How to Be More Intelligent in Marketing

As a data scientist, I’ve worked with many companies that are looking to implement AI and ML in marketing use cases. Though many marketers are excited about the possibilities of AI, they also have trouble understanding what AI is and how to utilize it in their jobs. There are a lot of great ways to use AI in marketing organizations. Some use cases I’ve seen include predicting the success of marketing campaigns, allocating and adjusting budgets, recommending content, improving search optimization, determining what drives sales, and so much more!

One of the most important advantages of AI for marketing is hyper-personalization, which is the ability to understand the exact preferences of each and every individual customer or prospect. In this new world in which brand loyalty has been eroded, and new regulations about consumer privacy are rolling out, marketing teams need the ability to personalize matters more than ever. Getting granular about understanding your prospect and customer segments allows for a lot more responsive reaction mechanism and, as a result, a better connection with your audience.

What Are the Tools That Can Support Marketers to Be More AI-driven?

Becoming an AI-driven Marketing Department requires more than an intelligent marketing point solution or traditional marketing automation software. It requires you to be able to take all of your data: customers, marketing, sales, and operations, and transform it into something that will give you a fast and intelligent leading indicator of where, who, and how to engage with your customers and prospects.

Let me share with you a real-world example. Beacon Street Services is the services arm (and one of a handful of affiliates) of Stansberry Holdings, and they produce financial publications that are exclusively available through purchased subscriptions. For its marketing and sales teams, there was an opportunity to improve on previous tactics and processes of selling subscriptions, with a clearer feedback loop and signal for marketers to optimize their campaigns.

They realized there was value to applying a data science approach to this, especially given the wealth of valuable data they already had. They hoped to identify better buying criteria with a modelling approach to help their marketing team run more targeted and effective campaigns.

Their hope was true, so when they decided to use an AI and ML tool, it led to improvement in accuracy and significant time saving for the team. Doing this process only in one week — a process that used to take them more than six months. And the business impact has been significant: Beacon Street Services is on track to realizing $15 million in additional annual sales, directly attributable to augmenting their marketing with AI.

Ethical AI in Marketing

As marketing teams adopt AI, there is also a responsibility to ensure that it’s truly representative of your customer and prospects, and that bias doesn’t creep in. What do bias and fairness mean in the context of AI-driven marketing? It can mean ensuring your marketing campaigns aren’t skewing to a certain profile that isn’t truly representative of your target market or inadvertently targeting promotional offers to one group and not another.

To understand how ethics factor into AI use in marketing, I invited Sarah Ladipo from the DataRobot’s Trusted AI group to share some of her experiences. “Marketing data and use cases often use sensitive features (features that are legal to use but which would cause reputation damage if they were used like gender, and race), which brings a variety of associated ethical risks. When working with this type of data, marketers need to understand how to use this data in a way that decisions that are made are not biased and that those decisions are having a good impact on society.”

“When marketers learn how to use data that is not biased, AI systems will choose unbiased algorithms if they follow the ideals of transparency and trustworthiness,” Sarah says. “Creating explainable AI that allows users to understand how it arrived at a certain outcome allows AI to be more trustworthy. The truth is that AI is only as smart as the information it has been given so far. It is entirely up to business executives to make the most of this technology’s enormous marketing possibilities.”

Sarah continues: “A part of marketing is evaluating your product and identifying which demographic out of a large population will be interested to target. This can have a number of drawbacks, including ethical implications to consider. A line can be crossed where marketing can be exploitative. In some cases sensitive information will not be used in marketing efforts but the way data can be used can be problematic.”

I’ve Been Hearing All About Becoming AI-Driven, but How Does One Actually Start on That Journey?

First, pick a use case and ask yourself — why is this important for you? Second, define the KPIs — what is success for you? For your business? I recommend choosing something you can quickly test if you’re new to the AI journey. Third, make sure you have enough of the right data. And lastly, use an AI platform to achieve your goal.

Becoming AI-driven in marketing doesn’t require a 12-month process to get started. It should be fast, quick, and like a flywheel, where you can quickly take a few use cases and get onto new ones.

We have a whole library of AI solution accelerators that can help you understand where and how you can get started.

About the author

Atalia Horenshtien

AI/ML Lead - Americas Channels, DataRobot

Atalia Horenshtien is a Global Technical Product Advocacy Lead at DataRobot. She plays a vital role as the lead developer of the DataRobot technical market story and works closely with product, marketing, and sales. As a former Customer Facing Data Scientist at DataRobot, Atalia worked with customers in different industries as a trusted advisor on AI, solved complex data science problems, and helped them unlock business value across the organization.

Whether speaking to customers and partners or presenting at industry events, she helps with advocating the DataRobot story and how to adopt AI/ML across the organization using the DataRobot platform. Some of her speaking sessions on different topics like MLOps, Time Series Forecasting, Sports projects, and use cases from various verticals in industry events like AI Summit NY, AI Summit Silicon Valley, Marketing AI Conference (MAICON), and partners events such as Snowflake Summit, Google Next, masterclasses, joint webinars and more.

Atalia holds a Bachelor of Science in industrial engineering and management and two Masters—MBA and Business Analytics.

Meet Atalia Horenshtien