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Daniel Goldsmith
VP Analytics and Innovation, Pearson Online Learning Services, Pearson
Supernova Award Category:
Data to Decisions
The Organization:
Pearson is the world’s largest learning company. Pearson Online Learning Services (POLS) is a division that offers advice, services and tools to educational institutions (primarily focused on adult-serving institutions like master’s degree programs and undergraduate degree completion programs) to launch new academic programs and help them attract, enroll and retain students. They also help colleges with strategy questions like how to work more closely with employers, how to reach new student segments, and how to evolve their business models for a changing world of work. POLS partners with 35 different colleges to reach over 60,000 learners annually, collectively creating over $700M in revenue for academic partners and Pearson.
The Problem:
Pearson needed to automate discovering which learner characteristics were the real drivers of outcomes, including student success metrics (such as graduation rates) and business partnership outcomes (such as enrollment growth.) They also needed to uncover some broad market insights across all the academic programs they manage - requiring a rethinking of a traditionally siloed data management and analytics teams - to determine how to deliver more value to college leadership, helping them take a more segmented approach to their own go to market strategy.
The Solution:
Daniel looked to Salesforce and Einstein Analytics to provide the AI engine and generate the required analytical insight (along with implementation partner Atrium); this took the form of predictive models across the “student lifecycle.” These models were then used across the business, with forecasting support for finance, operational support for our marketing teams, and strategic support for our account managers.
The Results:
From this innovation in data collection and analysis, Pearson saw 3 main types of results: a) better identifying student behavioral patterns; b) utilizing those factors to drive future data models and CRM adoption; and c) helping customers improve strategy and operations.
Initial insights focused on identifying the most important factors in increasing enrollment and retention for their customers, such as acquisition channel (i.e. Facebook vs LinkedIn), the program of application, and the learner's state of residency, etc. Armed with these insights, Daniel’s team can now design data models more effectively to serve internal users and their customers. The analysis also helps explain to internal teams why capturing those data points is important to the overall understanding of student outcomes (even when this wasn’t initially obvious.) This meant users could jump into deploying CRM faster and with greater confidence in the architecture.
As Daniel and his team work on customer projects, Einstein Discovery, a Salesforce AI product, helps them identify where the opportunities lie to improve the enrollment journey for prospective students and how to maximize student enrollment rate. By doing comparisons across colleges, they could see key differences that indicated where the student journey could be enhanced or improved right in the dashboard. Therefore, Pearson went from talking about analytics to showing customers quantifiable results and next best actions.
Metrics:
This work targeted several key metrics:
20% target improvement in the ratio of lifetime value / cost per acquisition for learners at partner colleges and universities (realization in process). This was enabled both by reducing marketing and learner acquisition costs, as well as helping learners find new pathways to academic programs (such as starting with “short courses” or a few academic classes to gain more experience and confidence.)
Improved client renewal. While the lengthy nature of relationships in the industry (5-15+ years) make this harder to quantify, the ability to engage with colleges as a strategic partner has driven improvements in client satisfaction and helped animate renewal discussions.
Other efficiencies: utilizing analytical results for the basis of team conversations helped increase productivity and reduce speed time to deploy new initiatives.
The Technology:
Salesforce: Einstein Analytics Plus, Einstein Discovery, Sales Cloud
Disruptive Factor:
While many aspects of this work were new for the organization, the most disruptive aspect was the fact that the initial Einstein Analytics implementation was conducted prior to an organizational wide Salesforce migration in order to “front load” analytical business value, help determine which data points matter the most to the business, and fundamentally inform migration plans. This meant taking a significant “leap” as an organization and making a true commitment to analytics. It also meant addressing organizational silos early in the process, but with the reward of rapid insight and analytics to help drive alignment around shared value and enable future transformation work.
Shining Moment:
This work had significant business impact for Pearson, but the best part of the work was enabling teams to sit down with a dean, provost, or college president and discuss how we can use data to help accomplish their mission and maximize students' potential success. This is the part of the job in which our teams and partners “light up." It is fantastic to ground these conversations in advanced analytics to truly move the needle and innovate the future of education.
About Your Organization
Pearson is the world’s learning company operating in 70 countries around the world with more than 24,000 employees, providing a range of products and services that help people make progress in their lives through learning.