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Building a Future-Proof CX Software Stack? Consider This


Lego Think It, Build It Campaign by Tina Kuczaj
Image: Lego Think It, Build It Campaign by Tina Kuczaj (Illustrator)
I’ve previously discussed how the customer experience (CX) imperative has been evolving for businesses. Now, CX is almost always interwoven with other digital transformation initiatives, both leveraging and depending on technology. For large enterprises and other companies, this means architecting a cohesive software stack, coupled with the transition to the cloud.
When it comes to building these stacks, these six important topics should be on your drawing board.
Rethink omnichannel
While omnichannel has been part of most projects for the past several years, practitioners continuously face a challenge to make the expected return on investment (ROI). If we look at the initiatives undertaken, most primarily focus on adding new channels — offering the choice to customers. Indeed, Gartner found that the average number of channels offered for customer service jumped from 3.7 in 2014 to 5.4 in 2018. However, it also found a few worrisome things we can relate to as consumers. There is an unfortunate correlation between the number of available channels and the number of contacts required for resolution. The more offered, the more interactions are needed to get things done. While technology is available to carry the context from one to another, it creates frictions and elongates resolution. Additionally, research shows that the breadth of channels offered isn’t moving the needle on customer satisfaction.
Shifting our focus to effortless resolutions means guiding the customer to the right channel for the job to be done. Consumers are very open to such recommendations. In the absence of directions, it is not uncommon that people try several channels simultaneously.
Enable continuous experiences
In the omnichannel world that we live in, consumers embrace messaging for its compelling conversation style of communication. It’s asynchronous, no longer requiring customers to wait on hold or for a response while an agent is looking for information. It also gives customer service representatives more time to handle tasks more effectively, and provides a persistent context allowing bots and humans to collaborate.
This model isn’t limited to messaging apps — it’s also available for webchat and mobile applications, and email. Solutions are now capable of delivering the messaging experience across multiple channels, and messaging should be implemented as a mode of communication rather than a specific channel to enable continuous experiences.
Build an automation framework
The sheer increase in interaction volume has made automation inevitable; this isn’t necessarily a bad thing — with artificial intelligence (AI) and bots coming of age. Moreover, consumers like to do things by themselves when exploring their options. While I’m bullish about these technologies, we don’t want to get ahead of ourselves. Full automation remains limited to simple interactions. Most start with self-service and requires an escalation to an agent or some further human-driven steps to be completed.
Enterprises need to build a framework for allowing customers or agents to leverage different automation technologies such as conversational AI, speech, biometrics, robotic, or low-code automation. The framework should let customers or agents harness them for either automation or assistance. It should eventually combine humans and robots for end-to-end resolution and fulfillment of customer service requests.
Create a context store
Assembling a 360-degree customer context has been the industry Holy Grail. It had been the promise of CRM for many years…until Salesforce acquired MuleSoft, conceding that another piece of software was needed to pull customer information from different clouds and applications. Contact center vendors have also been building up their customer interaction history repositories. A few new entrants have even created their contact center solution around a customer information and context repository. For example, Zendesk offers Sunshine, an open-source middleware option that lets you create a data structure tailored to customer experiences. Low-code software vendors have baked the ability to create customer profiles into their platforms. Eventually, in the marketing adjacency, we are seeing the formation of a new category — customer data platforms — aiming at a similar goal focused on serving relevant ads.
There are many options to choose from, but no out-of-the-box solution. Enterprises should pick a foundation on which to create their own customer context store.
Enable employee engagement
Gallup’s latest State of the American Workplace Report found that the top 25% companies with the most engaged employees enjoy 24% to 59% lower turnover, 17% higher productivity, and 10% increase in customer metrics. The changing profile of the workforce is turning it into an imperative.
Building engagement has three technology ramifications. First, it requires a consistent way to gather employee feedback. Second, enterprises must evolve their performance management. It should balance productivity metrics with measures of agents' impact on CX and positive attitude to driving improvement. Third, businesses must provide their customer service representatives with the right tools to take care of their customers. This often entails modernizing their desktop and giving them access to knowledge sharing, collaboration, and gamification tools.
Define your data architecture
The contact center is arguably the department that most relies on metrics to measure performance. Nevertheless, practitioners continue to wrestle with making sense of all the available data. Software fragmentation with already 12 apps on average in a customer service stack is part of the complexity. New data from speech analytics, voice-of-the-customer (VoC), and feedback management are adding to the mix.
Data plays a key role in guiding the design of effortless resolutions, personalizing customer journeys, and enabling employee engagement initiatives. It’s also what feeds AI. It’s critical to lay out a data architecture for collecting and using all the interaction and activity data processed by the various software. It includes defining how data should be staged to ensure quality, and how it can be correlated, aggregated, and fed into the context store. This data architecture should become the foundation into which applications need to connect and from which key performance indicators (KPIs) should be calculated.
The accelerating transition of customer care infrastructures to the cloud requires enterprises to think beyond migration and develop an architecture to enable great experiences.
With these practices, you can build a software stack that enables your business to meet rising customer expectations continuously, improving the overall customer experience and increasing customer satisfaction.

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This post is written on behalf of BCStrategies, an industry resource for enterprises, vendors, system integrators, and anyone interested in the growing business communications arena. A supplier of objective information on business communications, BCStrategies is supported by an alliance of leading communication industry advisors, analysts, and consultants who have worked in the various segments of the dynamic business communications market.