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It’s no secret that personalization
is the future of marketing.
98% of marketing pros say it advances customer relationships. 90% see a measurable uptick in results when they implement it. And 48% are increasing their personalization budgets this year.
But here’s a tricky question: How do we not only start to personalize our marketing, but do so on a large scale at a 1:1 level?
The answer is in data science and technology.
In our latest white paper, our data scientists explain why data science matters for marketers. The short answer is that it takes raw data and turns it into useful insights.
Because just having data that tells you Customer Ava is self-identified amateur home cook in their 40s doesn’t really tell you how, where, what, and when to market to them. Knowing that amateur home cooks are 25% more likely to purchase your mid-budget knife set, on the other hand, gives you insight you can act on. Knowing that Ava has a history of purchasing Christmas spice mix in October every year gives you insight you can act on.
This is what we mean when we talk about taking data and gleaning insights from it. And the process of turning that data into insights is what we call data science. It takes us from information to action.
But here’s the thing: If your goal is to personalize not just for a dozen customers or a hundred customers or even a thousand customers, but for thousands, hundreds of thousands, or millions, data science isn’t enough to get you there.
You need machines where that data science—and a way to automate personalization—are built in.
But don’t take our word for it. If we look at industry leaders who are knocking personalization out of the park—like Netflix, Amazon, and Spotify—it’s pretty clear that technology is the key to their personalization success.
They’re not selecting movie cover art on a per-user basis by hand. Their teams are not doing the labor-intensive work of coming up with Spotify discovery playlists for each individual. They’re not hand-selecting add-on products you might like.
Nope. They are trusting their MarTech systems to do that heavy lifting for them—using AI, machine learning, and data science to understand customers on a 1:1 level and automatically serve them the best experiences and products for their needs.
So, what does automating personalization look like? It looks a lot like the Lytics decision engine.
When you flip the switch, our decision engine uses feature-engineered data and proven data science models
to automate the entire workflow, predicting who needs a message, what message will resonate best with that person, and what channel they’re most likely to engage in.
It then pushes that messaging out to them in real time, allowing you to run multiple effective, profitable campaigns at the same time.
The machine knows that customer Jeff Brown is most likely to engage with email on Thursday at 1:30 p.m. and that he loves content about dogs. It knows customer Natalia De la Rosa has an affinity for hedgehogs and is mostly likely to engage on Facebook on Saturday. It knows that Ava loves cute aprons and is likely to purchase the “kiss the cook” apron you’ve just released. And it automates everything to deliver those experiences to those individual customers.
And while Lytics can create powerful, real-time results for any business size, the more you scale, the more automation matters.
The answer, according to our research, is a resounding yes. Lytics customers who use data science tools, machine learning, and automation tend to blow their own expectations out of the water. And so do industry leaders who have their automated systems.
Don’t forget that 35% of Amazon’s sales come from its data-driven recommendation engine. Or that Netflix saves $1 billion each year in customer retention costs because of algorithms that predict which movies we’ll like.
Of course, not every company will be ready to jump into automated personalization with both feet. Sometimes it makes sense to take it slow—give automation a test drive and see how it performs when compared to campaigns managed by your teams.
In our experience, the answer is that the machine is simply more scalable and automation frees teams up to focus on other important things like content creation, strategy, and aligning even more teams within the business with personalization strategies and customer data-driven insights.
Curious about how automation could help you scale your personalization? We’d love to talk about your specific use cases. Oh, and don’t forget that we wrote a whole (free) white paper on all things data science.