The same patterns. Different industries.
When we look under the hood of B2B companies, we tend to see the same patterns. The details vary, but the underlying issues are often similar, for example:
- Marketing and sales working from different definitions.
- Over-reliance on incomplete data.
- Tools added without a clear system.
- AI layered onto unclear processes.
The examples below show how these problems actually appear in practice – and what can change when they are understood clearly.
Marketing–sales disconnect
A mid– sized B2B company in the industrial sector that had grown quickly over several years.
They relied heavily on an experienced sales team, supported by a single overstretched marketing manager and a mix of external agencies handling SEO and lead generation.
On paper, marketing was performing well. Leads were coming in, and activity levels were high.
But there was very little connection between those leads and the deals the sales team was actually closing.
In practice, the sales team didn’t trust the leads being generated. They worked almost entirely from their own networks – past customers, personal contacts, and trade shows.
There was also very little structured feedback going back to marketing on why deals were won or lost.
On closer inspection, the agencies involved hadn’t been properly briefed on the company’s specific markets, and were applying fairly generic approaches. Most – though not all – of the leads coming through were low– value or irrelevant enquiries.
The few high-quality leads that did come through were often overlooked, largely because they were buried among a large number of lower quality leads.
- Marketing spend was reallocated away from low– value channels
- Sales feedback was built into the processes and lead definitions were aligned between marketing and sales
- Marketing activity was redirected towards the customers and deals already moving through the pipeline, rather than continuing to generate disconnected leads
- Leads began to be tracked properly through to revenue, with ongoing refinement
Marketing attribution and ROI unclear
A B2B company with multiple marketing channels running at once – paid search, SEO, email, and regular industry events.
There was a steady flow of enquiries, and the business was investing consistently in marketing.
But no one could clearly explain which activities were actually driving revenue.
Marketing reports focused heavily on first-touch and last-touch attribution. Channels that generated initial enquiries, or were easy to track digitally, were credited with most of the results. Most business was attributed to Google search or the website contact page.
At the same time, the sales team described a very different reality.
Deals often involved multiple conversations, referrals, repeat interactions, and long periods of inactivity. Customers would reappear months later, sometimes through a different channel entirely.
In practice, the journey was messy, non-linear, and only partially visible in the data.
This led to two problems:
- Overconfidence in certain channels that were easy to measure
- Underinvestment in activities that were clearly influential, but harder to track
There was also a growing frustration with marketing spend, as the reported ROI didn’t match the lived experience of the sales team.
- The limitations of first- and last-touch attribution were made explicit
- The role of different channels across the full journey became clearer
- Reporting shifted from trying to assign precise credit, to understanding contribution and patterns
- Marketing decisions were made with a better understanding of how deals actually developed over time
The new software that nobody actually uses
A growing B2B company had invested in a CRM and several supporting tools, with the expectation that it would improve visibility and coordination across the sales process.
In practice, the system wasn’t being used.
The sales team continued to manage most of their work through email, personal contacts, and in some cases even printed notes and business cards.
From the outside, this looked like a discipline problem.
From the sales team’s perspective, the system simply didn’t reflect how they worked.
Key fields were irrelevant or unclear, entering data took time with little obvious benefit, and the information captured wasn’t particularly useful in helping them close deals.
As a result, the CRM became an incomplete and unreliable record, and management had very limited visibility into what was actually happening.
There was also increasing pressure from leadership to “do more with data” and explore AI tools – but the underlying data wasn’t in a state where that was realistic.
- The system was simplified and restructured to match how the sales process actually worked
- Unnecessary fields and steps were removed
- Key information was made easier to capture in the flow of real work
- Repetitive tasks were automated where they added clear value
- The sales team could see direct benefits in using the system