What is predictive analytics and how it benefits B2B marketing?

What is predictive analytics and how it benefits B2B marketing?

Given the importance of data in today's marketing world, predictive analytics is becoming a powerful tool for marketers. Learn how predictive analytics can help you make the most of the wealth of data available to you.

What is Predictive Analytics?

Predictive analytics is able to forecast with more than 85% accuracy who is going to buy, when they are going to buy, and what they are going to buy1. The underlying principles are data analysis and statistical techniques, which basically means computers learning from customers past behaviour and their purchasing history. They recognize patterns in past data to project probability of future behaviour of customers.

The technology of predictive analytics is making a significant impact on the marketing industry. It's not just data scientists that use these techniques anymore. Business intelligence and data visualization tools are making Big Data analysis tools more accessible, more efficient, and easier to use than ever before.2 They deliver valuable insights to understand each individual customer better and to tailor content to the needs and interests of that customer. 86% of marketing executives have already stated that they have seen a positive return on investment of the technology in marketing3.

So, how could it help you with your B2B Marketing?

There is a broad series of use cases for predictive analytics in business today. One of them is predictive scoring. This prioritises known prospects, leads and accounts based on their past behaviours and purchasing history. It therefore allows you to spend more time on those accounts, that have the greatest revenue potential and less on the ones, that are less likely to convert. By analysing past data, predictive lead scoring adds a mathematical dimension to the conventional, basic lead scoring and is thereby even more accurate.

Furthermore, predictive analytics can enhance identification models, which identify and acquire prospects with attributes similar to your existing customers. It allows you to find profitable clients earlier in the sales cycle, uncover new marketers and prioritise existing accounts for expansion4.

Moreover, predictive analytics simplifies the segmentation of customers, which previously caused such high manual effort that it could only be applied to highly prioritised campaigns. Now, predictive analytics supports both complex and automated segmentation. As a result it also promotes personalised content targeting. It has been shown that marketers that deliver personalized content get double-digit returns in marketing performance and response5.

However, 82 per cent of marketers state that personalised content remains their biggest challenge6. Predictive Analytics can help with that as it delivers insights for establishing the tone, material and style of content each customer responds most to7.

This allows you to drive your outbound communications with relevant messages, enable meaningful conversations between sales and prospects and inform content strategy more intelligently8. This means your campaigns will be more successful and you are able to target your marketing spend to get the optimal results.

Last but not least, predictive analytics can help you to shape future products and solutions by leveraging pain points and industry trends to better meet the buyer's needs9.

Netflix, for instance, classified the key attributes of past and current products or services and then modeled the relationship between those attributes and the commercial success of the products. The result was a predictive model that provides the company with guidance about how likely the success of a new product or service is.

Examples of successful implementation

Cox Communications, the third-largest cable entertainment and broadband provider in the US, successfully implemented a predictive analytics model to create new offers targeted to the right customers. They were able to put more campaigns into the field and to measure the effectiveness of different offers and marketing techniques to different customer segment. Thereby, they generated an 18 percent increase in customers responding to the promotion.10

XO Communications, one of the largest communications service providers in the US, used predictive analytics to gain a deeper understanding of customers. They saw a 142 percent estimated reduction in revenue erosion for customers at most risk of churning and also $10 million+ estimated savings per year from increased customer retention and reduced customer service costs.11

Tipp24.com, Europe's leading lottery-based games provider, used predictive analytics to improve their customer targeting accuracy. The challenges, they were looking to overcome, were that they didn't have any in-depth knowledge of customer behaviour and their life-cycle. Furthermore, they wanted to improve the timings of their special offers and promotions and therefore needed qualitative insight into what customers were doing and what they were likely to do.

They developed predictive models with 800-900 variables including past behaviour, tickets bought, products played, gaming history and winnings along with customer demographics. With that they calculated scores of customers and predicted the right offer for the right customer at the right time. As a result, they saw a 200 percent uplift in targeting accuracy and a reduction in marketing cost.

Moreover, they were able to reactivate more customers and to prevent churn at the same time. The value of the customer increased as they successfully exploited upselling and cross-selling opportunities. Customer satisfaction also improved due to a high-level of customer personalisation in terms of offers. Customers were not receiving irrelevant messages with offers for products they were unlikely to buy, which generally improved the customer experience.12

Furthermore, it is said that the response to Amazon's buying suggestions, which are based on predictive analytics, generate an additional 10% to 30% in revenue for the business.13



Conclusion

Predictive Analytics can help you to understand your customers better and allows you to focus your budget and resources only on those that will buy. Getting started is easy. Many of the tools now available integrate easily within your existing marketing need. All you need is baseline data from your marketing and sales team.

1 Marketing Land: Exploring The Cutting-Edge: Predictive Marketing Analytics
2 PCMag UK: Predictive Analytics, Big Data, and How to Make Them Work for You
3 HubSpot: Is Predictive Intelligence the Future of B2B Marketing?
4 PCMag UK: Predictive Analytics, Big Data, and How to Make Them Work for You
5 Forbes: How Marketers Are Driving Growth Through Personalized Content
6 The Drum: Push or Pull? Create personalized content at its inception to pull in potential customers
7 HubSpot: Is Predictive Intelligence the Future of B2B Marketing?
8 PCMag UK: Predictive Analytics, Big Data, and How to Make Them Work for You
9 Marketing Land: Exploring The Cutting-Edge: Predictive Marketing Analytics
10 Forbes Insights: Why You Should be Using Predictive Analytics
11 IBM Communications: XO Communications takes control of customer satisfaction