You know your customers, or so it seems. But do you really know which ones are profitable? Engaged? Reliable when it comes to payments? Or, on the contrary, which ones are time-consuming, disengaged, and high-risk?
Most B2B companies still rely on traditional segmentation based on static criteria such as company size, industry, or revenue. But this information alone is no longer enough to effectively manage customer relationships.
This is where artificial intelligence comes in, specifically clustering, a method that segments customers based on actual behaviors rather than assumptions.
At Idealis, we tested this approach on our own data through a proof of concept. The result was much more accurate customer profiles that are actionable and useful in day-to-day operations.
What smart segmentation changes
Before: one customer meant one static category (SME, industry X, annual revenue)
After: one customer equals an observed real behavior (engaged, profitable, reliable)
Thanks to a machine learning technique called clustering, we automatically group customers according to their interaction patterns such as effort required, event attendance, and payment behavior.
Much like a conductor who arranges musicians not by section but by their tempo and style, clustering works the same way: it creates coherent groups without needing to guess the rules in advance.
What we did: a concrete proof of concept
We tested this advanced segmentation approach on our own customer base through a proof of concept focused on three key business areas.
1. Profitability
Why this focus?
Not all customers require the same level of effort. What we measure includes:
Time spent (meetings, follow-ups, tasks) / revenue generated
What AI revealed is a segmentation of customers into three distinct groups: highly profitable but low-demand clients, low-profit and inactive clients, and clients with uncertain profitability who consume a lot of time.
2. Event Participation
Why this focus?
Participation reflects genuine interest in our offerings and thus the potential for engagement. What AI revealed confirms this dynamic by identifying highly engaged and loyal customers, others who are less present, and finally profiles that are disengaged and difficult to mobilize.
3. Payment / Satisfaction
Why this focus?
Payment behaviors often reflect the level of satisfaction or maturity. What AI revealed are diligent customers, “distracted” customers with systematic delays, and at-risk customers exhibiting unpaid invoices or disputes. These combined factors provide a more nuanced and coherent understanding of action priorities.
What you gain
Making more accurate decisions allows you to prioritize the right customers and tailor your support accordingly. Optimizing resources prevents team burnout on low-value profiles. Personalizing actions adjusts the frequency of contacts, follow-ups, and offers.
Better risk anticipation covers disengagement, delays, and unpaid invoices. This low-risk, well-structured, and time-limited test can be implemented even on a small customer base.
Illustrative use case
A customer heavily engages the teams with frequent meetings and personalized requests but participates little in events and pays late. Thanks to AI segmentation, this profile is identified as having uncertain profitability, being disengaged, and posing financial risk. The decision made is to limit allocated resources, review commercial terms, and activate preventive monitoring. This fact-based approach benefits both the company and the customer.
Why choose Idealis?
Integrated data expertise
Our data science team specializes in leveraging data from the Odoo ERP. This enables us to analyze your existing data in depth without complex configurations.
End-to-end support
From data collection to clear reporting and integration into your CRM, we guide you through every step. After the proof of concept, we assist you in implementing the segmentations within your business tools.
Immediate business translation
No unreadable technical tables. Our deliverables are concrete, illustrated, and actionable by your sales, marketing, or management teams.
A progressive approach
This proof of concept lays the foundation for ongoing segmentation. You will be able to add new dimensions such as NPS, support, and product satisfaction, automate alerts, and integrate the logic into your CRM campaigns.
What you need to know before getting started
“Are my data clean enough? ”
No need for a perfect CRM. We start with your existing dataset and work with you to structure it.
“How long does it take? ”
Generally, 2 to 4 weeks, depending on data availability.
“Is it useful even with a modest database? ”
Yes. Sometimes small databases reveal the greatest contrasts in customer behaviors.
“Will I know what to do with the results? ”
Yes. We support you at every step and provide actionable, clear, and tailored recommendations suited to your organization.
And after the proof of concept?
This proof of concept is just the beginning. Once initial insights are gathered, you can integrate the segmentation into your CRM, automate alerts or follow-ups based on customer profiles, add complementary dimensions such as support, products, or satisfaction, and train your teams to take a more strategic approach to customer profiles.
This proof of concept is the first step toward continuous and evolving digitalization of your customer relationships.
Would you like to discover what your data can reveal?
Request a personalized, no-obligation assessment.
Within a few weeks, we work with you to identify the first actionable levers..
Contact Idealis Solutions and turn your customer base into a strategic advantage: solutions@idealisconsulting.com
Odoo and AI: Unveiling your customers’ true profiles