28 April 2020


How to Use Predictive Analytics for Better Marketing

In the world of marketing, knowledge is power. Having knowledge about customer behavior, preferences, demographics, and purchase history—among other things—is essential for any successful marketing team.

But obtaining this information is only half the battle. The other half is turning the data into actionable insights that can inform marketing strategy and improve the effectiveness of campaigns. That’s where predictive analytics comes in.

In its simplest form, predictive analytics is the practice of forecasting the future based on current and historical datasets, machine learning techniques, and statistical algorithms. This mathematical clairvoyance enables marketers to transform data into actionable insights, which they can leverage to iteratively improve their messaging strategies.

Being able to anticipate spikes in demand, analyze buying habits, or predict the lifetime value of potential customers unlocks many doors for marketing teams and allows them to make acquisition and nurturing strategies more impactful than ever before.

10 ways predictive analytics can be used to drive marketing performance

So how exactly can marketers leverage predictive analytics as part of their ongoing strategy? Virtually any marketing scenario can be a target for predictive analytics, provided the requisite data is available—and let’s be honest, what modern marketing team isn’t swimming in tons of data about their prospects?

Here are just a few examples of how predictive marketing analytics can be used to impact performance.

1. Segmenting customers

Customer segmentation allows marketers to create highly personalized and targeted messages for more effective acquisition and retention. When predictive analytics is used for customer segmentation,  marketers can make deeper analyses than simply grouping customers by age, geography, gender, or other shallow traits.

Predictive analytics allows for more nuanced groupings, considering many customer traits that are (or are not) present in specific groups, as well as identifying new features that might not be obviously important. This deeper analysis can reveal patterns that would be impossible to recognize otherwise.

For example, a bank’s marketing team could segment customers into different categories with varying receptiveness, income, age, and financial experience in order to determine what segments would be most interested in taking out a loan or opening a new savings account.

2. Understanding customer lifetime value

With the expansion and accessibility of digital advertising, the number of customers any given brand can reach has increased exponentially, even for smaller businesses. But this increased reach comes with some challenges. With such a high volume of potential customers, it’s simply not practical to treat every customer the same way.

Using predictive analytics to analyze customer data like average purchase value, purchase frequency, and likelihood of churn, marketing teams can calculate a customer lifetime value. This value projection helps better tailor messaging, offers and service levels depending on what customers are likely to have a higher lifetime value, maximizing future revenues and profit.

3. Analyzing trends & seasonality

An effective marketing team must be agile and proactive in designing strategies. In today’s marketing world, it’s not enough to simply look at previous years’ numbers and assume old patterns will continue as expected.

Predictive analytics helps teams make demand forecasts and predict market changes, allowing them to plan and execute on strategies before their competitors. This way they are setting the trend rather than chasing it.

And because it’s virtually infinitely scalable, marketers generate both high level insights and granular analysis from large swaths of data—more than would be possible using traditional market analysis.

4. Joining customer data

Marketing data is almost never able to encompass everything an organization knows about a given customer. This is due to the dispersion of data across the organization—from sales, tech support, product, finance, etc.

However, piecing together this information is required to build a comprehensive understanding of customers and more advanced marketing tactics. Predictive analytics helps ease the stress of such a large undertaking by:

This unified customer profile can then be used by marketers to produce more impactful campaigns. Marketing teams can look at a customer’s past purchases from sales data, recent product page views and clicks, and the sentiment of reviews left on purchased products to gain a greater understanding of that customer’s needs and preferences.

5. Scoring leads

With lead scoring, marketing and sales teams can better understand how likely potential customers are to progress through the customer journey. Sales calls, customer support, special offers, email campaigns, and other valuable resources can then be prioritized for those with higher probability.

Predictive analytics helps segment customer data for scoring, allowing recommendations to be automatically generated for marketing and sales teams to use. The power of predictive analytics can even be combined with widely used visualization tools to create custom reporting dashboards.

