How to anticipate the unseen customer experience
Direct feedback is an important part of any organisation’s understanding of the value of their customer experience. Unfortunately, there is no way to ensure that every single customer takes a survey or leaves a review. In addition to this, customers are more likely to provide feedback if they have had a particularly good or bad experience, potentially skewing NPS scores and an organisation’s overall perception of its customer experience.
Direct feedback is undeniably a channel through which team leaders can learn more about their customer experience, but to truly achieve a comprehensive, 360-degree view, organisations need to employ technology to predict the unseen customer experience.
Business intelligence is key to running a successful organisation, with the actionable insights derived from analysing data driving better business decisions across the board. Organisations can use the same analytical process to predict the unseen customer experience.
Direct customer feedback is not the only form of data important to companies. Experience data can (and should) be collected from all kinds of sources, including indirect feedback such as social media, employee feedback and inferred feedback, such as clickstream data and conversion rates. These are important to track if we are to achieve a truly holistic perception of the customer experience.
Something else to bear in mind is that from the customer’s point of view, any feedback they provide does not simply relate to a one-off issue, but instead represents a snapshot of part of a larger and ongoing relationship. And it is in the interest of companies to encourage this view. Not only does it cost more to acquire new customers than it does to retain them after acquisition, but current customers are more likely to spend more money with an organisation if they’ve interacted with them previously.
Customer churn is another crucial element to evaluate when seeking to optimise the customer experience. It may not be the most positive of metrics, but it would be pointless to try to measure operational success without also seeking to understand instances of failure. Key to doing this is gathering omnichannel data. By relying on only one or two channels of feedback, you limit the accuracy and depth of your customer insights, and risk restricting customer engagement to one-off moments instead of the ongoing journey. However, by gathering data from multiple sources, you can accurately use the information to rectify the situation and provide positive outcomes for your customers.
Training the model
The next step in this process is to combine all these data and use them to predict the holistic customer experience by starting to look for patterns. Operational data can be collated with experiential data to build out a model that locates early indicators of issues such as potential detractors or probable customer churn.
This can be achieved by deploying machine learning to investigate the data to train a model that can perform predictive analytics.
The key thing to think about at this point is data quality. When performing any kind of predictive analytics, if poor quality data is fed into a model, the insights revealed are going to be inaccurate and therefore worthless. This can even be harmful to the customer experience, as the actions that are subsequently taken to attempt to meet perceived customer needs may be inappropriate. In other words, what you put in is what you get out. The data needs to be cleaned to make training the model successful, by feeding it into a structured, high-quality data collection system.
Once it has the internal data, the model can train itself to find patterns in the data that match, for instance, specific customer perception scores that customers have given in direct feedback, e.g. Net Promoter Scores (NPS) in surveys. In a nutshell, it works by using machine learning to create customer profiles, through an analysis of several different parameters, including behaviours, products, interactions, usage and attitude patterns.
Predictive analytics provide accurate scores
These unique customer profiles can then be compared against internal data to predict a virtual NPS score for the large proportion of customers who haven’t provided direct feedback. By using AI and machine learning to forecast these virtual NPS scores, the analysis and subsequent insights are an extremely accurate predictor of future behaviour. With each training cycle, the model retrains itself to identify variables and add to its learning, delivering greater predictive powers each time.
Using predictive analytics delivers a truly holistic view of the overall customer experience, empowering organisations to take a more proactive approach to improving the customer experience. Organisations can make informed, customer-centric business decisions that will address every customer’s needs and reach out to the customers identified as potential detractors, addressing the issue before it has fully manifested, mitigating customer churn and driving revenues.