Current Challenges in Marketing
There was a study done by Dun & Bradstreet that summarized three challenges that global marketing teams face. Even in the era of data-driven marketing, the challenges are: difficulty in understanding the audience, tracking ROI, and lead conversion. The study found that out of the teams who participated, 57% are not able to understand their audience, 46% are not able to track the ROI, and, after all the marketing efforts, they say just 17% of their leads convert.
Right now, there are so many marketing automation tools in the market, but typically they are not able to help teams understand their customers or track ROI. As a result, teams use a bunch of different tools, which leads to data being scattered around. These challenges can be addressed by developing a predictive marketing analytics solution using RapidMiner.
Using Machine Learning at Every Stage of the Customer Journey
A simplified customer journey includes three stages: acquisition, nurturing, and conversion. How can data science improve the output of each stage? In the acquisition stage, data science can help you easily understand your customer and determine the channels that are working well. For the nurturing phase, it can prioritize your leads and determine products that are more suited to your customer. In the final conversion phase, it can help you reach out to your leads with a discount offer they’re highly likely to accept. Let’s break these concepts down further.
To see this in action, watch the Maximizing Lead Conversion Success Using Predictive Marketing Analytics webinar with our partner Anblicks.
Maximize lead generation success by using predictive analytics to identify your target audience and where they come from.
The concept of segmentation is basically slicing and dicing your audience to simplify your understanding of the customer base. What customers have in common isn’t always obvious, so applying machine learning can help reveal hidden insights and create truly meaningful segments. Customer segmentation allows you to understand your audience better: understand how they behave and what you can expect from them in terms of engagement.
Channel attribution, also a part of the acquisition stage, gives you an all-around perspective of your customer journey. It answers the questions that all marketing teams ask. What channels area performing best? Where do our best quality leads come from? With machine learning it is also possible to understand the effect of one channel on another and predict what would happen if a channel was removed from the system. Essentially, channel attribution helps to identify the best sources of leads, to optimize marketing spending, and can help in testing new strategies for lead acquisition.
Invest time effectively by engaging with your audience in the right ways.
Lead scoring is a methodology that identifies the top prospects coming in. With lots of leads hopefully coming into your system, it is helpful and effective to quantify the leads based on the probability of them converting by assigning a score to each profile through its learning on their path behavior. This way, you will be investing your time efficiently with leads that are highly likely to convert.
Product affinity, (product propensity) also a part of the nurturing phase, gives you an insight into what to offer. So once you understand that a prospect is likely to convert, you can offer them the product or service most likely to result in a purchase. It can give you insight into what products to recommend or even help you develop promotional strategies like bundling two products together, for example.
Make an offer they can’t refuse.
The recommendation concept comes in the last phase of the customer journey. It is that last step you need to push a customer to a finish line. The fundamental idea here is to furnish a discount, which is a monetary profit to both customer and the vendor. Not all leads require discounts, but you can identify certain leads as price sensitive. Machine learning can help identify those prospects, optimize the discount to them, help forecast costs and returns, and indicate the lifetime value of the customer.
Predictive marketing analytics is an essential piece of the marketing journey; the insights through PMA can expedite your efforts in winning a customer. But that is just one half of the battle. Keeping them engaged and ultimately retaining them is the real long-term success.
Watch the webinar with Anblicks to see how they maximized lead conversion for their client, an educational consultant that was looking to improve their marketing efforts and customer journey. They walk through all these concepts and also show how the insights helped their client in every phase.
Predictive analytics enables marketers to transform data into actionable insights & continuously improve strategies. Here are 10 ways it can be used to drive performance.
Don’t just make the best data science decision, make the best business decision. We’ll walk you through how to create a confusion matrix to understand your model’s results & account for business gains and losses.