The Role of AI in B2B Marketing
We’ve started World Wide Web evolution with Web1.0; It was mostly static . Then, we started 2.0 with dynamic content with interacting more with the end-user, such as blogging, tagging, social media, etc. Finally, web 3.0 started with more data, natural language processing, and behavioral analysis.
I am interested in the marketing applications of machine learning. I like to discuss the article “The Role of AI in B2B Marketing: Using machine learning and behavioral modeling to generate leads”
Why do we need Machine Learning?
The regular B2B marketing approach is simple just find a prospect give the right information at the right time and show your solution is the right one to solve the problem. If it was that simple why do some organizations are more successful than others?
” Turns out, identifying the perfect prospect at precisely the right time is similar to, but a lot harder than spotting a red umbrella in a sea of black ones. Prospects worth pursuing often can only be identified by subtle cues, patterns of behavior that can only be differentiated by careful analysis of just the right data.”
As we all know; using ML starts with understanding your data.
Marketing Funnel is a basic customer Journey. Customer starts with awareness, education, interest, differentiation. Only a small fraction become an identifiable lead by signup for a free trial or contacting the sales team. All the other visitors who never identified themselves with their company name, phone number, email, etc. Did we lose them forever? Or how can make this journey faster and more efficient?
Research and Proactive Outreach
Anonymous visitors which are non-converted visitors we have their ip address which is unique. and their ISP (Internet Service Provider) We can store these unique IP addresses for our research.
What do we have?: Unique IP address, ISP, and server logs (their referral page or search engine, time spent on the page, how many time visited the same page ) By tracing each visitor with the data we collect, we can make a fair analysis about the interest of the visitor.
We created a model on top of this data. Based on this model they learned:
“The point is that this approach doesn’t give us just a binary sense of should/should not have converted, it prioritizes our entire list for us based on the probability of predicted conversion.”
Results based on the model were the right start for the future approach of predicting leads.