How to Use Data APIs to Reduce Form Complexity
We’ve all been there… it’s your sixth time telling an insurer your details, answering the same 10, 20, or 30 questions over and over again… it’s painful, but some businesses have figured out how to avoid it. This article will show you how to too.
Customers hate answering questions, and that’s why so many companies are turning to products that can minimize the number of questions you need to ask while retaining the amount of information needed to provide quality service.
Sounds too good to be true?
The magic that makes all this possible is databases – if you already have information about the entire population and the commercial sector then you can easily look up the information you need about a person by simply using a few key pieces of information such as a search key to find the rest.
For example, asking a potential customer their name and company name only, and then looking those two things up in Doorda’s Database can reveal everything you need to know about that person to provide a great service.
Sounds simple? It is simple.
The majority of larger insurers are using this technology and technique today, but innovative data providers are now bringing down the cost of data, and are increasing the quality available to all business including startups, SMEs, mid-market and global enterprises.
The Economics of Form Auto-Completion
Measuring the value of form auto-completion is a relatively straight forward process with two major outputs:
- Lift in Revenue and Profit
- Happier Customers
- Accuracy of Quotes
If the cost of the data and systems to implement auto-complete is less than the increase in revenue, and if it provides more accurate inputs for your risk assessments, then it will also drive value long term via increase lifetime value (LTV) of customers.
Lastly, the cherry on the cake is that it gives your customers a better experience too.
The great thing is these are all relatively easy to test and prove value from. You can measure quote drop-off rates and quote success. You can also test the data used against historical data to measure uplift or decrease in quote accuracy.
So the good news is you can guarantee potential customers have a better experience, and you can easily prove return on investment (ROI) via a simple proof of concept.
What Are the Key Ingredients for a Successful AutoComplete System?
Data is King. If you don’t have good data, forget the rest. Do your due diligence with data providers. Make sure they can give you the provenance of their data, and make sure they are able to give you everything you need, in the format you need it.
IT matters. Your data technology stack matters a lot. Deploying new systems is never a simple process. Make sure you have a development team and environment which can deploy new technology quickly and easily. Look for products which make this integration work simple.
Testing is Vital. You are planning to swap a large percentage of your customer information you need to be prepared to do some rigorous testing to ensure your data and quotes are accurate and appropriate. This is why we suggest everyone adopting this technology should build a proof-of-concept. During this process, you can dial in your variables and measure your ROI and risk factors all on one go.
How to Do a PoC
The starting point for this kind of project is to build a PoC to prove value and test new data sources against historical performance.
It’s a relatively simple process to give your data science and developers access to data to compare the data you get through an auto-complete system to the historical data collected from your customers.
If you’re interested in finding out more or exploring our data for autocompletion, our team will be more than happy to help you understand the data needed and available through our data platform. Get in touch any time to book a demo and see our data catalogue.