The Model Assembly Line: A case study of Optimal Decisions Group

Optimizing for an actionable outcome over the right predictive models can be a company's most important strategic decision. For an insurance company, policy price is the product, so an optimal pricing model is to them what the assembly line is to automobile manufacturing.

Insurers have centuries of experience in prediction, but as recently as 10 years ago, the insurance companies often failed to make optimal business decisions about what price to charge each new customer. Their actuaries could build models to predict a customer's likelihood of being in an accident and the expected value of claims. But those models did not solve the pricing problem, so the insurance companies would set a price based on a combination of guesswork and market studies.

This situation changed in 1999 with a company called Optimal Decisions Group (ODG). ODG approached this problem with an early use of the Driv- etrain Approach and a practical take on step 4 that can be applied to a wide range of problems.

They began by defining the objective that the insurance company was trying to achieve: setting a price that maximizes the net-present value of the profit from a new customer over a multi-year time horizon, subject to certain constraints such as maintaining market share. From there, they developed an optimized pricing process that added hundreds of millions of dollars to the insurers' bottom lines. [Note: Co-author Jeremy Howard founded ODG.]

Drivetrain Step 4: The Model Assembly Line. Picture a Model Assembly Line for data products that transforms the raw data into an actionable outcome. The

ODG identified which levers the insurance company could control: what price to charge each customer, what types of accidents to cover, how much to spend on marketing and customer service, and how to react to their competitors' pricing decisions.

They also considered inputs outside of their control, like competitors' strategies, macroeconomic conditions, natural disasters, and customer "stickiness." They considered what additional data they would need to predict a customer's reaction to changes in price. It was necessary to build this dataset by randomly changing the prices of hundreds of thousands of pol-icies over many months. While the insurers were reluctant to conduct these experiments on real customers, as they'd certainly lose some customers as a result, they were swayed by the huge gains that optimized policy pricing might deliver. Finally, ODG started to design the models that could be used to optimize the insurer's profit.

Modeler takes the raw data and converts it into slightly more refined predicted data.

The first component of ODG's Modeler was a model of price elasticity (the probability that a customer will accept a given price) for new policies and for renewals. The price elasticity model is a curve of price versus the probability of the customer accepting the policy conditional on that price. This curve moves from almost certain acceptance at very low prices to almost never at high prices.

The second component of ODG's Modeler related price to the insurance company's profit, conditional on the customer accepting this price. The profit for a very low price will be in the red by the value of expected claims in the first year, plus any overhead for acquiring and servicing the new customer. Multi-plying these two curves creates a final curve that shows price versus expected profit (see Expected Profit figure, below). The final curve has a clearly identifiable local maximum that represents the best price to charge a customer for the first year.

Expected Profit

S Price

Expected profit.

ODG also built models for customer retention. These models predicted whether customers would renew their policies in one year, allowing for changes in price and willingness to jump to a competitor.

These additional models allow the annual models to be combined to predict profit from a new customer over the next five years.

This new suite of models is not a final answer because it only identifies the outcome for a given set of inputs. The next machine on the assembly line is a Simulator, which lets ODG ask the "what if" questions to see how the levers affect the distribution of the final outcome. The expected profit curve is just a slice of the surface of possible outcomes. To build that entire surface, the Sim-ulator runs the models over a wide range of inputs. The operator can adjust the input levers to answer specific questions like, "What will happen if our company offers the customer a low teaser price in year one but then raises the premiums in year two?" They can also explore how the distribution of profit is shaped by the inputs outside of the insurer's control: "What if the economy crashes and the customer loses his job? What if a 100-year flood hits his home? If a new competitor enters the market and our company does not react, what will be the impact on our bottom line?" Because the simulation is at a per- policy level, the insurer can view the impact of a given set of price changes on revenue, market share, and other metrics over time.

The Simulator's result is fed to an Optimizer, which takes the surface of possible outcomes and identifies the highest point. The Optimizer not only finds the best outcomes, it can also identify catastrophic outcomes and show how to avoid them. There are many different optimization techniques to choose from (see "Sidebar: Optimization in the real world" on page 6), but it is a well-understood field with robust and accessible solutions. ODG's competitors use different techniques to find an optimal price, but they are shipping the same over-all data product. What matters is that using a Drivetrain Ap-proach combined with a Model Assembly Line bridges the gap between pre-dictive models and actionable outcomes. Irfan Ahmed of CloudPhysics provides a good taxonomy of predictive modeling that describes this entire assembly line process:

"When dealing with hundreds or thousands of individual components models to understand the behavior of the full-system, a 'search' has to be done. I think of this as a complicated machine (full-system) where the curtain is withdrawn and you get to model each significant part of the machine under controlled experiments and then simulate the interactions. Note here the different levels: models of individual components, tied together in a simulation given a set of inputs, iterated through over different input sets in a search optimizer."

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Источник: Jeremy Howard, Margit Zwemer, and Mike Loukides. Designing Great Data Products. 2012

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