Understanding Customer Behavior with Machine Learning

The better a business knows their prospects and customers, the more likely they are to be able to generate additional leads, close sales, and generate revenue. The rapid development of new technology has made this easier than ever, giving teams across organizations access to tools and data they can use to ensure marketing, advertising, and retention efforts are targeted to the right people, at the right time, with the right message.

One of these types of technology is machine learning (ML). Read on to learn more about:

• What is machine learning?
• How to use machine learning to understand your prospects
• How to use machine learning to nurture your customers

What Is Machine Learning?

Machine learning is the development and use of computer systems that are able to learn and adapt without following explicit instructions. ML uses algorithms and statistical models to analyze and draw inferences from patterns in data, making them more effective at predicting outcomes.

ML systems automatically “learn” through the following:

• Supervised learning: In this case, you’d feed historical input and output data into ML algorithms, and processing between each pair allows for shifting in the model to create outputs more closely aligned with the desired result.
• Unsupervised learning: Here, the machine looks for less obvious patterns in data, making it useful to identify patterns and use data to make decisions. It’s primarily used to create predictive data models.
• Reinforcement learning: This allows a system to learn by interacting with its environment and getting a positive or negative reward, allowing the machine to understand the problem and environment better. Reinforcement learning requires less management from humans, but many ML platforms don’t have these capabilities yet as they are much more sophisticated.

Examples of ML include:

• Social media platforms. For example, Facebook notes a user’s activities, comments, likes, etc., and the algorithms learn from those activities to make pages and friend suggestions tailored to the user.
• Siri, Alexa, and Google Now are virtual personal assistants powered by ML. These devices collect information and refine it each time you interact with them, then use the data to give you more personalized results.
• Ecommerce sites like Amazon uses ML to make product recommendations and suggestions tailored to you based on past purchases, searches, and activity.

Are Machine Learning and Artificial Intelligence Different?

Technically, ML is a type of artificial intelligence (AI) that allows software applications to become more accurate at predicting outcomes without being programmed to do so, using historical data as input to predict new output values. AI solves tasks that require human intelligence while ML is a subset of intelligence that solves specific tasks by learning from data and making predictions.

How To Use Machine Learning to Understand Your Prospects

In order to generate leads and new customers, it’s important to be relevant. Companies can use ML to define the right features in their best products, and then use ML to find the best model for predicting that feature value and providing it to prospects.

In short, ML can determine the best predictive characteristics and run a data model against your existing prospect database or client population at large, and the resulting predictions can be used to segment prospects and/or prioritize those with highest closing potential.

Companies can use ML to better understand what aspects of their prospect data are strongest predictors of future profitably through information like purchase data, data about how the prospect was sourced, or prospect demographics. For example, if the model shows prospects that come from a particular channel or location are identified as the highest value, you can put additional budget or focus on those channels or areas.

Not only can this be used in real-time as prospects come into your system, but you can also run it against a large population to isolate the highest value prospects.

To summarize, machine learning can:

1. Extract relevant features about who your customer is, why they became customers, why they still buy from you, and why prospects choose you over similar product/services.
2. Label the data based on which leads took the least amount of time to convert to the maximum amount of time, and those who did not convert at all.
3. Use a supervised learning algorithm to classify new or existing prospects based on this data.

How To Use Machine Learning to Nurture Your Customers

The more you know your customers, the better. An organization can use ML similar to how they would with prospect data, as it allows you to create categories and group similar individuals/ You can use this data to better target existing customers with the proper retention/upsell/cross-sell messaging.

Understanding your customers means studying their behavior and identifying their preferences and choices to get insights in real time. Using these “clusters” of data, you can create customer score models based on behavior and purchasing data and use the patterns to target specific types of clients. This reduces the chances of customers getting confused while shopping, helping to ensure a satisfying experience.

Knowing past behaviors can also help predict and anticipate future buying preferences and behaviors, from identifying links they click to content they choose for sharing on social media. It allows you to deliver the right message at the right place at the right time, increasing the likelihood of repeat sales.

Examples of using ML to nurture existing customers include:

• Pinpointing associations in customer data (for example, a customer who buys a specific style of shoe may also be interested in a specific type of clothing).
• Serving advertisements based on past purchase behavior and related future behavior.
• Serving content based on how a customer interacts with existing or social media content.

Not only can this data be used in advertising and marketing purposes, but also in content production for social media channels, emails, or websites, website navigation decisions, calls to action copy, images, and more.

Understanding your customer behavior and nurturing future and existing customers is critical to the success of your business, and using machine learning makes this easier, seamless, and more accurate. Putting this abundance of data to good use can help you increase revenue and return on investment.

Contact Dragonfly Digital Marketing

If you are interested in improving your marketing efforts, reaching more potential customers, and improving overall operations, contact Dragonfly Digital Marketing. Let us help you develop a strategy to meet your organization’s specific goals. Call (800) 636-0347 to learn more.