3 Keys to Using AI and Data Science in Digital Marketing

The pandemic accelerated the shift to e-commerce, and brands are now competing more aggressively for consumers’ attention online. If you want to stand out in this crowded and competitive climate, using marketing data effectively to understand your consumers at a deeper level and create personalized digital experiences can help.

Many marketers are aware of the importance of consumer data, and they are already collecting lots of data from a variety of sources. However, some tend to struggle with the crucial next step: understanding how to use data effectively to identify and act on key insights.

Some marketers may feel overwhelmed by their company’s consumer data. This can be because they take a manual approach to data-driven marketing. A manual process starts by consolidating different data sources. Then, they analyzing data to create segments, rules, and personalized campaigns to act on. A manual process like this can be arduous and time-consuming.

However, thanks to AI and data science, marketers may be able to reduce the time and resources required to reach important segments of their audiences. If you employ the right technology and approach, you may be able to more easily target every corner of your audience without missing opportunities for relevant, personalized experiences that can drive business results.

AI-driven predictive personalization tools enable you to not only rely on historical data, but also act on consumers’ changing preferences and external events in real time while saving time and money on marketing resources.

Using Data Science in Digital Marketing

Data science helps brands define and uncover patterns that humans can’t see. If you pair AI and data science, your team can save time on manual analysis and execution and focus on the next step of your marketing efforts: putting that data to work.

Predictive AI tools can collect and study insights faster and more effectively than any human can. Plus, they can be fully implemented into your team’s workflow within a short timeframe. Although another option is to build internal teams of data scientists, this could take months or even years, and they may not be as productive or cost-effective as an AI tool. Automated technology becomes an extension of your team and serves as an extra set of eyes that captures opportunities you might otherwise miss.

If your brand has faced difficulties in data collection and implementation and is ready to start using data science in digital marketing to make the process smoother, consider these tips:

1. Define what success looks like.

The digital world is packed with data, but not every insight is relevant to your marketing initiatives. It’s important to know your goals before applying data-driven insights to your campaigns. Otherwise, you might veer off course and waste precious resources focusing on irrelevant details. Make sure there’s a clear link between your marketing initiatives and your business objectives. The data you collect and crunch should help your company achieve its desired outcomes.

Many organizations implement data science with one specific use case in mind, such as improving engagement on a particular webpage. This is a great place to start, but it only scratches the surface of what predictive AI is capable of. Instead, consider data science implementation as a holistic end-to-end process with broad implications for your digital marketing efforts. It does not have to be limited to collecting data or solving one particular problem, as it can help you acquire customers and grow multiple facets of your business.

2. Strive to understand your customers on a deeper level.

Understanding consumers beyond their demographic characteristics or purchase habits can be a critical part of creating meaningful experiences. It’s important to show consumers that they’re more than just a “35- to 40-year-old female wine drinker.” Some want you to understand them at a deeper level without feeling like you’re interfering with their privacy.

The first step to connecting with your consumers at this level is to collect data and understand how you can use it effectively. What connections can you make between different contextual data points to delight consumers, and how can you use technology to amplify the impact of this on your business?

One of our clients is an alcohol retailer that previously put its customers into three segments: wine drinkers, beer drinkers, and spirits drinkers. Although this makes sense, the business was actually sitting on a treasure trove of data that it didn’t know how to use effectively. I might usually drink wine, for example. But during summer weekends, I might buy spirits for parties and barbecues. Segmentation that doesn’t take this into account means missed opportunities to sell me spirits at the right time. After integrating AI into its marketing processes, our client can understand how its consumers’ preferences change with different contextual factors such as time, weather, current events, and more. These insights have empowered the company to reach new audiences and build loyalty with existing ones.

3. Personalize experiences for all consumers.

Although segmentation is the norm for many brands, personalizing experiences for individual consumers allows you to capture missed opportunities. For example, many brands typically spend a lot of time analyzing data and identifying insights for Christmas campaigns. Because this is a time-consuming process, they’re unable to do the same for other holidays such as Hanukkah, Diwali and Lunar New Year. This means they are missing out on building relationships with a large section of consumers.

Using predictive AI to create personalized experiences in these situations allows you to cater to the needs and preferences of more dynamic segments and individual consumers. It’s not just about having access to data, but also about using resources effectively – like combining AI and data science – to create efficiency and capitalize on otherwise missed opportunities.

Creating personalized experiences that are scalable can be a game-changer for brands. AI-driven predictive personalization tools enable you to not only rely on historical data, but also act on consumers’ changing preferences and external events in real time while saving time and money on marketing resources.

The idea of using data science in digital marketing might sound daunting at first, but it’s a necessary strategy to stand out to your consumers. If your campaigns are driven by demographics, psychographics, and contextual factors, your brand can connect with audiences and create meaningful moments that are also great for your business.

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