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The 84% figure does not mean that 84% of marketing leaders use predictive analytics. In a 2022 survey of 250 senior executives at large U.S. B2C companies already using predictive analytics, 84% said day-to-day data-driven decisions were difficult; another 84% said predicting customer behavior felt like guesswork. The survey found 95% reported some integration of AI-powered predictive analytics. Its lesson is about the gap between adopting models and turning them into timely, trusted decisions—not the share of all marketers using the technology.
What the report measured
Pecan AI commissioned the “Predictive Analytics in Marketing Survey,” with fieldwork conducted by Wakefield Research. The online survey ran September 13–21, 2022, and covered 250 U.S. marketing executives at director level or above. Their employers were B2C companies with at least $100 million in annual revenue that already used predictive analytics. The survey report and VentureBeat’s account of the methodology describe a selected group, not a representative sample of all marketing leaders.
That selection matters: organizations without predictive systems were not the population being surveyed. The results also reflect executives’ reported experiences and perceptions, not an independent audit of decision quality or proof that predictive analytics caused any business outcome. Because the fieldwork was in 2022, these are historical findings, not a measure of the industry in 2026. Pecan was both the survey sponsor and a predictive-analytics vendor, a commercial interest worth bearing in mind when interpreting the report.
What the percentages actually say
The figures describe different things: adoption, difficulty, perceived obstacles, and the time it takes to change programs. They should not be collapsed into a single claim about how many marketers use predictive analytics.
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| Survey finding | Share |
|---|---|
| Companies reporting some integration of AI-powered predictive analytics into marketing | 95% |
| Companies reporting complete integration | 44% |
| Executives saying day-to-day data-driven decisions were difficult | 84% |
| Executives saying predicting customer behavior felt like guesswork | 84% |
| Executives at companies reporting complete integration who still found day-to-day decisions difficult | 90% |
| Executives saying data scientists lacked time to meet requests | 42% |
| Executives saying model builders did not understand marketing goals | 40% |
| Executives saying data was not updated quickly enough to be valuable | 38% |
| Executives saying data scientists did not ask the right questions | 38% |
| Executives saying wrong or partial data was used in models | 37% |
| Executives saying models took too long to build | 35% |
| Executives wanting to extract more impactful analysis from data | 61% |
| Executives wanting specific KPI insights instead of searching through data | 60% |
| Companies able to adjust acquisition or retention programs within a week | 28% |
| Companies taking more than a week to change direction on acquisition or retention | 72% |
| Executives agreeing low- or no-code predictive tools could free data scientists for more complex work | 93% |
These are results within the survey’s defined sample; the percentages are not population estimates for all companies. The 93% figure records agreement with a proposition about low- or no-code tools. It does not show that those tools improved results. The sponsor-issued findings are also reported by Business Wire and MarketingCharts.
Why more data and models can still leave marketers guessing
Collecting data is not the same as making it useful. A company may have purchase, web, campaign, product, and engagement records, yet fail to join them reliably, refresh them in time, or make them accessible to the team responsible for a decision. Even clean, current data is not useful if the analysis does not answer a defined business question or arrive before the campaign window closes.
Predictive analytics uses historical and behavioral data, statistical methods, or machine learning to estimate likely outcomes: for example, conversion, churn, customer lifetime value, lead quality, campaign response, or demand. A model generally returns probabilities, rankings, forecasts, or segments—not certainty. A high churn score is an estimate, not proof that a customer will leave.
Likewise, data-driven decision-making is more than opening a dashboard. It means using evidence to choose an audience, prioritize sales attention, allocate budget, test an offer, or change a campaign. For a prediction to affect that decision, the team needs to understand what the score means, trust its limits, know what action follows, and be able to activate that action in its working systems.
Where the handoff can break
Stale or incomplete data
If customer records are delayed, duplicated, missing key events, or joined under inconsistent identifiers, a model may describe yesterday’s audience rather than today’s. Stale inputs can create outdated segments; partial inputs can make scores unreliable. The reported concerns about data freshness and wrong or incomplete data make data readiness a core operating issue, not a minor technical detail.
Slow model development and overloaded teams
When model work takes longer than the campaign or customer behavior remains stable, its output can arrive too late to act on. A data-science team with more requests than capacity may leave marketers waiting or push them toward spreadsheets and intuition. The survey’s 28% able to adjust acquisition or retention programs within a week contrasts with the 72% who needed longer.
