Technology has taken us closer to our customers in previously unimaginable ways. When customers abandon their carts, retailers typically mail them related offers to nudge them into reengaging with their brand. With Customer Journey Analytics, you can use machine learning to comb millions of customer interactions and pick out significant customer paths that led to purchase. This is data that can profoundly influence how you shape your CX campaigns. This data also informs UX design and helps you engage with your customers on a very deep level and individual level.
The customer journey map is a research-based tool that unravels how a customer engages with a product, service or brand over time. A CX expert will use this tool to understand the role of each department – the UX team, marketing team, logistics, sales, distribution, customer support etc. – to figure out how to best engage. There are many customer journeys that you can map but the trick is to hone in on the optimal and important journeys that can best lead to engaged customers.
A customer journey map includes many components. User personas, for example, play an important role, and so do time frames. You are mapping customer behaviour over months and even years, collecting extremely valuable and insightful data. This also helps verify patterns and key data points.
What channels are the customers using? This data , for instance, shows you a retail company’s data. It says that someone who uses mobile display as a channel is unlikely to use other channels to discover and engage with the product. The level of precision here is very high and the beauty of this approach is that it is just not a generalized, representative sample of customers in the retail space. These are your customers, engaging with you real-time and giving you crucial windows into their purchasing patterns.
Perhaps the most important task of a customer journey map is to understand customer touchpoints. Did your customer find you in a paid search ad, then see a display ad and come to your website? Or did they reach you organically? What were they looking for and what did they end up buying? Did they view reviews and ratings or chat with customer service? Customer touchpoints takes into account all these user matrices and trajectories. Interaction Design Foundation gives you a detailed list of factors to consider when listing out touchpoints, including creating empathy maps and affinity diagrams.
A step beyond this is Customer Journey Analytics which allows you to analyze both micro and macro patterns in your data. You can take a broader approach with your campaign, and make data-driven decisions to get to your customers and engage them.
There is another aspect of the Customer Journey Analytics that is crucial to your business – the fact that it doesn’t look merely at conversion but at loyalty, retention, enrichment, and the ultimate endorsement, which is advocacy.
The ingenuity of customer journey analytics is that it uses machine learning algorithms to mine data and help you generate important business calls-to-action, or ‘events’ in a customer’s journey and the frequent paths they take. You can use this data to predict future behaviour and trajectories too, and choose the best channel and approach to engage with them. A telecom company , for example, used machine learning algorithms (used by its CX and marketing teams, without the use of data scientists) and found that the customer experience during the installation phase is resulting in many negative calls to the customer care centre. They went beyond a static, macro-level journey map and used a dynamic micro-level journey map to chart the exact journeys customers take before making the calls, and what can be done differently.
Says Ross Quintana, Founder of Social Magnets says , “Analytics can’t just be data, it has to be understanding.”
Using these techniques to capture customer movement over time is crucial to understanding what people actually do versus what they say they do.
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