The Data Scientist Inside The Marketing Assistant

A man analyzing financial data on his laptop.

KPI dashboards are flashing in the office; flash reports are flush with beautiful data viz; and you’ve even got a job req open for your very own data scientist (I bet it has been open for a while, too).

You’ve successfully progressed to become a data-driven marketer. At the same time, your customers are providing a veritable treasure trove of interesting data. In fact, the typical customer is throwing off 17 events and is represented by six different identifiers in your marketing systems today.

This volume of riches is taxing, though. Making your way through this ever-expanding pool of data is fraught with inefficiency and can have you chasing shadows. The truth is, you could use a hand in separating signal from noise.

We created Zam for just that purpose — to assist you. Zam is a marketing assistant that provides the guidance you need to actually SEE the opportunities in your data.

Zam helps you get ahead and anticipate shoppers’ next moves to know when the moment is right to engage. Your path to data-driven excellence is not a race to mine the most data — the goal is to know when your customers are likely to buy and what your customers are looking for. Here’s how it all works.

Predicting Customer Behavior

Everyone wants to know what their customer is going to do next. And while shoppers can be unpredictable at times, data science provides you with an advantage when you look ahead.

Zam can predict when a shopper is likely — or not — to buy. Zam performs trillions (yes, with a T) of calculations, drawing on millions of events and more than one hundred thousand weights to model your customer behavior — including both known shoppers and anonymous visitors.

With this data in hand, you can create campaigns that target only the customers giving off strong buying signals for specific campaigns, or the ones who need some more encouragement. You can get incredibly granular and personalized with your campaigns because of the power of this customer behavior data.

“Now, with Zam, we have the ability to send out a very targeted email to a very specific set of customers and know we are going to be getting a very good return.”

Sam Sudman, Mizzen Main

Uncovering Product Opportunities

I’m sure you already know your best selling products. But have you found your hidden gems? What about the products that are duds? Looking across your entire product catalog, Zam surfaces which products are under- or over-performing according to both sales and product views. Products that are outliers — either in the bottom right or top left quadrant — are key products you should examine.

With this view into your product performance, you can re-merchandise the right products in the right channel. Now you know exactly which products you may want to either promote more heavily or scale back. That way, you can ensure you’re connecting your shoppers with the products that are resonating most.

Data Science, Trained on Your Brand

While all of these specific data-powered reports are powerful, they’re meaningless without context. Most importantly, your assistant needs to know your business and the nuances that make your brand unique and your customer engagements special.

Zam adapts to your business’ specific data, providing predictions and insights based not on a one-size-fits-all approach, but rather tuned to the sources and sequences of interactions between your brand and your customers. By incorporating point-of-sale, loyalty, and even data from guided selling applications (and more), you have a more complete view of your customers and can make better predictions.

With Zam, your marketing assistant, you can move faster, and grow faster. Zam turns your data into your advantage, offering a helping hand that predicts customer behavior and surfaces product insights. With data science powering your marketing, you can boost relevance and trim effort so you can design the engagement your customers need.

Want to see Zam in action? Watch this live demo.

This article was originally published by our friends at Zaius.

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