It’s not just consumers that want personalized buying experiences. As smart technology becomes more pervasive in our personal lives, all of us are starting to expect the same type of personalized, frictionless shopping experiences in our business lives, as well. We want recommendations. We want proactive answers to our questions. We want to know that businesses understand our unique needs. And that’s where AI and machine learning come in.
Just as consumer marketing teams have found AI-driven martech to help them reach their buyers where they are, B2B marketing teams are starting to realize that AI can do similar things to enhance their business customers’ journeys. This is why I’ve been so focused on key trends in analytics, big data, and technologies that can transform the buyer’s journey. And this is also why categories like business intelligence and Customer Data Platforms have become and will continue to be investment centers for the likes of Microsoft, Salesforce, Oracle, Twilio, AWS, and a plethora of the world’s leading technology companies.
Considering nearly 80 percent of leads never convert to sales, that’s a good—much needed—thing. The following are a few ways AI and machine learning are helping make the B2B buyers’ journey even better.
Recommendation Engines Drive Next Best Action for B2B
Most of us are familiar with the recommendation engines currently driven on AI, be it the ones that guide our shopping experience on Amazon, our TV viewing on Netflix, or our listening preferences on Spotify. Wouldn’t it be great if businesses could see the problems we’re experiencing at work and recommend solutions in a similar way? Thanks to applied ML and the continued maturation of AI, this is now possible. This is huge for both businesses and their B2B partners because it has the potential to find the right potential customer, and to save the customer time and mitigate the stress and complexity in finding the right solution. Technology leaders such as NVIDIA have been actively building frameworks to enable software makers and enterprises to leverage recommendation technology in a more seamless way. In fact, it’s the same technology that sits behind many of the consumer recommendation engines that I referenced above. The conversion of consumer based AI to be used for B2B has many overlaps, and these can be used to determine next best action in complex channel driven engagements, and it will be a differentiator for many businesses going forward.
SEO Automation and Optimization with AI/ML Tools
If you’ve ever tried to write SEO-optimized content to attract new customers, you’ll appreciate this emerging benefits of AI. Using AI and machine learning, businesses can now automate the process of tagging ads and determining the most effective keywords to reach their B2B customer base. IBM has been steadfast in this space with the advent of its Watson AI, launching IBM Watson Advertising. While tagging is only one of many features in the suite, it is helpful as it has become increasingly complex to figure out how to extract value from the hard work and investment that goes into content development. And frankly, it can be a real cost saver removing tons of tedious, painstaking time from marketing teams, allowing them to focus on higher impact workflows—after all, automation is designed to up-level and upskill the workforce. It is also worth noting that this type of ad-tagging technology is well designed for real-time optimization to continue updating content since AI can update lagging keywords in real-time.
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In terms of other key use cases for tools that can automate and optimize b2b marketing workflows, it also has the potential to remove potentially lost time and resources invested in campaigns with a high probability of failure by providing much needed analytical insights to halt ineffective campaigns early on.
Conversational AI to Answer Questions
Do you know at which point you lose your B2B customers on their buying journey? With continuously improving AI and ML capabilities, this is becoming increasingly possible. Much like NVIDIA’s efforts to simplify the deployment of recommender engines with its Merlin framework, conversational AI is another big focus of the company with its Jarvis framework for high quality, low-latency interactions between human and machine. This type of technology from NVIDIA and other companies refining these capabilities are layered into numerous martech solutions and chatbot tools and through their continuous development and improvement are increasingly capable of shepherding customers past identified pain points and throughout the entire process to make sure you don’t lose them at all. And with B2B, we understand the importance of quickly solving problems and improving processes, because many B2B ecosystems are highly engaged and interactions are continuous. The more process issues are quickly resolved and customers are able to quickly have problems addressed, the better it serves the ongoing business relationship.
And while it is far from perfect, AI is in many valuable ways, superhuman, it’s able to do things humans simply can’t do, like being in all places at all times. Indeed, thanks to AI-powered bots and chatbots, business consumers never have to be alone. It’s never too late to call. Someone (or something) is always up and ready to answer their question or concern. The company can guide them—by AI—through the journey at every touch point.
Price Analysis to Help You Stay Competitive
Lastly, wouldn’t it be great to know if you’re getting out-priced by a competitor—before it happens? Using AI, businesses can stay on top of things like competitor offerings, pricing, etc., in real-time so they can adjust their own sales strategy before losing the business. That’s basically what we call magic—the ability to stop a potential competitor from hijacking of business before it even happens. How much more profitable would your company be if it could do that? One enterprise software company, PROS, has actually built a global business on being able to help companies from consumer travel to enterprise software optimize pricing to micro levels to help their partners maximize every transaction. This is practical and high-value use of applied ML and AI at its finest.
The great thing about AI enhancing the B2B buyer’s journey is that the benefits are mutual. Businesses will benefit from being better able to meet their customers’ needs, and customers themselves will have greater support on every step of their journey. In business, where choosing the right vendor, product, or technology often has higher stakes than in our personal lives, that’s incredibly good news. Another benefit of AI: it increases your potential to hang on to your client base by making sure you’re always proactively meeting their needs. That, in turn, allows you to ensure that you’re maximizing their lifetime value—something that will keep your business in the game long-term.