New Car Loans on Steroids. Using Annotations – Why they’re valuable

Annotations are a GREAT way to describe events.  For example, below are two images showing a credit union’s 90-Day performance trend for New Car Loan Balances.

The information below came out of the Analytics Booth Website (  As you can see in the first image from the tool tip shown within the graph,  the balance dropped by $25,827 over a 43 day period.  In other words, basically performance was flat on a  9.3 million dollar balance.  However, over the next six weeks weeks a completely different story emerges when looking at the second graph.  This was a deliberate effort by the credit union to gain lift in this area of their loan portfolio.  They used a marketing campaign to achieve a $1.3M (14%) increase in New Car loan balances as of Nov 11, 2017.

This would be a great opportunity to describe the details of the campaign using the “Annotation” feature of Analytics Booth.  What was the credit unions expectation over this period of time?   How did they go about getting the new business?  Rate reductions?, How did they reach their target audience (emails?, phone calls?, billboards?, radio ads?).  I’m sure the key players involved with the campaign can answer all those questions as it is fresh in their minds.  Suppose this trend was from 2015, would those questions be as easy to answer?  Maybe some of the employees involved with the campaign are no longer with the credit union.  For these reasons, the “Annotate” feature provides a great way to describe activities within the credit union.


New Car Loan Balances Flat from August 22 until Early October (6 Weeks)

Huge gains in New Car Loan Balances starting in October and running for 6 weeks.




3 thoughts on “New Car Loans on Steroids. Using Annotations – Why they’re valuable”

  • Love the angle of your blog here Josh – first gets me thinking about how to use a generic tool and add value as an individual contributor. R.Karnes was here and did some work. And second you are reminding your readers that raw data without stored insight is worthless. Data might cycle the same way, but without the story on the catalysts to the results it might not. I thought I heard there were ways to denote comments as private or public? Is that true?

    You have a knack as a programmer to look at data and ask yourself does it make sense – and then the curiosity to chase it down and validate the story. Whether it comes from your work to QC code or from a genuine search for how things work, it is a great attribute. I hope your blog will continue to link the tools to ideas on how to see “why and how things work”!

  • I always think about the aftermath and wish we could schedule future annotations. In your example, you show a CU having a massive increase in new auto loans- will there be a massive flux again after 3-4 years, or whatever their average auto loan payoff period is?

    • That’s a good point point. Not only would it be helpful to anticipate higher than unusal balance runoff in the future, but it would also be a good reminder for marketing to run a preapproval program for our members who should be paying off soon.

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