This post is from our Voices of Customer Experience podcast with former FICO CEO and current Research Fellow, Larry Rosenberger where he discusses the evolution of data science, optimizing decisions, prescriptive analytics, the four pillars of behavior analytics and their impact on customer decision making.
Larry started his 40 + tenure with FICO in the early 70’s where he witnessed firsthand the progression of data science and analytics. Being a research fellow has given him an opportunity to brainstorm big ideas on how to use data analytics to create innovative solutions.
He has played an integral part in the evolution of business analytics at FICO and their focus.
1970’s to early 80’s – Predictive analytics
Mid-80’s – Credit bureau data and the birth of the FICO score
1990’s – Adaptive control (or duel control) based on Richard Bellman’s book, Adaptive Control Processes, which means taking complex problems and making them amenable to machine solutions and testing to see how they can be made better. However, FICO was missing how to mathematically model and optimize a decision.
2000’s – Decision analytics or Prescriptive analytics, which is the standard weaponry that B2C companies use to try to continue to improve decisions.
When you look at credit bureau data, demographics, individual transactions, all of it’s been plowed. So, the question is, ‘What’s next?’ Larry believes the next best thing to be behavior analytics, which he says is based on both art and science.
Here are Larry’s four pillars of behavior analytics and what they’re based on.
1. Behavioral Economics
This describes how people really make decisions. We know through the psychology and experiments that Nobel Prize winner, Daniel Kahneman, and psychologist Amos Tversky conducted, that there was a desire to find out whether you could design incentives. Can companies design decisions? Can they offer positive incentives that impact whether customers will buy? How do we make economic decisions?
2. Behavioral Psychology
Some may argue that this is the same as behavioral economics, but the two are slightly different. The former focuses on how people make decisions. This one looks more at the lives of the individuals and how they motivate themselves to make a change. Also, can they create new habits and when so, how do they sustain them?
Founder and Director of the Stanford Behavior Design Lab and author of Persuasive Technology, B.J. Fogg, has a behavioral model that says you have to put together motivation, something you can actually do, then create a trigger to move towards new habits and sustain them.
3. Advanced Gamification
Gamification has taken generations by storm. You have to pry kids off of games these days. Their focus and engagement is through the roof! How so?
"I watch my four grandchildren play games. They are into these games. When I heard the word ‘gamification’, I initially thought about wasting time until I saw someone speak on it and I discovered it from the angle of engagement."
“I saw Yu-Kai Chou speak on actionable gamification and human-focused design. A lot of business apps on our smart phones are process focused. It’s about what motivates people and how to focus on people, engage them, and keep them. These people know how to apply the principles of gamification; it’s human-focused design.”
You must design incentives that are engaging and motivate people to move towards sustainable habits. The question is how do you design experiences to provoke the other two pillars (behavioral economics and behavioral psychology).
4. Bilateral conversations
There’s a need for one on one conversations between a provider like a consumer, marketer or retailer versus their customers. So, when you engage, you start saying you’re focused on a small number of things so you don’t lose engagement. A key element is how you construct the conversation with minimum friction.
Make it easier for the consumer to say, “What do I care about?” “What are my interests?” and “Why do I buy?”
Companies are challenged to find the reasons why their customers are buying. At the start of market research it was too expensive to interview on a consistent basis. Now, you can have a conversation using technology where you can ask questions to make better, appropriate offers, you can understand more about them to tune your offers, communication, and experiences to be more effective. The company mindset can’t be stuck in the old technology.
You need fast, cheap, and comfortable dialogue vehicles like artificial intelligence and machine learning to better understand today’s customers.