Predictive Marketing Strategies To Add to Your Plan

Christy Walters


February 28, 2023 (Updated: May 4, 2023)

wooden coin with an eye and the word yes inside in a white person's palm to represent predictive marketing strategies

Predictive marketing is a process companies use to make educated guesses about upcoming sales and brand performance. Your marketing team can use many methods and strategies to draw these conclusions. They depend on what data you have and what predictions you want to make. Today, we’re looking at predictive marketing strategies you can incorporate into your marketing plans and what they can help you learn about your future performance:

Isn’t Predictive Marketing a Strategy?

You might wonder why we’re discussing predictive marketing strategies when predictive marketing is a strategy of its own. In reality, predictive marketing is an overreaching umbrella strategy with many smaller strategies underneath it. So, think of it as you would your content marketing strategy: within it, you may have an article writing strategy, a podcast marketing strategy, and a social media marketing strategy All three combine as part of your larger content marketing strategy.

The same thing happens in predictive marketing. You can use a bunch of smaller strategies to predict specific outcomes of your future marketing plans. They may focus on different areas of marketing or different tasks you complete in those areas. The data and predictions you find combine to make up your overall brand predictive marketing strategy.

Related: What Is Predictive Marketing and Why Use It?

13 Types of Predictive Marketing Strategies and How To Use Them

You can use predictive marketing to forecast nearly any marketing or advertising activity your brand uses. Here are some of the most common types of strategies you could use, including the ways to apply them to your marketing plan:

1. Classification

Classification strategies help you assess data quickly and effectively by producing responses to yes or no questions. This type of strategy uses historical data to give an estimated guess answer to a question. Classification is particularly useful when you’re trying to pinpoint your most qualified leads. You can use classification with clustering, which we discuss next, to determine if an individual prospect or an audience group holds qualified leads for your brand.

This strategy allows you to look at the characteristics your current and past customers have in common. Then you can compare the characteristics to those of your potential leads. If enough data points match, you can classify the new lead as “qualified.”

You can also use classification the same way for client or customer churn management. It allows you to look at customers or partners that have cut ties with your brand in the past and identify similar characteristics among them. If you find other customers with similar characteristics, you may choose to reach out to them or target them with different campaigns to prevent them from leaving and raising your churn rate.

2. Clustering

In predictive marketing, clustering uses algorithms to sort data into different categories based on similar characteristics. There are two types of clustering you might use, depending on the data you’re working with and the outcomes you’re looking for:

  • Hard clustering: Determines whether a data point belongs to a specific cluster completely.
  • Soft clustering: Assigns probability of characteristics to each data point rather than sorting them into absolute clusters.

Clustering may be the predictive marketing strategy you use most often in marketing. One of these uses you’re likely most familiar with is audience segmentation. You can use clusters to sort current audience members by their demographics and psychographics. When you uncover what each audience segment has in common, you can then develop marketing campaigns to better target each one.

3. Data-Driven Creative

Data-driven creative strategy is a fancy term for using past A/B testing to influence future campaigns. This type of predictive marketing focuses on choosing the right creative content, messaging, and displays to engage your audience. Looking at past data like what subject lines worked best for your email or what size banner ad got the most clicks to help you make decisions for the next campaign. Once you start to learn what your audience responds to or expects from your company, you can create more targeted campaigns that include all the features they respond to best.

4. Decision Tree

Decision trees are flowcharts that use algorithms to determine the outcomes of situations based on the decisions you make. The model gets its name from the visual structure, where each situational turning point has branches that lead to the next possible scenario. You’ve likely seen decision trees as funny social media memes about whether you should or shouldn’t do something.

Image via Smith Street Books

In marketing, decision trees help you predict all the possible outcomes of a campaign. They allow you to incorporate A/B testing and troubleshooting in the process before you even launch the campaign. Being prepared for all potential outcomes can help you change the course of a campaign quickly if issues arise. This way, you won’t lose time or potential conversions trying to rework your original campaign.

5. Filtering

Filtering is a predictive marketing technique often used with clustering to pare down data into smaller, more digestible segments for your team. Let’s look at audience segment clustering, for example. Based on the size of your audience and company, you could have tons of audience segments for your brand. But you’re not going to want to look at all of them all the time. You may only need to look at certain segments for certain campaigns.

Filtering lets you further review your clustered data to only pull up characteristics you need to look at for the current campaign. You can use filtering when trying to understand personalized recommendations for your audience. This is especially helpful for eCommerce businesses that want to recommend additional products to their customers based on past purchases. By filtering segments by their most-purchased products, you have a better idea of what to push to others in the same segment to get them to buy more.

