Based in North Carolina, Ingersoll Rand is considered 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, similar to scientific laboratories and cargo transportation firms. It also has a presence in over 175 countries, operating primarily within the B2B segment.

With that in mind, it is simple to assume how complex it could be to satisfy all of their customers, which is why Ingersoll Rand resorted to AI to grasp them higher.

By leveraging AI to segment their extensive and really diverse customer base, the corporate was capable of create tailored campaigns that performed a lot better on KPIs similar to open rates, click-through rates, and conversions. A few of these campaigns were segmented by geography, while others were by the kind or size of business, and yet others a mixture of the entire above. This helped the firm’s leaders comprehend that that they had some unique segments that that they had not taken the time to develop before. In actual fact, without AI, they may haven’t noticed these segments existed.

Ingersoll Rand’s success shows something that each one business leaders must understand. Today’s landscape is hyper-competitive, subsequently, understanding your customers is critical. Clients who don’t feel acknowledged or who should not getting their needs met by your services or products can easily be swayed to shift to a rival firm’s offer.

To enhance your odds of adequately comprehending what your clients expect, you will need to divide them into the best segments, as only that way you’ll know of course what their shared characteristics, behaviors, and preferences are. Based on these segments, you’ll be able to 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), corporations can improve their customer segmentation efforts. Nonetheless, like all technological innovations, they have to be adopted strategically.

Here’s a guide to allow you to accomplish that.

Why customer segmentation matters, and the way can AI help?

Principally, AI can assist us by transcending our biases and standard methods of segmenting our customers. Because its segmentation process is run only by data, we will then study customer segments that we hadn’t considered, and this uncovers unique details about our customers.

For example further, let’s have a look at the next example.

An organization that makes a speciality of agricultural equipment and supplies is aiming to expand its product offering. The firm is conducting segmentation to be certain that the brand new products are relevant.

Prior to now, the business relied on a traditional approach to segmentation, categorizing customers by geographic location, based on the underlying assumption that farmers from the identical region would have similar needs. For instance, they might advertise a tractor focused on the features they perceived as commonalities between the farms within the American Midwest, like weather conditions.

Nonetheless, upon implementing AI, the corporate realized that geographic segmentation was not the best approach. By collecting extensive data (including purchase history, farm size, varieties 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 vital critical aspects that influence a farmer’s purchasing decision. It may seem obvious: farmers with larger farms have distinct needs than those that have smaller properties. Nonetheless, the agricultural equipment company leaders were still set on selling through geographic segmentation, and by themselves, they may have never modified this process, although it wasn’t bringing the very best results.

Having said this, how can we run this process?

Different approaches to customer segmentation

To find out which model to use to your customer segmentation approach, it’s good to consider:

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

• What are my business’ goals?

• What do I learn about my customers?

Based on this, you’ll be able to either apply an unsupervised model, a supervised model, or follow the mixed approach.

• Unsupervised (K-Means clustering, DBSCAN, GMM): This model doesn’t depend on predefined labels and training data, but as an alternative calculates the optimal segments from scratch. You may apply the unsupervised algorithms:

  • While you don’t have specific segments in mind, especially once you apply AI segmentation for the primary time and don’t have previously trained datasets

  • When you might have a dynamic business with a rapidly changing customer base, and you should discover recent segments

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

The mixed approach combines using unsupervised learning to discover segments after which applying these segments as labels to coach a supervised model. This trained model may be used to categorise recent customers, or to create a segment for patrons from whom we don’t have complete data.

Please watch out when applying the mixed approach without random sampling. In case you only select those customers that you might have full data on, then, almost certainly, you’ll select your more loyal customers, which could not be a good representation of the entire group. This may lead to a biased selection, and people biases will only be passed on to AI.

Challenges and customary mistakes

AI isn’t without its challenges. From my experience, listed here are a number of the roadblocks that you simply are almost certainly to come across as you learn to master the ropes.

