Contact Center Expertise X Target Operating Model

Challenge is to find optimal change in contact center timings to cushion negative impact on contact center metrics and subsequently its impact on customer experience score while maximizing cost efficiencies.

EXL’s client, a Big Six UK energy provider, faced immense competition from a growing list of digital-first startups. These new entrants threated to encroach on the company’s market share, putting pressure on the client to actively search for ways to become more agile, with a focus on serving customers through digital solutions.

The client wanted to transform their contact center operations to reduce operating time and achieve cost efficiencies. This implied meeting excess customer demand through digital channels. Other implications of implementing this strategy were:

  • Customers driven away from their preferred contact channel likely to drive down Net Promoter score (NPS)
  • Deterioration of contact center (CC) metrics like call abandoned rate and average handling time and first-call resolution
  • Augmentation need for digital channels to resolve customer queries

To achieve the objective of finding optimal call center operating hours, EXL leveraged its analytics capability to formulate multiple scenarios to understand the impact on overall customer lifetime value.

Context

EXL’s in-depth knowledge of contact center operations coupled with analytics interventions to improve customer experience helped the client to formulate hypotheses, understand the impact of operational changes on customer experience, and recommend a strategy to smoothly roll out the call center operating hour changes with minimal disruption.

Orchestration

Optimal time to operate contact center will depend on multiple factors including peak operating CC demand, percent of calls deflected to online channels, workforce savings, impact of call deflection on CC and web NPS, and target cost savings. EXL looked into all these parameters to devise a comprehensive solution. Detailed process as below:

i. Impact on operational parameters:

Reducing contact center operating hours would mean an increase in demand for contact center during working hours. This would negatively affect CC metrics like average speed to answer (ASA), abandoned call rates, first call resolutions (FCR), call transfers, and average handling time (AHT).

The objective was to minimize the impact of changing CC hours on the above parameters. The team developed models to calculate the correlation between agent availability dependent on contact center timing and the above metrics. For example, this looked at how reducing operating times by one hour would lead to an increase in average speed to answer by five minutes keeping other factors constant. This exercise was carried out for all the possible inputs for contact center timings.

ii. Contact center customer satisfaction rating:

One of the crucial metric to gauge contact center performance is net promoter score, a reliable indicator for tracking customer satisfaction.

All of the operational parameters defined in the previous step would have some impact on NPS. For instance, a higher FCR would likely increase NPS.

Optimizing contact center operations


These parameters served as inputs to an advanced NPS simulator models to find out the core drivers with maximum impact on NPS change. The team calculated NPS scores for all possible operational parameter values driven from previous step.

iii. Performance of digital channels:

Reducing contact center operation times would mean a shift in demand to other channels. In this scenario, customers migrated to digital channels to get their queries resolved. The team calculated this potential excess demand through simulations on historical data.

Since some customers preferred interacting through contact centers and would be forced to move online, customer satisfaction ratings were likely to decline.

Three scenarios were created for these customers:

  • Best case scenario: All customers shift ed to digital channels had a good experience
  • As-is scenario: Equal mix of good and bad customer experiences through digital and through the contact center
  • Worst case scenario: All customers shift ed to digital channels had a bad experience

Digital channel NPS was calculated for all of the above scenarios.

iv. Optimization simulations:

Changing contact center times aff ected operational parameters, which in turn aff ected CC NPS. Further, excess demand shift ing to digital channels would also aff ect web NPS. Each point drop in NPS leads to lower upsell and cross-sell opportunities as well as higher churn. This would subsequently reduce overall customer lifetime value. This served as input for one side of the optimization equation. The goal was to minimize the overall NPS drop and prevent reduced customer lifetime value.

The last parameter to fit into the optimization equation was cost reduction achieved through reducing contact center timing. The goal was to maximize the cost reduction.

The team developed an optimization algorithm to maximize customer lifetime value through minimum reduction in NPS along with maximizing cost reduction for all possible scenarios of contact center timings.

Outcomes

Leveraging EXL’s analytics capability coupled with domain expertise in contact center operations, the client was able to implement the optimal contact center timings and make the best use of business resources by establishing a perfect balance between operational challenges and customer experience. This would bring about optimal cost reduction while maintaining brand value. The overall clientachieved NPS drop improvement was two points, with cost eff iciencies of £100K per month.

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