Data-Driven B2B Marketing: Insights from Intel, T-Mobile, and Hilti
In a recent English-language episode of the B2B Marketing Leaders Podcast, Olga Bondareva, founder of ModumUp, talks about data-driven B2B marketing with experts from different B2B companies:
Kristi Berg McCutchen, Vice President, Marketing - Enterprise, Public Sector & Partner at T-Mobile
Dina Habib O'Mara Global, Industry Partner Strategy, GTM Co-Sell Lead at Microsoft (at Intel when the episode was recorded)
Asked what they would want if marketing data were completely accurate, Dina Habib O'Mara described a single hub that brings together customer, partner, and sales data while meeting privacy and compliance requirements. Without a reliable data foundation, AI initiatives and marketing strategies are difficult to scale.
Kristi Berg McCutchen at T-Mobile wants to see the same connected customer journey across product, sales, customer success, and marketing. She also wants a clearer understanding of what matters to each person in the buying group.
Dina cited Forrester research indicating that the average B2B buying network includes around 12 to 14 people, rather than a single decision-maker.
Christiaan Davel at Hilti added that important influencers may never become the paying customer. In construction, engineers often specify products early in the process. Creating value for them can lead to better sales-qualified leads later.
Kristi also highlighted the importance of day-to-day users. Executive-level alignment may not be enough if the people who will use the product are not ready to adopt it.
Cases: GSIs, propensity scores, and project timing
At Intel, Dina rebuilt the go-to-market approach for global system integrators around three priorities: amplification, building a pipeline that matched limited sales capacity, and enablement.
Fragmented data made change management essential. The team also needed a privacy-compliant data foundation connected to its ICP systems and Salesforce.
Kristi, drawing on her experience at Google Cloud and AWS, used propensity models for accounts and buying groups. The models combined CRM, firmographic, and intent data.
The scores helped sales teams identify where the potential wallet share and likelihood of winning were highest. The approach was first tested through pilots and then scaled with the support of partners.
At Hilti, Christiaan connected software usage and construction project timelines with CRM and specification data. Better timing of handoffs to account managers improved lead quality and sales outcomes.
Before scaling an approach, the team still tested its assumptions directly with customers in the field.
The shift: buying groups, MQAs, and SaaS value
Dina said that the traditional linear funnel no longer reflects how many B2B purchases happen.
The phrase "the MQL is dead" does not mean that individual leads no longer matter. It means that marketers need to look beyond a single contact and understand the broader buying network around each opportunity.
Kristi at T-Mobile sees MQLs as activity signals rather than a complete view of the customer journey. She focuses on qualified buying groups, the experience each person should have, and marketing-qualified accounts that are ready for continued engagement.
In SaaS businesses, marketing also plays a role in onboarding users and supporting healthy product adoption.
Christiaan at Hilti cautioned that having more data does not automatically lead to better decisions. New information should be evaluated through a clear strategic lens.
In SaaS, usage data can also help teams continue creating value before declining adoption becomes a churn risk.
Sources, insight, and AI with humans in charge
Christiaan emphasized the importance of understanding where data comes from. Field feedback, social signals, and product usage data should not all be interpreted in the same way.
In one case, an incomplete interpretation of the data almost led the team to abandon an upsell opportunity that was still supported by field testing.
Kristi at T-Mobile put trust first: clean first-party data, carefully evaluated third-party intent data, and feedback from partners.
Dina starts with first-party, third-party, and financial metrics, then keeps the focus on company objectives rather than copying what peers are doing.
For analysis, all three speakers start with the business question.
Kristi looks beyond MQL volume to understand whether the right people at target accounts are actually engaging. Once the desired outcomes were clear, Dina reduced the content mix to a smaller set of proven formats.
On AI, Kristi described controlled pilots that used approved executive briefing materials to help product and bid teams access feedback more quickly.
Dina keeps people involved in reviewing AI output to protect the brand's distinctiveness.
Christiaan helped train and evaluate an AI model for structural engineers, rejecting answers that did not meet the required quality standards.
AI can improve productivity, but strategy and judgment remain human responsibilities.
When data and experience point in different directions, the speakers recommended investigating further before discontinuing a feature or initiative.
Christiaan also questioned attribution models that oversimplify complex buying cycles. Kristi found that gaps in CRM processes and duplicate accounts were behind some of the missing conversions.
You can check out the full episode on the B2B Marketing Leaders Podcast: