AI marketing automation has completely changed the game for digital marketers, allowing you to launch and scale campaigns swiftly, try out different ideas, and pivot quickly as new opportunities arise. The time-saving and cost-cutting potential is enormous. To really get the most out of your campaigns, though, you need data science.

That’s because automating marketing tasks and strategies only works if those decisions are based on solid insights from accurate, up-to-date data. Marketers are collecting tons of data about their customers, potential customers, campaigns, and broader market trends all the time. However, data science is how you get from amassing that data to truly data-driven marketing strategies. It’s how you build models that predict behavior in the first place, so you have a clear idea of who your AI marketing automation strategies should target, as well as when and where. It’s how you ensure that you’re basing your plans on the best data at all times, whether you’ve collected it yourself or are getting it from external sources. 

Let’s take a look at some of the key ways that data science improves your marketing automation strategies. 

How data science boosts your AI marketing automation 

1. Dynamic website optimization 

Today’s customers expect a level of personalization and customization from their online experiences that would have been unimaginable a few years ago. Web designers are rising to this challenge by building sites that automatically, creatively tailor themselves to the needs, tastes, and interests of each visitor.

The trouble is, of course, that you can only tailor an experience to someone if you understand that person and what they want from your website. For that, you need, firstly, the right data and insights. Secondly, you need a way to turn this data into predictive models that help you identify the key characteristics of each visitor the moment they land on your site, identifying the customizations most likely to appeal to them and tailoring their experience in the blink of an eye to maximize the chance of a conversion.

For example, let’s say you’re looking to enhance your marketing segmentation, targeting products on your site to visitors based on their intent, needs, age, and price point. You might already collect some basic demographic, behavioral, and transactional data on these visitors through your website and social media channels. However, by enriching this with external geospatial data, such as more nuanced demographics and information on that customer’s proximity to rival products from your competitors, you can add new parameters and generate more sophisticated models. This leads to far more accurate predictions of how a particular visitor will respond to what they see when they land on your site. As a result, you can offer a truly personalized experience, responding to the precise needs, interests, and behaviors of each visitor, rather than a few generalized options that loosely cater to a broad group. This has been shown to increase conversions and order value by up to 20%.

2. Lead scoring that optimizes marketing spend

There’s no sense in throwing resources at the wrong types of leads. Bringing a ton of new people into your advertising funnel might be a nice ego boost, but if these people are never going to make a purchase or download your app, or they’re likely to cancel their subscription before the free trial period is over, they’re no good to you. In fact, they’re actively bad for you, since your team will be wasting time and resources on these leads at the expense of others. Automation reduces that risk somewhat, but it doesn’t change the fact that, without a strategy backed by data science, you may simply be barking up the wrong tree.

Machine learning models can be used to frame core KPIs as data science questions. For example, you can build models based on Customer Lifetime Value (CLTV), Return on Marketing Investment (ROMI), or the churn rate. These help you to predict which leads are likely to become valuable customers and which are not.  In turn, you can transform these predictive insights into AI marketing automation processes, informing smarter, more effective campaigns. 

3. Lead nurturing 

Identifying the best leads isn’t enough on its own, of course. You still need to figure out how you will keep driving those potential customers through the funnel without losing them along the way. 

Getting this strategy right is a delicate art that combines figuring out exactly what kind of content, assets, and messages these people need at what stage in their customer journey – and then making sure you deliver this content to them at exactly the right moment. Once you know what to do, marketing automation tools will help you deliver and adapt your strategy, including through trigger emails, split testing, and so on. 

But the fact remains that you can’t know where to start with automation unless you have built insightful profiles and can predict how people will behave at various stages in the chain. Data science provides these answers, helping you account for a wide variety of factors based on multiple sources of information.

4. Social media retargeting 

It’s hard to overestimate the role of social media marketing for most companies today. Automated retargeting makes it easier to reach potential customers wherever they spend their time online. As these barriers to entry continue to drop and more and more businesses enter the playing field, you really can’t afford to make mistakes. There’s just too much competition. 

Ad retargeting is a handy way to narrow your focus to internet users who have already shown an interest in your product, service, or company. Even if they’re not receptive right now, the fact that you caught their attention once means they might be swayed later. Done badly, though, you may find yourself inadvertently putting off those customers more. People are increasingly privacy-conscious; reminding them that you know exactly what they’ve been up to online can feel creepy. What’s more, feeling hounded by a label because you happened to glance at a sweater eight months ago, or having a retargeted ad ruin a surprise gift to someone you live with, doesn’t exactly encourage brand loyalty.

Bringing data science into your strategy allows you to develop far smarter and more subtle approaches to ad retargeting. Firstly, by combining broader data on user activity, you can get a more holistic picture; one that goes beyond established but simplistic rules-based systems. Secondly, you can go beyond the obvious, predicting what other kinds of things this customer might be interested in later on, rather than simply hammering the same product they already reviewed and rejected. 

5. Ad bidding 

Getting the right ads in the right places at the right time is absolutely vital for any business operating online. The only thing more important than bidding for the right ad slots is making those bids in a time-sensitive manner. If you take too long, or you make the wrong call, you could wind up wasting a lot of money, while missing out on the opportunity anyway.

AI and machine learning not only make it possible to identify the ideal placement and timing for your ads. By keeping these models well-fed with fresh data, you ensure that you get those answers fast enough that they never lose their relevance before you’ve had time to bid. Combining this with automated bidding means you then apply that knowledge instantly, too, securing the very best ad placements at all times. 

Final thoughts

The value of automation is that it helps you scale up your efforts, getting more done, fast. This only benefits you if those individual actions are the right ones, though. Otherwise, you’re just taking something that doesn’t work and doing an awful lot more of it! 

Instead, you need to combine automation with valuable data and predictive models. Whether that means growing your data science team, investing in a platform to speed things up, or outsourcing the heavy lifting entirely through Data-Science-as-a-Service (DSaaS), you simply can’t afford to leave the data science out of your AI marketing automation efforts.