Making it Smart and Making Customers Happy in the Process
Having just completed another successful project in record time we were thrilled when our client described our work, exactly as we would have hoped they would when we started the project. – “Point Predictive more than met our expectations..”, “their team was available, responsive and really did an exceptional job of bringing their significant expertise to bear on our business”. These kind words were literally music to our ears and that’s why we started PointPredictive.
Our business model has always been to partner with our customers. We bring them our expertise in data, risk and modeling and help them solve business problems quickly in a way that they can understand and use. Through our most recent project we did just that by focusing on the business issues and working on a tactical and practical solution using smarter science.
There were many things that made this engagement successful but I felt there were 5 reasons that made it really work.
1. We started by understanding their business before we gathered the data.
There is intelligence you can derive from data but it first requires a deep understanding of the business issues to put that data into context. That’s why we spent an entire week, working hand in hand with our customers onsite with them. We needed to understand their business problems that they faced everyday before we could help them solve them. After the week was over, we were able to gather years of historic data and make sense of it when we got back to the PointPredictive lab to get to work.
2. We didn’t lock ourselves into a single analytic technique.
We realized pretty quickly on that to solve this particular issue, we were going to need to be creative and think outside of the box. So, we decided very early on to be analytic-agnostic. That is to say we would use any analytic technique that delivered the best predictive value while being simple enough to put into production quickly and be useable by the business. We tried scorecards, logistic regression and even experimented with deep learning techniques. In the end we found that a combination of techniques worked the best.
3. We scheduled regular meetings to discuss progress and raise issues.
We are big fans of iterating a solution until we get it right. So we scheduled weekly calls with our customers to report on results of the analytics and to get their feedback through the entire process as quickly as possible. We didn’t develop the model in a vacuum, we created the model in an iterative process hand in hand with our customer.
4. We measured model performance for them in many different ways and focused on performance.
We took great effort to deliver a full set of performance charts for our customer. Not only did we measure the models ability to detect risk overall, but as well its ability to reduce financial risk over time. A model that does not help reduce financial risk has a very hard time of adoption since their is no financial incentive. We made sure that our performance charts clearly demonstrated the benefit to improve adoption of the model.
5. We helped to operationalize the model.
The model was extremely predictive. We were able to show that with our performance charts. That only solves half the problem however and we needed to help our customers understand how to use that model in various aspects of their business. That’s were our business consulting backgrounds really came into play. Having worked with over 100 lenders, banks and financial companies on analytics and risk we were able to bring our significant expertise to bear. Operationalizing the model helped bring it to life in a way that made sense for the business to use.
It’s so great to be able to help our customers and enable them to achieve their business goals. That makes all the hard work we do everyday worth the effort.
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