Sales as a business discipline can occasionally feel like it’s only about people. Making connections, building relationships, and finding ways to align needs with offerings. And it is true that some sales professionals can have a productive career that consists of engaging with leads, closing deals, and almost nothing else. But as B2B industries increasingly treat data as its own form of capital, it becomes harder to ignore the ROI of B2B sales analytics.
With proper implementation, sales analytics can do more than almost any other approach or strategy for helping RevOps reliably build pipelines, shorten sales cycles, and improve win rates. It’s not quite predicting the future, but by aligning data, people, and processes, analytics brings us about as close as we can get to business foresight.
Stand up a reliable revenue data foundation (so analytics actually works)
Despite the extensive attention it gets, data isn’t actually all that helpful on its own. It’s just a collection of numbers, figures, and values. What turns it into a real asset is applying analytics to those values. Even then, though, it’s neither automatic nor easy. Put another way, genius sales insights don’t happen by accident.
Part of the issue is how difficult it can be to get good data. Data can be incomplete. It can be outdated. It can be siloed. Data can even be inaccurate, containing errors or redundant entries.
Data integrity (and how to achieve it) is its own can of worms. But it is worth noting here that our friends in technical fields regularly refer to “garbage in, garbage out” for good reason. All of this raw information serves as the foundation for everything that happens downstream, and issues at the point of entry will only compound as they move through the data pipeline.
Those businesses that are able to effectively harness commercial analytics, however, are 1.5x more likely to achieve above-average growth, and can see higher profitability
Actually achieving this will be an ongoing, iterative process (and we’ll touch on some of the critical steps as we proceed in this article). But taking the time to plan and layout critical details will better position you for success. Here are a few of those details to tackle:
- What data do you need, and how will you collect it?
- How will the data be recorded and aggregated?
- Who’s the owner for any given task/dataset/process?
- Do we have a CRM audit checklist (and if not, who’s going to tackle building one)?
Define the 12–15 core B2B sales KPIs that matter
There are numerous things you could be tracking, but not every metric is useful in every situation. Rather than try to record everything and hope that a few of them lead to positive outcomes, you’re much better off picking strategic KPIs, and keeping the list short.
Prioritize a small, relevant set of revenue KPIs, ideally broken down by stage and segment. “Vanity metrics” is as much a buzzword as it is a term with a functional definition. But you will want to avoid relying on KPIs that don’t directly correlate to meaningful steps in the buyer journey. Stick with metrics that reflect clear intent, and that can be reliably used to predict where a lead will go next.
For those unsure of why this all matters, it’s because you’re not reporting analytics on how well you are doing, or even how well your team is doing. You’re trying to measure the progress of buyers moving through the pipeline. Yes, looking good in the numbers is a positive result. But it will be more meaningful if your metrics result in feedback that can guide strategy, and allow you to deduce what’s working and what’s not.
Bottom line: outcome-aligned metrics correlate with business growth.
Instrument your CRM for clean, complete data capture
Your CRM platform should serve as the beating heart of your analytics processes. Strictly speaking, this is what such platforms were designed for. Unfortunately, despite the native B2B data analytics functionality, sales teams often reap little in the way of value from their CRMs, usually through no fault of the technology.
Three major pitfalls include poor adoption and usage, sales/marketing misalignment, and “over-engineering.” Here’s what they look like in action.
Adoption and usage often flags for the same reasons they do with other tech solutions. Professionals get used to doing things a certain way, can’t fully see the value in modifying their workflow, don’t always remember to follow the new process steps, or all of the above. If it all seems too bothersome without much in the way of benefit, teams eventually stop trying to use the shiny new tools in favor of what has always worked.
Even when the tool gets fully integrated into the workflow, some CRMs offer tools that are helpful to both sales and marketing, with both learning to use the software at the same time. This can lead to a situation where marketing builds a process prioritizing metrics they hope can lead to results, but that don’t align with the priorities of the sales team. It’s one example of why “marketing qualified lead” is often a term of derision in organizations.
