Predictive scoring was all the rage in 2016, before Account Based Marketing (ABM) became the new, new rage. The buzz around predictive scoring has died down drastically, and there's a good number of companies that have gone through a full life cycle with this product category, including many of Openprise's customers and prospects. There seem to be very few long-term success stories with predictive scoring. One of the reasons being that most marketers simply rely on predictive technology as a lazy shortcut to data quality. This assertion may sound strange, but hear me out.
With the rise of AI in the last few years, predictive scoring is back in the conversation — this time wrapped in the language of machine learning models, intent signals, and AI agents. The pitch has gotten shinier, but the underlying problem has not changed. The teams that struggled with predictive scoring in 2016 failed for the same reason most will struggle today: they treated the model as a substitute for clean data instead of a product of it. If anything, AI-powered scoring raises the stakes on data quality. A model trained on dirty, incomplete, or poorly segmented data will produce confident-sounding scores that are wrong in ways that are hard to detect until pipeline suffers.
Garbage in, garbage out
One of the mantras often repeated at Openprise is, "Garbage-in, garbage-out." Any process that's data-driven requires good data as input, and predictive analytics is no exception. Any data scientist will tell you that 80% of their work is data cleansing and preparation. Only 20% of the work is spent on modeling and visualization. So if your sales automation and marketing automation databases have poor quality, how can predictive lead scoring work? One of two ways:
- The initial and periodic manual cleansing is done with professional services.
- The vendor's product does generic cleansing in the background.
Many predictive analytics technologies, predictive scoring included, require a professional services engagement at the start to clean your database. This ensures that the product has good input data so that the predictions will be good. However, data decays at roughly 25% a year, and at a much faster rate in hot industries with rapidly growing companies. If this manual cleanup effort isn't kept up at least quarterly, the data quality will quickly deteriorate, and so will the quality of the predictions. Quarterly manual data cleansing is, of course, very expensive and exhausting.
Some vendors handle this data cleansing as embedded functionality in their product. These solutions take one of two approaches:
- Your data is cleaned transparently as part of a "black box" solution and your database isn't updated with the clean data.
- Your data is matched to, and enriched against, the vendor's database, which is usually data sourced from a number of data providers like Dun & Bradstreet, DiscoverOrg, and others.
Almost all the customers and prospects we've talked to adopted predictive scoring models before they launched a data quality program. Without a high-quality database that's well segmented along dimensions such as job function, job level, buyer persona, company size, and industry, it's impossible to implement a good demographic scoring model. So these customers had two choices, circa 2016:
- Implement a data quality program, then implement scoring, or
- Use a predictive solution that promised to work directly with your raw, dirty databases.
For marketers who are strapped for time and resources with data skills, offloading this problem to a predictive solution was a very attractive shortcut, and many companies took that route. After all, a "predictive" initiative sounds sexier than a "data quality" one. When data quality wasn't kept up, the performance and value of the predictive score deteriorated quickly.
Is predictive lead scoring right for your company?
Predictive lead scoring isn't the right technology for many companies. To help you determine if you are a good fit for the technology, consider the following factors:
Machine learning requires training data — lots of training data. This is another case of garbage-in, garbage-out. In the last few years, even the big tech giants had embarrassing issues with their voice assistant and facial recognition technologies that were traced back to poor training data. The challenge with applying machine learning in the B2B context, especially when the data in question is transaction data like opportunities and engagements, is that the database is generally too small to generate good training, and the data is very much company-specific, so a vendor can't leverage learning across their customers' databases. If you have less than 10,000 opportunities and 10 million engagements, your database is likely too small to generate a robust predictive scoring model.
Most machine learning algorithms are black box algorithms. The machine makes predictions based on the training data and it's actually not easy — or even possible — to say exactly why the machine came out with any specific prediction. Most companies we've talked to that have tried predictive scoring said the results correlated fairly well to close rate, however, they had two general issues:
- The scores were often obviously correlated with a few of the key segments that the customer already knew were important, like job level, company size, and industry. This leads to the question: what exactly was the machine algorithm telling them that they didn't already know?
