Customer Experience Measurement – Stand Alone v/s Rate of Change

Customer has become king again and is in a position to demand much more beyond better pricing and service from sellers. Sellers are also obliging in multiple unique ways to differentiate themselves in the eyes of customer. Several experiments are being made and significant dollars are set aside for this purpose. The ultimate motive of this push is to improve revenue from existing streams as well as add new revenue streams for the future. The success of the initiatives is measured in terms of how much the needle has moved.

There are two steps in this process; first step is, to define how we would measure customer experience and second step is, how do we connect this CX measure to Revenue movement. This kind of rigor is relatively new to CX and multiple options are being tried by organizations to connect it to revenue. It is not perfected yet, however learning from each experiment is improving the clarity.

An organization will need to answer these three questions to bring in clarity in their CX program as well as measuring benefits from the same.

1. Should Customer experience be measured as stand alone value or it should be measured in terms of rate of change in the CX measure?
2. Should we measure using Direct Customer Feedback or through Indirect (derived) feedback?
3. Should we use past data or future (predicted) data?

I have analyzed first question in this blog and identified possible way forward for companies.

Stand Alone or Rate of Change:

There are a few popular metrics to measure CX today; prominent being NPS and CSAT. Companies have devised ways to capture the NPS / CSAT score at definite intervals and use the feedback from each survey to create next round of initiatives. Some of the companies have built the necessary rigor in to their processes and collect the measurement at regular frequency. However, they still face some challenges like,

• The score is influenced many times by specific performance just before the survey and does not represent the entire period between two surveys 
• The highest frequency of such survey is Quarterly, but Half yearly or yearly is preferred due to investment of time and efforts in executing one cycle for company and customer
• Chances of bias in selection of respondents. Detractors or customers who did not make a purchase are missed out depriving the comprehensive CX level.

These challenges create limitations in terms of how fast one can respond, how comprehensively one can understand the trend as well as how to measure the actual impact on revenue movement.

If we want to understand the customer experience, how it is getting impacted by a specific initiative as well as how it is impacting revenue with minimal delay, observation of Rate of Change in CX is the answer. It provides us with the direction of impact and extent of impact together, which is critical to take the next course of action. Knowledge about success or failure of the initiative helps organization to make a kill or stop the losses, so faster is better. If the trend of CX is positive and rate of change is increasing, the initiative is working and impact is increasing exponentially. If the rate of change is zero, initiative has no impact on CX and mostly will not have any impact on revenue. Declining rate of change signifies decline in impact and possible revenue reduction.

The model in the adjacent figure depicts the status and revenue impact of a CX initiative through out its lifecycle. Typically, a positive burst in terms of increasing CX is seen at the time of introduction of new initiative and the relative impact reduces as the initiative matures. Slowly the impact is nullified and then ventures into negative territory. Once we map our initiative to the quadrant, we can determine our next steps.

Success of this model depends on frequency of capturing CX measure. Faster the capture, better the results.

With the current metrics used i.e. NPS or CSAT, it is almost impossible to capture the customer feedback at higher frequency due to the nature and mechanism of executing such initiatives. However, with the success of experimentation with AI/ML, certain algorithm-based metrics could be created using the data on inputs and outputs available with the company. This metric value could be calculated on daily basis or segment basis or geo basis to see how it is moving to plot it in the right quadrant.

Ref :    

• 1CX Life cycle management for continuous improvement in Customer Experience and Revenue/Customer https://personalandprofessionalexcellence.blogspot.com/2019/07/cx-life-cycle-management-for-continuous.html
• AAI for early detection of Customer Experience Fatigue and effective extension of CX lifecycle https://personalandprofessionalexcellence.blogspot.com/2019/08/ai-for-early-detection-of-customer.html