Article
2025 Predictions: GTM Becomes A Focused Team Sport
This week marks the end of 2024 and the start of 2025. So what did we learn in 2024 and what are the next big things that will drive GTM excellence this coming year?
Economic uncertainty drove prioritization and doing more with less and became the central theme in 2024. Departments moved to consolidate vendors, measure ROI and analyze the Revenue Data and AI signals that mattered the most. Some of the shine came off of company-level signals as GTM teams looked to find demand signals that are more actionable – intent at the city level not just country, persona signals not just department, product signals to indicate divisions. GenAI for content development became rampant and many organizations, departments and teams deployed multiple tools across multiple channels. But here’s the rub. Most of these efforts were done at the departmental level. Siloed efforts in Sales or Marketing.
How is this possible? Revenue operations or RevOps professionals are all championing joint strategies across sales and marketing, right? But how many revenue ops people are out there? If you search the hundreds of millions of titles in LinkedIn you will see that there are very few companies that have anyone with the title of revenue operations – less than 1 percent of the companies on LinkedIn. So most sales and marketing operations are left to making this happen on their own – in separate departments with separate budgets and often separate goals.
What does this spell? S_I_L_O_S. Data silos. Strategy silos. Budget silos. Performance silos. And what are the companies who are actively breaking down these silos doing?
I’ll share a story about a high tech company in the hardware industry. In this company, both sales and marketing operations report into the COO. The team was tasked to work together to reorganize the sales and marketing investments. They started first by identifying a TAM for every company out there that mattered and rank ordered them for a new sales and marketing coverage model. Once this was done, then all investments in both sales and marketing were tasked with focusing engagement efforts squarely on this prioritized set of accounts, prioritized buying teams and prioritized solution offers. Within a few quarters more than 80% of the inbound lead flow of the company fell within the TAM. This focus meant increased pipeline generation in the right accounts with the right buyers and the right value proposition. This spells F_O_C_U_S. So how will this be tackled in 2025?
Let’s face it. Sales, marketing and SDR teams are in the same company but on separate teams – from the data and tools they use to the targets and goals they have. Alignment is missing across most B2B organizations. We believe it will fall on RevOps or tight coupled sales and marketing ops teams to take the lead in turning Go-to-Market (GTM) efforts across organization into a team sport. CMOs and CROs will be responsible for tearing down the walls between sales and marketing. Why do we believe this? Let’s look at three main predictions we expect to see championed by RevOps in the B2B sales and marketing environment in 2025 to overcome organizational misalignment.
2. GTM teams will become aligned on actionable buying signals. They will agree on what they are and what they mean and prioritized into a shared resource across teams. Individual signals will not be good enough.
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Why speed-to-lead still fails without data intelligence
You already know response time matters. Inbound lead management teams have chased that metric for years. Faster alerts, faster handoffs, faster SLAs.
Yet lead routing still breaks. Good leads stall. Reps get the wrong records. Qualified buyers hit the wrong queue. Your team responds fast, but still responds wrong.
That gap comes from data quality. Speed helps only when the record is accurate, complete, and useful in the moment. If your lead routing runs on stale fields, weak matches, or thin firmographic context, your process moves faster in the wrong direction.
This is why speed-to-lead still fails. You do not have a time problem alone. You have a data intelligence problem.