6. Forecasting

Forecasting is a versatile type of predictive analysis that relies heavily on numerical and historical data to get results. This strategy lets you look at multiple variables at once to determine the outcome of a marketing campaign or action. You might use forecasting for predicting conversions and sales based on the success of a marketing campaign.

That activity is more complex than simply sorting audience members or qualifying leads. You have to look at a variety of variables such as the audience members, campaign content, how the audience crosses paths with the content, and external factors like the economy or political climate.

7. Gradient Boost

Gradient boosting, sometimes called next-best action, is a more advanced machine learning type of decision tree. It uses past decision trees to determine the next best model to predict a future situation with fewer errors. The goal of using a gradient boost strategy is to minimize errors within the decision tree. This type of strategy relies on AI tools to review and interpret historical data to predict a better way to handle similar situations in the future.

It might be most helpful when trying to predict the human behavior of your leads, audience, or customers. If you’re more certain about how your audience will react to a new campaign or initiative, you can feel more confident about launching it. Gradient boosting can also save you time and money by avoiding readjustments or correcting actions that didn’t unfold as you planned.

8. Linear

Linear predictive marketing strategies look at how one variable affects another. They’re a blast from your algebra past and take advantage of scatter plot graphing on coordinate planes. You’ll typically use linear predictive marketing strategies when working with statistical data. You identify your independent and dependent variables and then see how behavior between the two correlates over time.

You may use a linear model to show something like the correlation between website traffic and the amount of money you spend on paid ads. This could help you uncover more about your return on investment (ROI) for each paid marketing campaign.

9. Look-Alike

Look-alike strategies focus on more than reviewing your internal company data. They also rely on the information you learn from your competitors. The most common way to use look-alike strategies in predictive marketing is to identify potential customers who fit into your audience segments.

Doing look-alike modeling takes place before classification in most cases. You can use it to generate leads. After you identify those potential leads or customers that fit within your current audience segments, you can use classification to see if they’re qualified and a good fit for your brand. Look-alike modeling often uses machine learning and complex algorithms to find potential leads on a variety of channels, like search engines and social media sites.

10. Neural Network

Neural networks are complex models meant to resemble and work like the human brain. They use multiple algorithms to complete tasks like clustering data, developing categories across datasets, or identifying patterns. Because neural networking is so complex, it’s easiest to use this strategy when working with AI tools.

For example, you may use AI to help predict the accuracy of a search engine results page (SERP) for a specific keyword. Because Google and other search engines focus on search intent and try to tap into how searchers think and use their services, this is a great case where using a neural network strategy could come in handy.

11. Outliers

Outlier strategies operate similarly to “spot the difference” games. Instead of looking for the way everything is the same, as you do in clustering, you’re looking for data that doesn’t fit the current patterns. This type of predictive marketing can help you understand why the outliers exist in your data and what caused them. You could use an outlier model for a variety of reasons, like finding if there’s seasonality in your business or learning if your website is down.

For example, a sudden drop in website traffic, conversions, or sales should be considered an outlier in your data. But beyond determining it exists, you need to know why. When you comb through all the potential causes, you may find that your website experienced an outage that covered the dropped period. Therefore, you can ignore that outlier information due to technology error and not look at it as an ongoing issue with your website or content.

12. Time-Series

A time series strategy is a type of specific forecasting that looks at historical, time-based data to draw conclusions. It uses the same numerical and historical data as any other forecasting strategy but within a specific period. Time series is particularly useful for forecasting seasonal businesses or businesses that see a boom at certain times of the year.

Toy companies, for example, likely see more sales near the end of the year and spend more time marketing at that time to coincide. Time series forecasting can help these companies look at the data just for those boom times year to year. It provides a more accurate picture of how campaigns perform during those seasons rather than year-round.

13. Uplift

Uplift strategies build on multiple predictive marketing tactics to see how a brand intervention affects the likelihood of a specific outcome. You use it most often when looking for ways to upsell or cross-sell additional products or services to your audience. Uplift modeling is most common in predictive advertising because your brand’s intervention is an ad that plants the seed for the upsell or cross-sell.

This strategy can help you determine which audience segments are most likely to take the bait and which types of interventions, like pop-up ads or retargeting, are most likely to get them to convert.

Predictive Marketing Is an Educated Guess

No matter what type of predictive marketing strategies you use in your content marketing campaigns, it’s important to remember predictions are just educated guesses. It doesn’t matter how much planning and research you do, a campaign could still go sideways. The best part about predictive marketing, though, is that it prepares you for those situations in advance.

Plus, every new campaign provides more data, which fuels better predictive analysis for the future. You just have to make sure you’re collecting and analyzing it properly to get the most out of any strategy you choose.

Author Image - Christy Walters
Christy Walters

CopyPress writer

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