a person’s engagement with websites, resources, a school campus, and more. Most of these data are currently collected through a customer relation management (CRM) platform, websites, LMS and automated systems, among other sources. Behavioral data is dynamic—it represents what a person does while they are engaged in their learner journey. Demographic data, on the other hand, is stagnant data that segments people into different groups based on demographics such as age, gender, race, sexual orientation income, and zip code.   

 

What demographic data does  

Demographic data can be useful for generating insight into a person’s circumstances–it can tell you about their age, where they live, or what their gender identity is, for example. Demographics can provide additional contextual awareness of a student and what she may be experiencing in her individual learning journey. This type of information can also help understand high level group experiences 

 

But demographic data also has downfalls. Some demographic data serves as a proxy for information without truly telling you about a person or digging deep into the diversity of a large group. Gender, for example, can serve as a proxy for preferences. Using traditional demographic approaches could lead marketers to advertise baby products to middle-income women ages 20-35. Even with disaggregating the data, they could end up wasting their time and money advertising to women who don’t have children or are not interested in having children. If these same marketers used behavioral data instead, they could advertise to people who have shown interest in baby products and target older women with children, current or future fathers, and non-binary parents. Behavioral data digs deeper into the diversity within demographic groups.  

 

Demographic data can also perpetuate bias. Data is simply a quantitative representation of our world, so inequities in our society can easily be replicated. Even without using data on protected groups like race or disability status, some demographic data can serve as a proxy for this sensitive information and perpetuate inequities. As an example: zip code data can appear neutral at first, but given that systemic inequities have pushed certain groups into particular neighborhoods, that data can lead to similar results as having used race or socioeconomic status data.  

 

Why behavioral data is better 

Behavioral data, on the other hand, represents patterns in people’s behavior and give insight on their mindsets and decisions. Where demographics tell the “who”, behavioral data tells you “why” and “how”.  

 

Implementing behavioral data in your student success efforts is better than just using demographic data because it gives a more holistic view into your students. Behavioral data gives greater context into who your audience is, especially when combined with demographic data. With behavioral data, you can understand the causal factors of the decisions people are making and how those decisions affect their outcomes instead of just knowing people’s characteristics.  

 

Using behavioral data also allows your institution to create a better connection with students. You can understand how students are engaging with material, what motivates them to continue their education, and what is keeping them from progressing. Empowered with this information, you can provide scalable and one-to-one personalized messaging and support to help each student with exactly what they need. Much like the personalization engines driving value for many consumer facing apps (Uber, Spotify, Netflix), properly applied behavioral data fosters a better connection between students and the institution.  

 

Why using behavioral data is important  

Personalization, connection, and belonging are more important than ever in the education sector, and using behavioral data can help you foster that for better results. This year, just 12 percent of college students reported feeling a strong sense of belonging at their institutions, an essential component of student success. Connecting with students of all ages and levels is crucial during the post-pandemic recovery, and using the right data can help. Using behavioral data allows you to create a more personalized experience for learners at a time when students are seeking connection in ways that demographic data alone can’t.  

 

Using behavioral data to support students and learners allows you to meet them where they are at and better support their needs. Behavioral data offers deep insight into who a person is in the moment. Contact us to learn how we can help you offer more personalized support for your students.  

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

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

People's faces are unique like their attitudes

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 is like assembling a jigsaw puzzle

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 eBookArtificial Intelligence and Higher Ed
  2. How attitudes, “think-alike mindsets”, and nudging drive human decision making: get our free whitepaperWhy Attitudes Drive Decision Making: Reimagining Personalization

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

Personalized Nudges. Improved Outcomes.

4929 Bethesda Ave #200, Bethesda, MD 20814

202-505-1043

© 2022 All Rights Reserved.

Effective Personalization: Student Mindsets Matter Most

Effective Personalization: Student Mindsets Matter Most

By Keith P. O’Brien

8 minutes estimated time to read

Part two  of a two-part series

Head shots showing uniqueness of people

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.)

Four steps to identify a student's 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. 

 

Exhibit:  “Motivated Achievers” Mindset Snapshot  
Attitudes: highly motivated, very self-confident, low self-awareness, average capacity. Limited self-awareness and high confidence often lead these students to overcommit on tasks and responsibilities.  
Expected FAFSA Verification Behaviors: unlikely to track deadlines, have tendency to delay gathering documents because self-confidence underestimates task complexity, and misjudges capacity to get projects completed. Student’s view of personal capacity doesn’t match commitments and related workload.  

