Bridging the Gap Between Data Science and Marketing

Today’s consumers have high expectations from their favorite brands. After experiencing how leaders like Amazon, Netflix and Starbucks seem to anticipate and deliver offers tailored to their individual desires, consumers now demand the same from their other favorite brands. They want personalized experiences, whether they’re at a store, online or using a loyalty program app.

With so much customer data available, it seems like it would be simple to understand what each customer wants, and what’s required to make them take action. But it’s not that easy. Traditional approaches to offer creation are largely manual tasks. They require a lot of time and effort to developers that apply to segments or microsegments of customers who share similar characteristics, buying patterns or preferences.

How can you deliver a true 1:1 personalized experience without this kind of friction? The key is bridging the gap between data science and marketing. Though typically siloed organizations within a brand, these two groups can leverage artificial intelligence (AI) and machine learning (ML) to improve personalization and help their brand gain a competitive edge.

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