How Tech Companies Can Use Customer Insights

Enterprise hardware, software, and communications companies are under tremendous pressure, thanks to rising inflation and a tightening funding market, to understand and respond to their customers and prospects.

To compete, they must be attuned to the 24-7 omnichannel feedback loop that exists in the contact centre, on social media channels, and beyond. However, many are not equipped to effectively listen to the solicited and unsolicited feedback customers are already giving them.

Conversation intelligence is one way to truly understand the voice of the customer (VoC). This technology relies on a combination of machine learning and natural language processing to parse 100% of omnichannel feedback.

Instead of delivering only surface-level assessments of written or spoken customer interactions, conversation intelligence taps the power of AI to understand intent, sentiment, and meaning from customer data.

That way, teams can assess a high volume of interactions much more deeply, in far less time than manual approaches.

Let’s dive into five ways tech companies can use this technology to more effectively improve customer experience (CX), compete, retain talent, save costs, and generate revenue.

1. Driving Contact Centre Experience and Quality Assurance (QA)

Surveys, like Net Promoter Score (NPS), are a common practice to determine contact centre performance on a certain call.

However, surveys have an average response rate of  5 to 15%, and often only account for people who are extremely satisfied or extremely dissatisfied with their interactions. That leaves a huge middle ground open for interpretation.

Other common practices include manual approaches like listening to random calls for QA and agent performance. However, when the typical contact centre handles hundreds or thousands of calls a day, random call listening only covers a tiny fraction of overall interactions.

Conversation intelligence can be applied to 100% of omnichannel customer interactions, helping contact centre supervisors more effectively understand agent performance and process adherence for QA and compliance. From there, they can use data-driven coaching methods to hone in on exact areas of improvement or reward strong performers.

2. Improving the Employee Experience (EX)

In a challenging climate, it’s important to retain top performers, and help employees succeed along their chosen career paths.

Not to mention, happy employees equal happy customers. According to the CallMiner CX Landscape Report, 99% of companies believe that EX and CX are closely linked.  It’s important to create new opportunities for data-driven coaching for managers and supervisors.

Tech companies with customer service and support centres can have thousands of customer interactions per day, which are ripe with data-driven performance insights for coaches.

Capturing your employee’s side of conversations that occur beyond surveys can help drive more relevant employee engagement programs.

The right insights equip managers, supervisors and HR leaders to have more informed conversations about their future path and direction at your organization, while retaining valuable employees in the process.

3. Boosting Revenue Through New Logos, Expansion, and Retention

Generating revenue is top of mind for tech companies. The good news is that sales reps can use conversation intelligence to continuously improve their interactions with prospects, accelerating sales cycles. The technology can be applied to sales teams similarly to how it’s used to coach customer support teams.

Another area where conversation intelligence can be particularly helpful is retention. This technology helps sales and support teams understand certain signals such as churn-likely behavior in existing customers. Real-time guidance for reps can help guide them through potentially difficult situations for retention.

4. Detecting and Responding to Customer Issues and Brand Crises

Where there’s smoke, there’s fire! That doesn’t necessarily have to be the case for customer issues. While customers have more channels than ever in which to voice their frustration, conversation intelligence can be leveraged to monitor these interactions and take action before a larger PR or brand crisis emerges.

For example, if customers don’t respond well to a digital marketing campaign or strategy, the marketing team can make adjustments (e.g. replacing a controversial spokesperson or changing messaging that doesn’t resonate).

For issues such as warranties or product safety issues, product teams can detect signs of dissatisfaction and make adjustments before a larger event, such as a recall, needs to take place.

5. Driving Product Innovation and Improvements

While we’re talking about product quality, it’s important to note that conversation intelligence can be a powerful tool in a product team’s toolkit. Customers can be a critical voice in determining product roadmaps and prioritization of features.

The data collected from a conversation intelligence solution can uncover important insights for product quality and engineering teams, as well as supply chain partners, for continuous product improvement.

Tech companies that uncover and use deep customer insights effectively can better compete and retain talent, despite economic conditions or other challenges.

Not only can conversation intelligence differentiate the experiences and outcomes tech companies deliver to their customers, it can also drive cross-functional improvements across customer support, sales, marketing, product, and beyond.

This blog post has been re-published by kind permission of CallMiner – View the Original Article

For more information about CallMiner - visit the CallMiner Website

About CallMiner

CallMiner is the leading cloud-based customer interaction analytics solution for extracting business intelligence and improving agent performance across all contact channels.

Find out more about CallMiner

Call Centre Helper is not responsible for the content of these guest blog posts. The opinions expressed in this article are those of the author, and do not necessarily reflect those of Call Centre Helper.

Author: CallMiner