Predictive Analytics | An Overview

It has happened to all of us: a perfectly timed ad, email or recommendation that captures your attention. Typically, that kind of outreach leads to you to open multiple browser tabs and begin comparisons of features and pricing, resulting in a purchase, not to mention unintended items in your cart.

Marketing has done it’s job – a sale was made. There is plenty of credit to go around, but the unsung hero, the core of that transaction was the data which guided interaction.

Targeted marketing is nothing new. As marketers, we are witnessing exponential advances in behavior mapping technology and this is a good thing. Gone are the days of blunt-force outreach that relied on reams of wasted paper for direct-mail campaigns, spammy emails, and irrelevant content that depleted marketing dollars.meeks_analytics

Analytics driven marketing is already here, and predictive marketing is the future. Below I have listed three high-level points for your consideration.

Behavioral Clustering | What Makes You, You?

Thankfully due to modern processing power (and evolving software tools) algorithms can segment prospects based on many more variables then you or I could. Also, It’s not unusual for clusters to be different based on dozens of prospect dimensions, this auto-segmentation is the foundation of good, actionable data – and a time saver.

As an example, my prospect dimensions are: single male, Jeep owner living close to the North Georgia mountains with expendable income, so one could surmise I am in the market for off-road upgrades. Once off-road vendors learn this through actions such as type of email opened and click-through rate, a picture begins to form regarding the type of products that would be of interest to me.

Propensity Models | What Are You Likely To Do?

There are dozens of propensity models, but only three are worth mentioning here; will they engage, will they stall, or will they convert? The key take-away is propensity models allow you to anticipate future behavior based on action or lack thereof. As prospect data is refined within a CRM, targeting becomes more precise, always nudging towards action or removal.

As another example, I absolutely enjoy good old-fashioned catalogs. Personally, the tactile experience just can’t be replaced by a smart-phone screen. Good news is most online vendors are happy to mail out a catalog in exchange for the completion of a standard form. I was more than willing to provide email, zip code, year/model of my Jeep knowing I would be promptly added to their CRM and receive the requisite welcome email. I’m ok with that, and most consumers are if you offer something meaningful in return – more on this in future posts.

What’s the likelihood of a purchase? Should they entice with a limited offer/discount to spur action? The waters are being tested, a virtual case file is being built on me.

Collaborative Filtering | You Did This, Now Do That.

To understand what amounts to ‘recommendations’ one only needs look to Amazon for how this tactic was pioneered. In retail, it is commonly referred to as up-sell, cross-sell, and next sell. When collaborative filtering is utilized correctly, not only can you inspire more spend, you also begin to understand when that new customer is most likely to re-order.

Final example, I’d really like a new front bumper for my Jeep with a wench for self-recovery. So having clicked on a few emails of interest (and browsed the vendor website multiple times) their CRM has determined a first-time customer discount based on my known preferences would be appropriate. Another example of this would be clicking out of a shopping cart only to find those items listed in a follow up email with additional discounts, like free shipping. It’s the old adage, never leave money on the table.

The best part is a majority of CRMs, Automation, and Analytics Tools make quick work of the data crunching; it’s up to you and your team to utilize what is learned to move the needle for sales enablement.

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