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Data has the power to predict the future, or in this case, what your consumers might do next. Monitoring your marketing analytics is a key part of any digital marketing campaign, as it provides you with insights into your customers’ behavior.

Let’s go over the ways data analytics plays a role in predicting consumer behavior.

How to Use Data Analytics for Targeted Marketing

Here are five ways data analytics sets the stage for powerful targeted marketing campaigns:

Consumer Data Analysis

Data analytics makes it easier to understand your target audience’s needs. This can be done through artificial intelligence and machine learning models that gather data from consumer interactions and use it to create personalized user experiences.

For example, eCommerce brands might use predictive analytics to forecast what a consumer’s next purchase could be based on browsing patterns, purchase history, and cart behavior. With this information, your marketing team might run personalized email offers or dynamic website banners featuring products the customer is statistically likely to be interested in.

Healthcare providers use predictive analytics to identify which individuals are more likely to need specific services based on their medical history and lifestyle data. For instance, models can forecast which patients are at higher risk for diabetes and target them with educational content or preventive care campaigns before symptoms appear.

Real-Time Monitoring

You can use data to track user behavior (like clicks, scrolls, and time on page) to predict intent and trigger targeted offers instantly. For example, if a user lingers on a product page without purchasing, a discount pop-up can be shown to encourage conversion.

In another example, let’s look at the retail industry: Real-time data on product views, cart additions, and social media mentions can help forecast demand trends. These interactions provide immediate feedback on what your user may do next, which you can use to improve future marketing campaigns. This allows retailers to respond to evolving consumer trends and demands and create a more customer-centric shopping experience. 

Customer Segmentation

Customer segmentation involves collecting information about your customers and categorizing them into different groups. Some of the data that helps group people includes demographics, behaviors, and preferences. Predictive analytics then uses these segments to forecast future actions — like which group is most likely to respond to a new product, churn, or make a repeat purchase.

When conducting customer segmentation, use tools like CRM software, surveys, focus groups, interviews, and more to gather as much information as possible. This data can then be segmented into different categories, such as:

  • Demographics (Age, gender, occupation, income level, etc.)
  • Behavioral data (Brand interactions, transaction history, online behavior, etc.)
  • Psychographic data (Lifestyle, values, beliefs, etc.)
  • Geographical data
  • Specific pain points

Use this information to create a buyer persona, which is a representation of your ideal buyer. This helps you more accurately predict what your customer’s behavior will be based on your data.

Sentiment Analysis

Data can also reveal how users feel about a certain brand, also known as brand sentiment analysis. To do this, utilize social listening tools, media monitoring tools, and social media analytics tools to keep track of brand mentions, reviews, engagement rates, comments, likes, shares, follower counts, and more.

You can also use surveys, feedback loops, interviews, and focus groups to gather information about how a user feels about a certain product. Another useful tool to measure sentiment is the Net Promoter Score (NPS). NPS gauges how satisfied a customer is with your product and how likely they are to recommend it to others. 

You can send an NPS questionnaire to your users via email or use a pop-up on your website. NPS is usually broken down into:

  • Promoters (score 9-10): Loyal customers who would recommend your product
  • Passives (score 7-8): Satisfied users who aren’t overly enthusiastic about your product
  • Detractors (score 0-6): Unsatisfied customers could write negative feedback about your brand

As brands analyze the reception toward their product or service (or a specific aspect of it), they use it as feedback to build new products or improve current ones.

Predictive Pricing Analytics

Data and predictive analytics can be used to forecast pricing changes based on historical data, customer demand, price sensitivity, revenue potential, and more. This helps businesses come up with dynamic pricing models to improve profitability and retain customers.

One way to do this is by measuring relevant pricing metrics using market research, customer surveys, or feedback loops. These metrics include:

  • Willingness to Pay: The highest price a customer is willing to pay for a product or service
  • Feature Value: How much customers value individual features
  • Average Revenue Per User: How much revenue does each customer bring over a specific period? Compute this by dividing the total revenue by the number of customers.
  • Customer Acquisition Cost: How much does it cost to acquire a new customer?
  • Customer Lifetime Value: Potential revenue generated from a customer throughout their entire duration with the business

Target Better: Wrapping Up

Digital marketing isn’t just about coming up with creative ways to promote your product; it’s also about utilizing the current data you have to make objective, data-driven decisions that guide your future campaigns.

Say goodbye to guesswork and assumptions! When utilized properly, data can predict your customer’s next move, allowing you to capitalize and create the ideal experience.This article was written by Pranjal Bora, a fractional CMO in healthcare at Digital Authority Partners.