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Enabling AI-Powered Customer Segmentation for B2B Companies: A Roadmap

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Based in North Carolina, Ingersoll Rand is one of the world’s leading conglomerates. The firm boasts several business lines, including compressed air systems, HVAC solutions, and cutting-edge technological products that cater to diverse industries, such as scientific laboratories and cargo transportation firms. It also has a presence in over 175 countries, operating primarily in the B2B segment.

With that in mind, it is easy to imagine how complex it can be to satisfy all of their customers, which is why Ingersoll Rand resorted to AI to understand them better.

By leveraging AI to segment their extensive and very diverse customer base, the company was able to create tailored campaigns that performed much better on KPIs such as open rates, click-through rates, and conversions. Some of these campaigns were segmented by geography, while others were by the type or size of business, and yet others a combination of all of the above. This helped the firm’s leaders comprehend that they had some unique segments that they had not taken the time to develop before. In fact, without AI, they might have not noticed these segments existed.

Ingersoll Rand’s success shows something that all business leaders must understand. Today’s landscape is hyper-competitive, therefore, understanding your customers is critical. Clients who do not feel acknowledged or who are not getting their needs met by your product or service can easily be swayed to shift to a rival firm’s offer.

To improve your odds of adequately comprehending what your clients expect, you must divide them into the right segments, as only that way you will know for sure what their shared characteristics, behaviors, and preferences are. Based on these segments, you can craft tailored marketing campaigns and personalized product offerings, which highly enhance your conversion rates.

By adopting technologies like artificial intelligence (AI) and machine learning (ML), companies can give a boost to their customer segmentation efforts. However, like all technological innovations, they need to be adopted strategically.

Here’s a guide to help you accomplish that.

Why customer segmentation matters, and how can AI help?

Basically, AI can assist us by transcending our biases and conventional methods of segmenting our customers. Because its segmentation process is run only by data, we can then learn about customer segments that we hadn’t thought about, and this uncovers unique information about our customers.

To illustrate further, let’s look at the following example.

A company that specializes in agricultural equipment and supplies is aiming to expand its product offering. The firm is conducting segmentation to make sure the new products are relevant.

In the past, the business relied on a conventional approach to segmentation, categorizing customers by geographic location, based on the underlying assumption that farmers from the same region would have similar needs. For example, they would advertise a tractor focused on the features they perceived as commonalities between the farms in the American Midwest, like weather conditions.

However, upon implementing AI, the company realized that geographic segmentation was not the right approach. By collecting extensive data (including purchase history, farm size, types of crops grown, irrigation methods used, technology adoption, automation rate, and more), and letting AI algorithms analyze it, the firm detected that farm size is one of the most critical factors that influence a farmer’s purchasing decision. It can seem obvious: farmers with larger farms have distinct needs than those who have smaller properties. However, the agricultural equipment company leaders were still set on selling through geographic segmentation, and by themselves, they might have never changed this process, even though it wasn’t bringing the best results.

Having said this, how can we run this process?

Different approaches to customer segmentation

To determine which model to apply to your customer segmentation approach, you need to consider:

  • What data do I have available? In other words, what do I know?

  • What are my business’ goals?

  • What do I know about my customers?

Based on this, you can either apply an unsupervised model, a supervised model, or follow the mixed approach.

  • Unsupervised (K-Means clustering, DBSCAN, GMM): This model doesn't rely on predefined labels and training data, but instead calculates the optimal segments from scratch. You can apply the unsupervised algorithms:

    • When you don’t have specific segments in mind, especially when you apply AI segmentation for the first time and don’t have previously trained datasets

    • When you have a dynamic business with a rapidly changing customer base, and you want to identify new segments

  • Supervised Machine Learning (regression model, decision tree, random forest): We can apply this approach if we have a labeled training dataset, e.g. from previous segmentation or domain knowledge. The supervised ML model can then be applied to new customers, or customers for which segment is not clear

The mixed approach combines using unsupervised learning to identify segments and then applying these segments as labels to train a supervised model. This trained model can be used to classify new customers, or to create a segment for customers from whom we don’t have complete data.

Please be careful when applying the mixed approach without random sampling. If you only choose those customers that you have full data on, then, most likely, you will choose your more loyal customers, which might not be a fair representation of the whole group. This will result in a biased selection, and those biases will only be passed on to AI.

Challenges and common mistakes

AI is not without its challenges. From my experience, here are some of the roadblocks that you are most likely to encounter as you learn to master the ropes.

  • Clear segmentation: Many companies are not clear on why they’re segmenting. Without this purpose, it is hard for an AI-run process to be effective. In those cases, a traditional  approach run by humans can work better, especially if you mainly have qualitative data. The same applies if you only have a small number of customers.