Nudging Strategy: calls to action target overcommitment so capacity issues don’t undercut FAFSA verification obligations. Nudges highlight the importance of timeliness in task completion and the support resources available. Nudges also engage the student’s task-orientation and proactivity by emphasizing the benefits of a using a step-by-step approach to the process. 

  

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.  

How engaging student attitudes boosts FAFSA verification

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 eBookArtificial 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 

Personalized Nudges. Improved Outcomes.

4929 Bethesda Ave #200, Bethesda, MD 20814

202-505-1043

© 2022 All Rights Reserved.

Your campus and AI: 6 Questions to Prepare for Success

Your campus and AI: 6 Questions to Prepare for Success

By Keith P. O’Brien

8 minutes estimated time to read

 

Light bulb showing power of new technology                

 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.
  1. 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]

 

Personalized Nudges. Improved Outcomes.

4929 Bethesda Ave #200, Bethesda, MD 20814

202-505-1043

© 2022 All Rights Reserved.

Three Questions to Vet AI Vendors

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!

 

Leader faces key choices in picking AI vendor

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:

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]

Personalized Nudges. Improved Outcomes.

4929 Bethesda Ave #200, Bethesda, MD 20814

202-505-1043

© 2022 All Rights Reserved.

Financial Services’ Missing link: Personalization at Scale and Mindsets

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 20th Century banking with our digitally focused financial services but rather analogizing them.

 

Digital banking becoming the norm

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]

Personalized Nudges. Improved Outcomes.

4929 Bethesda Ave #200, Bethesda, MD 20814

202-505-1043

© 2022 All Rights Reserved.

Will they show up? Fighting pandemic summer melt by engaging attitudes

Will they show up? Fighting pandemic summer melt by engaging attitudes

By Keith P. O’Brien

6 minutes estimated time to read

Group of students standing by school building

First, the point of this blog series isn’t to confirm the extraordinary challenges universities face in enrolling the Fall 2020 class. This series shares the ongoing experience of one regional private university in the Mid Atlantic, and their approach to forming an incoming class in these unprecedented times. The university’s challenges—drive deposits, battle summer melt—are commonplace today but the approach to engaging admitted students is rare: understanding their attitudes or mindsets towards matriculation. And then personalizing communications to those attitudes.

COVID-19 Undercuts all Predictive Modelling: New Data Needed

Is there an admissions or enrollment management department that isn’t awash in data on admits? Yet COVID-19 has blown away all the conventional wisdom on student behavior and class projections as students and families grapple with economic contraction, public health concerns, and disparate reopening timelines. Traditional predictive analytics aren’t helpful when decision making is upended by a pandemic and a rapid recession.

People’s decisions are driven by their attitudes, which are rooted in values, not zip codes, income level, age, or race. If you are or know a procrastinator, this mindset shapes how choices are viewed and selected. If Admissions knew in advance that a student’s mindset around college selection was procrastination, then tailoring messages to that mindset would likely be more effective. Attitudes are dynamic and can be influenced by messages that nudge people to make different choices.  Procrastinators can be motivated to act by presenting choices and support appropriately.

Earlier this year and before the pandemic had erupted, the regional private (let’s call it Private U) asked Discourse Analytics (DA) to profile the attitudes of their admitted students using existing behavioral data in Slate and other systems of record. Put simply, a student’s attitudinal profile illuminates the reasons behind an enrollment choice—depositing, registering for orientation, going somewhere else, or taking a gap year.

Admitted Students’ Enrollment Mindsets 

DA’s artificial intelligence-platform took behavioral data on 3,000 admits (no personally identifiable information was used) creating a profile of each student’s mindset. While every student has a unique attitudinal profile, there are likeminded people and the algorithm identified four “Think-alike” clusters based on shared attitudinal attributes. Here’s an overview of the four enrollment mindsets among Private U’s admits:

Mindset ClusterStriversGlidersPlannersLaggards
 

Confident and motivated with a strong growth mindset. College is a key opportunity to develop skills and networks. Lacks self-discipline so takes on too much and gets behind on things.

