What is account scoring?
Account scoring ranks target accounts by how well they fit your ideal customer and how much evidence shows they are in motion, so budget and sales time go to the right accounts first.
A target list is always longer than anyone can work. Marketing can fund ads for some of those accounts, sales can call a few of them this week, and something has to decide which. Scoring gives each account a number, a tier, or a stage, and the accounts at the top get the money and the calls first. The form of the output matters less than whether the person receiving it can act on it, which is where most scoring goes wrong(opens in a new tab). A rep who opens an account, finds nothing that matches the number, and goes back to a personal hit list has voted against the model. A score earns its place when the reasons travel with the rank, so a rep can check them against what they already know about the account.
Fit and activity answer different questions
Fit asks whether the account looks like the companies you win. The inputs are firmographic and technographic: industry, headcount, the tools it runs, the markets it sells into, plus whatever your closed-won history says about the accounts that stayed. Fit changes slowly, so it can be recalculated on a schedule. Activity asks what is happening at the account now. Site visits from identified people, ad engagement, form fills, a job change into a buying role, CRM activity, and research on someone else's property all count. Some vendors split these into separate engagement and intent scores. Activity can change by the hour.
Read the two apart before you combine them. A perfect-fit account with no activity is a good account for next quarter. A noisy account that doesn't fit is a distraction with a number attached, and a model that blends the two into one figure hides which case you are looking at. Settle what counts as firmographic, technographic, or intent data(opens in a new tab) before you assign any weights.
Account scoring versus lead scoring
Lead scoring rates one person, usually from form fills, email clicks, and page views, and hands the contact to sales once a threshold is crossed. B2B purchases are made by committees, and a lead score cannot tell an intern's visit from a VP of finance's unless someone adds that logic by hand. An account score can weigh who is involved, how many of them are active, and whether the roles that sign off have shown up at all.
Hand-weighted points versus predictive models
The oldest approach is a point system. Someone on the team decides that a pricing page visit is worth some points, a webinar registration is worth fewer, and ten points of fit are worth a tier. It is easy to read, which is its real strength, and it drifts. Buyer behavior changes, the product changes, and nobody goes back to retune the weights until sales complains.
A predictive model learns the weights from outcomes. It needs enough closed-won and closed-lost history to train on, and it is only as good as the opportunity data in the CRM behind it. Some predictive scores come with an explanation attached and some return only a number, so whether you can see why an account moved depends on the product you pick. Either kind needs decay, so that last month's visit counts for more than last year's. It also needs a periodic check that the top tier really converts better than the middle one.
What a score can't tell you
A score ranks accounts against each other. A high rank earns a closer look and promises nothing about a purchase. The score also reflects only the signals you can see, so an account that researches through channels you don't track will look cold. Ask why the account is hot(opens in a new tab) before you act on the number alone. Ranking is only the first step in prioritization(opens in a new tab), which still needs a decision about what each tier receives: paid media, a sequence, a call, or nothing yet.
How contact-level evidence rolls up to the account
The score stays at the account level, because the account is where you decide how much to spend. The evidence behind it can be contact-level. Say three named people from one company read your pricing page in the same week, and one of them is the economic buyer. The account's score rises because specific people did specific things, and the rep sees who they are. Without that detail the same rise is a bump in anonymous company traffic.
Vector's scoring model, Specter(opens in a new tab), is built this way. It places each account in one of six buying stages, re-scores daily, and writes a plain-language reason for every stage change along with the people driving it.