Predictive Modeling

How to supercharge your marketing with predictive analytics

by
Craig Wisneski
,
June 27, 2022

Want to know how to supercharge your marketing with data? It's easier than you might think.

Marketing is an essential part of any business. It’s how you connect with customers, increase sales and grow your business. By leveraging machine learning, you can analyze your data and make predictions, automate repetitive tasks, improve customer experience, increase sales and market share, and more. 

This is because AI algorithms can analyze far more data much faster than humans can, and it can make predictions based on this data. This article is going to show you how to supercharge your marketing with predictive analytics.

What is predictive analytics and how does it help your marketing?

In recent years, the term “predictive analytics” has become increasingly popular in the business world. But what is predictive analytics, and how can it help your marketing efforts?

Predictive analytics is a form of data analysis that uses historical data to make predictions about future events. By understanding past patterns and trends, businesses can use predictive analytics to make more informed decisions about the future.

For example, let’s say you own a clothing store. You might use predictive analytics to predict how many people will buy sweaters in the next month based on past sales data. Or, you might use it to identify which customers are most likely to respond to a new marketing campaign.

Predictive analytics can be used for all sorts of marketing decisions, from product development to pricing to customer segmentation. And because it is based on data, it can help you make decisions that are more informed and less risky than relying on intuition alone.

If you’re not already using predictive analytics in your marketing efforts, now is the time to start. It can help you make smarter decisions, improve your results, and stay one step ahead of the competition.

Examples of predictive analytics in marketing 

There are a number of different ways that businesses can use predictive analytics in marketing, from targeting ads to understanding customer behavior. Here are a few examples in detail.

To understand customer behavior

There are many potential benefits of using predictive analytics to better understand customer behavior. For example, businesses can use these insights to personalize offers and promotions to individual customers, or to identify leads who are more likely to convert into paying customers.

One of the most important questions that businesses must answer is: who are our customers? Without a clear understanding of who their target market is, firms will struggle to effectively market their products or services. Predictive analytics can be extremely helpful in this regard, as it can provide businesses with detailed insights into the characteristics of their best customers.

For example, say you run a SaaS company that sells accounting software. Using predictive analytics, you might analyze your customer data to discover that the vast majority of your best customers are small business owners in the United States. Armed with this information, you can then adjust your marketing and sales efforts accordingly to focus on acquiring more customers who fit this profile.

In addition to understanding who their customers are, businesses must also have a clear understanding of what those customers like and dislike. This information is essential for developing effective marketing and sales strategies. Once again, predictive analytics can be extremely helpful in this regard.

For instance, a luxury car dealer could use predictive analytics to analyze customer data, discovering that the majority of their customers who purchase luxury cars are also interested in purchasing high-end jewelry. With this information in hand, the dealer could collaborate with a local jewelry store to offer customers special discounts on jewelry when they purchase a luxury car. The firm could also market the right cross-sell or upsell opportunities, at the right time.

Finally, businesses need to understand why their customers buy the products and services that they do. What are their motivations? What needs are they trying to satisfy? Answering these questions is essential for developing effective marketing messages and offers.

Predictive analytics can be extremely helpful in customer behavior optimization. By analyzing customer data sets, businesses can gain valuable insights into who their customers are, what they like and dislike, and why they purchase the products and services that they do. 

To make better decisions

Predictive analytics is an incredible tool that can help firms make better decisions. By analyzing data, predictive analytics can provide insights that help organizations make more informed decisions about marketing strategy, content, shopping cart flows, and more.

For example, consider a situation where a company is trying to decide whether to run a particular marketing campaign. Predictive analytics can help by taking into account factors such as customer demographics, purchase history, and even web-browsing behavior. By understanding how likely customers are to respond to the campaign, the company can make a more informed decision about whether or not to proceed.

Content decisions are another area where predictive analytics can be extremely helpful. For instance, imagine that a website is considering publishing a new article. Predictive marketing can analyze past user behavior to understand how likely people are to read and engage with the article. This information can then be used to determine whether or not the article is worth publishing.

Shopping cart abandonment is another common problem that predictive analytics can help with. By tracking user behavior, predictive analytics can identify patterns that often lead to shopping cart abandonment. This information can then be used to intervene and prevent abandoned carts, resulting in more sales for the firm.

Finally, predictive analysis, and particularly cluster models, can take the guesswork out of targeting audiences. By understanding, in real-time, which users are likely to respond to a given message, marketers can send targeted content that is far more likely to result in conversions. The same is true for salespeople, who can use predictive analytics tools to understand which leads are most likely to close. These actionable insights can be used to focus efforts on the most promising prospects, resulting in more sales and happier customers.

How can you apply predictive analytics to your business?

