So, you are collecting data on your insurance customers and prospects? Brilliant. But what are you doing with it? Is it just sitting there untouched, costing you money to gather and store, without earning its keep by giving back to your business?
Collecting data on your insurance customers and prospects is essential in this highly competitive marketplace. The trick is in ensuring it is earning its keep by giving value back to the business. No insurance provider can afford to be sitting on data without making it work hard for them (especially if its sitting on a cloud-based service).
Wherever you are in the data mining journey, from starting out to having swathes of analytics and algorithms, insurance providers need to keep investing, keep working and keep developing more strategies to better-utilise that data.
To stay in the insurance race, you need performance enhancers: entirely legal and data-driven ones, obviously.
Putting the data on the table: where to start
We all know the wise man built his house upon the rock, not the sand. So, in theory, we know we must start with strong foundations and that whatever follows will be limited or boosted by those foundations. When it comes to data, starting with the right architecture in place for data capture and storage is vital, even if you do not quite know how you will use the data once you have got your hands on it.
Now, it’s not always easy to get buy-in from the rest of the business, but before you can get the architecture in place certain decisions must be made and that needs input from every area of the business. You need data from each area if you want to build a 360-degree picture of customer’s journey through the insurance continuum which will add value to the data and provide insights to help with future business decisions. So, the method of collection and storage must incorporate every business area.
Having the entire business involved in the process will also smooth the road ahead for future investment requirements. Once you have a clearer idea of what you can get out of the data and how it can be utilised across the business, you may find you want to add additional infrastructure, maybe to allow better interrogation of data.
The big data dos and don’ts
- Do carry out detailed cost/benefits analysis before each investment
- Don’t invest where there is little or no value to be returned
- Do plan and lay out your logic before you begin coding
- Do not dwell on impossibilities. If you cannot do something yet, invest in the architecture and start collecting that data, then get on with the next thing
- Do pick the right sized net. If your net is too small you might not catch anything, if you make the net too big you might capture the wrong fish
- Don’t replace or overwrite data: historical information is invaluable
- Do understand, accept and work within your limitations. You don’t want to spend five months building a product based on data which doesn’t contain the historical information you need.
There is no hiding from the fact that decent data analysis takes some level of investment. Whether you handle the data internally or employ a third-party to help you, there are going to be cost implications. Of course, this must be weighed-up against the potential short and long-term benefits and a strong business case.
A highly-valuable investment is in automating Exploratory Data Analysis (EDA) at different parts of the customer journey, understanding the inputs and outputs and having a traffic light style system in place to identify oddities in throughput.
It is all very well capturing data from every stage of a customer journey and using it to target certain customers and prospects, but that is hardly ground-breaking stuff to transform your business or set you ahead of the competition by getting ahead of the unknown unknowns. Only through good EDA can an insurance provider learn from previous decisions and use that additional knowledge to benefit future risk assessment and underwriting.
When it is properly collected, stored, cleansed and analysed, data gives you the ability to time travel. No ‘Tardis’ or flux capacitor required. Detailed analytics can answer the question: Had we known this information before writing the policy, would we have priced the policy differently? This can provide invaluable opportunities to learn and improve in future. Without these unique insights, insurance providers risk facing inaccurate pricing and high loss costs or low conversion rates and delivering inferior customer service: something no insurance provider can afford in this highly competitive market.
Benefits of enhanced data analysis
- ‘Time travel’
- Smoother customer journey
- Accurate risk assessment and underwriting
- Lower loss cost
- Reduced cancellations and operational overheads
- Increased customer loyalty.
Follow the link to the LexisNexis Risk Solutions website to find out more about how we support insurance providers.