SolutionsGetting in
You have the account. You do not have the people.
The logo is on the list. What you have against it is four contacts scraped two years ago, a title taxonomy that means something different at this company, and no idea which of them still works there. Before you can run any play, you have to answer a question the data cannot: who actually decides here.
01What is really happening
A title is a label, not a role in your deal.
The same title carries different authority at different companies, and directories decay at roughly a third a year through job changes alone. So the list is wrong in two ways at once: some of these people have gone, and the ones who remain are not necessarily the ones who decide. Buying a fresher list fixes the first problem and not the second.
- A third of a contact list goes stale within a year.
- The same title signs at one company and cannot at another.
- Nothing in the data says who decided last time.
02What Adrata reads
Who decided in the deals you already won.
Your closed-won history records which title was genuinely the economic buyer or the champion. Adrata turns that into a prior for your business, smoothed so one deal cannot become a rule and isolated so it only learns from your own wins, then applies it to the new account alongside the committee the deal actually requires.
- "Economic Buyer in three of your last eight wins in Fintech", with the count.
- The committee this purchase needs, against the names you hold.
- Who has moved on, so you stop working a person who left.
03The move
A shortlist you can defend, not a list you bought.
The output is a small number of named people, each with the reason they are on the list and how confident that call is. Where your history is too thin to support a prior, it says so and falls back to the transparent heuristic instead of inventing a decision-maker.
- The likely decision-maker, and the history that justifies it.
- The gap that still needs filling before this is a real deal.
- An honest confidence, including when it is low.
The model behind it
You should be able to argue with the recommendation.
A move you cannot interrogate is a move you will ignore the first time it is wrong. These are the models doing the work in this scenario.
Priors learned from your own wins
In your business, who actually turns out to be the decision-maker?
Closed-won history records which title was genuinely the economic buyer or champion. That becomes a per-workspace prior, smoothed so one deal cannot become a rule, weighted by how much history stands behind it, and isolated so a workspace only ever learns from itself. It carries its own provenance: "Economic Buyer in three of your last eight wins in Fintech".
Stable matching
Given limited attention, who should reach whom?
Assigning outreach across a buyer group is a matching problem with preferences and capacity on both sides. Adrata solves it with deferred acceptance (the Gale–Shapley algorithm), so the assignment is stable rather than greedy, and no two touches quietly compete for the same person.
The right to say nothing
Is there enough evidence here to justify a recommendation at all?
Learned components stay inert until they beat the simple heuristic on held-out data, and every model has a floor below which it abstains rather than guessing. On a new workspace the honest answer is often "not yet", and the system is built to say so instead of manufacturing confidence.
Where it runs
The surfaces this scenario uses.
Who this is for