For the uninitiated, AI is a machine’s ability to perceive a set of inputs and process that information in order to reach a desired outcome. Facebook analyzing your photo, identifying the faces, and pulling data to suggest who you should tag is an example of AI you’re likely familiar with.
AI and martech
As AI continues to evolve, data scientists and software engineers are working to apply it to new business areas in order to automate processes and increase efficiency. Already, AI is beginning to transform the martech space.
TIP: Read our in-depth breakdown of what martech is!
Responsive paid search ads
As a digital marketer, chances are you’ve run your share of A/B tests to determine the paid search headlines and descriptions that drive the most clicks or calls. You’ve endured the lengthy cycle of running ads side-by-side, comparing results, tweaking the ads, running them again, and so on, until you discovered that perfect combination.
Currently in the beta phase, Google’s Responsive Search Ads are designed to eliminate time-consuming A/B testing. Rather than running different headline and description combinations in various ad sets, Google allows you to run a series of headlines and descriptions in one constantly evolving search ad. To set up the ad, simply enter up to 15 headlines and four descriptions; AdWords will automatically show different combinations depending on the search query.
Over time, Google will test various headline and description combinations and calculate which perform best. The multiple headline and description options allow your ads to compete in more auctions and match more queries, broadening the base of potential customers you can reach. Additionally, Google will optimize your message for various device widths, enhancing the experience for customers on mobile, tablet, and desktop.
Audience segmentation and optimization
If you’ve ever tried to target an audience programmatically, you’ve likely faced your share of frustrations with the intricacies involved. First, you had to create your segments by hand-selecting attributes from countless options and a broad range of data sources.
Then, you had to manually track and optimize the segments as you went, losing time and money throughout a lengthy testing process. And, because today’s standard solutions rely on prolonged data refreshes, as well as static cookie pools, the audience segments you targeted did not adjust with the constant changes in consumer attitudes and behavior.
Now, companies have begun to introduce AI-optimized audience segmentation solutions to address these common frustrations. These solutions integrate seamlessly into any demand-side platform, and they continually refresh audiences based on campaign performance, new data, and machine learning.
They can create customized campaign segments using predictive algorithms and millions of live consumer profiles, each comprised of proprietary search and shopper data, as well as data from third party sources. By utilizing artificial intelligence and machine learning to optimize audiences throughout the duration of campaigns, these solutions deliver superior performance to static segments.
Conversation analytics for inbound calls
For many businesses, calls drive the most valuable leads — according to BIA/Kelsey, calls convert to revenue 10-to-15 times more than web leads. Therefore, in order to report businesses full return on investment (ROI), it’s essential for marketers to understand how many revenue-generating calls their campaigns are driving. However, manually listening to calls is a lengthy and inadvisable process. Instead, companies can gain these crucial insights through AI.
Through the use of AI-powered conversation analytics tools, you can learn which channels, ads, keywords, and webpages drive the best sales calls; if new promotions are resonating; and why calls did or did not convert. These insights allow you to allocate your budget to marketing campaigns that are driving high-converting calls. Additionally, in future campaigns, you’ll be able to retarget callers who didn’t convert and exclude callers who became customers.
You can also use these AI tools to analyze how calls are handled, allowing you to detect issues that negatively impact ROI. Analyzing voice interactions between consumers and your business locations enables you to see if agents are answering calls, asking the right questions, following the right scripts, and what tactics are working best to convert callers to customers. You can then democratize best practices across your business while working with underperforming locations or agents to improve their closing rate.
To maintain a competitive advantage in the ever-evolving martech landscape, digital marketers need to leverage their time by automating their processes, from A/B testing, to audience segmentation, to call analytics with AI solutions.
Louise is the Public Relations and Content Manager for DialogTech, where she tells brand stories through earned media. She has a background in e-commerce, social media, corporate communications, and brand content, and has a passion for strategy. Louise is an alumnus of the University of Tennessee and spends her free time traveling, baking, and watching documentaries.