A new way to run competitive intelligence.
Create an autonomous operating system that turns competitor moves into sharper decisions — not quarterly reports nobody reads.
A flare is a brief burst of bright light, fired into the dark to spotlight where you need to see — only when it matters. Competitive intelligence should work the same way: illuminate the one thing you need to act on, not floodlight everything until you're blind to all of it. This is the method for doing it that way.
The status quo
How competitive intelligence is done
For most teams, competitive intelligence is a project, not a practice. It's something you spin up once in a while, then forget. Someone kicks off a research sprint, ships a deck, and files it in a wiki, where it starts going stale the day it's published. Almost everyone does it the same way, whatever tool they bought:
Broad alerts: monitor everything every competitor does.
Project-based: a big competitive deck or battlecard gets built once, then forgotten by the next quarter.
Analyst-centric: one person or team does the research and becomes the keeper (and the bottleneck) of competitive knowledge.
Passive distribution: newsletters and wiki pages nobody opens in time, or even remembers exist.
Document as the output: the deliverable is a big competitive report nobody reads.
Reactive: fire-fight when a rep loses a deal, or a prospect tells you about a competitor's latest move.
It feels rigorous. It produces a lot. And it quietly fails, not because the research is bad, but because the model is wrong.
The problem
Why it breaks
Six predictable cracks:
Staleness: the battlecard or competitive report is out of date the day it's published.
The last-mile gap: intel never reaches the rep during the live deal, the only moment it mattered.
Noise over signal: teams drown in data and still can't answer “what changed, and why does it matter?”
The bottleneck: CI dies when the one analyst is on PTO or leaves.
Vanity intelligence: beautiful reports that change zero behavior and trigger no decision.
Lagging, not leading: you learn about competitor moves after they've already cost you deals.
One pattern runs under all six: traditional CI processes optimize for more — more sources, more coverage, more reports. But the expected output is actionable signal for sharper decisions. Drowning in too many irrelevant alerts is the one thing every CI tool's customers complain about.
Source: Flares CI Research, 2026 — The State of Competitive Intelligence Tools: A G2 Review Study of Klue, Crayon, Kompyte & Contify (full study published soon).
That's where the Flares Method starts.
The shift
A new method: intelligence in motion
The fix isn't more discipline applied to the old model. It's a different model, a new paradigm.
Shift competitive intelligence from a periodic research project that produces static documents to a continuous operating system that delivers sharper decisions when it matters.
The unlock was never more data. It was relevance, timing, and distribution. Surface less, not more: only the handful of changes that actually matter. Turn each one into a tailored, actionable recommendation, a sharper decision, not raw signal. And deliver it to the right person exactly when they decide. Intelligence in motion, not intelligence in storage.
That changes how the work is built and run. You design it backwards and run it forwards:
Design backwards: start from the decisions that need competitive context, derive the schema those decisions turn on, and watch only the dimensions that feed them.
Run forwards: Sense → Interpret → Distribute → Act → Learn, a loop that never closes, sharpening itself as outcomes feed back into what's worth watching.
Eight principles make it concrete.
The principles
The eight principles
Start from the decision, not the data.
Most teams collect first and hope it's useful — then drown. Invert it. Begin with the decisions that need competitive context: which deal to defend, what to put on the roadmap, how to price. From there, reverse-engineer the smallest set of observations that informs them. Done well, that produces something concrete. A competitive schema: a handful of dimensions, per competitor.
Watch the schema, not mentions.
That schema is what you monitor (pricing, positioning, distribution, hiring, messaging, ads, product), not every passing mention of a competitor's name. A signal is a change to the schema, not a new headline. And because the input is structured by definition, filtering stops being editorial and becomes mechanical: you can't drown in alerts you've designed out.
Hunt signal, not noise.
A schema doesn't just filter; it lets you aim. A good system surfaces fewer things, not more: the one or two schema changes per competitor each week that genuinely deserve a decision, with everything else suppressed by design. So flip the metric too. The question is never “how much did we catch?” but “what did we miss that mattered?” A flare is a brief, bright light aimed where it counts — never a floodlight.
