This notebook aims at analyzing the content of a real E-Commerce database that lists purchases made by 25,500 customers over a period from November 2018 to April 2019. Based on this analysis, I develop a RFM model that allows to anticipate the purchases that will be made by a new customer, during the following year and this, from its first purchase. RFM (Recency Frequency Monetary) analysis or RFM segmentation is an effective marketing technique to identify your best customers. Instead of reaching out to 100% of your audience, target only specific customer segments that can prove beneficial for your business in future. Deduct most recent purchase date from today to calculate the recency value.

Using AI to boost sales is not a new idea. In fact, back in 2017, the Harvard Business Review did an extensive story about one sales office of motorcycle maker Harley Davidson that was able to increase sales from two bikes per week to 15 per weekend using AI-powered marketing support. If predictive analytics increased sales leads by 3,000 percent back in 2017, what's it doing for businesses now? AI and predictive analytics are still making an impact for businesses in terms of identifying customer trends, building customer profiles, and constructing tighter potential target audiences. However, it's also doing a lot more, both in terms of how it's able to gather data and how it's able to use it to offer customers the personalization they want and need.

Some of the best opportunities for go-to-market teams center around uncovering inefficiencies in the business -- e.g., reducing marketing waste, accelerating lead or account qualification, optimizing channels and programs. Since the potential returns of improved sales and marketing performance are significant, there's no reason to wait. It's time for every marketer to recognize that an arms race for data is underway, and those who don't evolve the way their businesses operate based on data will soon fall behind.

Among the multitude of new technologies entering banking's sphere, machine learning likely will quickly come to the fore. Imagine some poor bank employee sitting at a desk, peering at multiple computer screens. On those screens come waves and waves of charts, transaction lists, voice recordings, geographical data points, and social media posts, all constantly changing--and all focused on a single bank customer. The bank employee's job, at the moment: Decide whether that customer actually used his own credit card to buy a big screen television, or if that transaction, which just occurred, is fraudulent. And he only has a moment to decide.

Some of the best opportunities for go-to-market teams center around uncovering inefficiencies in the business -- e.g., reducing marketing waste, accelerating lead or account qualification, optimizing channels and programs. Since the potential returns of improved sales and marketing performance are significant, there's no reason to wait. It's time for every marketer to recognize that an arms race for data is underway, and those who don't evolve the way their businesses operate based on data will soon fall behind. Some opinions expressed in this article may be those of a guest author and not necessarily MarTech Today. Staff authors are listed here.