It’s no secret that machine learning is revolutionizing marketing. Gone are the days when marketers had to sift through reams of data manually, segmenting them into distinct groups and applying the parameters themselves. Now, algorithms can take care of most of the legwork, leaving you to handle those jobs requiring the human touch.
Machine learning is a branch of AI that can read all your data and identify ways to improve your marketing efforts. It can enhance your marketing analytics to improve your overall customer experience and help you truly connect with your audience.
Marketing analytics is the study of data gathered from marketing campaigns to determine if your marketing efforts are working and to optimize future campaigns. In this article, we’re going to look at how machine learning can be used to improve your marketing analytics to achieve the best results.
- Customer segmentation
- Improved depth of data
- Predictive analytics
- Chatbot analysis
- Conversion rate
- Final thoughts
Customer segmentation has long been a tool in every marketer's belt. It divides customers into sub-groups typically based on some shared characteristic, such as gender or age (mainly in B2C), or by company type or industry (for us B2B cool kids), allowing marketers to serve targeted content to those groups. It’s a basic way of personalizing your marketing efforts. But it’s often done manually, which can be a very time-consuming process, not to mention inaccurate if the marketer is unknowingly choosing the wrong metrics to focus on.
Machine learning algorithms can detect similarities within your customer base and automatically and much more accurately group them together. It measures customer activities on your website to create more personalized and specific metrics to group your prospects.
While a marketer may look at a few broad metrics, a machine can look at many more metrics in mere seconds and apply them to your marketing efforts. It can look at all website users across certain time periods and predict, based on previous behaviors, which users are likely to make a purchase and when.
With all this data, marketers can craft the perfect campaign that reaches their customers at exactly the right time. The algorithm will cluster audiences into segments based on real-world data rather than general assumptions for more effective targeting and messaging, providing an overall improved ROI.
Improved depth of data
Data is a crucial tool for marketers, but they can often be limited by their ability to access and analyze it. Gathering and analyzing data manually is a time-consuming and labor-intensive process, which can often be inaccurate because data changes and updates so quickly.
Machine learning can provide marketers with large, up-to-date data sets that they can use to obtain deeper insights, such as predicted next steps, helping you to understand your consumer behavior and your campaign performance.
By diving into deep data, you can better develop personalized marketing strategies for the highest possible ROI. This kind of data not only reveals your customers’ preferences but also helps you to establish which channels are the best for engaging them.
With machine learning, marketers can respond more quickly to changes in the quality and quantity of customers brought in by marketing campaigns. All of this can be done in real-time, and you can adapt to any changes or new data, improving the overall quality of your data analysis in less time.
Predictive analytics uses data and machine learning to predict future events. For marketers, this can be used to better understand which campaigns are working and which will lead to an increase in sales in the future. Almost half of B2B marketers are currently using predictive analytics for customer insights.
It links to customer segmentation, providing insights to consumers based on their past behaviors so they can be sent targeted messaging at the right time. With predictive analysis, marketers can determine where to focus their marketing efforts based on the predicted value of a customer. Predictive data identifies the right kind of advertising channels and the right times to increase marketing spending or reduce investments.
It can help you qualify and prioritize leads, allowing you to devote more attention and spend on the right prospects rather than wasting it on those that won’t respond to marketing.
Predictive analytics can also help retain customers, which is essential for marketers as it’s five times more expensive to gain a new customer than it is to retain one. It helps you better understand your customers’ needs, providing you with upselling opportunities to keep them spending in the future.
Chatbots are a great way to engage with customers and improve customer support, as they have the benefit of being available 24 hours a day to offer help and assistance. In fact, chatbots have seen a 92% increase in usage since 2019, and 87.2% of consumers rate their chatbot experiences within the range of neutral to positive. As we well know, once consumers have given the thumbs up to something, it’s only a matter of time before it becomes prevalent in B2B. Machine learning significantly improves the performance of chatbots while also gathering vital data.
Machine learning uses the data of previous chatbot interactions to help it improve its responses to future customers. It can be “taught” to “talk” with human customers and respond effectively to their questions and problems. By using algorithms and conversational inferences, the chatbot can become smarter and more human-like with its responses.
Aside from improving your customer service, you can utilize chatbots as a tool to learn about your customers and adjust your service strategies accordingly. Your chatbot can record, store, and retrieve customer data better than a human can manually, providing you with a detailed analysis of trends and behaviors. With all this data, you can gain a deeper understanding of your customer base and any recurring pain points to address.
A conversion rate looks at the likelihood that a customer will make a purchase. When you harness this data, you can focus your marketing efforts on those more likely to make purchases and avoid wasting time and effort on those much less likely to do so.
Increasing conversion rate with machine learning can be done in a few ways, such as predicting which version of a landing page will perform best for each visitor; using dynamic pricing to predict the best price for each user, and predicting which free users will convert into paying customers.
The algorithms use extensive data points for each user to work out the likely conversion rate of each one, classifying converters and non-converters within chosen variables. Marketers can then adjust their strategies to focus mainly on the users determined to become converters, increasing the likelihood of purchase.
Machine learning is a truly powerful tool that is ever-growing and improving to provide marketers with limitless ways to enhance their marketing efforts and ROI. With analytics and forecasting, there are endless optimization opportunities.
Machine learning in marketing promotes efficiency, allowing marketers to provide better customer service and improve their customer retention. To stay competitive in today’s data-driven world, marketers need to take advantage of the predictive analytics that machine learning provides.
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