Daniel Saks
Chief Executive Officer
ML models don’t stay good forever. When you first train a model on historical data, it might perform brilliantly in testing. But once deployed, the real world keeps changing – and your model might not keep up. This phenomenon is known as inference drift (or model drift). In simple terms, inference drift means the model’s predictions gradually become less accurate or reliable as time goes on. The causes vary: customer behaviors change, new competitors emerge, data patterns shift, or your own business strategy evolves. The model is still relying on the old patterns it learned, so its “understanding” of what a good lead or a likely buyer looks like becomes stale.
It happens more often than you think. In one analysis, researchers observed temporal degradation in 91% of ML models they studied(1). Most models start to lose their edge within 1–2 years of deployment if they aren’t retrained. In a GTM context, that could mean the predictive model that used to pinpoint your hottest prospects last year might be missing the mark this year. Perhaps it’s still prioritizing leads based on last year’s economic climate or outdated customer profiles. The result? Your sales team starts following misguided predictions.
Imagine you built an ML model in 2024 to score inbound leads for your SaaS product. Initially, it gave high scores to leads from tech startups (because many early customers were from tech). It worked great. But by 2025, market conditions changed – perhaps healthcare and finance companies are now more interested in your product. If your model wasn’t updated, it might still under-score those healthcare/finance leads (missing real opportunities) and over-score tech startup leads (many of whom may no longer have budgets). This is inference drift in action: the model’s “understanding” of a good lead drifted away from reality, quietly and without warning.
Inference drift is dangerous because it often happens silently. Unlike a software bug that crashes a system, a drifting model doesn’t announce its failures – it just starts giving poorer results. Unless you’re monitoring performance, you might not realize anything is wrong until quarterly results slip or pipelines dry up. By then, the damage is done. This is why proactive monitoring is crucial. It’s far better to catch a drifting model early (and retrain or tweak it) than to find out six months later that your sales team was chasing sub-par leads.
Inference drift doesn’t just mean lower accuracy – it cascades into data quality problems. When an ML model drifts, the predictions and insights it produces essentially become bad data. In a GTM scenario, that could be mis-scored leads, incorrect product recommendations, or misclassified ideal customer attributes. Your systems and teams feed on these model outputs as if they were truth. But if the outputs are flawed, you now have garbage data polluting your workflows.
Compounding this issue, the underlying business data that ML models rely on is itself a moving target. Customer and prospect data decays at an alarming rate. B2B contact databases experience decay rates between ~22% and 70% per year – meaning nearly three-quarters of your contacts can become outdated in just 12 months. (Think about job churn, promotions, email changes, company moves, etc.) In fact, by some measures 70.8% of business contacts change within a year. If your ML model was trained on last year’s CRM data, a significant chunk of that data is now invalid.
What do bad model outputs + stale data add up to? Poor data quality at scale. And poor data quality is a revenue killer. IBM researchers estimate that poor data quality costs U.S. businesses about $3.1 trillion annually. On a company level, Gartner finds it costs the average organization around $12.9–15 million per year in wasted marketing spend, sales productivity loss, and operational inefficiencies. These are jaw-dropping numbers that highlight how bad data isn’t just an IT headache – it’s a business growth killer.
Crucially, data quality issues erode trust and efficiency. If your sales ops or marketing ops team starts noticing that lead lists are full of bounces and outdated info, they’ll spend time scrubbing and fixing data (time that could have gone into selling or campaigns). A recent survey of data professionals found 77% of organizations have data quality issues, and 91% say those issues are impairing performance(2). When data is unreliable, employees understandably lose confidence in it. Fewer than half of professionals in that survey said they highly trust their company’s data(2) – meaning most people suspect the numbers in their CRM or dashboards might be off. That skepticism leads to duplicated efforts, manual double-checking, and general hesitation in following data-driven insights.
In summary, an unmonitored ML model can turn into a factory of bad data. Inference drift gives you subtly wrong predictions; those predictions combined with rapidly decaying contact data create a pool of erroneous information about your customers. And once bad data enters the system, every downstream process suffers – marketing segments, sales cadences, analytics, forecasting – you name it. It’s a true domino effect: model drift -> bad data -> weaker decisions. The next domino to fall? Your team starts chasing the wrong signals.
In sales and marketing, we live by signals. A signal could be a score that indicates a lead is “hot.” It could be an intent insight like “this company is researching solutions in your category.” Or it might be a prompt from your AI assistant that says “reach out to Contact X, they fit the ideal profile.” We rely on these signals to decide where to focus our precious time and budget. But what if the signals are lying?
When an ML model drifts or data quality falters, you start getting bad signals. The model flags the wrong prospects as high-priority, or it prompts your reps with misleading insights. Essentially, the compass guiding your GTM team goes haywire. This can manifest in many ways: sales reps get alerts about accounts that “show intent” but in reality have no such intent, or your lead scoring ranks a bunch of leads as 90/100 who turn out to be dead ends. It’s like a GPS that’s a few miles off – you end up in the wrong place.