• Clear segmentation: Many corporations should not clear on why they’re segmenting. Without this purpose, it is tough for an AI-run process to be effective. In those cases, a conventional  approach run by humans can work higher, especially in the event you mainly have qualitative data. The identical applies in the event you only have a small number of consumers.

• Data Quality: The standard of the outcomes yielded by AI will only be nearly as good as the standard of the info that you simply feed the system. Due to this fact, in case your data isn’t accurate, your segmentation won’t be, either.

• Ethical considerations: Be certain that you simply don’t include sensitive data and criteria into the model. This can be a mistake many corporations have made, and it has cost them each money and their fame. For instance, within the US, mortgage corporations 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 should not equipped to handle it. Due to this fact, a correct integration of segments into business operations (marketing campaigns, touchpoints, sales strategy) requires additional work. Again and again, owners jump in instantly without considering all of the processes involved, and this results in hiccups when attempting to leverage AI.

• Worker Training: Employees have to be trained further so that they can fully understand AI segmentation approaches. Also, it is probably going you’ll find some resistance because AI results might contradict their intuition. To beat the trust barrier, showcase a few of its positive applications, and use AI responsibly.

• Segment quality: Much like 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 corporation’ knowledge could be very essential at every step of the best way, from data preparation to validating the model’s results. Also, take note that even an ideal machine learning model won’t provide you with 100% accuracy. Here is where your domain expertise is required, and why it is rather essential for AI and humans to work together. One other mistake I’ve seen often is that decision-makers delegate every little thing to AI, and blindly implement their suggestions without further query. This may likely result in unfavorable outcomes. Also, let’s keep in mind that at the tip of the day, we’re humans, and our biases are still present when interpreting the info. Being aware of this may help us be less vulnerable to potential mistakes.

• Model updates: If you might have a dynamic customer base or you might have a high customer turnover, your customers behaviour and preferences often change. Hence, be certain that that you simply update the model commonly and don’t depend on outdated segments.

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

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

1. Define your segmentation goal. This includes understanding the several criteria under which you’ll classify your customers. Here, again, each the insights generated by AI and your perspective as an authority on the sector are needed. Together, you’ll uncover recent customer segments and give you the chance to customize your marketing campaigns to perform higher outcomes.

2. Guarantee data availability: Be certain that AI has access to comprehensive customer data, or in case your data is incomplete, discover a solution to take care of it. One solution to accomplish that may be using the mixed modeling approach. We said it before, nevertheless it can’t be emphasized enough: The outcomes will only be nearly as good as the info that AI has to work with.

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

4. Select your modeling approach and apply the chosen model to the info obtained

5. Select the optimal variety of segments: There are numerous techniques to calculate the optimal variety of segments. The most well-liked ones are the Elbow rule and gap evaluation.

6. Understand the segments’ differentiating criteria and interpret the outcomes: What are the important thing variables by which your customers might be identified? What are their perceptions, and the way can they be marketed to? For the segmentation process to work, after validating the model’s accuracy, it’s good to review the several segments and determine whether the variables driving those segments adequately apply to your corporation model.

Last, but not least, as a resource for adequate segmentation visualization, I apply parallel coordinates, by which I discover 4 segments: high-value shoppers, budget shoppers, tech enthusiasts, and occasional shoppers. I measure categories like monthly spending and frequency of purchases for every of those segments as this helps me have a greater understanding of my customers.

Final Thoughts

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

By following the roadmap outlined on this guide, you’ll be able to leverage AI algorithms to spice up your corporation’ segmentation processes and make data-driven decisions that propel your growth and increase your customer satisfaction KPIs, fostering a greater connection along with your clients and a solid sense of loyalty to your brand.

That is increasingly essential within the B2B world, and particularly for high-tech products, for the reason that needs of consumers change rapidly and technological expectations are evolving fast. Adequately segmenting your customers could make the difference between delivering a top-notch product and something that fails to achieve the relevant product-market fit.