Finally, assuming you clear both of those hurdles (or possibly contributing to the challenges both of them present), it is entirely possible to build a process that is too sophisticated. Remember, most people choose sales as a discipline because they prefer the more sociable, people-first labor involved. Not everyone gets excited at the sight of a well-organized spreadsheet, even if they can appreciate the value of the same. So trying to build too much complexity, information density, or additional process steps into the workflow will almost always backfire in the end.
97% of leaders say better tools/data would improve forecast accuracy. Getting to that point depends on making the data, and the tools used to refine it, work for you, not the other way around. Your analytics shouldn’t feel like an entirely new manager to keep happy. It should be a power tool that reduces the effort needed to reach realistic goals.
Put RevOps in charge of schema and validation, then give sales managers authority to inspect and coach to standards. Give your sales teams the responsibility of providing feedback to help refine the system. The old joke about “give a hard job to a lazy employee and they’ll find an easier way to do it” applies here, and will do wonders for helping you engineer the workflow to make the most of the automation.
Unify marketing + sales data for buying‑group visibility
Speaking of friction between marketing and sales, something as simple as choosing different (and unrelated) KPIs to measure can frustrate attempts to build a cohesive pipeline. Done right, marketing can dramatically reduce the necessary time and effort required to find and close on leads. But, yet again, this doesn’t happen by accident.
In an ideal scenario, every marketing initiative should be leading to measurable results for the sales team: faster sales cycles, warmer incoming leads, easier negotiations, happier clients, and improved retention rates. In turn, feedback from sales should provide actionable insight so marketing can refine their efforts.
So, don’t operate in isolation. Move beyond leads to analyze accounts, opportunities, and buying groups across channels. Purpose‑built B2B journey analytics can visualize stakeholders at account, buying group, and opportunity levels (Adobe, 2025). You likely already have everything needed to make this happen; it just all needs to be properly calibrated.
Your marketing team is investing quite a bit of energy and resources into research, testing, measuring, and reporting; don’t let all of that effort go to waste simply because they aren’t sure which direction to point it in. Compare notes, and help them see where their metrics match with your KPIs.
Make RevOps the one responsible for integrating the data, and handling the really technical details. Let Marketing Ops maintain the measurement tools that track intent, engagement, and other clear signs of buyer intent. And let sales make use of the highly refined data (and highly qualified leads), providing feedback on what’s producing slam dunks, and what’s striking out.
Operationalize across the funnel: Your 7‑Step Playbook to better B2B sales analytics
While there’s obviously no shortcut or magic solution to any of these challenges, having a proven process in place, and taking time to make adjustments as needed, is still a reliable way to achieve your objectives. And, since this is B2B analytics we’re talking about, the good news is that once it’s working as intended, you’ll have all the data you need to make the right calls, meaning that getting things started is often the biggest hurdle to clear.
Step 1: Set a baseline. Audit data sources, field hygiene, and stage definitions. Lock KPIs and formulas.
Step 2: Establish pipeline visibility. Build stage‑level dashboards (volume, conversion, time‑in‑stage) by segment.
Step 3: Track and measure buyer behavior. Implement account‑level engagement scoring and buying‑group coverage.
Step 4: Implement forecasting. Standardize categories, roll‑ups, and risk flags. Institute weekly forecast calls with RevOps and Finance.
Step 5: Coaching. Use conversation and activity analytics to coach to next best action and deal quality.
Step 6: Experiments and iterate. A/B test outreach sequences, proof assets, and ROI models. Measure cycle impact, and make adjustments.
Step 7: Governance and QA. Perform quarterly schema reviews, dashboard refactoring, and enablement refresh.
Playbook QA: avoid these failure modes
Once the gears are turning, it’s critical that you don’t simply “set and forget” the process. Review results and check regularly for possible avenues of refinement and optimization. As you do, stay vigilant against the following pitfalls and common mistakes.
- Don’t skip stage definitions; ambiguous stages break velocity and forecast roll‑ups. There’s value in specificity.
