AI, startup hacks, and engineering miracles from your friends at Faraday

How customer-centric marketers use machine learning

Alexis Hughes on

You've probably noticed the growing hype around artificial intelligence (AI) in marketing. From chatbots to content creation to programmatic advertising — it seems like every other MarTech or AdTech platform is baking in some sort of AI capability.

With so many applications, it's easy to lose sight of what's most important in implementing an effective, optimized marketing strategy: deeply understanding your customers.

machine_learning_customer_insights_graphic

According to Forbes Insights, only 13% of businesses express a high degree of confidence that they are making the most of their customer data.

Stepping back, what does AI even mean for marketing?

At a high level, AI refers to a computer's replication of some aspect of human intelligence — pretty ambiguous, right? AI, as it exists today, is an umbrella term for a range of computer-enabled data analysis techniques — the most relevant and widely-practiced in marketing being machine learning.

Machine learning (ML) is the process of training computers to “learn” to recognize important patterns and trends in large datasets, with the goal of developing data models that can quickly categorize new data inputs and predict likely outcomes.

So, what does that mean for you, the customer-centric marketer? When using your customer data, or training data, as the basis of machine learning models, you can start to generate deeper customer insights and make better behavioral predictions. These can be around your prospects' and customers' likelihood to convert on certain campaigns, increase their purchase frequency, churn or lapse, or something much more specific.

Leveraging machine learning in your marketing strategy is no longer a luxury — it's a necessity. As competition increases and ad space gets more crowded, consumers have more choices of businesses to engage with, making machine learning critical to efficiently reaching the right people and keeping your customers engaged.

ML-driven insights marketers can't ignore

It should come as no surprise that the world's top brands are efficiently scaling growth by leveraging machine learning to prioritize their resources and personalize experiences across their customer lifecycles.

Here are some of the most important ML-driven insights marketers are using to craft better customer experiences and optimize their performance.

Behavioral insights and predictions

A vital piece of giving your prospects and customers a memorable experience with your brand is knowing who to engage with and when.

Have you ever been in the position as a consumer where you're targeted with ads that don't align with who you are or where you are in your customer journey? Perhaps you see an ad trying to get you to buy an item you've already bought, or offering you a first-purchase discount code when you're already a customer.

As a consumer, these experiences can be annoying and frustrating. And as a marketer, your message can end up diluted instead of impactful.

Behavioral predictions are crucial to proactively engaging your customers. Rather than reacting to your customers' behaviors, you're able to anticipate them and market to your most valuable customers at the right time, with content that corresponds to where they are in their journeys. This helps you prioritize your resources, optimize your marketing spend, and cut through the noise to better reach your target audience.

How can machine learning help predict customer behavior?

machine_learning_decision_tree_icon

Relatively straightforward machine learning algorithms can uncover predictive patterns hidden deep in your customer data. The random forest, or random decision forest model, analyzes your historical customer data, building a series of decisions trees to predict the likelihood that future inputs (e.g. new leads) will result in a target outcome (e.g. make a purchase).

If you advertise on Facebook, you've likely come across lookalike audiences, which are generated in a similar way. Using lead conversion as the example outcome, these models essentially predict the degree to which a new lead best “looks like” leads who've successfully converted to customers in the past.

Naturally, the data used to train or build your behavioral models will influence how they make predictions. Done properly, these predictive insights can have a tremendous impact on your return on ad spend, overall customer acquisition costs, and customer satisfaction.

saatchi_art_lead_gen_case_study

Persona-based insights and predictions

While predicting your customer's next move is immensely helpful in reaching the right people at the right time, it's not where the road to truly optimized marketing ends. Effective and thoughtful engagement requires an understanding of who your customers are as real people, so you can create hyper-personalized experiences that evoke emotional responses.

Salesforce Research revealed that 84% of customers say that being treated like a person is very important to winning their business. If you show your audience that you understand what motivates them to interact with you, whether they're an early prospect or a loyal customer, the relationship and trust between you and your customers grow stronger.

So how can machine learning help you personalize experiences at scale?

Customer clustering

Buyer personas, semi-fictional representations of your target customers, are instrumental in creating personalized content and creative that truly resonates with them. Traditionally, personas were created using basic demographic data, some psychographic data from surveys or focus groups, and a good amount of human intuition. While this approach has worked for years, it leaves too much room for human bias.

Customer clustering leverages a different machine learning technique than behavioral modeling: unsupervised machine learning. Rather than uncovering patterns that are predictive of a known outcome (e.g. likely to convert), unsupervised algorithms, like K-means, sort data into distinct groups based on shared attributes. The resulting groups, or clusters, form the foundation of unbiased, truly data-driven personas.

machine_learning_cluster_icon

As time goes on and new data is collected, running the clustering algorithm again may reveal new emergent personas, enabling you to refresh your messaging, creative, and other personalization efforts to stay relevant as your customer base evolves.

Burrow, a disruptive direct-to-consumer furniture brand, uses ML-driven personas to identify what color couches their audience segments see in targeted ads. They found that customers who are older, live in single-family homes, and have kids are more likely to buy couches in darker colors; customers who are younger, live in apartments, and have few or no kids are more likely to buy couches in lighter colors. With these insights, Burrow was able to push creative that reflected these attributes to the audiences that possessed them.

burrow_omnichannel_case_study

Location-based insights and predictions

Virtually all consumer-facing companies need to consider location in their marketing strategies, whether it's out-of-home advertising, brick-and-mortar marketing, or geotargeted search campaigns. However, these tactics are often expensive and difficult to do well.

geospatial_intelligence_icon

Geospatial intelligence refers to a suite of geospatial analysis techniques enhanced with a combination of behavioral and persona-based insights. Whether you're considering increasing investment in your existing markets, expanding to new ones, or looking to drive foot traffic to specific retail or branch locations, incorporating predictive insights into your geospatial analyses can drastically improve ROI.

  • Predictive penetration analysis aims to identify the maximum return you can expect from further investments in your existing markets. The resulting insights can help you understand the performance of your existing sites and determine whether you should increase investments or cut back entirely.
  • Predictive market analysis identifies specific hotspots of existing customers and customer lookalikes to guide where you should put new sites and optimize acquisition-focused out-of-home and geo-targeted SEM campaigns.
  • Predictive trade area analysis identifies the maximum distance customers are likely to travel to your existing retail sites. The resulting insights can help you optimize individually-targeted and geo-targeted campaigns focused on driving foot traffic to those sites.

Implementing machine learning

While several complex applications of AI are still years — or even decades — away from being fully developed, the democratization of machine learning is enabling nimble marketing teams to generate these predictive customer insights without having to spend millions on expensive consultants or hire large data science teams.

If you're considering adopting machine learning or looking for ways to expand your analytics team's bandwidth, our article, Is your marketing team AI-ready?, discusses important considerations to ensure successful implementation.

how_to_build_ai_marketing_stack_guide