How can I use AI?
Artificial Intelligence, and its building blocks of predictive analytics and machine learning, currently deliver the greatest rewards as organizations move towards improving efficiency. You get to keep doing what you’re doing, with whom you are doing it, but you use the feedback from your activities to train a model that will (soon) help make segmentation and prioritization decisions on your behalf in your sales and marketing campaigns.
Here’s an example of what we mean: if your inside sales team makes ten million dials a year, you can use AI to find the properties of the accounts that predict a higher conversion rate, such as company size, revenue, previous contacts, or if you’re using MRP Prelytix, online research that those accounts are conducting. MRP’s customers have seen a 25 – 35% increase in conversion rates, measured from lead to close, just by using predictive analytics.
You can also use that same data to create marketing segments for your direct mail/email and digital advertising campaigns. Successful content marketers drive downloads and engagements by building as many as fifty pieces of content for a given campaign. By classifying your content by segment and using AI, you can reduce and prioritize the content creation for your campaign, based on demonstrated intent, while delivering the same results.
MRP clients have had successful direct response campaigns with only a few pieces of content, just by focusing on the segments that are most likely to respond to that content. They are seeing a 25% conversion rate of leads to pipeline, and overall campaign goals exceeded by 65%.
How do I Implement AI?
We’ve all see the warnings on certain television ads, “Do not attempt this at home!” It’s the same for implementing truly effective artificial intelligence. To get the best results, you really need to engage experts in this process.
If you’re like most marketers, you don’t have the budget to hire data scientists or business analysts to design, build, and implement an AI solution. However, you can get access to these resources on a fractional basis by working with the MRP Prelytix team.
Here is how MRP recommends getting started with AI.
Step 1: If you want to get data-driven advice that will drive your marketing and sales efforts, get your data together. Ideally you will have “close” data consisting of leads identified and qualified to varying levels, and a number of close-categories such as wins, losses for budget, losses against a competitor, not-ready-to-buy, and so on.
Step 2: An MRP data scientist can help combine this data into a model, and bring that model into our massive MRP Prelytix intent data set. We can help you analyse the online original research to see if predicts the wins so you can call them first; or predicts the losses so you don’t waste time contacting them.
Step 3: Next, we will test the results of the model and keep track of how well it predicted your successes. Where it succeeds, you’ll want to feed that data into the next iteration of the model. Because machine learning enables the algorithm to learn from its mistakes just as we humans do, it is crucially important to collect both positive responses and data on where the model fails.
Step 4: A good business analyst can help diagnose the model faster to identify where more parameters (inputs) might help (the more inputs the merrier) or whether we can filter our data to look at the results in different way. AI isn’t quite ready to tell us why our model works or doesn’t work – we still need humans for that. But it’s important to add transparency and understanding to the process by decoding the model on a recurring basis.