Customer surveys used to be a mainstay of marketing, largely because they were the only way to directly connect with the customer. But recent research from OpinionLab suggests that shoppers are pushing back on surveys in ways that are severely hampering their effectiveness:
To further exacerbate the problem, as Roger Dooley points out in his article in Forbes, many of us are subject to unconscious influences that restrict our responses. And, as Dooley goes on to explain, people are often incapable of articulating why they do things or how they would behave in the future.
If that weren’t bad enough, surveys suffer from a structural problem too. They’re static, siloed and retrospective. They don’t allow for how your competitors, for instance, might have changed their offering to reduce the impact of your user experience.
And yet, this information is critical to the business. It’s routinely used to inform marketing strategy, to construct demographics and user personas, and to strategically adapt the company’s offering of its goods and services.
There really has to be a better way.
And fortunately there is. By combining machine learning with unique customer identification, we can now obtain deeper and more granular information on customers than ever before. Data can be gathered and processed not just at one point in time, but continuously and repeatedly to create much more realistic personas and to understand customer behaviour in a much deeper way.
Machine learning – a subfield of artificial intelligence – takes raw data and analyses occurrences over time to correlate and improve the accuracy of that data, as well as providing the ability to make predictions on that data.
By assigning the customer a unique identifier the moment they land on the website it becomes possible to track the customer journey, showing where they came from, where they went on the site, what they looked at and for how long, and whether that resulted in conversion to a sale. Additional information such as the device they are using can also tracked and this information can be linked to the customer’s account to personalise their viewing experience.
Going forward, it’s this customer journey that holds the real value for the business. Focusing on the customer journey and their actual behaviour provides insights into what works and what doesn’t, where sales have been narrowly missed and why, and will indicate the success of marketing drives.
Rand Fishkin at marketing firm, Moz, says metrics such as CRO (Customer Rate Optimisation) has eclipsed the customer journey for too long: “measure your customer journey, not just your conversion path. So many folks look at paths to conversion. You have your reports set up in Google Analytics, and you look at assisted conversions and path to conversions, but you don’t look at customer journey, which is what do people do after they convert… .”
The beauty our machine learning solution AVORA is that you get precisely the level of detail that you would from Google Analytics joined with the customer journey data to give a much deeper and richer view of the customer experience.
Suddenly the customer journey now becomes a complete trail of accessible touchpoints that the business can use to accurately create real personas based on real-time behaviour rather than the limited and flawed information volunteered in a survey.
With AVORA customers can, for the very first time, be accurately tracked according to the device, referral source and touchpoints across the website. And it’s this visibility into the entire customer journey that will improve persona mapping and refine your marketing drives even further.
By combining this real-time data with a machine learning infrastructure, you can create a much more effective user experience. And as we all know, a better user experience lays the foundations for better conversions that lead to more satisfied and loyal customers.
So say goodbye to those boring old customer surveys and hello to the new world of AVORA!
Topics: Customer Engagement, Machine Learning