Always ship the “so what.”
A signal isn't finished until it carries a decision. Never deliver a change without the implication and a recommended move: not “Competitor X changed pricing,” but “Competitor X moved up-market ~30%, so tighten our SMB messaging, or test a matching enterprise tier.” To provide such tailored recommendations, the system needs to understand your own schema. The translation of a signal into actionable insight is the entire value. Skip it and you've just moved the work onto the reader.
Treat intelligence as a flow, not a file.
Step back from any single signal and the system has a shape: continuous, not a one-time deliverable. Competitors change constantly; a document can't keep up. Kill the quarterly deck and make CI a routine: fifteen minutes a week compounds across 52 passes a year, each building on the last, where a quarterly sprint forgets itself four times a year. Small and regular beats big and rare.
Earlier beats complete.
A flow lets you act before the picture is finished, and you should. A directional read today beats a perfect analysis next month. Move on clusters of weak, early signals instead of waiting for a certainty that arrives after the deal is already lost. Roughly right and in time beats exactly right and too late.
Optimize for the last mile, not the library.
None of this matters if the insight never arrives. A perfect report nobody reads at the right moment is worth zero. Value is the right insight reaching the right person as they act. So the CI tool is never the destination: the CRM is, the deal review is, the pricing meeting and the AI assistant the team already uses are. Push competitive context into those moments; don't park it in a library people have to remember to visit.
Everyone's sense, one owner's system.
If intelligence has to reach people the moment they act, then everyone is part of the system. Every rep, sales enabler, PMM, and PM both feeds it and draws from it, so it doesn't die when one analyst is on PTO. But shared doesn't mean ownerless: one person curates the schema, tends the flow, keeps the bar high. Distributed sensing, single spine.
At a glance
How it's different from the common practice
| Dimension | Common practice | The Flares Method |
|---|---|---|
| Starting point | Collect everything you can | Start from the decision |
| Unit of observation | Endless competitor mentions | Meaningful schema changes |
| Volume goal | More coverage, but noisy | Less coverage, but sharper |
| Output | Outdated battlecards & reports | Routed actionable recommendations |
| Cadence | One-off quarterly projects | A continuous weekly habit |
| Timing | Reacting once it's too late | Anticipating the next move |
| Destination | An unknown portal & wiki | Right where the decision happens |
| Ownership | One — bottleneck — analyst | Everyone senses, one curates |
| Success metric | Vanity alerts nobody reads | Smarter decisions made |
Old CI asks:
“What's happening with our competitors?”
The Flares Method asks:
“What should we do about it — right now?”
The payoff
What you get
Run competitive intelligence this way, as a living system rather than a quarterly project, and the everyday outcomes change:
You win more of the deals you're in.
Competitive context shows up inside the live deal (in the CRM, on the call, in the rep's AI assistant) the moment an objection lands, not in a quarterly refresh nobody opened. The rep always knows what to say, while it still counts.
You see moves before they land.
Watching a schema for change means you catch the up-market pricing shift, the new enterprise hire, the repositioned homepage as they happen, and respond while it's still a choice, not a fire. You lead instead of react.
Your team stops drowning.
When the system surfaces the one or two changes a week that deserve a decision and suppresses the rest, attention goes to deciding, not digging. Less noise, less alert fatigue, more of the signal that actually moves a number.
Every insight ends in a decision.
No more beautiful reports that change nothing. Each signal arrives with its “so what” and a recommended move, so it leads to a concrete decision (sharpening your messaging, running a pricing test, re-prioritizing the roadmap) instead of sitting unread.
It survives your people.
CI no longer lives in one analyst's head, or stalls when they're on PTO. Everyone feeds and draws from the same system; one owner keeps it coherent. Distributed sensing, single spine, resilient by design.
It compounds instead of going stale.
