An account prioritization matrix that crosses fit with timing tells a team which few accounts deserve work this week, and which ones can safely wait.
Place every account on two axes: how well it fits your ideal customer profile, and whether a dated public signal gives it a reason to act now. Accounts strong on both are worked this week, strong fit alone is watched, a signal with weak fit gets a light test, and the rest wait. We call it the Torke Matrix.
An account prioritization matrix answers a question every revenue team asks on Monday morning: out of the few hundred accounts on the list, which ones deserve real work this week? Most teams answer it with fit alone, sorting accounts by how closely they match the ideal customer profile, and then spread the same effort across the top of that list every week. Fit tells you who could buy, though, and says nothing about who has a reason to buy now, so the best-fitting accounts get contacted in weeks when nothing has changed for them and ignore the message.
We call our version the Torke Matrix. It places every account on the account list, which we call the account book, on two axes. The first is fit, meaning how well the account matches the profile the client agreed. The second is force, meaning whether a dated public signal, such as a new plant announced, a senior operations hire or a published tender, gives the account a reason to act in the coming weeks. Crossing the two gives four boxes, and each box comes with its own instruction.
Accounts with a strong fit and a fresh signal are worked this week, with outreach that opens on the signal and covers the whole buying group rather than one title. Accounts with a strong fit and no signal are primed and watched: they get light visibility work, and the moment a signal fires they move to the first box. Accounts with a signal but a weak fit get a light test, because a fresh event sometimes reveals a use case the profile missed. Accounts with neither are left alone on purpose, since every hour spent there is taken from an account in the first box.
Implementing it takes four steps. First, score fit for every account from evidence you can link to, the way our Method page describes, so nobody argues later about why an account sits where it does. Second, decide which signal types count for your market and how long each one stays fresh, because a funding round and a building permit do not age at the same speed. Third, place every account in a box each week, using the Torke Score as the number behind the placement. Fourth, attach one action and one owner to each box, so the matrix changes what people do on Monday rather than decorating a slide.
Take an account list of 400 accounts as an example. If 120 of them score a strong fit and, in a given month, a dated signal fires at 15 of those 120, the first box holds 15 accounts. That is the work for the month, and it is small enough for one person to cover every buying group properly. The other 105 strong-fit accounts are watched rather than chased, and the 280 weaker-fit accounts get either a light test or nothing, which frees the budget and the hours the first box needs.
The matrix has limits worth stating. It only works if signals carry a date and a source, because an undated hint from intent data will fill the first box with accounts that have no real reason to act, and the team will lose faith in the whole grid within a month. It also assumes the fit score is honest: if the profile was written to include everyone, the strong-fit column holds most of the list and the matrix sorts nothing. And in a market where signals are rare, the first box can stay empty for weeks, and that empty box tells you something true about how often your market changes.
For a revenue leader, the consequence is a smaller, sharper weekly list and a clear reason for every account on it. Sales stops receiving a ranked spreadsheet of 400 names and starts receiving a handful of accounts with a dated reason attached, which is the same idea as the week something changed, turned into a weekly routine.
The idea holds anywhere a small number of buyers decide large purchases, from robotics to banking software. This week, take your current account list, mark the accounts with a strong fit, then mark the ones where a dated public event happened in the last thirty days, and count the overlap: that number is how many accounts your team should be working right now, and it is usually far smaller than the list it is actually working.
Each answer stands on its own, so you can forward one to a colleague, and an assistant asked the same question can quote it as it is.
Fit on one axis, scored from evidence you can link to, and timing on the other, meaning a dated public signal such as a hire, an expansion or a tender. Using two fit measures on both axes sorts nothing new.
Weekly. Signals age fast, so an account placed in the top box a month ago may have no reason to act today. Fit changes slowly and can be reviewed quarterly.
Watch them and keep light visibility work running, so the right people already know your name when a signal fires. Chasing them with outreach before then usually burns the contact.
Sometimes. A fresh event can reveal a use case your profile missed, so give it a light test and update the profile if it converts, rather than ignoring it or giving it full coverage.
Tell us what your team sells, who buys it and where deals stop moving. You'll get a written reply saying which way of working fits you, or whether a Diagnostic makes sense, and if neither does, the reason.