Improving First Contact Resolution – An Analytical Model
In today’s contact centers the concept of First Contact Resolution or FCR for short, is well known. And many understand the value of high FCR scores both in terms of customer satisfaction and loyalty as well as reduced operational costs. Most contact center want to improve on this element, but how? Where do you invest effort to achieve the best results? This case study provides some ideas as to how to tackle improving FCR in a contact center.
Many contact centers do not look at FCR because they do not have a solid and accurate method to measure it. This desire for ‘accuracy’ blinds them to the benefits of acceptable measures. In this case absolute accuracy is not needed and a ‘reasonably accurate’ measurement is good enough. Also, regardless of the level of accuracy, the measurement identifies the movement (improvement or deterioration) correctly.
One way to measure FCR is to ask your customers! Keep in mind that only a small percentage of customers complete surveys (higher percentage if calling). So you need to initiate enough surveys to get a reasonable sample size. At this point you are not looking for a statistically valid sample but a sample that is indicative. A much smaller sample with lower degree of confidence would still be good enough to provide reasonable measurement (of course the sample size relates to the size of the centre and the overall number of contacts). On top of the sampling issue is also the cost of collecting data directly from customers.
Another method, could be using internal data from CRM system (assuming that the organization has employed such system). Using CRM and disposition codes (call/contact types), one can look at the calls coming back from the same customer regarding the same topic in a pre-defined timeframe. The assumption is that these calls are not FCR. The benefit here is that there is a much larger population of data to work with. The shortfall, on the other hand, is the assumption itself. What if a repeat call was coded incorrectly? Or the repeat call came after the defined timeframe? Again, one can argue that even though the results are not 100% accurate, they are good enough to be used in our analysis; and that errors of coding or other exact definitions should wash out over the sample. Remember, when looking for indications and not absolute accuracy, good enough will work.
Regardless of how FCR is measured, the key is the ability to relate results to the associated contacts. In another words the need to know the nature of each contact when customer ranks it as a FCR or not. Having an overall FCR number without knowing which contact / account was or was not an FCR is not helpful. Overall FCR figures will tell you how you are doing, but it is the analysis of the contacts that can tell you how you got there and how to improve your FCR
Let’s stat analyzing the results. Looking back at 3 to 6 months of data, separate the contacts by their disposition codes. It does not matter how many disposition codes are used. Although too many (say more than 30) makes the analysis time consuming. While not having enough (say less than 5) makes for less granular analysis and inadequate details. What is required, is a clear break between the call/contact types so that the contact handling process for each call/contact type and be reviewed.
Now to perform two sets of operations: first calculate the frequency of each contact type as a percentage of the overall contact volume and as a raw number; second tabulate the FCR results and calculate FCR for each of those categories or contact types.
Combination of these will show how much impact each of the call/contact type has on the overall results. Perhaps there is a call type with very low FCR but it won’t matter too much if it is a very infrequent call type. On the other hand, improvement on a highly frequent or volume call type can have a significant improvement on the overall results. The sample chart below presents ‘Frequency’ versus ‘FCR’ for 5 different call types with overall FCR of 83%.
Without this analysis, one might see call type ‘D’ as a prime candidate for improvement since currently it has the lowest FCR. However, when we consider the weight of the frequency, it is call type ‘C’ followed by call type ‘E’ that have the biggest potential!
Of course, we have yet to consider the complexity of the process for each call type. So, let’s dig deeper into each call/contact type by reviewing the contact handling process for each call/contact type. Compare it with the actual results. Then place the calls into several categories. Categories and their definitions can vary from centre to centre but in general the following four are commonly used:
- Achieved – Simply 100% FCR
- Achievable – The contact handling process indicates that it should be 100% FCR but the results are slightly short of 100%. The cause for lack of consistent results could be traced back to quality and can be improved by additional training, coaching and motivating agents (in order to promote higher consistency).
- Potential – The contact handling process does not lend itself to FCR but with minor (or major) adjustments in the process (perhaps with the help of technology and/or additional training) the process has the potential to achieve higher FCR results.
- Non-FCR – The contact type requires multiple contacts which is by design and will not change unless there is a significant change in the contact centre operational philosophy (for example requiring a customer to physically sign a form).
When doing this analysis also go back to the voice of the customer (either through the customer survey or perhaps reviewing the comments within CRM) to pinpoint the point of failure more specifically. Look at improving the processes. Focusing on removing those points of failure. This is where up-to-date and clear process map show their true value. Use the maps to identify the points of failure and minimize the number of steps in any process, call and contact types.
A realistic review of these potential improvements can separate them into short-term (less than 6 months) and long-term (longer than 6 months) based on the complexity and/or difficulty to implement. Extending the review, one can predict how many points of failure will be eliminated in short and long-term. The analysis now can set the target for potential FCR for each call type in short and long-term timeline. Calculate a potential ceiling (maximum percentage of FCR contacts) under current structure. Combining all the individual targets (using the contact frequency) will gives the overall FCR target. Of course, focus any efforts on improving processes to get the highest/quickest benefits.
It is noteworthy that certain improvements (such as IVR self-service) changes the distribution of calls and could impact the overall results negatively if FCR is only measured for live contacts. Maybe automating easy contacts with high FCR, leaves the low FCR as part of the live contacts. A common error and observation in these exercise is to only focus on live contacts. IVR is part of the customer experience and the overall FCR. Ensure that the IVR answered calls are part of the overall report. How many of us dislike the IVR / Auto Attendant experience and like live agent contacts? This too is an important point to surface. Many a firm has automated to reduce costs but reduced the customer experience at the same time and ending up with lost brand loyalty.
The above analysis proves to be effective in providing a road map in improving FCR. The improvement is almost immediate and consistent. The analysis also gives organization reasons to think about their overall operational philosophy as what should be the overall customer experience.
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