How to Make Your Call Center More Efficient with Data and Analytics

The call/contact center plays a vital role in the customer experience journey, yet it’s often overlooked from a budgetary perspective. Call center leaders are constantly expected to do more with fewer resources. 

When resources thin, quality almost always takes a heavy hit, leading to skyrocketing overtime costs, agent turnover, and worse of all, customer churn.

Luckily for the industry, leveraging data and analytics has a consistent track record of helping call center operations improve efficiency without exhausting budget and resources.

While this blog is not going to solve all of your call center’s challenges, it will point you in the right direction of how your organization can better use data and analytics to become more efficient.

How Data & Analytics Improve Call Center Efficiency

Call centers generate enormous amounts of data but very few can wield it properly to improve outcomes. This is mostly due to all that data living in separate (siloed) places. Held back by data silos, legacy data and analytics infrastructure, call center agents and leaders are not able to get the full picture of a customer’s experience. Without data-backed insights, so many golden (efficiency-boosting) opportunities are left at the table.

Luckily for you, we’re here to help you uncover a few of those golden opportunities by walking through three common areas where data and analytics can have a major impact on efficiency gains (AKA, doing more with less).

Capacity Planning

Staffing the ideal number of agents at the right time (with the right training) is one of the largest opportunities for call center operations to boost efficiency, yet so many are not able to do this effectively. The challenge is that it’s really hard to anticipate call volume (including spikes in demand) without relevant data. Scheduling software helps but it doesn’t go deep enough due to siloed data. 

Ideal Analytics Environment 

Ideally, analytics should flow into iterative capacity planning processes that routinely drive  strategic decisions that better anticipate and address risk before results are impacted. By leveraging historical data, you can get a clearer look into the root causes of service excesses and deficits, and in turn, shape the accuracy and effectiveness of planning for future weeks and months.

Efficiency Goal: Staffing hits targeted occupancy levels that balance cost with results.

Bonus Freebie: Call Center Capacity and Service Analytics Tableau Dashboard

We put together this free Tableau dashboard to help you get better insight into the service performance of your call center, its capacity and demand requirements over time, and any level of detail from the overall call center down to individual teams within it.

Call Quality and Outcomes

It’s estimated that 30 percent of customers will need to interact with a call center representative each year through 2024. To your customers, the call center agent(s) that they interact with is their best representation of your company. Each of those call or chat interactions is nestled at the front lines for driving customer retention and influencing NPS (Net Promoter Score). 

In a world where nearly 74 percent of consumers will switch providers after a single poor customer experience, arming every rep with the tools and data to provide an exceptional customer experience is a must.

Contact center operations need a clear picture of how well customers are served once the contact is handled. Metrics like first call resolution, post-call survey results, post-contact account actions, and general call quality metrics help draw a line to NPS results, which loop back to contact rate drivers.  

Poor call quality will in turn drive additional call volume and increase staffing requirements as well as customers making more calls to get the outcomes they are seeking.

Ideal Analytics Environment 

Analytics that promote better customer outcomes will focus on extracting the highest value from interaction data to draw a clear line from upstream drivers. Effective platform integration strategies are critical here, as that interaction data needs to be enriched with context from IVR, ACD, CRM, and survey/CSAT data. This will clarify what factors are influencing outcomes and highlight potential outliers and opportunities for process improvement.

Efficiency Goal: Lower customer churn and contact rates, support higher NPS and engagement, and lower the cost for the contact center to deliver those outcomes the first time.

Digital Transformation

It’s no secret that customers prefer digital channels vs. traditional, phone-based contact methods. This presents unique opportunities for creative solutions in workforce optimization, as well as challenges in maintaining high quality and favorable outcomes from those digital channel interactions.  

Chat interactions remove the need for digital transcription, making sentiment analysis and other Natural Language Processing (NLP) models more accessible for managing quality and outcomes. However, the nature of chat makes the perception of wait time more complex to understand and manage effectively. On a phone call, customers have high sensitivity to wait times, and responsiveness is considered a “must-have.” 

With chat, customers feel “on hold” after a question they type, and each reply now has its own response time to measure. There may also be an observed shift in overall handle time and resolution rate across channels, as customers with more difficult or complex issues may opt to call rather than chat. 

This leaves the phone lines as a reliably critical channel to support and increases the challenge of converting those calls into positive interactions as the “easy-wins” stay in digital channels.

Ideal Analytics Environment 

Effective multi-channel service solutions will provide an even deeper look into metrics like service level and occupancy than a voice-primary staffing model allows. Measures for customer engagement and satisfaction in digital or cross-channel interactions should look at how the employee staffing model supports the customer-facing experience for both front-side queuing and mid-interaction response waits. This will facilitate improved and more consistent outcomes for your customers.

Efficiency Goal: Create a model that leverages the best of digital and traditional channels to meet the needs of the customer.

Conclusion

Call center leaders have so much to gain by transforming their call center data into efficiency-boosting insights. From optimizing staffing to perfecting call outcomes, data and analytics are the keys to the future. 

If any of the topics covered in this blog interest you, feel free to reach out! At phData, we have a team of honest and experienced data and analytics professionals who genuinely love helping call/contact center leaders better leverage data to make more informed decisions. 

FAQs

Improving the customer experience means that call center reps and leaders have access to enriched, accurate, and relevant data at their fingertips to give a consistent and personalized experience for every customer. In order to build a center of excellence for call center operations, you need the right tools to promote healthy and effective analytics capabilities.

Call center operation teams need to invest in three critical components, having a single source of truth for their data (cloud-based data storage), achieving data interoperability, and deploying actionable dashboards. When all three of these elements are achieved, call center leaders are equipped with information from various sources and can personalize the customer experience. 

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