  • Data Quality: The quality of the results yielded by AI will only be as good as the quality of the data that you feed the system. Therefore, if your data is not accurate, your segmentation will not be, either.

  • Ethical considerations: Make sure that you do not include sensitive data and criteria into the model. This is a mistake many companies have made, and it has cost them both money and their reputation. For example, in the US, mortgage companies have been under fire for alleged racial profiling of their AI algorithms.

  • CRM Readiness: Because ML is such an incipient technology, many CRM (customer relationship management) systems are not equipped to handle it. Therefore, a proper integration of segments into business operations (marketing campaigns, touchpoints, sales strategy) requires additional work. Many times, owners jump in right away without considering all the processes involved, and this leads to hiccups when attempting to leverage AI.

  • Employee Training: Employees need to be trained further so they can fully understand AI segmentation approaches. Also, it is likely you’ll find some resistance because AI results might contradict their intuition. To overcome the trust barrier, showcase some of its positive applications, and use AI responsibly.

  • Segment quality: Similar to traditional segmentation, the segments you get from ML model should satisfy key criteria and be validated:

    • Actionable

    • Stable

    • Big-enough size

    • Differentiable

  • Domain knowledge and interpretation: Integrating and adequately managing your business’ knowledge is very important at every step of the way, from data preparation to validating the model’s results. Also, keep in mind that even a perfect machine learning model will not give you 100% accuracy. Here is where your domain expertise is needed, and why it is very important for AI and humans to work together. Another mistake I’ve seen often is that decision-makers delegate everything to AI, and blindly implement their suggestions without further question. This will likely lead to unfavorable outcomes. Also, let’s remember that at the end of the day, we are humans, and our biases are still present when interpreting the data. Being aware of this can help us be less vulnerable to potential mistakes.

  • Model updates: If you have a dynamic customer base or you have a high customer turnover, your customers behaviour and preferences often change. Hence, make sure that you update the model regularly and don’t rely on outdated segments.

Step-by-Step Guide to AI-Enabled Customer Segmentation

Now that you’re aware of the challenges, here’s a step-by-step guide to help you implement AI and successfully integrate it into your customer segmentation processes.

  1. Define your segmentation goal. This includes understanding the different criteria under which you will classify your customers. Here, again, both the insights generated by AI and your perspective as an expert on the field are needed. Together, you will uncover new customer segments and be able to customize your marketing campaigns to accomplish better outcomes.

  2. Guarantee data availability: Ensure that AI has access to comprehensive customer data, or if your data is incomplete, find a way to deal with it. One way to do so can be using the mixed modeling approach. We said it before, but it cannot be emphasized enough: The results will only be as good as the data that AI has to work with.

  3. Handle data limitations: If you have limited data, select a random sample from your customers database and collect additional data from them. Then, apply the mixed approach to maximize your results.

  4. Select your modeling approach and apply the selected model to the data obtained

  5. Select the optimal number of segments: There are various techniques to calculate the optimal number of segments. The most popular ones are the Elbow rule and gap analysis.

  6. Understand the segments’ differentiating criteria and interpret the results: What are the key variables by which your customers will be identified? What are their perceptions, and how can they be marketed to? For the segmentation process to work, after validating the model’s accuracy, you need to review the different segments and determine whether the variables driving those segments adequately apply to your business model.

Last, but not least, as a resource for adequate segmentation visualization, I apply parallel coordinates, in which I identify four segments: high-value shoppers, budget shoppers, tech enthusiasts, and occasional shoppers. I measure categories like monthly spending and frequency of purchases for each of these segments as this helps me have a better understanding of my customers.

Final Thoughts

As we’ve discussed, AI-powered customer segmentation can help B2B companies gain clearer visibility of who their customers are and the drivers behind their decision-making. Once you have this information, you can leverage it to craft personalized campaigns and experiences that add more value to your clients.

By following the roadmap outlined in this guide, you can leverage AI algorithms to boost your business’ segmentation processes and make data-driven decisions that propel your growth and increase your customer satisfaction KPIs, fostering a better connection with your clients and a solid sense of loyalty to your brand.

This is increasingly important in the B2B world, and especially for high-tech products, since the needs of customers change rapidly and technological expectations are evolving fast. Adequately segmenting your customers can make the difference between delivering a top-notch product and something that fails to attain the relevant product-market fit.

Veronika is a senior data scientist and business strategist with nearly 20 years of experience in international consulting and business intelligence. She has worked with leading companies in industries such as pharmaceutical, logistics, heavy industries & technologies, agriculture, financial markets, and has a proven track record of developing successful go-to-market strategies.