Contact Centre Analytics Solutions: Six Ways Analytics Impact Business Outcomes

Overall contact centre performance across major KPIs, such as quality score, average handle time, and contact resolution rates all suffer when a company doesn't have a clear view of the full customer journey. And yet, fewer than 10% of companies have a 360-degree view of their customers, and only about 5% are able to use this view to systemically grow their businesses. As a result, when looking at ways to improve customer experiences and optimise call centre operations, better understanding and utilisation of contact centre analytics and call centre analytics is a great place to start.

This was originally posted as an article in the Customer Strategist Journal.
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Of course turning data into meaningful customer analytics is easier said than done. Not to worry. This article provides best practices and next steps so your organisation can better utilise your contact centre analytics to positively impact business outcomes. We'll provide a brief overview of some of the main types of analytics, outline key strategic first steps in how to think about, organise, and operationalise the call centre analytics that arrive into your centres every day, and dive into six ways that contact centre analytics software can provide improved customer experiences, customer loyalty, and centre operations.

To maximise your contact centre analytics requires looking at many types of customer and centre data

As the number of customer engagement channels grows so too has contact centre volume, and it has become even harder to maintain a consistent experience across interaction touchpoints. These poor customer experiences and channel breakdowns can seriously impact churn and in turn, revenue, so it is critical that your company better connect the dots between channels in order to improve business results. For example, in the infographic we've highlighted some key research findings that show how much contact centre analytics can positively impact business outcomes, while also outlining just how few companies are actually maximising the value customer analytics can provide.

Before we discuss how analytics impacts business outcomes by helping you better understand your customers and increase customer loyalty and lifetime value, it is important to first define three general categories of analytics. In isolation, each of these provides useful insights, but when joined together, like a collection of puzzle pieces, they form an invaluable picture of the entire customer journey and provide actionable insights on how to improve call centre operations and customer experiences.

What are Contact Centre Analytics?

Contact centre Analytics are the analysis of standard contact centre metrics to identify trends, impacts, causes and results.

What are Customer Analytics?

Customer Analytics is the analysis of multiple customer-related data sources to identify customer trends, interaction opportunities, and to serve as a source for modeling. They can be historical or predictive. Data sources include voice of the customer, behavior data, demographics, and purchase data.

What are Speech Analytics?

Speech Analytics are the analysis of transcribed voice and ingested text engagements along with metadata including CRM and notes to identify trends, voice-of-the-customer insights, performance drivers, and other insights.

Want Actionable 360-Degree Customer Views? Start with these best practices

With the massive volumes of data out there, organisations have an opportunity (and challenge) to create a 360-degree view of the customer that produces actionable insights and decisions. If this seems like a daunting task—it is—but our call centre business intelligence consulting experts have developed some best practices for simplifying customer data into meaningful customer insights.

Don't mistake the part for the whole

For many businesses, data is both the key to important insights and an obstacle to getting answers. While there isn’t an easy way to connect disparate data, call centre analytics software that incorporate both structured and unstructured in the centre environment can go a long way in helping employees present information and make data-driven decisions.

Have a partner fill in the gaps

Even companies that are rich in data and analytics may need help optimising their insights or fixing a data blind spot. Customer centric, call centre analytics companies can help aggregate digital and offline customer data into a common architecture and gather a 360-degree view of customer needs, behaviors, and preferences.

Less is more

How do you decide which insights to pursue and when? Instead of setting a broad goal for better insights, remember that less is more. To figure out which types of insights to pursue first use a prioritisation framework to evaluate your needs. Questions to consider include: What types of insights does your team hope to gain? Which ones are likely to deliver immediate versus future results? What data/channels/sources are available and what holes need to be filled? Answering these questions early on will make it easier to stay focused on the plan.

Make sure employees are ready to act on the insights

Insights gained through analytics are only useful if they’re deployed and implemented. But how many employees are ready to regularly embed analysis, data, and evidence-based reasoning into their decision-making processes? Data-driven insights and decision-making can create better customer experiences that differentiate a brand but only if the right strategy, technology, and people are in place.

Smarter call centre and contact centre analytics equals smarter business

Analytics tools hold the key to working smarter and having real impact across channels. Big data gathered during a service interaction can offer incredible insights to the rest of the organisation. This business intelligence allows companies to understand their customers more clearly and influence proactive and predictive customer strategies. We believe there are six key principles—driven by technology-enabled analytics—that can guide contact centre leaders in their organisational decision making:

1. Get to the root of the interaction

When customers contact your company, even if for apparently mundane reasons, they become a valuable source of information about your business and operations. Most contact centres desire deflection of such contacts to low-cost channels and do not necessarily pay attention to root causes. Root cause analytics can highlight fundamental issues and inform solutions that if implemented can reduce overall interaction volume, call times, and significantly increase customer satisfaction.

Root cause analytics may reveal the need to make changes outside the contact centre, such as coordinating marketing communications across channels, redesigning bill layout, making Web self-service more intelligent, and issuing proactive alerts.

2. Self-service can, and should be, intelligent

Both companies and customers desire self-service tools. They exist as FAQs, searchable knowledgebases, or even avatars, and they can help lower hold times and abandonment rates by letting customers explore topics and find their own answers.. However, many self-service options powered by artificial intelligence are actually dumb-—they do not provide the right answers or provide too many possible answers, leaving customers frustrated. Advances in text analytics and speech analytics software and machine learning can help self-service tools to select highly accurate responses to customer inquiries. These are highly automated systems that are self-learning, so accuracy improves over time, as the amount of historical data increases. The one pre-requisite for these systems is a rich knowledgebase. But when implemented effectively, intelligent self-service is a great deflection strategy by reducing the number of calls.