Very self-confident so welcomes challenges and likes being decisive. Sets goals but is disorganized and must play catch up on tasks.

Pursues challenges and opportunities to improve—growth mindset.  Acts decisively as highly self-confident and likes to be organized. Not afraid to ask for help.Low motivation so thinks short-term and rarely plans. Lacks self-discipline so struggles to follow through on commitments. Needs support to take on challenges
% of Admit Pop.20%19%13%4%

These four mindsets aren’t specific to Private U. DA’s AI and machine-learning platform use a set of attitudinal dimensions specific to higher ed to profile each student’s attitudes. Therefore, it’s very likely that these four mindsets exist among your own institution’s admitted student population.

The “Think-alike”clusters gave Private U an unprecedented view into their admitted student population:

  1. Predicting how students will behave as their attitudes provide the “why” for their actions;
  2. Identifying message strategies to engage the attitudes that motivate students to take the desired action; and
  3. Enabling personalization at scale through messages tied to the cluster the individual admit belongs to.

Student attitudes aren’t static: the admitted students’ mindsets react to the stimuli provided by Private U’s attitudinal messaging.  DA’s algorithms use the student response to communications (open rates, click throughs, follow ups) as additional inputs into that student’s attitudinal profile. Behaviors can be seen but attitudes must be revealed. As these behavioral data get richer, the AI rescores the attitudinal profiles and updates the clusters regularly.

The insights into the student mindsets helped Private U to begin to understand their admitted population differently.  But what will Admissions do with this information?  How will they convince these students to deposit, to not withdraw and ultimately to matriculate in the fall? 

In the next blog, we’ll share how the mindset clusters continue to evolve as student (and parental) attitudes shift with the ongoing tumultuous changes in the world and their effects on higher education. Additionally, we’ll look at how Private U uses the DA outreach recommendations to radically shift their messaging approach to students.

Download our free whitepaper,  Why Attitudes Drive Decision Making: Reimagining Personalization  to understand how student attitudes and “think-alike” mindsets explain student behaviors. 

To learn more contact: [email protected]

 

Personalized Nudges. Improved Outcomes.

4929 Bethesda Ave #200, Bethesda, MD 20814

202-505-1043

© 2022 All Rights Reserved.

Will they Deposit? Mindsets, May 1st and Melt

Will they Deposit? Mindsets, May 1st and Melt

By Keith P. O’Brien

7 minutes estimated time to read

Blog two in a series

Female student ponders where to enroll

As mentioned in our previous blog, Private U, a regional private university in the Mid Atlantic, is navigating the challenges in recruiting its undergraduate class for fall 2020. Challenges that are exacerbated by the public health crisis and attendant recession. Private U is employing a new approach to get admitted students to deposit by personalizing communications to each student’s mindset or attitudes, not their demographic characteristics. Next we’ll share some of the initial results.

Nudging Student Mindsets in a Pandemic: Early Findings

Private U deployed its first attitudinal messaging in April with calls to action focusing on the traditional May 1st deadline; like many privates, the deadline was extended given the circumstances.

Pandemic Affects Student Mindsets

COVID-19, shelter-in-place regulations and the economic downturn clearly affected admitted students and their families. Consequently, Private U received updated financial data for parental income, Pell-grant status, reported EFC and work status. These new data are ingested by Discourse Analytics’ (DA) artificial intelligence (AI) model and the algorithms refine the scoring of each student’s attitudinal profile. The model is updated with the student response to the attitudinal messages.

Large shifts occurred in the “Think-alike” clusters as societal and economic volatility reshapes attitudes towards enrollment. Two of the larger changes involved:  

  • The number of “Laggards” grows as uncertainty increases while motivation and financial comfort weakens
  • The number of “Strivers” contracts as confidence wanes and fall 2020 plans become tentative

An entirely new mindset cluster emerges—“Ambivalent Searchers.”  Students who are indecisive and risk averse and try to accumulate college-going options to manage uncertainty. Despite being admitted, they tend to have low affinity for many of these colleges and so manage their options quite passively. Private U must gain these students attention, build a connection, and motivate actions.

