The 2018 Customer Interaction Management Landscape: Part 2 - Looking Forward

By Nicolas De Kouchkovsky
7 Aug 2018

We continue our exploration of the key trends shaping up the customer interaction management market. In the first part of this article, we looked at the accelerating transition to the cloud and the impact of digital transformation initiatives. In this second part, I would like to explore four other driving forces.

Self-Service First

One of my thesis, when putting together this industry landscape two years ago, was that self-service would become the preferred starting point for getting service. It has been validated with the rapid development of virtual agents and chatbots. Consumers like the convenience of self-service. The coming of age of Artificial Intelligence (AI) and the viral adoption of messaging apps are enabling conversational experiences. Self-service has become an investment priority for enterprises and the number of virtual customer assistant providers has surged to a whopping 80. The convenience of messaging is transformative for customer service. It offers a near real-time mode of conversation, always in the context of past exchanges. It is mobile friendly. It eventually gives their time back to customers since they no longer need to wait on hold.

Self-service is not only for digital interactions. Interactive Voice Response (IVR) has remained a prominent and yet frustrating channel. AI has not only improved the human-machine dialogue, but also speech recognition. It is now possible to offer much better voice self-service. Options were limited to a few specialized vendors such as [24]7, Interactions, or Nuance. Their number has swollen with several new entrants including CallDesk, Interactive Media, Omilia, SmartAction, and Verbio. The maturing of technologies and this broader set of options is triggering a new modernization cycle for IVRs.

These technologies are still young and irrelevant answers can have a very negative impact. Businesses are finding that assistants and bots work better when focused on specific requests and used together with people. A new assistance model is emerging with specialized bots working together and supported by humans. It changes the role of agents, shifting to assisting the self-service channel and handling high-value moments with customers.

The rise of Automation

If self-service reduces the volume of interactions requiring human assistance, it is also changing their pattern. Live interactions tend to be longer and more complex. In addition, customers have higher service expectations after having attempted to self-help themselves. It is creating new challenges for agents.

Businesses are turning to automation technologies to better support their customer service representatives. Examples include offloading simple tasks using robotic automation, assisting agents with relevant recommendations, or suggesting the next best actions. Robotic Process Automation (RPA) in particular is poised for wider adoption. The technology has made leaps and bounds. Moreover, solutions can be overlaid on top of existing investments with minimal changes. Most RPA vendors have been focused on the back-office market. As a consequence, the education and awareness of the possibilities of robotic automation for customer service have remained low. I expect this to change.

Proliferation of Analytics

There is no shortage of metrics in the customer service and interaction management world. Technology underpins every step of the process and provides detailed tracking of all activities. Nevertheless, customer service practitioners continue to add analytic applications. It is driven by three needs: consolidate end-to-end views, simplify report customization, and translate data into improvement recommendations.

If many Workforce Optimization (WFO) capabilities are now included in contact center software, several other analytics are purchased separately:

  • Performance management uncovers the attributes of top-performing agents. They can then be translated into best practices for training and coaching.
  • The development of the service economy has created a new Customer Success Management (CSM) category. These applications track the usage of products and enable a more proactive approach to service customers.
  • Customer Experience Management (CEM) and Voice of the Customer (VoC) allow enterprises to gather and consolidate customer feedback across all touchpoints and departments.
  • Customer Journey Analytics (CJA) provides the visualization of all the steps customers go through to resolve their service issues. It helps identify critical moments and prioritize the experiences that need to be changed. Many projects are driven outside of the customer service department, most often by marketing.

Enterprises are also building data marts and data lakes to consolidate and unify access to this wealth of data. They layer on top of them data visualization tools from the likes of Looker or Tableau. This approach is gaining a lot of traction within large organizations.

The proliferation of analytics remains overwhelming for many companies. They are not just struggling with the sheer volume of data. Individual metrics are hard to tie back to quantifiable experience improvements or desired outcomes. The progress of speech technologies is stimulating the emergence of a new generation of interaction analytics. Historical providers CallMiner, Nice, and Verint are joined by a plethora of new players including Cogito that just raised $37 million funding round, Gridspace, Tethr, VoiceBase, and Xdroid. AI transforms how data gathered from every interaction can be turned into information. All calls can be transcribed with a high degree of accuracy and voice conversations are no longer a blind spot. Natural Language Processing (NLP) provides insights into voice and digital interactions. AI can eventually be used to uncover patterns and correlations from what used to be a disconnected set of data points.

Artificial Intelligence

AI has made its way into the customer interaction management stack. We already covered several use cases in this article. I consolidated them in a heatmap:

One very promising application leverages AI to find the best agent to handle an interaction. Two flavors were pioneered by Afiniti and Mattersight. Mattersight, in the process of being acquired by NICE, uses speech analytics to associate predefined personality types with customers and agents and match them. Afiniti builds models using information about customers from both commercial and in-house databases to best pair them with the best associate. Genesys also recently introduced its own offering while Avaya struck a strategic partnership with Afiniti to leverage its technology.

The pace of changes has reached an all-time high. It is making the evolution of the market both exciting and captivating.

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