Customer journey mapping is often seen only as a design thinking tool that allows organizations to understand their markets and the ways customers interact with them. It can be made even more powerful when it is augmented by a data analytics tool. These insights can then be used to increase sales or reduce churn. Journey analysis is so important that, according to Gartner, it was the top customer analytics priority among business and IT leaders in 2018.
A customer’s ‘journey’ involves all of their interactions with a company, product or service. This is normally done by reference to ‘touchpoints’ — that is, every point at which the customer comes into contact with a brand. This means not only the product or service, but associated social media, marketing, points of sale, billing, support and anything else that might touch on the experience.
Typically, marketers put themselves in the shoes of a customer and try to identify touchpoints, then create a visual ‘map’ of what they expect typical customer journeys through the organization to look like. This map is important because it allows the organization to identify problems and areas where the customer journey could be improved. If there are gaps between touchpoints (for example, the website does not lead easily to a point of sale), points where customers have a difficult experience, or touchpoints that do not lead naturally to the next step in the customer journey, these sources of friction can be identified and fixed.
Thus, by leveraging customer journey maps customer experiences can be made as pleasant and frictionless as possible.
The traditional method of journey mapping has a serious limitation, however. An ‘expected customer journey’ is just that — expected, not observed. Though it does incorporate data analytics, expected journey mapping is a fundamentally qualitative method of divining customer experience.
In practice, this often means trying to understand the hows and whys from a user’s perspective via workshops, surveys and interviews, with data providing additional clues (for example, a lack of web traffic between touchpoints showing that there is a gap, or a lot of clicks and time spent with a touchpoint suggesting that it might be awkward or confusing).
While these methods can be useful, the results are highly speculative. Businesses hope that customers will take the expected journeys that have been so carefully mapped and planned, but that may not be what is going on in the real world.
“Businesses hope that customers will take the expected journeys that have been so carefully mapped and planned”
At the same time, the data organizations are able to collect about touchpoints are constantly increasing. Multiply the number of touchpoints by the number of customers who encounter them, and you have a dizzying amount of information to process. Sorting through actual customer journeys to find meaningful insights is now a problem in itself.
This is where a quantitative, data-driven approach becomes necessary. At ODAIA, we leverage a technique called process mining to analyse touchpoint data and provide useful journey maps based on actual journeys. By using the latest in machine learning development, we analyze customer behavior, allowing us to help companies prevent churn, measure the similarities between customers and provide recommendations for next best actions .
By using the latest in machine learning development, we analyze customer behavior
I’ll talk more about data mining approaches to customer journey mapping in the future — for now, the important takeaway is that, if you want to understand actual customer journeys and predict future actions, a quantitative approach should be favored over the more qualitative approach.
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