- Don’t report without owners; each metric must have a single accountable role and SLA. Remember, if everyone owns it, no one owns it.
- Don’t silo up; collaborating with marketing on strategy will help unify the pipeline. And Cross‑functional forecast reviews with Finance reduce misses (Gong, 2024).
- Don’t overcomplicate it; segment by deal size, industry, and channel to find true bottlenecks, but avoid bogging the workflow down in needless nuance and “sophistication” that only adds more work.
Decode buyer behavior to personalize outreach and move stakeholders
As we mentioned at the start, data by itself is just a collection of input values. It’s not “analytics” until you start squeezing the juicy insights out of that harvest. And the most important insights are the ones about buyer intent and buyer behavior.
What most teams find once they hit this stage is, if they hadn’t already realized it, their prospects come to their buyer’s journey from very different directions, with different objectives in mind. Each one is unique, but they do tend to fall into a finite number of loosely related buckets. Sales/marketing 101 stuff, certainly. What’s different is that now, you have the data to back it up, and to illustrate what they want and what they respond to.
Use the data to define your buying groups. Maybe the data reinforces the segmentation you’ve already done, but it might suggest revisions to those established notions. Don’t change things for the sake of changing, but don’t be too precious about “the way we’ve always done it,” either. Personal experience is valuable, but the data is concrete evidence, so be prepared to make some calls in places where the two don’t reconcile neatly.
Your analytics should guide the who, the what, and the when. Some changes may seem unnecessary or counterintuitive at first (and don’t be shy about reviewing and iterating as you go). Even so, the data is likely bringing details to light that have been overlooked by human eyes. By switching to data-driven decisions, you’ll see more relevance per touch, faster consensus, and fewer stalled deals.
The tools you use and the data you collect should be empowering segmentation and multi-threading. It should be clarifying intent. And it should bring cross-channel, buyer-group-level views that provide reliable, actionable insights. If that’s not happening, make the necessary recalibrations until it is.
Map the buying committee and intent signals
Human bias can be a major stumbling block to the sales process. Especially in situations where sales teams have long-standing preconceptions about what the sales cycle should look like. The business landscape across virtually every industry and vertical has seen countless often unforeseen changes. And without the visibility provided by, say, effective B2B sales analytics, it’s entirely possible the world has moved on and left your sales pipeline behind.
That’s why it’s important to let the data tell the story. Your analytics should be defining and determining your multi-threading, rather than used to justify preexisting models. Content consumption, pricing page views, repeat visits, and competitor comparisons are all values that are “vanity metrics” in the wrong hands, but powerful buyer intent indicators when used effectively. And don’t be surprised at all if your analytics starts revealing missing roles (e.g. finance approver, security, etc.).
Have AE map roles, SDR support net-new contacts, and RevOps maintain role taxonomy. Just be sure you’re not treating intent as a qualifier all on its own. Otherwise you’ll spend more time than is prudent chasing luke-warm leads.
Build engagement scoring and next‑best actions
If your team members are looking for “the proof in the pudding,” so to speak, this is for them. Analytics isn’t just a value-add for the higher-ups. It’s something that can give individual sales professionals an edge in their efforts, and improve their performance. Professionals who operationalize analytics for marketing and sales to drive growth are consistently found to outperform their peers.
The key is to give them guidance that’s clear, actionable, and repeatable. Case in point: using weighted, account-level scoring. Contact-only scores miss consensus, and without a standardized method of measurement and comparison, there’s no way to compare apples-to-apples (even across a given team member’s own historical figures).
Achieving alignment on this is a team effort. Have Marketing Ops configure, Sales Ops calibrate actions, and sales staff follow playbooks. And, like before, charge sales reps with the responsibility of taking notes, both on how leads respond to the new approach, and the difficulties they personally may experience in the transition. It is them, after all, who will be doing the legwork on all of this, so smoothing out the rough edges is in everyone’s best interest.
Turn conversation intelligence into coachable insights
At some point, you’ll have to address two major challenges in this endeavor: data tied to the more “human” side of the sales process, and coaching team members specifically based on those figures.
