Comments
Top 10 Lessons OMIG Learned From Analytics
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Anne R Gabriel
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Anne R Gabriel,
User Rank: Apprentice
12/30/2014 | 12:24:09 PM
Re: Analytics driving retention
Yes, that's all true, Kelly. I believe Dave would also say the barriers to entry have never been lower. Although OMIG had internal resources, Dave clearly believes the right vendor (for your firm) can provide excellent support. I'm also hoping we'll see ongoing adoption and innovation by insurers as they discover the tools are ready for prime time and the needed support is available.
Kelly22
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Kelly22,
User Rank: Author
12/29/2014 | 3:34:41 PM
Re: Analytics driving retention
Thanks, Anne, these are all valuable lessons for insurers, whether or not they're large businesses or on the cutting edge of new technology. As Dave notes, it can be tough to jump in and get started, but the right resources and support can make a huge difference. Hopefully we'll see a wider range of insurers take on projects like this in 2015.
Anne R Gabriel
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Anne R Gabriel,
User Rank: Apprentice
12/23/2014 | 1:06:33 PM
Re: Analytics driving retention
Excellent insights, thank you. In addition to OMIG proving the theory that analytics can work to improve pricing strategies and, hence, profitability, I was also struck by their emphasis on the maturity of the analytics tools. Not long ago, only large or bleeding-edge organizations attempted this type of analytics initiative. According to OMIG, a much broader range of insurers could now take this path. This creates significant implications for leaders vs stragglers.
janderson088
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janderson088,
User Rank: Moderator
12/23/2014 | 12:41:35 PM
Analytics driving retention
This is a good summation of the opportunity and advantage I see on a regular basis. Executed properly I see an increase of 6-10% in the retention rate for personal lines carriers.

The opportunity comes when analytics incorporate more behavioral influences, including efforts by the carrier to predict the impact of a rate increase on a micro-segment. In the desired state, rate increases are not pushing away profitable customers but asking for less profitable to bear more of the cost of their participation. If only it was that easy...

Before Big Data analytics, actuary develops a needed increase in rate. Using historical experience, they consider the probable impact to retention which ultimately influences written/earned to probable loss cost. Regressive analysis helps develop an acceptable price and retention to rate balance, leading to an optimized plan. (Or relies on the underwriter/agent to sel the rate increase and hold on to the best risks...)

Better Insights / feedback from Big data starts to identify behavioral considerations and creates a new micro-segmentation that predicts the response to rate changes allowing Actuary to adjust their models to a more precise pricing point and written/earned impact prediction, ultimately for better profit.

 


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