How analytics and machine learning are transforming the modern call centre

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Call centres have been around for a good few years now, and have had their fair share of bad press. Most of us have horror stories about the length of time spent on hold, or our inability to speak to the right person or get a helpful response, or even just the unavoidable canned music. But views on the roles of call centres are now changing. Many companies have come to see that they may be key to managing customer experience more effectively.

Enter ‘contact centres’

Perhaps the most obvious sign of change is that call centres are now often known as ‘contact centres’. This reflects the increasing use of text- and messaging-based interactions with customers, alongside the more traditional, but still very popular, telephone contact. It also reflects the move towards consideration of the ‘customer journey’, rather than individual channels of communication: Customers do not view interactions as ‘different’ via website or phone, so neither should companies.

There is increasing recognition that contact centres have multiple roles, including pre-sales, sales, complaints, and post-sales services. Each of these needs to be handled slightly differently, which means that contact centre agents have one of the most challenging jobs around. It is perhaps best to view this as involving a cycle of meeting customer needs and resolving problems.

Getting it right - at the right time

For example, in handling complaints, contact centre agents need to get to the bottom of customers’ displeasure: Finding the ‘root cause’ of the problem, whether that is an issue with the product, or an inability to find the necessary information. They need to be able to pinpoint and resolve issues that lead to inefficiencies, both on the customer journey, and in the contact centre more generally. They also need to be able to target offers at the right customer and at the right time. There is no question that when contact centre agents get it right — when, in essence, they do their job better — customers will be happier, and happy customers are more likely to come back.

This is the part where the story gets a bit challenging. Despite state of art infrastructure and software to run it, the wealth of data available in the contact centre is still mostly untapped from an analytical perspective and rarely go beyond the intelligence of providing some basic operational & KPI reports – based on agent populated data.

Enter machine learning & natural language processing

But how can contact centres be improved? Many companies are finding that analytics, artificial intelligence and chatbots are a major part of the answer. These technologies are having both a direct and indirect impact on the way that contact centres operate.

One of the biggest complaints of many customers is that they have to wait too long to speak to someone at the contact centre. Companies have made huge efforts to direct customers towards self-service channels such as websites, but research from Forrester confirms that around three quarters of customers still prefer to phone. This is particularly true for financial services, where queries can be complicated. Companies in this sector have already started to deploy chatbots to deal with the easier queries, such as password-resetting, to reduce customer wait-times. Real-time analytics allow human operators to work in partnership with the chatbots, and step in quickly as soon as the conversation gets too complex.

Real-time analytics and AI

Artificial intelligence and analytics are also being used to improve the way that operators relate to customers. For example, voice, speech and text analytics allow interactions to be analysed in real-time, so that ‘coaching’ suggestions can be passed immediately to staff, to change the way that they are interacting with customers at the time. This is a huge change from recording calls and playing them back later, as a way to provide effective feedback to staff. Technology that can sense behavioural signals and emotions can be particularly helpful in improving empathy. Getting the ‘tone’ right will improve that customer’s experience, and so result in fewer complaints. It is also likely to reduce the length of calls, because customers’ issues will be resolved more readily, resulting in shorter customer wait times.

Contact centre analytics, particularly text analysis and data mining techniques, can also provide useful information about customer churn, competitive threats, and refusal or purchase reasons. This information, in turn, can be used to improve the customer offer, both individually and overall, and therefore make customer experience better. This creates a ‘virtuous circle’ of using information to drive improvement.

The heart of customer experience

Ultimately, contact centres are at the centre of the customer experience. Recognising that, and acting on it, is likely to improve customer satisfaction. Equally importantly, as a result of its position at the heart of the operation, data from contact centres has a huge role to play in improving products and services. The role of the modern call centre has very definitely changed, and the impact is huge.

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About Author

Yigit Karabag

Regional Director, Business Solutions - Middle East & Eastern Europe

Yigit is the director of presales for SAS Middle East. He has 15 years of domain experience in enterprise level information management solutions and has been involved in large scale projects dealing with structured and unstructured data across the banking, telco and public sectors respectively.
Yigit is also a board member for various data management organizations and a regular speaker at data management & data governance events. He holds a degree in Computer technology and Programming from Bilkent University in Ankara, Turkey. Aside from being a fan of sci-fi authors such as Isaac Asimov, Philip K. Dick, he is also a musician and an avid model train builder.

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2 Comments

  1. Creating Leads from Inbound call data is always straight selling. As customer already showed his interest in the product line.

    The challenge start with Out Bound calls and Qualifying the right leads to customer. On top of it reaching to the right customer and right authorized person another constraint. Surely the inbound called data helps you to some extend to profile your customers and later use these details for future contacts.

    Hope to hear from you more on Qualifying right leads through Data Analytics.

    • Yigit Karabag
      Yigit Karabag on

      Hi Humayun,

      You are spot on about the inbound lead qualification. We are already at early stages of exploring this with some large organizations, not only to analytically qualify the leads but also to assign them to the best matching operator to maximize the chances for the right business outcome

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