Rank #3
Questions that do not match marketing goals
A model can be technically sound and still be commercially irrelevant if it predicts an outcome marketing cannot influence or does not need. “Who is likely to buy?” is different from “Whose purchase is likely to change because of this offer?” A useful project begins with the decision and KPI, not with the desire to build a model.
Scores without an action or activation path
A number in a notebook or dashboard does not tell a marketer whom to contact, when to do it, or what message to use. Each score needs an owner, an operational threshold, a defined action, and a place to use it—such as a CRM, email platform, ad system, or sales workflow. Without that translation and delivery layer, adoption can be high while practical use remains low.
A practical way to make predictive analytics actionable
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Define the decision first
Write down the decision that should change, who owns it, what actions follow different score ranges, and how quickly the output is needed. For example: “Which existing customers should receive a retention offer this week?” Identify the relevant KPI and the costs of false positives and false negatives before selecting a model.
Rank #4
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Check whether the data can support it
Review completeness, freshness, customer identifiers, duplicate records, missing values, historical outcome labels, and whether the target is defined consistently. Confirm that the data reflects the current business model and that consent, privacy, and access restrictions are understood. A model cannot repair a mislabeled outcome or an unreliable customer history.
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Set a baseline to beat
Compare the proposed model with the current business rule, lead score, marketer-selected audience, or a simple recency-frequency-monetary approach. Where feasible, keep a control group. A more complicated model is not an improvement unless it adds value over the process already in use.
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Measure business lift, not only model accuracy
Track an outcome tied to the decision: incremental conversion, revenue or margin per customer, retention, acquisition cost, return on ad spend, contact rate, or incremental lifetime value. Calibration, precision, and recall can help assess a model, but they do not establish that using it improved marketing performance. Test treatment and control groups when possible to separate campaign impact from customers who would have acted anyway.
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Put predictions where the work happens
Deliver scores or audience lists to the systems that execute the action, such as a CRM, customer data platform, email service provider, advertising platform, or sales workflow. Confirm that delivery cadence, identity matching, permissions, and ownership work end to end. A prediction refreshed monthly may be unsuitable for a fast-changing campaign.
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Review performance and refresh the process
Assign responsibility for monitoring data drift, score distributions, missing inputs, model decay, user adoption, and performance across customer groups. Revisit the model when customer behavior, campaign mix, or the business changes. Involve privacy, legal, and security teams where predictions use personal data or affect customer treatment.
Choose the operating approach that fits the bottleneck
The survey does not establish that buying a platform solves the problems it describes. The relevant choice depends on whether the constraint is modeling capacity, marketing workflow, shared data infrastructure, or customization.
| Approach | Best fit | Main trade-off |
|---|---|---|
| Build models in-house | Organizations with data engineering and data-science capacity, unusual data, bespoke requirements, or a need for infrastructure control | Requires specialist staffing and ongoing responsibility for deployment, maintenance, monitoring, security, and explainability |
| Use a specialist predictive platform | Teams with usable historical data and limited modeling capacity, especially when common use cases and faster activation matter | Adds vendor cost and dependence; automated modeling does not remove the need for sound labels, governance, experiments, and data quality |
| Use an all-in-one marketing platform | Teams whose core problem is connecting customer information to campaigns and workflows in a system they already use | Predictive features may be bound to the platform’s data model or edition, and advanced needs may require higher tiers or add-ons |
| Use a warehouse-first stack | Data-mature organizations that need governed, reusable data and technical control across functions | More components to maintain; marketers may remain dependent on data teams, and activation may require additional integrations |
For any option, assess data freshness, identity resolution, activation integrations, model monitoring, experimentation, explainability, privacy controls, and total cost—not just the presence of an AI feature. Low-code tooling may help analysts iterate, but it cannot determine the right business question or prove incremental value by itself.
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Pecan’s current pricing page describes a specialist platform and makes vendor-provided comparisons about time to market and in-house staffing costs; those comparisons are not independent benchmarks. Its conversion, upsell, and cross-sell page describes sending predictions into databases, warehouses, CRMs, and marketing systems. Treat these as product capability claims to verify against your own integrations, contract, data-residency needs, and workflow—not evidence that a platform will deliver lift in a particular company.
What the finding means for marketing leaders
The report’s central tension is that broad predictive-analytics adoption coexisted with difficulty making daily decisions. That is plausible when data is not ready, model work is slow, business questions are misaligned, or outputs never reach the people who can act. A prediction creates value only when it is relevant, timely, trusted, operationalized, and shown to improve an outcome against a credible baseline.
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