Calls that run on too long, or with unfavorable talk ratios. Letting objection themes slip through the cracks. Failing to read between the lines to find subtle but definitive dealbreakers for leads. These are often the factors that separate the top reps from the middle of the pack, but they’re also what’s keeping the sales team as a whole from achieving better results.
Win-loss rates by objection category are a prime candidate for this. Better tracking and analysis can lead to data that can enable better coaching for the whole team, allowing you to focus enablement where losses concentrate. If, for example, “security review” stalls 30% of late-stage deals, you can rework your process to add earlier technical validation, and instruct reps to prioritize this as part of the vetting process.
You’ll likely see push back, and one of the points of contention is sure to be “but won’t this result in fewer sales?” It’s a mistake seen both on the sales side and on the marketing side: numerical increases are synonymous with positive results, irrespective of other factors. This is your opportunity to help sales reps at every level of performance to see that closing sales is more profitable when you close the right sales. Increase quality, and quantity will usually follow.
Manage pipeline health, risk, and accuracy to forecast with confidence
Your forecasts should trigger deal inspections, resource shifts, and executive support, not half-hearted responses in meetings and email threads.
The ultimate objective with analytics is producing accurate insights that help drive meaningful results. By understanding your target market better, you learn how to better deliver what they need, making it easier both to expand your clientele, and better serve those currently doing business with your brand. In abstract, it sounds easy and straightforward. That isn’t how it feels in practice, however.
Case in point: 4 in 5 leaders missed at least one quarterly forecast last year. For any sales team wanting their forecasts to be anything more than a “best guess,” standardizing data and processes is the most direct path to improving their accuracy and reliability.
Use pipeline velocity to spot where deals stall
If we had to pick a single metric as the one likely to provide the most ROI, it would be velocity. Velocity blends volume, value, win rate, and cycle. Simply put, it is the best single efficiency metric.
Here’s the formula for calculating it for those who aren’t overly familiar: Sales velocity = (Opportunities × Win Rate × ACV) ÷ Sales Cycle Length.
Even if all you do is track velocity (which would in turn require tracking several other KPIs for that calculation), this is the data point that most effectively illustrates the returns you see on all of your efforts as a sales team, and how long it takes to see those returns. This is to your B2B sales initiatives what an hourly rate is to an individual employee. It’s a measure of what your time and energy is worth (or at least what it’s currently earning you).
Improving your velocity is a bit of a balancing act. Ostensibly, anything that increases the positive values (i.e. leads, win rate, ACV), or decreases your time to value should improve the figure. But gains for one often come hand-in-hand with losses for another. Maybe you’ve boosted your ACV, but now your sales cycle is twice as long. Maybe you’re driving up the number of opportunities, but closing rates don’t match pace.
The beauty of velocity as a metric is that it can help highlight when you’re spinning your wheels. Even if one number goes in the direction you intend, if the velocity doesn’t change in kind, it’s your cue to reevaluate and see what unintended effects are involved.
Lumping all of your market segments together can muddy the waters and dilute your analytics on this point. Separate out the reporting along segment lines, and be sure you adjust your targets to match the baseline of a given segment.
Standardize forecast categories and risk scoring
From a team management standpoint, reporting on metrics can be a bit of a double-edged blade. Measuring performance and results makes it easier to improve performance and results. But unless it’s implemented carefully, it can also generate apprehension and alarm for the team.
This is one of the reasons a sales team may initially resist adopting more robust analytics processes. Analytics can certainly reveal bottom-rung performers that previously hid behind nebulous KPI objectives. But once numbers start going up on a scoreboard (i.e. a spreadsheet), even dedicated and productive employees may experience anxiety about coming up short, and be tempted to sandbag their numbers to minimize their risk of receiving a pink slip.
Obviously that’s less than ideal if you’re hoping to cultivate an environment of cooperation and well-being. Even with the human factor aside, though, it’s the ultimate Achilles heel of your data pipeline. No amount of process, tools, or top-down enforcement can fully mitigate the damage that results from cooking the books.
