What does an analytics-driven contact centre look like?
What are the foundational elements of the latest, analytics-driven contact centres - and how are they improving customer experiences?
Businesses often talk about identifying customer pain points and call centres are a clear opportunity as most customers still view them with dread. The best organisations recognise this as a chance to proactively differentiate themselves from the competition - but they cannot get there without advanced analytics.
To put the customer first, customer service managers require accurate and detailed performance information that real-time analytics can provide. The good news is that basic data and analytics tools are becoming standard practice in call centres. However, most organisations are not taking full advantage of the technology, meaning they are not applying advanced analytics in ways that truly put the customer first.
There is a real difference between first-generation data and analytics already in place at many companies and the advanced analytics techniques and methodologies now available. Unlike earlier data and analytics solutions, which helped businesses understand what is currently happening within their call centres, advanced analytics help them generate actionable insights about what will happen next, through both internal and customer-facing applications.
Companies that have already applied advanced analytics have reduced average handle time by up to 40 percent, increased self-service containment rates by 5 to 20 percent, cut employee costs by up to £4 million, and boosted the conversion rate on service-to-sales calls by nearly 50 percent—all while improving customer satisfaction and employee engagement. While analytics is only one of a broader set of improvements, including operational changes such as coaching and process simplification, it is a powerful tool for companies to implement.
So, what should businesses consider when it comes to implementing new tools and ensuring the right foundations are in place to make the most of their data and improve call centre customer satisfaction?
Traits of the analytics-driven contact centre
There is a large and growing pool of available vendors and technologies for contact centres that are generating a lot of data but struggle to make sense of that data. It is important for contact centres to build the right foundation if they are to generate the maximum potential benefits. Those foundational elements include:
- A clear vision and strategy: Contact- centre organisations need a coherent, enterprise-wide vision for analytics. That vision must have a clear link to the overall business strategy, along with a road map for implementing specific use cases, such as improving FCR or offering more self-service options to reduce the demand on call centres.
- An agile organisation with internal analytics capabilities: Companies need to build strong in-house talent capabilities in analytics that align with the organisation’s strategic goals. And, companies need agile mechanisms to capitalize on analytics-driven insights. For example, a leading credit card company has set up an interactive voice response (IVR) analytics lab that allows it to immediately assess changes in customer satisfaction and containment after every change in the IVR.
- Platforms and data sources: Leading organisations also require a comprehensive data strategy and ecosystem that can support the broader analytics strategy. Platforms and data sources call for best-in-class data governance, data or IT architecture, and infrastructure and data security frameworks. Many top-performing contact centres have built data lakes as a single source of all data on customers, agents, product performance, surveys, and other sources.
- An ecosystem of partners: Few companies can meet all of their data and analytics needs internally. Rather, they must determine which needs can be handled in-house and which should be outsourced to expert partners.
- A culture of objective decision-making: Leading call-centre organisations make their day-to-day decisions based on data, rather than gut instincts. Examples include analytically driven hiring, targeted coaching, performance-based bonuses, and other initiatives to improve outcomes.
How to get started
To begin, companies can identify the potential value pools from an analytics initiative and prioritise them based on measures such as the payoff relative to required effort, data availability, customer demand, and competitors’ moves, among other things.
Prioritising will help a company focus on a specific use case—for example, improving first-call resolution (FCR) by 20 percent at a particular call- centre site—and map the data requirements it will need, such as agent notes, voice-of-the-customer information, routing data, and automatic call distributor information. Most organisations will not have perfect data, but that should not be an excuse for a lack of action. Rather, organisations should begin by working with whatever they have and refine their data over time.
With the goal of using advanced analytics to improve FCR performance, a company can begin to generate hypotheses—for example, calls may be routed to the wrong queues—and then analyse the data to prove or disprove each hypothesis. Next, it can build an analytics model and test it with users, gauging results and refining the model based on user feedback (in close collaboration with IT and the line organisation).
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Finally—and most importantly—a company can scale up successful pilot tests across the entire call- centre organisation to maximise their potential impact. This rollout requires working with managers at other sites, applying lessons learned, and—when possible—automating analytics use cases to improve efficiency.
Operating in the rapidly changing field of analytics – don’t wait
Building the right foundation is crucial if call-centre organisations are to generate the biggest benefit from advanced analytics, but companies should not wait until all of these elements are in place.
On the contrary, analytics is a rapidly changing field, and organisations must start applying advanced analytics tools and techniques right away and learn through experience. Implementing these new tools, companies can more accurately predict what’s coming—allowing them to literally control their own future.
*The author would like to thank Guy Benjamin, Avinash Chandra Das, and Vinay Gupta for their contributions to this article.