Most Viable Enrollment Outreach Strategies Emerge

Private U’s messaging encouraging depositing is guided by outreach strategies tied to attitudes. DA’s AI-model assigns one of four outreach strategies to each admit. The two most relevant strategies are:

  1. Career Outcomes (about 60% of students get these messages)
  2. Fit for Me (about 25% of students receive these messages)

The career focus is unsurprising as COVID-19 stokes uncertainty about job security and unemployment. This prominence also reflects issues around affordability—the cost of private education must deliver well-paid job opportunities. The fit between Private U and the student’s desired campus experience is always a major factor in determining college choice. 

Outcomes: Deposits Trending Positively

Private U’s attitudinal communications boosted deposits through April and the May 1st deadline. The deposit numbers are impressive given:

  • The irrelevance of traditional predictive modelling given pandemic induced volatility; and 
  • The withdrawals created by COVID-19’s impact on families and finances.

Private U's Student Deposits

From Mindsets to Messaging: How Private U Does It

DA helps Private U develop communications targeting admitted students in stages. First, DA’s AI-model evaluated each student’s individual attitudinal profile and grouped likeminded students (sharing attitudes) in these four think-alike clusters 

Private U 4 Mindset Clusters

The first blog describes the four clusters.

Next, the AI assigned an outreach strategy—categorical messages that nudge students to enroll—to every admitted student. The following graphic summarizes the AI model’s steps and outputs.

Process to create attitudinal nudging

Four Enrollment Outreach Strategies

Based on the student attitudes shaping enrollment outcomes, DA developed four outreach strategies, which are applicable to nearly all 4-year institutions:

  1. Career Outcomes targets students prioritizing the ability of college’s graduates to obtain good jobs. The outreach underscores Private U’s portfolio of career exploration, career services and experiential opportunities that enable students to acquire real-world work experience. 
  2. Invest/Affordability engages students wanting to ensure the value of their degree justifies the cost of a private college. The outreach underlines Private U’s high-quality private education and its affordability. 
  3. Core Academics targets students focused on academic reputation. The outreach highlights the range of liberal arts and professionally focused programs enabling each student to pursue the major that matches their skills and interests. 
  4. Fit for Me targets students assessing the fit between Private U and their desired college experience. The outreach highlights how the environment, learning opportunities and campus services enable a student to find the right place for their learning style, social character, intellectual interests and talents.

The next stage involves developing messages activating the different outreach strategies.

Creating Personalized Messaging aligned to Student Attitudes

Private U’s Admissions and marketing teams develop messages to nudge admitted students to deposit. The messages target student attitudes by:

Step One—Understanding the attitudes characterizing the four mindset clusters; and

Step Two—Developing a set of four outreach strategy messages for each mindset cluster. This entails shaping the subject lines, the language, the calls to action, and positioning of Private U’s value proposition to the mindset clusters.

Private U’s communication plan for admitted students is summarized in the following attitudinal message portfolio:

Private U Outreach Cluster

The message portfolio is updated weekly based on DA reports identifying the number of students in each mindset cluster and their assigned outreach strategy. Additionally, DA provides a weekly payload document specifying the outreach strategy message for each non-deposited student in Private U’s CRM system, Slate.

DA’s ongoing reporting enables Admissions to prioritize the students (clusters) and the messaging (outreach strategies) within its communication plan.

In the next blog, we’ll discuss the (on-going) lessons from Private’s U summer melt campaign, including:

  • Using nudging (behavioral economics) in crafting the outreach messages
  • Crafting deposit calls to action that tie to other enrollment steps

To learn more contact: [email protected] 

Private U’s summer melt campaign exemplifies Nudge Tech, using AI to personalize nudges that improve student outcomes.  Nudge Tech and Discourse Analytics feature in Gartner’s Top 10 Strategic Technologies Impacting Higher Education in 2020.

 

Personalized Nudges. Improved Outcomes.

4929 Bethesda Ave #200, Bethesda, MD 20814

202-505-1043

© 2022 All Rights Reserved.

Gartner stresses value of Higher Ed Nudge Tech: 6 recommendations

Gartner stresses value of Higher Ed Nudge Tech: 6 recommendations

By Keith P. O’Brien

6 minutes estimated time to read

Gartner, the research and advisory firm, recently published its Top 10 Strategic Technologies Impacting Higher Education in 2020.

