More and more businesses and organisations are using analytics data driven approaches to understand how their customers interact with their products.
The capabilities of this new field are wide-ranging, and the applications exciting, to the point where some have suggested that older methods like traditional online surveys are obsolete. This overlooks both the limitations of a machine learning approach and the opportunities for greater understanding that a multi-channel approach offers.
Limitations of a pure Quantitative approach.
Whatever you want to call it, the modern approach of crunching vast amounts of data has allowed organisations to drill into their users’ habits like never before, and see connected behaviours that otherwise would not have been apparent.
A frequent use of this is determining when a user may be about to unsubscribe, leave, or otherwise “churn” from using a service. For Example, a company that sells a Software-as-a-Service product may be able to tell that a user is beginning to display the signs that they are at risk of leaving. That company can then target that user for some sort of intervention.
But what should that intervention be? An discount offer via email? By observing the analytics data, we know this user is at risk, but because we don’t know why this user has started on the path to leaving, we may not choose the right intervention. We’re at risk of wasting our effort, or even making things worse, if we don’t understand the underlying reasons for the behaviour. A pricing offer may be of no value to a user who is struggling with your user experience. Perhaps they find your product too difficult to use, or can’t find a feature they need.
Qualitative data as part of the mix
By combining the data programme with regular surveys, a much better picture can be drawn about not only user behaviour, but motivations and satisfaction.
Understanding motivation can be the most important thing because every act taken by a user of your service has been motivated by some future goal, and it’s not often clear what that is. If a user is coming back to your site, or a particular page, every day – why is that? What need are they fulfilling? Is it a chore to them, or something they look forward to?
Understanding why people use your service, buy your product, or whatever it is you do will make it much easier to plan your future strategy. A pure data approach will tell you what people do, but will tell you little about what they want to do.
The wonderful thing about people, that sometimes makes this field so fascinating to work in, is the capability to surprise you. One of the most valuable things about surveys is the result you weren’t expecting. The left-field answers that open up new avenues of investigation.
This is often the value of an online survey tool such as SmartSurvey. It’s not just about identifying answers, it’s also realising that there are questions you never thought of asking. And then being able to ask them.
Regularly surveying users to track sentiment, and gathering feedback about how your users perceive their use of your app, service or product is the most important thing you can be be doing. Keeping surveys short and to-the-point is always a benefit, but if you’re already using data analytics, you should already have a sense of the areas of concern.
SmartSurvey has powerful features that allow an integrated approach between analytics and surveys.
Custom variables allow organisations with means of identifying users a method of inserting that ID into their survey responses, allowing the qual and quan data for a user to be matched up. Exporting the data, and deleting any other identifiers allows this processing to be done on an anonymous basis, but it’s important to remember that the data won’t be anonymous at the point of collection.
Web-embed, intercept, or pop-up surveys also allow this data to be collected alongside the quan data. Of course, you need to bear in mind that the implementation of these needs to be done with care not to disrupt the user flow you’re trying to measure.