6. Cross-selling

One of the best marketing strategies is to leverage an existing customer base into new sales. It’s a win-win for you and your customers, not to mention it’s far less expensive than acquiring a new customer.

Through predictive analytics, marketing teams can generate more accurate and personalized suggestions for their customers. Perhaps the best-known examples of this type of cross-selling are the automated suggests from providers like Netflix and Amazon.

Providing these kinds of suggests to your customers can dramatically increase cross-sales while also strengthening customer relationships and trust in the organization’s products.

7. Predicting customer behavior

Knowing the most likely actions a customer is going to take helps marketers formulate a tailored message before the customer has made a decision.

By combining a customer’s recent online behavior with historic purchase data, marketers can accurately predict what products a customer is most likely to purchase in the future. Marketers can then expose the customer to said products with cross-selling recommendations and make other promotional strategies more effective, allowing marketing teams to take a proactive role in guiding customer journeys.

8. Personalizing content

Personalization is no longer just an option for marketers, it’s a necessity for running successful campaigns. Consumers are used to receiving blast marketing aimed at a broad audience – offering personalized and engaging content is required to catch their attention and encourage them to take action.

Marketing teams can use predictive analytics to consider the collected data on past behaviors and then anticipate the next best action with regards to a customer. Teams can then craft more personalized and targeted interactions with which to engage and treat their audience.

9. Determining pricing

Optimizing the price for a given product is a complicated process. Marketers must strike a balance between demand, profit, and risk when they set a price, considering multiple variables, trends, and demands from both their own organization and competitors. Knowing the best possible price can often be the deciding factor in the success of a campaign, giving a brand the competitive edge that it needs.

Predictive modeling can streamline the entire pricing process through price optimization, using all possible factors and historical data to arrive at the optimal price. Marketers can use this information to plan promotions around price changes or optimize the timings and discounts of sale events.

10. Optimizing campaigns

All the above uses come together for marketers to create more efficient, optimized campaigns through predictive analytics.

It’s worth noting that having the right predictive analytics solution is key, as marketers must be able to streamline processes and unify their strategy. This will ultimately help marketing teams better reach their audiences with more personalized and engaging messaging, while maximizing the efficiency of marketing budgets through more targeted campaigns.

Having a unified predictive analytics solution implemented also helps eliminate redundancies and resolve any internal strategies that are in conflict, making sure the overarching strategy is as streamlined and effective as possible.

Common challenges that marketers face with predictive analytics

Although predictive analytics is a powerful tool for marketing teams, like any strategy, challenges do arise. Here are the most common challenges we see marketers face.

Understanding the audience

An algorithm can predict customer behavior, but it can’t understand a customer base’s sense of humor or the cultural sensitivities of an advertisement. When implementing analytics-based insights, marketing teams still need a deep understanding of their audience in order to best implement predictive analytics insights.

Keeping track of ROI

Even with predictive analytics, it’s important for marketing teams to keep track of how their campaigns are performing and make continuous adjustments. Often, even if a campaign proves effective in the short term, its costs can outweigh the benefits as time goes on. Tracking campaign ROI is vital to getting the best value out of predictive marketing analytics.

Tracking conversions

Again, no algorithm has perfect accuracy and there are usually many algorithms to choose from, so the first predictive analytics model implemented might not be the best. Accurately tracking lead conversion rates associated will help avoid wasting time and resources on faulty leads down the road, as well as helping you iterate through different models and strategies to determine what works best.

How RapidMiner can help with predictive marketing analytics

From the many uses highlighted above, it’s clear that predictive marketing analytics is a must for the modern marketing team. A major key to success here is adopting a data science platform that is easily accessible for team members of all skillsets, even those with little or no experience.

Luckily, RapidMiner Go is just that. All you need is a data set and something you want to predict. The automated and guided experience helps you select the best model for your business and use that model to uncover insights and inform business decisions.

Getting started with a machine learning project can be overwhelming. Request a live demo with one of our data science experts and learn how RapidMiner can help your enterprise.

Related Resources