How AI Is Transforming Phone Calls Into Data

How AI Is Transforming Phone Calls Into Data

Share this:

“This call may be recorded for training and quality purposes”  We’ve all heard a version of this ubiquitous  message a thousand times.  Chances are we don’t even notice it any more due to a form of audio “ad blindness”.  Still, if we stop and think about it, interesting questions arise.  What are these recordings actually used for?  Who is listening?  Perhaps most importantly, how can we use these calls to improve our marketing strategies and our business as a whole?

Certainly, businesses use recorded calls for a variety of reasons including: to document verbal consumer approvals, as reference for dispute resolution, and as a resource for training call center agents.

However, with advances in AI technology, calls can now be converted to data which in turn can be mined to yield profound, actionable insights.  In fact, there is no clearer picture or more authentic reflection of consumer engagement than what happens on phone calls to your business.  if you are a MULO company and calls are an important lead source or touch point, then phone call analytics are arguably among your most valuable data assets.

The Basics Of Speech Analytics

Let’s explore how call analytics works.  In what is now considered a relatively basic AI function, call recordings can be transcribed in real time.  Once transcribed, AI driven keyword rules are set up to tag a broad range of queries and outcomes.  These include but are not limited to:

  • Marketing source of the call (achieved through unique tracking lines)
  • Requests for specific products and services
  • Pricing/quote inquiries
  • Mention of specific incentives or discounts
  • Outcomes/Conversions (appointments set/declined, sales executed, etc.)
  • Monitoring call agent performance such as offering loyalty programs or add-on services

With the query and keyword structure in place, the opportunities to mine and layer the data are almost limitless.  To illustrate, we’ll use a multi-location Plumbing company as an example.  Using speech analytics, the company can measure the percentage of calls requesting specific services such as drain clogs, leak detection, pipe repair, etc.  Of those calls, they can then determine what percentage convert to appointments for each service and how that compares with the company’s overall conversion rate.

In addition, call data can unlock insights on the impact of pricing and discounting.  For example, if a caller requests pricing or a quote on the phone, does that lead to a higher or lower conversion rate to booking an appointment?  Or, perhaps the company wants to know if discounts/incentives drive calls and whether those callers are converting or just shopping price?

Drilling down even deeper, the data can be analyzed on an enterprise, regional or location specific level.  Utilizing this information, we can uncover market level nuances and customize hyperlocal marketing accordingly.

Enterprise-Wide Use Cases

Obviously, the data is quite deep and wide as are the opportunities to activate it to improve results.   Call analytics insights can benefit numerous departments across the enterprise.

Marketers can use the analytics to better understand consumers’ needs and adjust content related to services, messaging, and media campaigns to both accentuate the company’s strengths and align with future business goals.  In addition, insights into conversion activity can inform call script development geared toward overcoming objections.

Sales can evaluate areas of strength and weakness in order to prioritize and bundle services, adjust pricing and develop/test discounts and incentives.  Operations can use examples of recorded calls to assist in field level and call center training.

Where Does Human Intelligence Fit In and For How Long?

Interestingly, our entire discussion so far has focused on call data with absolutely no mention of actually listening to the calls. For companies that are mining their call data, insight into consumer sentiment is a frequent request.

Ideally, it would be beneficial to know if a caller was frustrated, delighted, angered or satisfied.  Understanding a caller’s emotional reaction can be invaluable in developing communication strategies  that address consumer pain points and enhance customer experience.

While some might disagree, at this point, evaluating caller sentiment remains in the purview of we humans.   However, with the staggering pace of AI evolution, that is subject to change quickly.  We will be watching…and listening.

Michael is DSG's VP of Marketing. DSG has built a multifaceted marketing playbook featuring unique expertise in online presence, brand building, and hyperlocal marketing strategies for SMB’s and multi-location businesses.
Previous Post

City Mattress Rests Easy with Agital

Next Post

Fat Brands Blasts Off for Success