Ingersoll Rand – a global industrial manufacturer with a large HVAC (heating, ventilation, and air conditioning) division – faced a classic B2B marketing challenge. The company serves a diverse range of customers with very different needs, yet its marketing team had been relying on broad, one-size-fits-all campaigns.
Melanie Fox, Ingersoll Rand’s digital engagement leader, noted that an HVAC service technician and a facilities owner have vastly different interests; a generic nurture approach meant messages that resonated with one audience often fell flat with another. The pain point was clear: valuable prospects were being treated homogeneously, and the team knew that if they could tailor content to each segment’s specific interests and context, they would see far better engagement.
However, doing this manually was impractical – personalising at scale with a small marketing team was time-consuming and inefficient. Ingersoll Rand needed a new approach to lead nurturing that could deliver relevant, contextual communications to each prospect without ballooning the workload. This requirement led them to explore artificial intelligence as a solution.
AI solution: personalised nurturing with IBM Watson
To overcome this challenge, Ingersoll Rand turned to an AI-powered marketing automation platform. They implemented IBM’s Watson Campaign Automation, which brought machine learning intelligence into their email and campaign workflows.
Watson’s AI capabilities enabled the team to automate labour-intensive processes like audience segmentation and content targeting, which had previously been done by hand. As Fox explained, “one of the things that impressed us most about the IBM solution was its ability to automate time-consuming processes such as segmentation – enabling us to build personalised campaigns at speed and scale.”
Crucially, the platform allowed Ingersoll Rand to create dynamic, personalised content within their lead nurturing campaigns. Rather than sending the same follow-up emails to every lead, the marketing team could reuse a single campaign template but have the content automatically tailor itself to each recipient’s profile.
For example, an email promoting a new HVAC service would highlight ROI and high-level benefits for a CFO, while a technical contact would see detailed service features – all driven by AI rules that swap in text and images based on the lead’s job role. This level of personalisation ensured that each prospect received information aligned with their priorities, making the nurturing touchpoints far more relevant.
The AI solution also integrated contextual data to trigger timely outreach as part of the nurturing workflow. Ingersoll Rand connected IBM’s WeatherFX data feed to Watson Campaign Automation, allowing the system to use local weather events as nurture triggers. If severe weather struck a customer’s region, the platform would automatically send a helpful, tailored message – for instance, suggesting an equipment check after a hailstorm and providing a direct link to schedule maintenance with a local dealer.
These AI-driven triggers effectively added a real-time layer of personalisation based on external signals. The lead nurturing process thus moved beyond a static drip sequence to an adaptive journey that could respond to each lead’s environment and behaviour in real time.
Integration into the lead nurturing workflow
Implementing the Watson-powered solution meant that AI became embedded in Ingersoll Rand’s day-to-day lead nurturing operations. The marketing team set up their email nurture streams within the Watson Campaign Automation platform, which handled the heavy lifting of deciding which content variant to send to which lead and when. Segmentation that used to require manual list-building was now continuously performed by the AI – grouping prospects by industry, role, buying stage, and even by inferred intent or engagement level.
This ensured that a CTO from a large enterprise might receive a different sequence of nurture content than a maintenance manager at a small business, all without marketers having to manually create dozens of segments. The content itself was populated dynamically. Email templates were built with AI-driven content blocks that would show or hide sections based on the lead’s attributes. In practice, this felt like having a custom campaign for each prospect, but it was fully automated behind the scenes.
The workflow also incorporated the WeatherFX-triggered messages into the nurturing calendar – essentially an automated “if/then” rule where if a certain condition (like a temperature drop or storm alert) was met, the system would inject an extra nurturing email addressing that scenario. These integrations meant that AI was orchestrating both the content and timing of lead communications, augmenting the marketers’ strategy with machine-driven decisioning.
Fox and her team remained in control of the overall strategy – they determined what content was available and the rules or logic – but the AI continuously optimised the execution, ensuring each lead got the right touch at the right time. The result was a highly personalised lead nurture programme that operated at a scale and responsiveness impossible to achieve manually.
Results: higher engagement and conversion rates
After rolling out the AI-personalised nurture programme, Ingersoll Rand saw significant improvements in lead engagement. Within a short time, the team reported substantial improvements in campaign performance metrics such as open rates, click-through rates and conversions on their nurtured leads. In other words, more prospects were opening the emails, clicking through to content, and ultimately converting into sales opportunities or qualified leads.
