You may have had the experience of filling out a long form on a website. For example, creating an account to make a purchase, or applying for a job, or renewing your car insurance.
A long form can lead to customers losing interest and taking their business elsewhere. Each additional field can result in up to 10% more customers dropping out instead of completing the form.
If you have a business with a form like this, one reason why you’re not able to simplify your form is because the data you are requesting is valuable.
There are lots of ways to address the problem, such as improving the design of the form, or splitting it across multiple pages, removing the “confirm password” field, and so on. But it appears that most fields can’t be removed without inherently degrading the data you collect on these new customers.
However with machine learning it’s possible to predict the values of some of these fields, and completely remove them from the form without sacrificing too much information. This way you gain more customers. You would need to have a history of what information customers have provided in the past, in order to remove the fields for new customers.
A few examples
- On a small ads site, you require users to upload a photo, or fill out a description of the item they’re selling. With machine learning you can suggest a price from the description, or a title from the photo, resulting in less typing for the user.
- On a recruitment website, you can use machine learning to deduce lots of data (name, address, salary, desired role) directly from the candidate’s CV when it’s uploaded. Even salary can be predicted although it’s not usually explicit in the CV.
- On a car insurance website, it’s possible to retrieve make, model, car tax and insurance status from an image of the car.
If you are interested and would like to know more please send me a message.
For an example of how data can be inferred from an unstructured text field please check out my forensic stylometry demo.