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AI vs. Machine Learning: How Do Marketers Use Them to Increase ROI?

Alain Stephan Senior Vice President, Growth & Innovation, DialogTech

A survey of 300 B2B marketers, performed by EverString and Heinz Marketing, found that less than one-fifth of respondents truly understand the difference between artificial intelligence, machine learning, and predictive modeling.

If this confusion is keeping marketers from adopting AI, it’s unfortunate. AI is such a powerful asset to marketers — it’s helping brands and agencies analyze marketing data with unprecedented precision, providing them with the insights to make smarter optimizations that drive more revenue at lower costs.

Need a refresher on AI vs. machine learning and how marketers are using these technologies to drive results? Read on. There’s also a deep dive into one of the most powerful ways leading marketers are using machine learning and AI: to capture insights from inbound phone conversations at scale.

What Is AI?

Short for artificial intelligence, AI is a branch of computer science concerned with building machines that are capable of imitating intelligent human behavior. AI systems typically imitate some of the following behaviors associated with human intelligence: learning, planning, problem-solving, knowledge representation, perception, and/or creativity.

What Is Machine Learning?

A subset of AI, machine learning is a machine’s ability to process data and make adjustments to its behavior based on that data to eventually reach a desired outcome. This “data” can take the form of numbers, words, images, clicks — almost anything that can be digitally stored.

Since machine learning is a subset of AI, when we talk about “AI for marketers” in this post, we’re also covering tools that leverage machine learning.

What Are the Top AI Marketing Tools?

1. Bid Management Tools

Tools like Google Ads Smart Bidding allow marketers to set their PPC advertising goals — for instance, a target cost per acquisition — and AI will automatically adjust your bids to reach the goal. This allows marketers to take a hands-off approach to bidding and allow the AI to deal with the manual work.

2. Website Personalization Tools

Website personalization tools like Optimizely analyze data points about a single user (including location, demographics, device type, past interactions with the website, and more), and use AI to display personalized offers, content, and recommendations for each user. AI can also personalize push notifications to deliver relevant messages at the right time.

3. Churn Prediction and Customer Engagement Tools

AI can help you identify disengaged customers that are about to churn or leave for a competitor. These tools can help gather data, build a predictive model, and test that model on real customers. That information can indicate what stage of churning the person is in and marketers can reach out to their most at-risk customers with special messaging and promotions.

4. Call Tracking and Analytics Solutions

Call tracking and conversation analytics solutions capture the marketing source that drives each inbound call and use AI to analyze the content of phone conversations at scale. You can leverage this data to optimize for the marketing sources driving your best calls, route calls more efficiently, and target callers based on the content of their conversations. In addition, you can integrate call data with your martech stack, including some of the tools mentioned above. More on this below.

How Marketers Use AI to Categorize Inbound Calls

Your marketing generates a lot of calls, but are they all sales leads? How do you know what marketing sources generate the most sales leads vs. support calls? How does your marketing team know what products or services each caller is calling about from each channel (for example, home insurance vs. auto insurance vs. motorcycle insurance)? Knowing the answers can help you make smarter targeting, messaging, and budgeting optimizations for your campaigns.

With DialogTech, marketers can use AI to automatically analyze the content of phone conversations and detect key insights, including if the call was a lead, the lead score, the products or services they expressed interest in, if they expressed urgency, if they expressed price sensitivity, if they ultimately made a purchase, and more. Not only is AI far faster at detecting patterns than humans, but it’s also more accurate—machines are not prone to fatigue, human error, or other biases. Therefore, using AI to locate trends from your phone conversations will provide you with a competitive edge.

How Do AI-Powered Conversation Analytics Work?

Step 1: A consumer places a call from a DialogTech trackable phone number.

Step 2: Artificial intelligence automatically “listens” to the call and transcribes the conversations into highly accurate text between the caller and the agent.

Step 3: Our algorithms identify dozens of proprietary sales call indicators within each transcription, including key phrases, patterns, and the business outcome (such as “appointment set” or “purchase made”). Using these indicators, we provide you with metrics like lead score, sales opportunity, conversion rate, and more.

Step 4: The resulting insights are published in your DialogTech dashboard, allowing you to see a holistic picture of the impact calls are having on your marketing, sales, and service departments.

Step 5: You can also integrate these insights directly into your martech stack and turn these insights into optimizations that drive more quality leads and revenue. DialogTech offers integrations with major platforms, including Google Ads, Bing, Facebook, Salesforce, LiveRamp, and many more.

What Insights Can I Gain from Using AI-Powered Conversation Analytics?

Let’s say you’re a marketer at a national home services franchise with hundreds of franchisee locations. Prior to using call analytics, you were allocating most of your marketing spend to paid search ads, since they were driving the most calls to your franchisees. However, upon integrating AI voice analytics you realize that, although Facebook ads are driving fewer total calls, these calls have nearly double the lead score of your paid search ads. So, instead of optimizing your marketing campaigns for call quantity, you optimize for call quality, resulting in higher revenue.

AI-powered report of call quality by marketing source

[Sample Report] See which marketing sources are driving the highest quality leads.

Continuing with the example, let’s say your average lead score for these Facebook ads is 7 out of 10. However, you notice that a significant number of these leads aren’t converting. You can drill down and view the conversion rate by franchisee location, allowing you to understand how calls are being handled. You may notice that a particular location is underperforming—you can then dig deeper and search the transcriptions to understand why. Are your franchisees using the right scripts on calls? Are they mentioning the right offers or even asking the caller for appointments? Based on your findings, you can provide additional coaching to improve the conversion rate of phone calls.

AI-powered report of call handling by location

[Sample Report] View sales conversion rates by location and monitor how many calls are sent to voicemail.

Finally, you can use the AI-generated data to assist with customer retention. AI-powered conversation analytics can automatically detect when customers are likely to churn. Using this information, you can alert franchisee locations when issues surface during phone calls and have their owner/manager take action, perhaps calling the lead back to provide a personal touch or a special offer.

AI automatically identifies CX issues at scale

[Sample Report] View a real-time list of frustrated or at-risk customers, curated by AI.

Through this holistic view of the call channel, companies can ensure their marketers are driving high-quality leads, their sales staff is closing them at a high rate, and their customer service department is retaining these customers.

How GE Appliances uses AI to Drive More Appointment Calls

GE Appliances has a team of technicians in more than 300 cities nationwide who specialize in repairing appliances. Around 70% of the service requests digital agency DAC Group generates for GE Appliances come in over the phone. To attribute the marketing sources driving each call, they use DialogTech.

In addition to leveraging DialogTech for call attribution, GE Appliances uses DialogTech’s AI to understand the lead quality of each caller, their product interests, their call experience, and the call-to-appointment conversion rates. By using these new insights to make optimizations, DAC has further increased GE Appliances’ monthly volume of quality phone leads by 42% and their total sales-related calls by 41%.

“DialogTech’s AI provides a wealth of powerful insights from phone conversations. Instead of just seeing how many calls our keywords drive, we can now see how many sales conversions. We can see what percentage of calls from each source are sales leads specifically from out-of-warranty customers, what appliances they need repaired, if they converted to an appointment, and much more,” said David Mabry, DAC’s account manager for GE Appliances.

To learn more about how GE uses AI-powered call analytics data to drive more appointments, check out the full case study.

Want to learn how you can use AI-powered call data in Google Analytics? Download our eBook, The Ultimate Guide to Tracking Phone Calls in Google Analytics.

Download our eBook, The Ultimate Guide to Tracking Phone Calls in Google Analytics, to learn how you can use AI-powered call data in Google Analytics.

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