3. Empower and transform your front line contact centre associates

Too often call centre agents have high attrition. They work in a high-stress environment and are treated only as a cost by companies, and as a result, agent performance suffers. Instead, contact centre associates should be viewed as employees who can earn the trust and loyalty of customers during critical moments of truth when customers expend a lot of emotional energy. Success during these types of calls or interactions can drive significant positive business outcomes.

Examples of these moments could include events like a missed flight to attend an important meeting, a lost credit card when traveling overseas, or a stalled vehicle late at night in an unfamiliar area. During these moments, smart technological solutions alone won't cut it, because complex solutions are required, and because centre technology alone cannot create an emotional bond. However, emotionally intelligent frontline employees, empowered with the appropriate technology and data at their fingertips, can excel in these situations and create sustainable differentiation. Take the time to determine who within your organisation has the emotional intelligence to succeed as brand stewards during the most fragile service interactions.

4. Discover the right channels for customer interactions

Many companies have begun to right channel contact centre interactions, or assign specific interactions to specific channels. But analytics tools are required to make the correct workflow decisions in routing a customer call. It is instructive to look at four factors: customer channel preference, customer profiles and behaviors (commonly available in transactional and other data), interaction complexity (e.g., interactions that require significant diagnostics to solve), and moments of truth, where the outcome will have significant implications for customer retention and customer loyalty.

Only very high complexity interactions that occur during extremely critical moments of truth should be handled by frontline live employees, with the rest moved to other low cost channels like self-service, chat, or SMS.

5. Follow and inform the customer journey across channels

Today's call centre operations are siloed by channels, and sometimes even within a channel. While sophisticated omnichannel contact centres with robust channel orchestration provide a better view of the full customer journey, they also often lack the centre solutions needed to analyse data as effectively and quickly as needed. Without powerful contact centre analytics software in place, companies miss out not only on successfully resolving customers current needs and questions, but also on anticipating their future needs and next questions. Many senior leaders understand the problem and want to turn the multichannel experience into a true omnichannel solution, but are often limited by behemoth legacy infrastructures supporting their centre environments. This makes getting a holistic view of all relevant contact centre analytics difficult.

Chart showing how predictive contact centre analytics optimised call deflection and improved NPS scores

Companies can enable a scalable, massively parallel processing, cloud contact centre analytics software solution that includes three advanced customer insight capabilities.

To make the most of this solution, first companies must enable real-time and batch data ingestion (both structured and unstructured) from multiple channels, touchpoints, and enterprise systems like CRM. This will facilitate a live customer profile and context as customers move along their journey with the company.

Next, get predictive. Analytics-enabled recommenders can implement a multitude of analytic engines, such as interaction reason prediction, sense and respond, next interaction predictor, or next best offer optimiser. These tools are designed to provide the best response in the best channel and takes into account context, customer behavior and history, customer preference, and issue complexity.

Finally, make sure channels are connected. Use channel connectors that seamlessly connect via open APIs/custom integration with various channels to orchestrate response. This applies to self-service channels as well as live channels where the actions may be orchestrated in the IVR or at the associate's desktop. And be sure to allot for future experience channels, to be as nimble and flexible to changing customer behaviors.

6. Pre-empt customer interactions with contact centre predictive analytics

Most customer service interactions are inbound. So what if we were able to pre-empt them and solve the issue before the customer reaches out to interact? This would not only reduce interaction volume, but also delight customers. Predictive analytics can help. They can be used to predict interaction reasons even before customer and agent interactions happen.

For example, if contact centre analytics showed that customers who had Problem X (e.g., install issues with Internet service) also tended to have Problem Y (e.g., wireless network setup issues), then a customer who reached out with Problem X would be prompted to check and solve for Problem Y during the same interaction, thereby reducing repeat call incidence, improving FCR, and improving customer satisfaction. Or, a company that monitors its self-service interactions can identify an issue alert customers who may be experiencing similar issues, even if they haven't contacted the company about the problem.

Next gen call centre analytics at work

Recently, a large telecommunications provider wanted to increase call deflection and improve first call resolution (FCR). We analysed its call volume, taking into account both where customers were in their lifecycle with the firm, as well as their value to the company as determined by product mix owned. This analysis led to several recommendations that would lead to a reduction in total interaction volume, a deflection of interactions to low or no cost channels, and a 50 percent overall estimated annual cost reduction. In addition, an analytic engine designed to predict likelihood of a repeat call was developed, thereby enabling an improvement in FCR.

And a leading automaker's contact centre was exhibiting deteriorating NPS performance, which had been identified as the key metric to influence. Data collected from customer surveys determined which customer experience improvements would lead to a significant boost in NPS. Predictive analytics identified prioritised improvements that would lead to an NPS improvement from – 29 to +70 (see the above chart).

Conclusion: Contact centre analytics solutions can supercharge your business operations

Contact centres today are ready to lead a paradigm shift and drive transformative customer experiences for companies and customers. TTEC's Call centre and Contact centre Analytics team can help you establish a strategic approach to analytics in order to get there.