Consider what bad signals do to a sales team: Reps might pursue a prospect intensely because “the AI said they’re ready to buy,” only to find the prospect was a poor fit or not in the market at all. That’s hours (or days) of effort per rep, wasted. Or marketing might pour budget into a segment that the model identified as high-converting, only to see abysmal conversion because the signal was based on outdated patterns. These misfires not only hurt results – they also create skepticism. After a few wild goose chases, your salespeople will start doubting the model’s recommendations entirely (“Last week it told me SaaS startups in fintech were hot leads – but none of them panned out. Maybe these AI scores are garbage.”). This breakdown in trust means even when the model does have a valuable insight, your team might ignore it.
Bad signals also distort your internal priorities. For instance, if the model erroneously signals that Industry A is where most high-value deals are, your strategy might tilt towards Industry A – shifting resources away from the truly profitable segments (this bleeds into the ICP issue, which we’ll discuss next). In essence, bad signals send you on strategic wild goose chases as well as tactical ones.
A classic example: A predictive lead scoring model starts giving high scores to contacts who downloaded a certain whitepaper, because historically that correlated with deals. But suppose over time, your competitors also offer similar content and that behavior is no longer special – the model doesn’t know this without retraining. So it keeps saying “hot lead!” for those downloads. Your SDRs jump on those leads, finding most of them are nowhere near purchase-ready. Time wasted, and genuine leads might be sitting neglected because they didn’t trigger the now-outdated signal.
The data bears this out: In many companies, sales reps spend more than a quarter of their time on leads that never should have been pursued. (One study quantified it as reps wasting 27.3% of their time on bad leads.) This is a direct consequence of chasing bad signals – hours that could’ve gone into real prospects or building relationships are instead burnt on dead ends.
In short, bad signals = team running in the wrong direction. It’s frustrating and costly. The immediate impact is lost productivity and missed opportunities. But if bad signals persist, they start to warp something even more fundamental: your understanding of who your ideal customers really are.
Your Ideal Customer Profile is the foundation of targeted sales and marketing. It’s the answer to “Who should we be selling to, and why?” If your ICP is off, a lot of effort goes to waste on the wrong prospects. Now, imagine building your ICP based on flawed model outputs and bad signals – you could end up with a fundamentally misguided go-to-market strategy.
This is the long-term fallout of not monitoring your ML models. Over time, if your model is drifting and your team is chasing the wrong signals, you might start to observe (incorrect) patterns and draw (incorrect) conclusions about your ideal customer. It’s a bit like having a distorted lens: the data coming from your model suggests your best customers look a certain way, so you recalibrate your ICP to match that picture. But if that picture was skewed by drift, you’ve now institutionally baked in the wrong target profile.
For example, say your AI model (when it was fresh) correctly identified that your best conversions were coming from mid-market tech companies. But as it drifted, it started favoring a lot of leads from, say, the education sector (perhaps due to some spurious correlations in new data). If you aren’t aware of the drift, you might tell your team, “Hey, looks like education is an emerging vertical for us – our ICP should include education industry organizations!” You might even shift marketing dollars to try to acquire education-sector leads. But in reality, those were false positives from the model. The result: your ICP definition broadens or shifts in the wrong direction, diluting your focus on the truly ideal customers.
The consequences of a bad ICP are severe: Marketing campaigns reach people who are unlikely to buy, sales reps struggle to close deals because the “ideal customers” they’re courting aren’t actually ideal, and your product team might even get misguided input on which features or use-cases to prioritize. It’s a cascading effect on strategy. Companies with misidentified ICPs often see longer sales cycles, lower close rates, and higher churn, because they’re selling to folks who don’t perfectly need or value their product.
On the flip side, getting your ICP right delivers tangible gains. Organizations that clean up their data and refocus on the truly ideal customers see significant improvements in outcomes. For instance, after a data quality overhaul, businesses have reported 12% higher conversion rates and 15% higher close rates within just six months(3). Those stats underscore how crucial an accurate ICP is – it directly boosts win rates and revenue. Conversely, a bad ICP (driven by bad model insights) will reduce conversion and close rates, even if your sales team is excellent, because you’re aiming at the wrong targets.
If you let an unchecked model run your targeting, you risk redefining your ICP based on fiction. And a bad ICP sets the stage for massive inefficiency – tons of outreach and marketing spend directed at prospects who either can’t or won’t become high-value customers. It’s like fishing in the wrong pond; no matter how great your fishing skills are, you won’t catch much. This brings us to the final (and most visible) consequence: wasted outbound effort.
All the issues above ultimately hit you where it hurts – in the pipeline and the wallet. Wasted outbound is the culmination of inference drift, bad data, bad signals, and a bad ICP. It means your sales development reps (SDRs) are making call after call to prospects who never convert, your account executives are giving demos to companies that aren’t actually qualified, and your marketing team is nurturing leads that will never turn into revenue. It’s the GTM equivalent of throwing spaghetti at the wall to see what sticks – only it’s expensive spaghetti, and nothing is sticking.
