Revenue timing is the estimate of when a specific B2B account's buying window opens and how long it stays open, judged from the recency and sequence of events at that account. A buying window is the period when a problem owner, authority and budget line up and no decision has been made. The estimate helps prioritize outreach. It is not a purchase date.
What is a buying window, and how long does it stay open?
A buying window is a condition at the account, and no date on a calendar marks it. Someone owns a problem, a person with authority is paying attention to it, money or headcount is available, and nobody has chosen a vendor yet. While all four hold, a new vendor can be heard. Before that, your message arrives too early. After the choice is made, it arrives too late.
Windows usually open after something changes: a new leader takes over a function, a team starts hiring, a core system is replaced, money arrives. They close when a first decision is made, a budget is committed or the urgency fades. How long a window stays open depends on the company, the size of the purchase and who is involved, so any fixed number of days is a starting guess.
Why are fit and intent not enough?
For example, imagine three accounts, all a strong ICP fit, all showing some interest.
| Account | Situation | Fit | Intent | Timing | What happens if you reach out today |
|---|---|---|---|---|---|
| A | New VP Sales started 40 days ago, hiring four SDRs, migrating CRM | High | Medium | Window likely open now | A conversation is plausible |
| B | Same stack for three years, incumbent vendor renewed last month | High | Medium (someone read a comparison) | Likely closed for months | A polite decline, and a spent first impression |
| C | Funding closed last week, no hiring yet | High | Low | May open in a few weeks | Lost among the congratulations |
A score built only on fit and intent ranks these three nearly equally. A timing view puts A first, C on a short watch, and B on a renewal-date watch. Timing is one of the factors that separates outbound that gets replies from outbound that just keeps running.
How is timing judged from recency and sequence?
A timing estimate comes from three properties of the events at an account: type, recency and sequence.
Type sets a base window. Each kind of event tends to open a characteristic window. These ranges are our working assumptions for a manual method you can apply yourself, not research findings and not an output of PravRaha, so adjust them. They are drawn together in the buying signals guide:
| Event | Window may open | Window may close |
|---|---|---|
| New leader in the buying function | About 30 days after start | About 90 to 120 days after start |
| Hiring surge in the function | Immediately | About 60 to 90 days |
| Technology-stack change | Immediately to 30 days | About 60 to 90 days |
| Funding round | About 60 days after close | About 180 days after close |
| Expansion | Immediately | About 120 days |
| Partnership or acquisition | About 90 days | About 365 days |
Recency decays the event. A hiring surge from last week is a live problem. One from four months ago has probably been solved by someone. As an illustrative starting rule, not a measured result: full weight for 30 days, half weight to 90 days, quarter weight to 180 days, then none. Tune the numbers to your market.
Sequence sharpens the estimate. Two events inside about 45 days that point at the same function narrow the window and raise confidence. Sequences worth watching:
- 01Leadership change, then a hiring surge in that leader's function (mandate and headcount look real)
- 02Funding, then a leadership change or hiring (money is being pointed at a function)
- 03A CRM migration, then a vendor dropped in an adjacent category (the project has scope)
- 04Any of the above, then first-party engagement such as repeat visits to your site (someone is looking)
For example, imagine a hypothetical account moving through that kind of sequence:
| When | Signal | What it implies |
|---|---|---|
| Day 0 | New Head of RevOps starts | Base window: may open around day 30 and close around day 120 |
| Day 18 | Posting: 'own our data and enrichment stack' | The function is confirmed; the window now looks like day 25 to day 90, with more confidence |
| Day 40 | Previous enrichment vendor's script removed | A decision looks to be in progress. By the manual rule above, this is a reasonable moment to act, though the evidence is circumstantial |
| Day 41 | Two visits to your integrations page | Someone is evaluating. Worth a personal message today. |
How do you prioritize by window instead of by score?
A single score hides timing. As a manual method for you to apply, not a PravRaha output, use two axes: probability (fit plus event strength) and window state (your own judgment of whether the window looks open, upcoming or closed).
| Window open now | Opens in 30 to 90 days | Closed or unknown | |
|---|---|---|---|
| High probability | Personal outreach this week. Economic buyer plus technical evaluator. | Light-touch drafts to warm the account; personal outreach scheduled for when the window opens. | Watch. Set a trigger on the next event. |
| Medium probability | Outreach drafted now for a person to review and approve; a person steps in on reply. | Watch list with a dated check. | Ignore until an event fires. |
| Low probability | Drafted outreach, reviewed and approved by a person, if capacity allows. | Ignore. | Ignore. |
The top-left cell is small. For many teams it is a small share of the territory. That is where a salesperson's afternoon is best spent, and many outbound teams never isolate it because their tooling only produces a ranked list. The decision-maker intelligence guide covers who to approach once a window is open.
What are the limits of timing prediction?
A timing model can rank accounts by how likely they are to be in a window this month, and it can improve as reply and meeting data feed back into the weights. It cannot name a purchase date, see budget cycles that are not public, or know that the new VP is a friend of your competitor's founder. Treat the estimate as a reason to spend attention rather than a forecast, and let people who know the account override it.
How does PravRaha approach revenue timing?
PravRaha looks at the events it can see at an account (hiring, funding, leadership change, technology change, expansion), notes when each happened and in what order, and shows a sales team which accounts appear to be moving toward a purchase and why. It then recommends the next action and drafts the first message against the trigger. A person on your team reviews and approves every outreach message before it is sent from your own connected mailbox (Gmail, Microsoft or SMTP) or WhatsApp. PravRaha does not label accounts with window states or dates, and does not claim a validated buying date or a hit rate. It attaches the business event to the account, so your team can see what sits behind a recommendation. The platform page describes the product, and the how it works page shows the sequence. For the wider picture, see revenue intelligence.
Where to go next
- →The inputs: buying signals and buyer intent
- →A common window opener: leadership change signals
- →Who to engage when the window opens: decision-maker intelligence
- →The whole loop: revenue intelligence
See how the loop runs inside PravRaha.
Signals, intelligence, prediction, timing, decision-makers and action, as one system rather than eight tools.