In recent years, businesses have become increasingly reliant on data-driven decision making. With the advent of big data, organizations are now able to collect and store large amounts of data more efficiently than ever before. However, making sense of this data can be a challenge. This is where predictive analytics comes in.

Akkio's no-code AI platform makes it easy to build predictive models with just a few clicks. In this section, we'll explore some ways in which businesses can use predictive analytics to their advantage.

Lead Scoring

The modern sales process is more complex than ever. With so many moving parts and new technologies to keep track of, it's easy to see why lead scoring has become a necessary part of doing business.

Lead scoring is the process of assigning a numeric value to a lead, based on factors such as engaged behaviors and demographic information. The higher the score, the more "sales-ready" the lead is considered to be.

Lead scoring can help sales teams in a number of ways. For one, you can prioritize your follow-up efforts and save time by focusing on the hottest prospects first. After all, speed is critical when it comes to converting leads into customers. 

It's also important to note that not all leads are created equal. Some may be ready to buy immediately while others may need more nurturing before they're ready to make a purchase. Lead scoring can help you route leads to the right salespeople, based on their readiness to buy. 

Traditionally, lead scoring was done manually, with sales teams using checklists to keep track of various factors. However, with the rise of artificial intelligence, lead scoring has become much more sophisticated. AI-powered tools can analyze a vast amount of data and identify patterns that would be impossible for humans to spot. 

This is critical for sales teams who need to make sense of large amounts of data in order to find the best leads. AI-powered lead scoring can help you close more deals, faster.

Churn Reduction

Reducing churn is a top priority for any business. After all, it's far more expensive to acquire a new customer than it is to keep an existing one.

There are a number of ways to reduce churn. For one, you can provide your customers with an impeccable experience from start to finish. This means making sure their needs are met at every touchpoint and going above and beyond to deliver exceptional service. 

You can also keep churn in check by being proactive about addressing customer concerns. This may involve conducting regular check-ins or surveying customers to get feedback on their satisfaction levels. By taking action before problems arise, you can head off potential issues and keep your customers happy. 

It's also important to have a clear understanding of why customers are leaving in the first place. This can be gleaned from exit interviews or surveys. Once you know the reasons behind churn, you can put policies and procedures in place to prevent it from happening again. 

The problem is that there are many, interrelated factors that can contribute to churn. This is where AI can be particularly helpful. AI-powered tools can analyze customer data and identify patterns that may be indicative of future churn. This allows you to take preventive action and keep your customers longer. 

AI can also help you understand why customers are leaving, so you can put policies in place to prevent it from happening again. By reducing churn, you can protect your bottom line and keep your business growing.

Predicting Renewals and Customer Lifetime Value

Understanding your customer's lifetime value is critical for any business. After all, it costs far more to acquire a new customer than it does to keep an existing one.

There are a number of factors that can affect customer lifetime value, such as retention rates, engagement levels, and average order value. To get a clear picture of your customers' lifetime value, you need to track these metrics over time.

AI can help you identify patterns in consumer behavior data that may indicate a high lifetime value, and predict future behavior. For example, someone who regularly interacts with your brand on social media or visits your website frequently is likely to be a more valuable customer than someone who doesn't.

By using AI to predict customer lifetime value, you can focus your acquisition efforts on those who are most likely to stick around (and spend more money) in the long term. This will not only save you money on acquisition costs, but it will also increase your overall customer lifetime value.

Building an ML Model for Predictive Analytics

Now that we've seen some examples of how businesses can use predictive analytics, let's take a look at how you would build an ML model for this purpose. 

The first step is to gather data. This data can come from a variety of sources, including customer surveys, transaction data, website usage data, and more. Once you have this data, you'll need to clean it and prepare it for modeling. 

Next, you'll simply select the column you want to predict (the "target variable"). Akkio will then automatically pre-process your data, select a model through Neural Architecture Search, train and test your models, and select the best one for you. 

Finally, you can deploy your model into production and start making predictions. With Akkio, there's no need to code; everything can be done with just a few clicks.

Predictive analytics is a powerful tool that can help businesses make better decisions and improve their bottom line. Akkio's no-code AI platform makes it easy to build predictive models with just a few clicks. Try it out today and see how predictive analytics can benefit your business.

Conclusion

Predictive analytics is a powerful tool that can help businesses make better decisions by using data to predict future outcomes. Akkio's no-code AI platform makes it easy to build predictive models with just a few clicks. In this article, we've explored some ways in which businesses can use predictive analytics to their advantage.

If you're not already using marketing analytics in your business, now is the time to start. It can help you make smarter decisions, improve your results, and stay one step ahead of the competition. Sign up for a free trial of Akkio today and see how predictive analytics use cases can benefit your business.

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