Smarter Closed Loop Feedback

This white paper explores how to optimise the design of Enterprise Feedback Management and Voice of the Customer programmes to maximise the ROI and reduce customer complaints and churn.

Smarter Closed Loop Feedback

Many companies now deploy real-time Voice of the Customer or Enterprise Feedback Management (EFM) programmes. This enables them to generate timely flags or ‘hot alerts’ when a customer experiences a poor level of service – we call these Critical Incidents.

Closed Loop Feedback or ‘Closing the Loop’ is when organisations enable their staff to contact a customer following a critical incident and take appropriate action. But customer facing staff are rarely given good guidance about which actions are the most appropriate. The economics of Closed Loop Feedback has not been properly addressed:
Maximise-your-ROI

Do we need to contact every single customer, every time they give a low score? When is an apology good enough? When should some form of compensation be offered?

Ineffective interventions lead to wasted efforts and resources, extra workloads for staff and increased costs. Companies need to work out how to optimise the investments they make in ‘Closing the Loop’ in a way that maximises the return on that investment – returns in the form of avoiding customer defection and reducing negative word-of-mouth.

Ipsos conducted a study in the USA evaluating over 10,000 critical incidents across seven different industries. The key findings from this study are:

Customer experience matters

Nearly 2/3 of customers said that personal experience influenced their decisions about which brands to choose. This was more than twice as high as most other ‘influences’ (e.g. family and friends, social media, etc.).

Many had negative experiences

30% of customers reported a negative critical incident. This was closer to 50% for cable and telecommunications and around 20% for hotels, automotive and bank branches.

Critical incidents impact behaviour

52% of people who had a negative incident say that they told friends, family or colleagues about this bad experience. 50% contacted the company to complain. 24% state that they started to use the brand less or stopped using it.

Companies need to do more to resolve customer issues

In over 1 in 3 cases companies are not even aware of a complaint or negative critical incident, meaning that they do not have the right systems in place to capture customer feedback following an interaction.
About 50% of customers who experienced a negative critical incident are dissatisfied with how the issue was resolved.

Different types of critical incidents warrant different interventions

Not all critical incidents are equal. Companies need to discriminate between different types of incidents and prioritise interventions based on likely impact. They also need to account for customer value, customer profile and past transaction history.

The customer: company effort ratio is more important than the customer effort score

One of the new ‘buzz metrics’ is the CES or Customer Effort Score - this is the amount of effort a customer says they have to make to deal with a particular issue. But this does not take into account the amount of effort the customer thinks the company has made. We found that the CES in isolation is not enough. The Customer: Company Effort Ratio has a much stronger relationship with a customer’s likelihood to continue to use that brand.

Algorithms built into systems can improve ROI

Algorithms based on a combination of factors, such as type of critical incident, customer profile/value or transaction history, can then be created to help companies deploy intelligent Closed Loop systems that can suggest the most appropriate response in any given situation.

In summary, there is still a lot to improve upon in the nascent EFM industry. In particular, the economics of Closed Loop Feedback has not been properly addressed. Companies need to work out how to optimise the investments they make in ‘Closing the Loop’ – in a way that maximises the return on that investment. This requires a greater understanding of which interventions are appropriate for different situations, enabling a suggestive algorithm to be built into EFM systems.

Customer Experience