Any customer interaction generates a wealth of data, hidden inside of which are vital insights into improving the customer experience. That data is chiefly in the form of speech and text, so revealing those insights requires an analytics solution that can trawl through the increasingly vast amounts collected and separate the wheat from the chaff.
In this edition of the CX Today round table, we welcome:
- Sharon Einstein, Vice president and general manager, NICE Experience Analytics
- Dan O'Connell, CSO, Dialpad
- Paul Lang, Senior Director, Contact Centre Solutions Marketing, Avaya
- Frank Sherlock, VP of International, CallMiner
Our panellists will discuss the major hurdles for businesses wanting to adopt speech and text analytics, how the market is developing, and how the deployment of machine learning has impacted growth.
What are the major hurdles for businesses wanting to adopt speech and text analytics as we move into 2022?
Einstein: Analytics adoption is becoming widespread in contact centres, while at the same time businesses are prioritising customer satisfaction over more traditional contact centre metrics. Still, some organisations are not taking advantage of the recent advances in speech and text analytics, which can effectively advance customer experience strategies. The main barrier to implementation continues to be for those businesses that persist in using legacy premise-based IT solutions.
Many businesses continue to have contact centres that function in silos. The centres themselves are generating plenty of data, but there’s no systematic approach to collect that data into a single source of truth from which analytics experts can analyse and devise actionable insights. Advanced speech and text analytics are only really achievable with a single, integrated cloud platform that is pulling data from across the contact centre ecosystem.
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Sharon Einstein[/caption]
O’Connell: Voice data is the last offline dataset. So much data is lost in voice calls, whether that’s group meetings, one-to-ones, outbound sales conversations and inbound service calls. You end up repeating meetings to get to decisions, forgetting actions or decisions that were made, or missing out on valuable customer insights. Even with all the values of speech and text analytics, businesses are still slow or hesitant to adopt.
Providing a clear, efficient and cost-effective solution to this problem is critical to proving the value of speech and text analysis to businesses. Even once businesses realise the value of voice, cost can be another hurdle.
We’ve found that technology stacks built internally instead of using other platforms prove to be much more cost-effective. Our technology has been built entirely in-house instead of relying on other outsourced products so we’re able to control more and drive costs down to give our customers the most value.
Accessing the insights hidden in voice interactions can lead to huge cost savings, increased sales, time savings, and improved productivity, so it can affect every corner of the business and impact the top line and bottom line simultaneously.
Lang: One of the biggest challenges in customer services is being able to collect, collate, analyse, interpret tons of data in real time and do something meaningful with it. One thing that can make a big difference is conversational AI which can process words and voice conversations in real time then respond with contextual information, unlike traditional speech analytics applications that mine calls after they occur.
Conversational AI incorporates technologies like chatbots or voice assistants, with machine learning to improve customer interactions. Research shows the most successful companies are moving cautiously with AI, starting with agent assist or chatbots (nearly 40% of companies are leveraging AI-powered chatbots to interact with customers). Although many companies are not considering conversational AI, today’s biggest brands, such as Facebook, Apple, and Google, are signalling AI’s rise by making key investments in conversational design.
Conversational AI is something organisations are going to have to contend with for future success in customer experience.
Sherlock: A key hurdle that contact centre leaders will continue to face in the coming year is getting executive buy-in to invest in speech and text analytics.
While contact centre leaders are well versed on the benefits of conversation analytics, such as automated QA, improved agent performance, increased customer satisfaction and more, the budget for these solutions often falls outside of the contact centre, such as with customer experience executives or even at the C-suite level. Getting them to allocate budget isn’t always easy, until they understand how the solutions can deliver ROI and impact the bottom line.
Another hurdle is getting internal teams to embrace the solutions, especially the agents who interact with the technology on a daily basis. It’s important to explain and show the people engaging with these tools most frequently how the insights that are uncovered are to their benefit. It’s not about monitoring them – it’s about helping to support them in their roles, making their jobs easier and giving them access to the objective, data-driven feedback it takes to improve.
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Frank Sherlock[/caption]
How will the speech and text analytics market develop over the next year and what will this mean for businesses and their customers?
Einstein: Speech and text analytics are an indispensable part of the modern contact centre’s toolkit. Text and voice interactions comprise the largest percentage of unstructured data in most contact centres in the UK. Making sense of all the data being generated about a company - from social-media channels, chats with customer-service agents, surveys, forms, warranty claims, and more - is going to become a key differentiator for businesses.
Advanced analytics allow companies to conduct end-to-end analytics on millions of customer data points to proactively identify potential improvements. Initially, such benefits may be incremental in some areas of the business while in others it may be more noticeable. However, what is clear is that those who start applying these tools now and begin learning through the experience will reap the rewards down the line. Having clean, usable data is akin to the idea of compound interest, it’s continually built upon with long-term benefit.
O’Connell: Beyond the practical applications of call transcription, real-time coaching and sentiment analysis, voice data is rich with data and can be leveraged like any other data set- and it will only get smarter over time.
Once voice becomes a company’s data set, that data can become used like any other set - you can get instant access to the number of times a word is mentioned and then trigger technology in other parts of the business to action (ie. If there is a large increase in customers saying the word “refund”).
Being able to dig into the analytics of customer satisfaction, for example, is a game-changer. With embedded AI, we can think about how to automate certain workflows which is where additional gains will be made. We really are in the early innings of AI, but we can definitely picture a future where users can say they need to reschedule a meeting during a call and the calendar immediately finds the meeting participants' availability and schedules the time, or a future where technology has evolved to the point where sales teams no longer have to input data into a CRM, but instead, the data could be auto-populated.
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Dan O'Connell[/caption]
Paul Lang[/caption]
