We often talk about the benefits of joining web browsing data to customers for better targeting and personalization, which is great for digital and CRM teams, but the value add does not stop there.
3 ways to improve your research with web data
At Fusion, we find that the benefits of integrating web data with your market research panel data can be significant. In this article, we look at how our tool WebFusion can provide the link between all the activity for all browsers online (page views, clicks etc), with the detailed questions asked to smaller research groups.
According to survey monkey, you have 7-8 minutes max before your completion rate of a survey drops through the floor. This is the equivalent of around 15 questions.
If you want to ask a question to a specific segment of your customer base, or customers that have partaken in a specific activity, you may find that you spend more time defining the group than actually getting the answers you want.
For example, if you want to talk to existing high-value female customers regarding a recent transaction, you may have to spend 3-4 of your 15 questions (20-25%) on working out if you actually want to ask that question to that person.
Using WebFusion to group digital, customer, transnational and survey data together we can define the segments prior to launching the survey. This can free up the valuable question space in your survey.
We all have moments when we forget where we put the keys, trying to remember the last date you transacted with a brand is naturally going to have a health warning.
But this information is vital to research projects and understanding trends and customer habits.
The ability to have detailed engagement information, with an understanding of exactly what people have seen and when they have brought enhances the output of insight projects.
There are some things that CRM just cannot do without research, predict the response of a new product/creative (assuming no similar data), explain the why behind category decline. Research like this can be attributed to a customer base, as long as there is enough shared data on all customers. Web browsing data is rich enough to provide this currency between the research panel and the rest of the customers.
To give an example, assume that you have a drop in sales in a specific category, you can see this is mainly in younger groups, your research explains your buying process is too complex (people are impatient). We can now model this outcome across all of your web browsers using variables like average time per page as an indicator of patients (if statistically significant). Those likely to be affected by the time can now be targeted with quicker sales processes.
By targeting the affected group with a more relevant process we protect the rest of the customers (who are transacting normally) and can test to ensure we are getting an incremental benefit in the new quicker process.