Because it runs as a flow, not a one-off project, context accumulates: each week builds on the last, and outcomes feed back into what's worth watching. The system gets sharper the longer it runs, the opposite of a deck that's out of date on arrival.
Add it up, and competitive intelligence stops being a cost center that ships stale documents. It becomes an unfair operating advantage you can measure — in deals won, moves anticipated, market share gained, and decisions made.
Implementation
How to implement it, step by step
Standing the system up is mostly design, then a few integrations. You build it backwards, from the decisions, not the data:
Start from the decisions.
List the recurring decisions your team makes that need competitive context: which deals to defend, how to price, what to put on the roadmap, how to position. Everything downstream serves these.
Derive the schema.
For each competitor, turn those decisions into the handful of dimensions they actually turn on: pricing, positioning, distribution, hiring, messaging, ads, product. If a dimension doesn't trace back to a decision, leave it out. That shortlist is your competitive schema.
Point monitoring at the schema.
Set up automated, continuous watching of those dimensions for changes, not keyword alerts on a competitor's name. Then set the bar: aim for one or two signals per competitor per week, and suppress the rest by design. Structured input is what makes filtering mechanical instead of a daily judgment call.
Name one owner.
Assign a single curator to maintain the schema, tend the flow, and keep the relevance bar high, while everyone on the team both feeds and draws from the system. Distributed sensing, single spine.
Wire the last mile.
Map each type of signal to the decision it serves and the tool its owner already lives in: the CRM and the open deal for reps, the messaging workspace for PMM, the roadmap for product, the chat or AI assistant for everyone. Route it there automatically; never to a portal.
Make the “so what” mandatory.
Set the rule that no signal ships without its implication and a recommended move attached. Bake the translation into the process so it never lands on the reader.
Retire the quarterly deck, and start the loop.
Declare the old artifact dead and the new default in place: CI is continuous now. Run the first weekly cycle. From here on, measure decisions changed, not documents produced.
Set up once, the system then runs on a simple weekly rhythm.
Operation
How to run it
With the schema built and monitoring pointed at it, competitive intelligence runs as a loop, not a project. Five steps, and the fifth feeds the first:
Sense: watch the schema, continuously.
Automated monitoring tracks each competitor across the dimensions you defined (pricing, positioning, hiring, messaging, ads, product) and flags changes, not mentions. This runs in the background, every day, so nothing depends on a person remembering to check.
Interpret: turn changes into signals.
Once a week, triage what moved. Keep only the one or two changes per competitor that genuinely deserve a decision, and suppress the rest. For each keeper, attach the “so what,” a recommended move, and a confidence level. Cluster the weak ones too, since three small signals pointing the same way beat one loud one. This is the fifteen-minute habit, and it's where noise dies and meaning gets added.
Distribute: route each signal to the decision.
Push it to where and when the decision actually gets made: the rep gets it in the CRM or during a call, the PMM during the positioning meeting, the PM while prioritizing the roadmap, everyone in the AI assistant they already use. Never a portal someone has to remember to open.
Act: make the call.
The recipient decides: adjust the pitch, update positioning, reprioritize the roadmap, test a price. The output of the loop is a decision, not a read. And if a signal reliably produces no decision, that's your clue it never belonged in the schema.
Learn: feed outcomes back.
Track which signals led to decisions, and which decisions paid off (deals won, moves anticipated). Feed that back in: sharpen the dimensions that earn their place, drop the ones that don't, retune the threshold for what's worth surfacing. Keep asking the only metric that counts: “what did we miss that mattered?” Then adjust. The loop gets sharper every time it turns.
The rhythm: sensing runs daily in the background, interpretation and distribution take about fifteen minutes once a week, acting happens in the moment a decision is on the table, and learning recalibrates the schema every quarter. Designed once, backwards. Run every week, forwards — a system that compounds instead of going stale.
Flares is competitive intelligence, built on this method.
A continuous system that watches your competitors' schema, surfaces only what deserves a decision, and pushes the recommendation to where you act.