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coding, AI in Marketing Use Cases

AI in Marketing Use Cases

Jennifer Trou
Head of Marketing

The best and most successful marketing is rooted in insights pulled from data. From social media engagement to purchase history, the data generated across hundreds of platforms has given marketers multiple ways to understand and reach current and potential customers. However, analyzing and interpreting that data is a resource intensive task, meaning many marketers only use some of the data available to them and potentially miss out on opportunities to maximize their reach. This challenge makes marketing an industry ripe for AI deployment to help marketers harness the full power of their data to reach, target and engage customers. The below are some of the top applications for AI and Machine Learning in marketing.

Recommendation Systems

Personalization works! Spotify, Netflix, and YouTube can make content recommendations with scary accuracy. In fact, 80% of movies watched on Netflix come from their recommendation system. According to a Salesforce study of 150 million shopping sessions, personalized recommendations led to 7% of total site visits, 24% of purchases, and 26% of revenue. Recommending products based on previous purchases or even search history has long been the model for marketers to suggest a product the consumer might consider. This is usually done via the search bar or a filter search, which require extensive use of tags assigned to products and can vary by retailer. However bad personalization can be detrimental. According to an Accenture Study, 41% of consumers switched companies last year over a lack of trust and poor personalization, costing businesses $756 billion!

Enter AI techniques such as computer vision, NLP, and embeddings which can understand both structured and unstructured data. More data means better recommendations. Newer deep learning approaches to recommendation systems can account for images, product descriptions, user reviews, product similarities, and past user behaviors to make more accurate recommendations for relevant products based on deep understanding of the product and it attributes helping shoppers to find items of a similar or complementary style.

For example, if a consumer likes a particular style of shoe they see online but doesn’t know how to describe it in text search, their quest to find a similarly styled shoe will either be difficult, time-intensive or just unfruitful. When deploying a tool that uses computer vision, a recommendation for a similar shoe could be served to that customer in a matter of seconds, increasing the likelihood of the purchase.

Audience targeting and segmentation

As platforms continue to segment, audiences are becoming increasingly niche and expect to be targeted as such. Maintaining this level of analysis manually requires significant time and resources, making it a perfect use case for machine learning. Deploying machine learning allows marketers to more quickly understand their customer’s behavioral patterns and more accurately predict what will be most relevant to them, increasing the ability to micro-target and personalize content to consumers.

In addition to the ability to micro-target consumers, AI can also enable dynamic segmentation. As consumers’ behaviors are constantly changing, dynamic segmentation accounts for changing customer behaviors and their ability to take on different personas at different times for different reasons.

For example, if a young person browses for a gift for an older relative, traditional segmentation might begin to serve content related to that purchase, while dynamic segmentation will allow for outliers in behavior and group behavior appropriately, giving marketers the ability to continue sharing relevant content.

Lead Prioritization and Prediction

For many B2B marketers, identifying and prioritizing leads requires significant legwork or the capital to purchase lists from third-party sources, which can quickly become outdated.

AI-powered lead propensity models can create a more accurate target list by identifying factors that make a customer most likely to be profitable or relevant. These models use customer and potential customer data to identify the variables that make someone more likely to purchase so marketers can reach them with timely, relevant content. It can also track the changes in those variables over time to ensure the target list stays current.

Churn Prediction and Customer Engagement

One of the biggest challenges marketers have is identifying customers who are about to discontinue the use of their product before they actually do so. Once they have stopped purchasing, the window to revive that customer is often closed. Add in the fact that it is 10 times more expensive to acquire a new customer than maintain a current one and this problem creates an even bigger impact on the bottom line.

AI can use customer data to identify what stage of “churn” a particular customer might be in, whether slowing down in product use, decrease in dollars spent or recent inactivity, giving marketing the opportunity to intervene and connect with them before they disengage - potentially saving a long-term customer.

Dynamic Pricing and Incentives

Anyone who has purchased a plane ticket, booked a hotel room or used a ride-hailing app like Uber or Lyft is familiar with dynamic pricing, the practice of adjusting prices to align supply and demand.

Previously this could be achieved by purchasing expensive dynamic pricing software and large data centers and deploying huge teams to understand when supply or demand would change and how to adjust the price.

AI systems can automate this process and include a broader set of data including annual calendars, consumer behavior, competitor pricing and even weather to calculate a price point that makes consumers more likely to buy or maximize their cart value.

Increased adoption of AI will increase marketers’ ability to target, engage and gain loyalty from their key audiences that can drive tremendous value and ultimately impact the bottom line.

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