How much will your customers spend on Product A next quarter? Or on Product B? Knowing, or at least having a rough idea, not only helps you make informed decisions, it can also help you boost revenue.
It’s also why many businesses are turning to customer behavior prediction to create a clear picture of the future.
By having an idea of how your customers will behave, you can make adjustments to different areas of business operations. But how does the method work? Perhaps more importantly, how can you leverage it to boost your sales and revenue figures? Let’s dive in.
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Human behavior is not static. It can be influenced by multiple factors, such as social media trends or economic downturns.
You may already use approaches such as Customer 360 to gain a deeper understanding of your customers. It’s worth using knowledge you already have about customers, but you also need some idea of how those customers will behave in the future.
Customer behavior prediction requires you to dig deep into the factors that might influence customers to change their behavior or maintain previously identified patterns. There are several benefits that can come with using behavioral prediction, all of which can increase revenue.
You will probably already be segmenting your customer base. However, most segmentation works by analyzing historical behavior from the records you have in your customer relationship management (CRM) system.
Predictive analysis can add another layer to that by accessing dynamic data that shows where current behavior is likely to lead.
Your buyer personas are usually built on the historical data you have as well as more generalized demographical identification. Adding the predictive angle to that persona gives you something that is less static and that can inform you about what that customer is likely to buy or have an interest in.
Predictive analysis can help you identify groups that may be high-value that will be worth investing further time and effort in.
Another thing to consider is that by careful segmentation, your brand can also identify customers who have the potential to become brand ambassadors or who will at least leave positive reviews and posts.
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Knowing your customers' likes and dislikes means you can personalize the customer experience and ensure that any marketing efforts target their specific interests.
Personalized experiences, combined with other factors such as high-quality products and customer service, help build customer loyalty, improve retention rates, and lead to higher average CLVs (customer lifetime value).
Today, predictive models use AI deep learning to get an idea of what tastes and motives drive customers to decisions.
Using the results, you can personalize your products and content to those tastes and motives. With tools such as AI-powered customer service and other AI tools, businesses are increasingly focusing on personalization. Predictive analysis adds to this ‘arsenal’ of tools that bring you closer to your customers.
What is your current marketing strategy? Are you just advertising and posting on multiple channels in hopes of getting clicks and conversions? You may get some results that way, but your ROI won’t be high. You need a more targeted approach.
Monitoring and analyzing customers’ behavior gives you that focus. It guides you on what marketing customers will be receptive towards and on what platforms.
It also helps you home in on those people who have yet to become a paying customer. They may have shown some interest or have put products in their basket at some point, only to drop off at some point in your sales funnel.
By predicting where people will leave your funnel, you can adjust your approach. That could involve pop-ups or messages that address their pain points.
It can also guide special offers to move customers through the funnel. For example, you might offer 50% off a brand logo for customers that haven’t yet made a purchase.
With the information you have and the insights provided by predictive models, you can focus efforts on the places most likely to give results.
If you use channels such as SMS or email marketing strategies, having these insights means you can optimize everything from subject lines to personalized content. Plus you can add more focused CTAs to create a sense of urgency.
Every organization is constantly looking for ways to boost revenue by increasing sales and conversion rates. So, how can predictive models help you achieve those goals?
There’s data and there’s quality data. It’s estimated that we will be generating 147 ZB (zettabytes) of data by the end of 2024, so it’s clear that there’s a lot of chaff in with that wheat.
The data you use for predictive analysis has to be pertinent to customer behavior. You can collect data from a number of sources including your website, CRM, and mobile apps. Even the best sales prospecting tools will be a goldmine of information.
This data can take many guises, too. It’s worth combining quantitative and qualitative data when running an analysis:
Direct customer feedback: This could be from email surveys, conducted by bots on a call, or through website forms.
Analytics: Resulting from any conversations between your business and the customer.
Previous purchases: This includes patterns/preferences shown for particular types of products or services.
Browsing history and other digital footprints: How often customers have visited your site, the pages they visited, and time spent on each page.
Social media engagement: This can cover any interactions between customer and brand as well as customer reviews and online discussions about your brand (social listening tools can be useful here).
You can only make informed decisions when you have a comprehensive view of the user journey and likely future behavior. So you have to use collected data to construct predictive models. There are six main steps to this:
Ensure you have datasets made up of data relevant to your needs.
Organize and combine all the data you plan to use into one large dataset.
Clean the data so that any predictions will be as accurate as possible.
Add any variables needed to understand your data and records.
Decide on the methodology or algorithm that meets your needs and suits your dataset.
Build your model.
Good algorithms combined with ML (machine learning) can identify potential patterns in the future behavior of your customer segments.
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Using your completed customer segments, leverage machine learning to predict future customer actions. This includes high or low purchase intent, churn, and preferred products.
Look at this similarly to lead scoring. Assign a score to each metric (between 1-5 or 1-10). Doing so means you can more effectively assess where each customer lies in terms of being a qualified vs unqualified lead.
Take what you’ve learned and apply it to your marketing efforts. Identify factors such as product preferences and recommend products to your segments or highlight their wants and needs in your marketing campaigns.
Use tools to your advantage here as they can significantly strengthen the impact of your efforts. For example, mobile game developer Nord used My Tracker Personalize to personalize its in-app pitches and convert non-paying players into paying customers.
Reducing customer churn and increasing customer retention go hand-in-hand. Chances are you’re running multiple campaigns – or one campaign across multiple channels. So you need to understand what works and what doesn’t.
Identifying campaigns (or channels) that are not improving your retention rates goes a long way in increasing ROI along with other metrics.
Use tools like this free LTV (lifetime value) calculator to achieve this. Predict the revenue of your ad campaigns immediately after launch. Use those insights to disable campaigns that will be unprofitable, thus decreasing churn.
Robust behavioral analytics offer you the opportunity to boost revenue by upselling or cross-selling other products and services. Analytics show customers who are high value or who will have interest in increasing their average spend.
For example, your analysis could reveal that customers who bought outbound sales software from you are also interested in associated products such as intelligence tools or scorecards. It’s a case of knowing what to sell, to whom, at what time.
Customers contact your customer service team for a number of reasons, from technical help to complaints about shipping. Predictive analytics combined with other data from your CRM show what people expect from these communications or where you are falling short.
Good customer experience and customer satisfaction relies on good service. So this is an area you should focus on.
While excellent customer service may not directly generate revenue, it can influence loyalty and lead to future purchases from loyal customers.
Customer behavior prediction can help you better understand your customer base. In the competitive business landscape, that gives you an edge.
Effective predictive analytics lead to reduced churn and increased customer retention. Having insights into future behavior allows you to build more targeted campaigns. It also guides your segmentation and personalization strategies.
The result of all of this is that your marketing, messaging, and overall approach will be more effective. The more effective they are, the more you can boost revenue.
