The most consequential information in any competitive or commercial environment is rarely the information that everyone already has. By the time a competitor’s strategic pivot appears in mainstream trade coverage, the organisations that will respond most effectively have already begun preparing their response because they saw the signal three months earlier in an earnings call commentary, a regulatory filing, a patent application, or a cluster of job postings that pointed in a direction nobody else had yet connected into a coherent picture. And by the time a marketing team’s budget allocation decisions are vindicated or undermined by quarterly results, the teams operating with the best measurement infrastructure have already made the adjustments that the results will eventually confirm were necessary because their measurement system told them what was actually driving revenue while the quarter was still in progress rather than after it had closed. Valona Intelligence and Sellforte are two platforms that have each built their product around this same fundamental insight that the value of intelligence is entirely a function of when it arrives relative to the decisions it needs to inform, and that the infrastructure determining this timing is worth building with the same seriousness as the decisions themselves.
Valona Intelligence The Competitive Intelligence Program That Arrives Before the Market Does
Most organisations that run something they describe as a competitive intelligence program are running monitoring service alerts when competitors publish press releases, summaries of what appeared in trade media last week, notifications when a competitor’s website changes in a way that a crawler happens to notice. This is better than nothing and considerably less useful than what the term competitive intelligence program implies when it is taken seriously. A monitoring service tells an organisation what its competitors have chosen to announce. A genuine competitive intelligence program tells an organisation what its competitors are doing before they announce it and, more importantly, before the implications of what they are doing have been priced into the market conditions the organisation is competing within.
Valona Intelligence was built for the second version. The platform monitors more than 200,000 verified sources across 115 languages trade publications, patent filings, regulatory announcements, earnings calls, academic research, procurement notices, job postings, social signals, and the full breadth of publicly available information that, taken together, allows the strategic direction of a competitor or the emergence of a market shift to be detected months before it becomes visible in the mainstream signals that everyone monitors. Agentic workflows continuously process this source landscape and deliver role-specific intelligence to the decision-makers who need it in the format and at the frequency their role requires strategy directors receive competitive positioning analysis, CMI managers receive market trend signals, and sales directors receive the competitive differentiation intelligence that shapes how individual deals are positioned and won.
The earnings analysis capability reflects the depth of what this source coverage enables. A quarterly earnings call is a public document that every competitor has access to simultaneously. What differentiates a competitive intelligence program that extracts genuine value from earnings calls from one that does not is the ability to read the strategic signals embedded beneath the financial performance data the forward guidance that implies a market entry, the R&D spending commentary that precedes a product category expansion, the margin discussion that signals a competitive repositioning that will be visible in pricing behaviour two quarters from now. Valona’s AI-assisted earnings analysis extracts these signals systematically rather than depending on an analyst happening to notice them while reading a transcript. The platform’s early warning capability identifies disruptions three to six months ahead of when they become obvious which is precisely the window in which strategic responses are still possible rather than merely reactive.
Named a Leader in the 2026 Gartner Magic Quadrant for Competitive and Market Intelligence Platforms and a Leader in the Forrester Wave with top scores in 17 of 31 criteria, Valona Intelligence serves organisations including ABB, BASF, Bosch, Goodyear, Unilever, and Philips. The scale and sophistication of this client base reflects the scale at which the difference between a competitive intelligence program that sees what is coming and one that describes what has already happened carries strategic consequences that make the infrastructure investment not merely justifiable but essential.
Sellforte Incrementality Testing That Replaces Attribution Bias With Causal Truth
The marketing measurement problem has a structure that most organisations recognise but fewer have built the infrastructure to address. Every major advertising platform Meta, Google, TikTok reports attribution data that is systematically biased toward showing the platform in the best possible light. Last-click attribution overcredits the lower-funnel channels that appear at the end of the customer journey and undercredits the awareness channels that drove the customer into the funnel in the first place. And incrementality testing the methodology that measures the actual causal impact of each channel by varying exposure in controlled ways rather than inferring causality from correlation between ad exposure and conversion events is typically run as a separate workstream that produces numbers which contradict the attribution data rather than correcting it, leaving the team uncertain which set of figures to act on.
Sellforte’s architecture addresses this at the foundational level by treating incrementality testing not as a separate analytical exercise but as the calibration layer of a unified measurement operating system. The foundation is an always-on causal Bayesian MMM that runs continuously rather than in the quarterly or annual cycles that traditional marketing mix modeling implementations operate on measuring the true incremental impact of every channel across time as the baseline that everything else is calibrated against. Geo lift experiments, conversion lift studies, and A/B tests run through a unified experiments hub and feed back into this foundation as Bayesian priors, each experiment sharpening the accuracy of the MMM model rather than producing a separate figure that needs to be independently reconciled. The incrementality testing results do not sit alongside the attribution data in a separate report that raises more questions than it answers. They are the mechanism through which attribution is corrected at the campaign and ad set level, producing true incremental ROAS figures that reflect what each campaign actually drove rather than what each platform claimed credit for.
The practical consequence of this architecture is a marketing team that can make budget allocation decisions on the basis of what is actually driving revenue rather than on the basis of what each platform’s reporting suggests is driving revenue, a distinction whose financial significance compounds with every budget cycle that runs on corrected rather than biased data. FCP Euro used Sellforte’s always-on measurement approach to drive a 26.6 percent increase in media-driven US sales with over 90 percent forecast accuracy. Represent Clothing achieved a 44 percent lift in Black Friday incremental revenue through MMM-driven optimisation. C&A built always-on measurement across 18 markets, replacing annual reporting with continuous causal insight. Farmacity unlocked 240,000 dollars in incremental revenue with 63 percent more retail impact. These outcomes are described in specific and auditable terms that reflect what incrementality testing at the foundation of a unified measurement system produces when the architecture is built correctly rather than added as an afterthought.
The Sellforte platform extends the incrementality testing and MMM foundation into AI-powered media planning and buying a Media Planner Agent that builds granular budget recommendations at the campaign and ad set level, a Media Buyer Agent that executes real-time buying decisions directly in Meta, Google, TikTok, and other platforms, and an Experiments Agent that designs, detects, and analyses experiments automatically to continuously strengthen the evidential foundation that every budget decision rests on. The NPS of 76 achieved in 2025 reflects the consistency of this value delivery across a client base that includes Lidl, C&A, Bonprix, Intersport, Tchibo, Douglas, Musti Group, and Represent.
The Measurement Standard These Two Platforms Share
A competitive intelligence program that surfaces strategic signals months before they become consensus, and incrementality testing unified with MMM into a measurement system that tells marketing teams what actually drove revenue rather than what each platform claimed credit for. Valona Intelligence and Sellforte are solving different measurement problems in different domains, but the underlying standard is identical information that arrives early enough to inform a decision rather than to describe what the decision should have been, delivered through architecture that was built to close the gap between data and action rather than to widen it with more impressive-looking reports. The organisations that build their intelligence and measurement infrastructure to this standard make better decisions than the ones that do not, and the compounding advantage of consistently better decisions is the return that justifies the investment in both platforms.