- In the cases where the score was not obvious, customers couldn't figure out how to act on it.
White box solutions are usually not truly machine learning. A number of predictive solutions say they're white box solutions. With very few exceptions, this is not truly machine learning. It's really just glorified search against a database based on your own Ideal Customer Profile (ICP). So most white box technologies are really just static scoring models wearing a shiny marketing label.
Most companies have a decent idea about what their ideal customer looks like. Even if they don't know for sure, with enough historical data in their CRM, they can easily produce that profile based on a segmentation analysis. Companies can't do this analysis or develop a static demographic scoring model, usually because their database quality is bad and segmentation is even worse.
How to do predictive lead scoring right
Whether you're contemplating adopting predictive scoring or a "recovering user" thinking about giving predictive scoring another chance, here are the important things to consider that can save you the agony of a failed project.
First things first: predictive lead scoring isn't the right technology for many companies. If you don't have the training data volume noted above, or if your data quality foundation isn't in place, you will not get reliable results from a purely predictive model. But that doesn't mean you have to give up on scoring altogether — it means starting with a foundation that actually works.
Before any scoring project, make sure you have:
- A clean, deduplicated, and normalized database
- Job function, job level, company size, and industry segmentation in place
- A defined Ideal Customer Profile (ICP) based on your actual closed-won data
- Continuous data enrichment so quality doesn't decay between cleansing cycles
- A process for keeping enrichment ongoing — not a one-time cleanup
Once those foundations are in place, there are better and worse ways to build the scoring model itself. Openprise's lead scoring platform reflects lessons from working with hundreds of RevOps teams who tried predictive scoring and ran into the problems described above. A few things differentiate a scoring approach that holds up over time:
- Run multiple models simultaneously. Most marketing automation platforms only let you run one scoring model at a time. Running multiple models — and A/B testing them against real sales data — lets you validate which signals actually predict conversion rather than assuming your first model is right.
- Use time-based and momentum scoring. A static score that decays at a fixed rate misses a critical signal: the rate of change in a prospect's engagement. Momentum scoring — tracking how quickly a score is rising — is often a better leading indicator of near-term intent than the score itself.
- Build composite scores that blend data sources. Scoring just website and email activity misses most of what your best prospects are doing. The most predictive models incorporate intent signals (such as Bombora and G2 data), technographic fit, support ticket activity, free trial usage, and other signals that standard marketing automation tools don't capture.
- Keep the scoring logic in your database. One of the most common failure modes is "black box" scoring where the vendor's model updates your scores but not your underlying data. That means your CRM and MAP are still dirty, and you're fully dependent on the scoring vendor for results you can't audit or explain to sales.roof point
A pragmatic alternative: how Openprise scores its own leads
Openprise doesn't use a purely predictive model to score its own leads. Instead, the team built a grading system that's transparent, explainable, and that sales actually trusts — which turns out to be the harder problem.
Leads are graded A, B, C, or D based on demographic fit against Openprise's ICP. If a lead also shows recent engagement activity, they receive a plus modifier — A+, B+, and so on. Activity scores decay automatically after four weeks: if a rep hasn't been able to follow up within a month of the engagement, the system treats the lead as not truly active and removes the plus.
Accounts are graded the same way. An A+ lead at an A+ company gets immediate follow-up. An A+ lead at a B company — an engaged individual at a company that doesn't fit the profile — gets lower priority, because converting that deal would require convincing more people at an organization that may not be a fit at all.
This approach sidesteps the two most common failure modes of predictive scoring: it doesn't require a massive training dataset, and it produces scores that sales can understand and act on. The model is also easy to audit and adjust when sales feedback suggests something isn't working.
For teams with sufficient data volume and a solid data quality foundation, layering in predictive signals on top of a working demographic and behavioral model is a reasonable next step. But the grading foundation has to come first.
Want to go deeper on building a scoring model your sales team will actually use? Download the Openprise comprehensive survival guide on lead scoring for a framework covering demographic grading, behavioral scoring, time-based decay, and account-level scoring.
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