Let’s quantify it. We already mentioned sales reps wasting ~27% of their time on wild-goose-chase leads. That’s like having a 5-day work week and more than one whole day each week spent on fruitless tasks. Across a sales org, that is a huge productivity sink. In monetary terms, one analysis pegged it at 550 hours or $32,000 of wasted effort per sales rep per year on average(3). Multiply that by a team of 10 or 50 reps, and we’re talking hundreds of thousands to millions of dollars in opportunity cost, just gone.
Marketing fares no better. Ever run an email campaign where half your list bounced or got no engagement? That’s bad data and ICP at work. Or perhaps you sponsored a webinar aimed at the wrong persona because the “AI insights” pointed you there – money down the drain. These are hard costs that add up. The average organization loses $12+ million a year to bad data and the miscued efforts it causes, much of that in marketing/outbound spend.
The revenue impact is real and measurable. A 2024 industry survey found that companies are losing about 6% of their annual revenue on average due to underperforming AI models – essentially, models that aren’t doing what they should, because of issues like drift and poor data. For large firms in that survey, 6% equated to $400 million in lost revenue on average. Even for smaller businesses, take 6% of your revenue and imagine it being burned because your targeting and outreach machinery is misfiring. It’s a painful thought.
Beyond the numbers, think of the human toll. SDRs grinding and getting frustrated by constant rejection from bad-fit leads. Account execs missing quota because the pipeline is packed with junk opportunities. Marketers losing faith in their campaigns. Morale and confidence take a hit. Your team starts to question the whole data-driven approach (“Why are we relying on this model at all? It’s just creating more work.”). That cultural backlash can set back your AI/automation initiatives significantly.
To be blunt, if you ignore monitoring your ML models, you’re letting a slow leak turn into a flood. The leak starts with a bit of model drift and a few bad predictions. Left alone, it floods your CRM with bad data, steers your team off-course with bad signals, corrodes your targeting strategy, and finally drowns your ROI in wasted effort and spend. The good news? None of this is inevitable. By implementing some smart monitoring practices, you can catch issues early and keep your revenue engine humming.
Now that we’ve seen the risks and costs, let’s turn to solutions. How can you, as a GTM leader without a dedicated MLOps department, keep your ML models in check and avoid these pitfalls?
The prospect of monitoring an ML model might sound technical, but you don’t need a full-blown MLOps team to do it effectively. Here are some practical, doable strategies to ensure your models stay healthy and your data stays accurate – even on a scrappy team. (Bonus: many of these can be automated or handled with light support from a data-savvy colleague or affordable tools.)
By implementing these practices, you essentially create a safety net for your ML initiatives. You don’t need a legion of engineers – just a thoughtful approach and possibly some targeted tool use. The result will be that you catch problems early, continually improve your model’s performance, and maintain the trust of your team in these AI-driven systems. Remember, the goal of using ML in GTM is to accelerate and enhance your team’s success, not send them on wild goose chases. A bit of monitoring ensures the models remain the wind at your team’s back, not weights on their ankles.
Finally, let’s look at how these best practices come together in the real world by examining how Landbase monitors its ML models and data quality. This will give a concrete sense of what “good” looks like in action.
Landbase – a B2B go-to-market data platform powered by an agentic AI model – has made model monitoring and data quality a core part of its DNA. They’ve essentially built the “pro-level” MLOps practices into the product so that users (sales and marketing teams) don’t need to worry about drift or bad data. Here’s how Landbase avoids the pitfalls we discussed:
Thanks to these practices, Landbase avoids the cascade of problems we discussed. The model doesn’t get stale because it’s retrained and fed fresh data; the signals don’t lead the team astray because they’re vetted and accurate; the ICP defined by Landbase’s outputs is precise because it’s grounded in up-to-date insights, not last year’s news. The proof is in the outcomes. Early users of Landbase reported they could build targeted prospect lists 4–7× faster than before, with up to 80% less manual effort, while still maintaining over 90% accuracy on the data(3). In pilot campaigns, teams saw 2–4× higher lead conversion rates using Landbase-qualified leads compared to their old methods(3). Those are massive improvements in efficiency and effectiveness – achieved without needing an in-house data army, because the platform handles the heavy lifting of model monitoring and data quality.
In essence, Landbase serves as a case study for how to proactively monitor and maintain ML models in a GTM context. They verify the data, validate the signals, refresh constantly, and bake in feedback loops. The takeaway for any GTM leader is that these are the same principles you can apply, even if you use different tools. And if building this yourself is impractical, solutions like Landbase are there to act as your “outsourced MLOps,” ensuring your targeting AI stays on target.
By now, it should be clear that monitoring your ML models is not an academic exercise – it’s a business imperative to prevent wasted effort and missed revenue. The good news is that you can do it without an army of specialists. With the best practices outlined and real-world examples to emulate, you’re equipped to keep your models performing and your team executing on reliable insights. Don’t let inference drift and data decay silently undermine your go-to-market strategy. A little vigilance goes a long way to ensure your AI continues to be a force multiplier for growth.
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