What is call analytics software and how is AI giving marketers more power to measure inbound call data?


Why it matters. While phones are now a platform for a huge number of things, letting one person talk to another is still the primary reason people have them. That is especially true when someone wants information from a business. Phone calls provide businesses with an opportunity to offer deep in-the-funnel prospects fast answers and connections to real people.

The study found that 35% of American adults, or around 100 million people, owned a smart speaker in 2022, up from 21% in 2019. Use of those devices seems to drive additional use of smart assistants on mobile phones, with 46% of smart speaker owners with smartphones saying they used the phone assistant more often since getting the speakers.

Although both smart speakers and virtual assistants are most frequently used for music, weather, directions, games and setting alarms, about one-third of users are finding local businesses, while around a quarter of users use them to order food.

What they do. Call analytics platforms help marketers identify and activate the rich data hidden in the growing volume of inbound calls. Call analytics platforms track both online and offline leads, following a call from its source (i.e., website, social media and click-to-call search or display ads) to a sales representative (i.e., based on geographic location or product line and on to a conversion or lack thereof).

The ability to track calls is a core use case of call analytics technology. However, advances in machine learning and artificial intelligence (AI) are driving more sophisticated applications, including the following:

Call fraud prevention is another important feature, as automated dialers, fax machines and even computer programmers can hack into carrier networks to fraudulently inflate call volumes and revenue for pay-per-call services. In response, vendors have developed proprietary call fraud detection and prevention tools that identify, monitor and block suspicious call patterns and routes.

Vendors begin to differentiate their platforms by offering more advanced capabilities, often requiring additional investment, which includes – but are not limited to – the following.

Call analytics technology has evolved from providing basic analytics to providing “conversation intelligence” based on AI-driven algorithms that extract and predict caller intent, and measure caller tone, sentiment and emotion. AI is increasingly being applied to analyze and “spot” keywords, phrases and speech patterns for positive or negative signals of conversion intent.

These signals can also include the length of time a caller speaks versus how long the sales rep speaks. Many call analytics platforms use a variety of natural language processing (NLP) and machine-learning algorithms to automatically assess calls and score leads. The results can be used immediately to help sales reps on the call by using whisper messages heard through the rep’s headset to influence call outcomes. The data can also be used post-call to feed CRM systems and trigger nurturing campaigns.

Machine learning-based or “intelligent” lead scoring and routing systems are now being used to optimally route a call to the rep or location most qualified to close a sale or other conversion action (i.e., make an appointment). These types of scoring and routing tools automatically qualify and distribute calls to the appropriate sales reps or departments based on variables such as caller source (i.e., website, social media, search ad), geography, demographics (i.e., age, gender, income) or intent. Some of the tools used in intelligent call scoring and routing include interactive voice response (IVR), which prequalifies callers before they are routed to a rep through a short series of automated questions, and whisper messages that alert reps to relevant caller data before they pick up the call. Calls can be tracked through the system to follow conversions and other qualifying events.

Call analytics software vendors are leveraging the growth in native social advertising and click-to-call to more seamlessly integrate social media and call analytics. Most vendors offer Facebook and Instagram call tracking software to attribute calls back to social media ads.

Chat applications — either mainstream messengers like Facebook Messenger or WhatsApp, or chat functionality on websites — have become key channels for customer service and pre-sales inquiries. Additionally, many such interactions are now enabled by SMS. Some call analytics vendors incorporate connections and data gathering from these sources, to give marketers a more holistic view of customer interactions and sentiments.

Several call analytics software platforms are rooted in sales coaching and evaluation, and use call recording tools to maximize efficiency in the call center and among sales staffs. Today, the platforms provide machine learning-based call handling analytics tools that assess location and/or rep performance, and provide scoring/grading systems, script optimization and real-time alerts that flag lost opportunities.

Explore platform capabilities from vendors like CallRail, Invoca, CallSource, DialogueTech and more in the full MarTech Intelligence Report on enterprise call analytics platforms.

Call tracking software and analytics play a vital role in bridging the gap between online and offline channels, leading to more efficient marketing resource allocation and improved sales staff effectiveness. The specific benefits of using an enterprise call analytics platform include – but are not limited to – the following:

Get MarTech! Daily. Free. In your inbox.

See terms.

The post What is call analytics software and how is AI giving marketers more power to measure inbound call data? appeared first on MarTech.


Leave a Reply

Your email address will not be published. Required fields are marked *