How predictive analytics will change marketing

In a previous blog post, we discussed why marketers should be excited about data analytics. However, one thing we did not explain was how data analytics can transform the marketing industry. In this blog, we will be doing just that – more specifically, we are going to explain how predictive analytics works, and how it helps the marketing industry achieve better results.

What is predictive analytics?

Data analytics is a broad term referring to powerful platforms that process terabytes of data to yield unique insights. However, predictive analytics refers to a more specific form of analytics where platforms use historical data to predict future marketing.

So, what does this mean for you, the marketer? It means you will understand your customer better than they understand themselves, allowing you to sell products to them before they realise they need it. Moreover, you can find the optimal time to sell and set the perfect price structure to entice your customers. Major retail chains have been using this technology for years. For example, Target used data to identify female shoppers who were pregnant when they entered their second trimester, allowing them to market baby products to them. They were even able to accurately tell that a teenage girl was pregnant before her father even knew about it!

Predictive analytics in marketing

Predictive analytics works in marketing through a process called regression analysis. With regression analysis, a data analyst will take two variables and perform a regression coefficient, to discover the chances of the customer purchasing the product based on that variable. For example, a data analyst can take income level and product demand to perform a regression coefficient. If there is a strong connection between the two variables, it indicates that income level is a massive factor in product demand.

However, seeing the major contributing factors to customer demand is just the tip of the iceberg. With predictive analytics, it’s possible to map past data and predict future shopping trends. Whether you are in B2B or B2C marketing, you can anticipate what customers will buy before a purchase is made. In other words, you can easily identify high-value customers before they have even purchased from you. How can predictive analytics perform so many functions? By using a combination of response modelling, affinity analytics, churn analysis and historical data.

By investing in predictive analytics, you can perform several functions more efficiently. One function is customer segmentation, which involves dividing customers into different niches for more precise marketing campaigns. You can also create more detailed customer personas, which are the lifeblood of marketing. With analytics, it’s possible to incorporate information about future purchases, thereby creating personas that are more detailed than ever before.

You can then develop more precise marketing campaigns that will connect more closely with customers. It’s also possible to eliminate inefficient processes and reduce churn. Reducing churn ensures that customers don’t drift away. You can gain better insight into customers in danger of leaving and develop marketing campaigns that prevent them from leaving. It also becomes possible to automate market processes to find shortcuts, cut costs and save time.

Predictive analytics models in marketing

Predictive analytics identifies new marketing opportunities through three different models.

Propensity models: Analytics platforms can use historical data to see the chances of a particular customer closing a sale. Not only that – you can also identify the chances of a customer taking different actions like subscribing, unsubscribing, paying for the top-tier service and more.

Collaborative filtering: Predictive analytics can anticipate the type of products and services a customer is most likely to buy based on their past purchasing history. Thus, providing a fantastic opportunity to upsell and cross-sell. You already see this technology in action because digital giants like Amazon and Netflix are using collaborative filtering to sell additional products and services.

Cluster models: With this model, you can divide the customer base into different niche segments based on any variable like age, income level, demographics, and average order total. There are several different cluster models including brand-based clustering, product-based clustering, and behavioural clustering.

Key takeaways

Predictive analytics is an excellent tool that will change marketing because of its ability to take historical data and use it to make future predictions. Whether it’s digital or conventional marketing, analytics can deliver better insights, allowing you to develop more precise customer personas, identify high-value customers and precise marketing techniques that will yield better ROI in marketing efforts.

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