Have you prioritized the use of artificial intelligence (AI) in your small-to-medium business (SMB) marketing plans to improve customer experience (CX)? While you may think the collaboration between the two is far-fetched, the connection may be closer than you can imagine.
Do you remember the Gary Kasparov vs Deep Blue chess match in 1997? In one of the first artificial intelligence (AI) case studies, Kasparov — one of the world's greatest chess players — lost to IBM chess machine Deep Blue. It was in this moment, over twenty years ago, that the world opened their eyes and began to see that it, in Kasparov's words, it's "impossible for humans to compete", with intelligent technology especially considering humanity's predisposition for error.
Hey, Siri. Hey, Alexa. Everywhere you turn, there's a good chance you'll see how AI is fully integrated into our lives. When a music-streaming app recommends songs to listeners, it has all the data needed to make good decisions. In a chess match, piece placement and relativity to the other pieces are the data points that players need to make the right moves at the right times.
So why have companies been so slow to adopt AI into their business strategies? Quite possibly, it's due to disconnected customer data, which makes it difficult to facilitate appropriate AI decisions. Marketers using fragmented data have limited visibility, leading to a customer experience that isn't well-designed and broken customer journeys.
Marketing decisions help guide prospects and current customers along a journey, and there are many different types of decisions that go into a successful marketing strategy, such as:
Here is where aggregating and connecting data is so important when B2B (business-to-business) marketers begin to adopt and depend on AI. Predictive scoring can help identify when purchasers are likely to buy, and the more data attached to the customer profile, the more likely these scores will be effective and drive sales. The same can also be said for predictive segments, content, or any time an AI-assisted decision needs to be made.
As a graduate student, one of the projects I worked required that I manually assigned numbers to keywords from interviews. Then, I counted how many times those keywords were used. The intent of this project would show valuable information gleaned from the interviews. However, doing this manually was tedious. It required I spend hours in front of a computer screen, and took weeks to complete. Remember the propensity for human error that Kasparov mentioned? It was sure to have caused some imperfections in that study. If a machine were taught to work through that process, it could have completed it much more quickly — and without error.
There's also the opportunity for machines to take assignments to a level deeper with Deep Learning. Using my example, the data collected was connected and related in a easy-to-understand manner. What's happening now is that companies are collecting data from a multitude of channels, sources and devices, but once they've accumulated it, they often don't know what to do with it. Valuable data gets discarded when instead there was an opportunity to connect and mine for deeper insights to help inform decisions.
Imagine knowing that when your chess opponent wears a certain brand of watch, it tips their strategy and moves. While this piece of information may appear unrelated, and even unimportant on its own, data is most valuable when looked at in a larger pool.
B2B marketers who are active on social media may know about Latent Semantic Analysis or Natural Language Processing which looks at things such as sentiment and possible actions based solely on social posts.This is an example of deep learning in action. Where humans in a small-to-medium business might take months or years to find connections, AI does it rapidly and in real time, which reduces human error and identifies subtle nuances, such as not allowing a tweet which was intended for customer service publish simply because the user hadn't drank their first cup of coffee yet.
As salespeople work to expand within a key account, predictive lead scoring helps them to identify who the right contact is to engage with next. When AI is used to email the prospect and drive them to a website, personalized and predictive content can then enhance the customer experience. Though, this experience only takes place based on properly connected data, which means the predictive scores and content need to be aligned with marketing efforts.
Imagine what it would be like to attempt to predict content for someone based solely on his or her job title. While you may occasionally see some success, you'd certainly be better equipped if you also had access to information such as their location, company size and any other relevant data points.
The partnership between humans and intelligent machines is inevitable, and it's necessary to figure out which use cases make sense for AI, as well as which don't. The decision often comes down to having the necessary data available, and also connected, for maximum effectiveness. The time and effort a SMB invests now into connecting data allows companies to work more harmoniously with AI solutions and apps as they hit the market.
Are you ready for the impact an AI partnership can have on improving your company's customer experience? The next step could be as simple as working with your sales counterparts to determine where AI fits. Integrating your strategy with AI efforts will allow you to work alongside the machines instead of against them.
By Mark Tarro, Senior Solutions Engineer, Oracle