Effective recourse here requires a two-pronged approach, one to tackle the SOPs, one to address the human element.
For the process-oriented fix, set firm and quantitative definitions for the important stuff. Commit, best case, pipeline, etc.; all should have explicit criteria. Be explicit, too, on disallowing sandbagging or “wishcasting.”
On the human side, this will need to be a little more bespoke for your given circumstance. Establishing a minimum threshold is to be expected; whether you coach or downsize below that line is up to you. For the bulk of the team that occupy the middle of the bell curve, though, their transparency will be impacted (at least partially) by the perceived level of risk regarding “underperforming.”
Address the process issues, certainly. Just be aware that you’ll see more accurate self-reporting as the fear of job loss is reduced.
Operate a weekly forecast and deal‑risk cadence
One more factor that can frustrate your efforts to measure progress, effectiveness, and improvements over time is, well, measuring things over time. A regular schedule of reporting, reviewing, evaluating, and planning will do wonders for setting the pace, improving consistency, and ensuring accuracy.
Be careful to avoid spending more time in meetings than necessary, though. At some point, the value to be gained from discussing work diminishes considerably, and your time is better spent doing said work. That being said, remember that rhythm beats intent, and running a consistent, data-first call structure will help you make the most of your analytics efforts.
Shorten sales cycles and lift win rates with data‑driven plays
Data and analytics isn’t a cure-all. It won’t magically resolve every issue you face. But it will address a number of core challenges you face. And it will make it easier to align teams, departments, and entire cross-functional teams to pursue unified goals.
Use analytics to refine ICP targeting. Or to optimize outreach sequencing. Or improve proof packaging, or deal execution. Done well, analytics can help your teams conserve effort, time, and resources by redistributing away from false priorities. It empowers you to reach consensus faster, with fewer surprises, to achieve clearer ROI.
Targeted sequences by segment and stage
Be sure to tailor your efforts and procedures to match the market segments, and their stage of the sales funnel. This is one of the biggest advantages of applying B2B analytics in sales and marketing. Accurate results make it much easier to identify clear distinctions in market segments and what they respond to.
Put those insights to good use. Segment by industry, size, tech stack, and trigger events as appropriate. Align your messaging to stage jobs-to-be-done. This is a joint effort, so get SDR leadership and Marketing Ops to collaborate on this, with sales validating the insights on live deals.
Again, resist the inclination to overcomplicate; this is meant to multiply your results, not your workload. You don’t want a one-size-fits-all approach, but you also don’t want too many segments (and sub-segments) to keep track of.
Put ROI modeling early in the deal
Your results shouldn’t just come from the deals you close, either. Knowing what works is good, but there’s plenty of valuable insights to be gained by looking past the survivorship bias. Collect data from leads in progress, even the ones that don’t ultimately sign an agreement at the end. And start collecting that data early. This can be helpful both for future leads, and even for the ones you have right now.
Quantify impact during discovery, and use customer data to co‑build an ROI case. Deals with ROI models presented early tend to close faster. This isn’t universal; quite the opposite. In fact, it will likely serve as a highly effective filter. But the faster you can separate the hot leads from the questionable ones, the less wasted effort you’ll have.
Plus, you’ll be collecting data on all of this (obviously), and you’ll eventually have indicators to help predict which leads will be filtered out.
Remove friction with enablement and deal‑desk analytics
As a final point of guidance, there are steps you can take to reduce externally imposed friction.
Instrument legal, security, and procurement to anticipate blockers and compress review cycles. With the right data, you can more effectively predict what opportunities are likely to snag in the flow, and get ahead of those issues before they even start.
Get Deal Desk and Legal involved. Have RevOps handle reports, and sales adapt and conform to submission standards. And be clear about what you’re trying to do; odds are if you tell Legal that you’re trying to help them speed up their process and reduce their workload, they’ll be more than happy to collaborate on that point.
Just be sure to avoid ad-hoc approvals. Enforce submission checklists to minimize unnecessary rework and resubmissions.
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Stephen Porritt
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