Higher ed: AI, behavioral economics and nudge tech

Nudge Tech is prominent on the list and defined as: “a collection of technologies that work together to achieve timely, personalized interaction with students, staff and faculty, such as a just-in-time text (SMS) reminder for class. Technologies used include chatbot, texting, algorithmic analytics, machine learning and AI CI [Conversational Interfaces].”

Nudge Tech represents a remarkable fusion of advanced technologies and behavioral economics: AI and data analytics activating insights on human decision making—nudging. In Nudge: Improving Decisions about Health, Wealth, and Happiness, Thaler and Sunstein define a nudge as “any small feature of the environment that attracts people’s attention and alters their behavior but does so in a way that doesn’t compel.”

All but the most selective Higher ed institutions face numerous challenges to their continued operations from the immediate effects of COVID-19 to the long-term demographic declines in college-going populations. Nudge Tech must be a component in student engagement strategies seeking to boost enrollment and success outcomes. 

“Above all, nudge tech is a concrete example of how to achieve personalization at scale, which is becoming a key competitive advantage in an increasingly global digital education ecosystem.”

Lowendahl, Jan-Martin and Morgan, Glenda. “Nudge Tech.” Top 10 Strategic Technologies Impacting Higher Education in 2020. (March 2, 2020)

Uniquely within nudge tech providers, Discourse Analytics delivers attitudinal personalization at scale. Our key insight is that humans make choices based on how they think, not their demographics. We harness artificial intelligence to deliver attitudinal personalization: nudges tailored to each person’s attitudes towards an issue. 

For the second year in a row, we are honored to be referenced by Gartner in their analysis of Nudge Tech.  

Gartner’s Recommendations on Using Nudge Tech in Higher Ed

Gartner provides a set of recommendations for higher ed CIO to capitalize on nudge tech, which are applicable to all institutional leaders seeking to improve enrollment management, advancement and student success. These six recommendations align with DA’s core operating principles:

  1. Make sure to understand nudge theory, as well as pros and cons of nudging. Look for a solid understanding of nudge theory from the vendors you invite. DA’s work is predicated on the research of leading theorists, including Amos Tversky, Daniel Kahneman and Richard Thaler. 
  2. Ensure users’ trust by implementing transparency policies and interfaces, as well as opt-in and opt-out procedures for personal data. DA’s AI-platform only uses existing behavioral data on a university’s systems of record. Moreover, DA’s model does not use any Personally identifiable information (PII) such as name, age, gender, race for any student. 
  3. Build a nudge tech business case by identifying narrow use cases that have clearly measurable outcomes on which to judge value. DA’s attitudinal nudging has helped colleges and universities enhance outcomes across a range of challenges—summer melt and yield management, FAFSA verification and financial aid processing, student persistence and retention, and alumni relations and advancement. 
  4. Assess suitable data quality by identifying data sources, such as Q&A forums, SISs and LMSs that can act as the foundation for machine learning. DA ingests available student behavioral data from systems of record including the LMS, SIS, the CRM, and card swipe data from libraries and gymnasiums. 
  5. Design for a virtuous learning cycle by capturing all interactions in a machine-readable format. DA’s AI-model uses machine learning to map new behavioral data, including responses to nudges, to the student’s individual attitudinal profiles throughout the engagement. 
  6. Design the implementation so that statistically valid data can be collected for the “test group” as well as control group. The gold standard is to do a randomized controlled trial. DA benchmarks the results of every nudge campaign against a randomized control group from the target population.

Now more than ever, higher ed institutions must use AI to unlock the student insight buried in their systems to personalize communications. As Gartner emphasizes, AI is a particularly good case for nudging, as it allows analyzing increasingly complex data for opportunities to impact behaviors.”

To successfully remain relevant and financially viable, institutions should deliver more student-centric personalization–motivating mindsets to attain desired outcomes.  It’s time to embrace Nudge Tech. 

To learn more about DA’s approach to Nudge Tech, contact: [email protected]

Download our free whitepaper,  Why Attitudes Drive Decision Making: Reimagining Personalization  to understand how student attitudes and “think-alike” mindsets explain student behaviors. 

 

Personalized Nudges. Improved Outcomes.

4929 Bethesda Ave #200, Bethesda, MD 20814

202-505-1043

© 2022 All Rights Reserved.