This was a direct result of leads receiving content that spoke to their interests – they were more inclined to read and act on emails that felt custom-made for them. Exact numbers were not publicly disclosed, but the qualitative gains were clear: the AI-driven personalisation was turning previously indifferent contacts into actively engaged prospects.
Moreover, the intelligent cadence of outreach helped Ingersoll Rand sustain longer-term conversations with leads. By responding to each lead’s behaviour and context (such as sending that maintenance prompt after a hailstorm), the company stayed relevant throughout the buyer’s journey. Fox noted that the new AI-enabled platform allowed them to maintain ongoing long-term dialogue with both prospects and existing customers, driving both engagement and loyalty.
In essence, leads did not drop off after an initial contact; the personalised nurture stream kept adding value, which kept the prospects interested until they were sales-ready. This translated to a healthier pipeline and more nurtured leads moving to the next stage.
It’s worth noting that Ingersoll Rand’s success with AI personalisation is echoed by other B2B firms that have taken a similar approach. For example, ServiceMax, a B2B software company in the field-service industry, used an AI-based recommendation engine on its website to guide visitors to the most relevant content, resulting in a 70% decrease in bounce rates and over 100% increase in pages per session – ultimately leading to a big uptick in demo requests. Such outcomes underscore how tailoring the journey to each prospect through AI can yield dramatic improvements in engagement metrics and lead quality.
Key takeaways for B2B marketers
Ingersoll Rand’s case provides several practical lessons for B2B marketers looking to implement AI-driven lead nurturing.
Segmentation and personalisation at scale: One of the greatest advantages of AI in lead nurturing is the ability to micro-segment your audience and deliver content variations to each segment (or individual) automatically. In the past, creating dozens of versions of an email for different roles or industries would be prohibitive. With AI, Ingersoll Rand was able to let the system do this segmentation and content matching work in real time, which dramatically improved relevance and thus engagement. B2B marketers should identify key differentiators in their audience (job role, company size, industry, behaviour patterns) and leverage AI tools to tailor messaging along those lines. The more precise your targeting, the better your conversion outcomes – in fact, Salesforce found that businesses using AI for lead scoring and qualification see up to 30% higher lead conversion rates.
Automate the nurture process (with a human strategy): AI doesn’t replace the marketer; it augments them. Marketers still need to define the nurture strategy – for example, what content to send for different scenarios – but AI can automate the execution and adapt it in ways too complex for manual campaigns. Setting up automated triggers (like Ingersoll Rand did with weather events) or using AI to respond to lead behaviours (for example, sending a specific whitepaper if a lead spent a long time on a related webpage) can ensure no opportunities slip through the cracks. The key is to map out the customer journey and decide where AI can inject a timely touch. This leads to a multi-touch, always-on nurture engine that engages leads 24/7 across their journey. Importantly, marketers should monitor the AI’s suggestions and performance – it’s a continuous learning loop where the strategy can be refined as the AI reveals what works best.
Content must be relevant and helpful: Personalisation isn’t just inserting a name into an email; it’s about truly aligning with the lead’s needs or pain points. Ingersoll Rand achieved better results because the content they delivered was genuinely useful to the recipient (like maintenance tips keyed to local weather). B2B marketers implementing AI should ensure they have a rich content library and data signals to draw from. Whether it’s leveraging external data (industry news, events, weather, market trends) or internal data (past purchases, web behaviour), use those insights to craft nurture content that addresses the lead’s context. When your nurture stream consistently provides value, leads are more likely to respond and trust your brand. As the case showed, this approach drives not just short-term clicks but long-term loyalty.
Measure and iterate: Finally, treat an AI-driven nurture initiative as a test-and-learn process. Track engagement metrics (open rates, click-through rates, conversion to opportunities) and compare against your prior benchmarks. Ingersoll Rand saw strong uplifts in these KPIs, and those wins helped justify further investment in AI. Be prepared to tweak segments, adjust content, or update your models based on what the data shows. AI tools often provide analytics or even recommendations for optimisation. For example, many modern AI marketing platforms highlight which content is most effective for which audience, or suggest new audience groupings you might not have considered. Taking advantage of these insights can further refine your lead nurturing. In sum, continuous improvement is part of the process – AI might get you big gains quickly, but sustained success comes from ongoing optimisation and strategy alignment.
Ingersoll Rand’s experience illustrates that AI can be a game-changer for B2B lead nurturing when applied thoughtfully. By automating segmentation, personalising content, and responding to real-world triggers, the company transformed a fragmented, generic marketing approach into a cohesive program that caters to each prospect’s journey.
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