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Posted April 26, 2021
Posted April 26, 2021
By Praveen Balla, Vice President, Production, Anteriad
AI innovation is transforming every aspect of B2B sales and marketing, from content hyper-personalization to honing customer personas and campaign segments. And as the technology becomes smarter, so do the various applications that B2B sellers find for Big Data analytics. Some are at the edge of going mainstream, and the view ahead is exciting.
In this post, I look at some of the emerging uses for AI that will likely change the way you do business in the next few years. Some you’ve heard of; some you may already be testing. But in each of these areas, AI will keep learning, and your business will keep improving as a result.
Let me give you some numbers on how AI innovation is being employed today that illustrate how wide-open the potential for the technology truly is.
In its mid-year 2020 global AI spending guide, IDC broke down the top use cases of AI among survey respondents. The big winner? “Other,” at 62 percent. The highest-ranking specific use case was Automated Customer Service, at just 11 percent. (We’ll talk about chatbots in just a moment.) Companies use AI in so many new and innovative ways that it almost defies categorization. The number-two use case? Sales Process Recommendation at just 7 percent.
IDC predicts that the hardware that fuels all this analysis will account for the bulk of the spend in 2021, but evolving software and services will be the fastest growing category, with a forecast CAGR of 22.45 percent.
Overall, global AI spend is expected to more than double to $110 billion over the next four years. Wide-ranging innovation in new service categories will drive that growth.
Now, let’s talk about chatbots. You may well already have these in place for customer service, and probably have experienced them for yourself.
“Dumb” bots that walk site visitors through simple, rule-based transactions have been around for years. They’ve proved useful, but definitely have their limitations. A confused customer often ends up being told “I don’t understand what you’re asking.” In many ways, these “dumb” bots really are nothing more than a slick UI for what could be presented as a series of on-screen buttons.
AI bots, on the other hand, learn from past user interactions to generate solution paths that developers could never anticipate in building fixed rules. This technical overview from an AI developer explains the pros and cons of building AI chatbots in simple terms. (I particularly like the example of how a smartbot can learn to get out of a dead-end conversation gracefully.) Smartbots can quickly answer questions and provide resources, creating the “on-demand” experience B2B customers have come to expect.
This results in effective upsell to existing customers and higher conversion to qualified leads on landing pages. Most companies still employ triggers that pass along either particularly promising or problematic conversations to human agents, but as bots get smarter, the need for human escalation will decrease. This collection of case studies from AI chatbot provider, Drift, showcases how one customer was able to intelligently route customer engagements to either direct upsell or lead nurturing, to the tune of 4,000 leads and $1 million in sales.
AI chatbots help customers find what they want – even if they don’t know exactly what they want when they first contact you.
One of the most common applications of AI innovation is voice recognition and search. In fact, more than 20 percent of people use voice search at least once a week, according to a recent survey. Alongside smart chatbots, adding voice search to your site navigation will give you an edge over competitors (as well as making it more accessible, which is also a plus.)
Waiting a day or two for inside sales to respond to an email inquiry can cool off an otherwise hot lead. Semantic AI is getting better at crafting the kind of highly personalized messaging a customer would expect from a human salesperson, with a personal touch that’s not possible with template-based auto-responders.
I discussed semantic AI extensively in my post on content personalization. That piece focused mainly on how semantic AI helps you craft one-to-one outbound campaigns by identifying contacts’ interests. But as with learning chat bots, AI email responders now can understand complex direct questions. And they are getting pretty good at writing copy, too.
There’s still some work to do. This interesting piece from Stanford University details research into bias and over-simplification in AI-generated long-form communications. But the tech is constantly improving, and may soon provide a solid alternative for human responses to every email.
Google is the biggest AI company in the world, and trying to decipher its search algorithm has been the stock in trade for SEO practitioners for decades now.
With AI-powered tools, SEO has gone well beyond simple best practices and keyword density to semantic analysis of content. Google aspires to actually read your landing pages and understand what they are about, as a human would.
New AI-powered tools can build sophisticated SEO strategies based on user personas and a few targeted topics. Using propensity modeling across vast pools of first and third-party data, they can suggest the types of terms your best customers are likely to search on.
What’s more, they can just go ahead and write your page copy for you. Many of these tools claim they can “replace” writers and create compelling narratives. I’m not confident they’ve reached that level of sophistication, but they certainly can generate solid drafts of SEO-friendly content that human editors can use as a starting point. This AI innovation an enormous time-saver if you create a lot of long-form SEO copy.
AI’s growing ability to understand open-ended questions also positions it as a great tool for evaluating open-ended responses to survey questions, one of the biggest time drains for market researchers.
A recent survey found that analyzing survey comments is one of the areas of market research where AI is likely to have the greatest impact. Overall, the survey found that market researchers are eager to pass off data analysis and programming tasks to AI, but want to retain writing surveys as a human speciality, at least for now.
Within 10 years, a large majority of respondents think AI innovation will take over other tasks, such as brand awareness tracking (67 percent) and sample sizing (77 percent). And propensity and predictive modeling will likely cut back on the number of panel respondents needed to create a meaningful statistical basis for analysis.
Overall, about four-fifths of respondents said they think AI will have a positive, but certainly transformative impact on the industry.
The impact of AI innovation on business is just getting started. Sales and marketing teams are eager to embrace AI’s potential and explore this powerful technology to find and engage customers. It’s evolving so rapidly that within five years, there will be new applications of the technology we haven’t even glimpsed today. Get current fast with the on-demand version of our recent summit on AI and B2B marketing.