SolutionsAfter the close
You landed. The rest of the company has never heard of you.
One team uses you and likes you. Ten more have the same problem and no idea you exist. The expansion is not a new sale, it is the same sale to a neighbour, with a reference sitting one floor away. Most teams never run it, because nobody owns finding the neighbour.
01What is really happening
Expansion is a break-in problem that starts warm and gets treated as a report.
The adjacent team is a new buying group with its own budget, its own priorities, and no obligation to care that another department is happy. What makes it easier than net-new is the one asset almost nobody uses systematically: a colleague, inside the building, who will vouch for you.
- Whitespace reports name accounts, not people, and nothing happens.
- The internal referral is the highest-yield asset in the business and gets asked for by accident.
- The adjacent team receives the same cold sequence as a stranger.
02What Adrata reads
The whitespace, drawn from what they actually bought.
Adrata builds the matrix of divisions against products from real won business rather than a guess at account potential, overlays the relationships you already hold, and watches for the change that makes one of those gaps urgent this quarter.
- Which divisions hold which products, and where the gaps are.
- Which gap has a live reason to move now.
- The colleague inside the account who can open the door.
03The move
One earned introduction beats a hundred cold ones.
The action is an introduction request to a named person, with proof of what their colleague achieved, at the moment the receiving team has a reason to listen. It is the same break-in engine as net-new, run with an advantage most teams leave on the floor.
- The referrer, the recipient, and the ask.
- The outcome from the existing team, stated as evidence.
- Timing driven by a real change rather than a quarter end.
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.
Break-in probability
Of every possible way into this account, which one actually opens?
Each candidate route is scored on seven components: relationship strength, how relevant the connector is to the target, freshness, consent safety, social cost, evidence quality, and expected lift. A calibrated model converts them into the probability that the route breaks in, with each component’s contribution visible.
Causal uplift, not correlation
Did the action cause the outcome, or did the good deals just get more attention?
Treatment effects are estimated with propensity weighting and doubly-robust estimators, adjusting for deal size, buyer-group size, whether the deal was intro-sourced, whether it was an existing customer, and known competitive pressure. Estimates that have not been adjusted for confounders are quarantined so they can never be presented as a cause.
Thompson Sampling on what to send
Which version of this actually earns replies, for this kind of buyer?
Message decisions (template, tone, call to action, send time, subject style, opening hook) are treated as arms with Beta priors and sampled rather than fixed. Where a workspace has thin data for a specific context, the estimate falls back through archetype and industry to a global prior instead of pretending to know.
Where it runs
The surfaces this scenario uses.
Who this is for
The people who live in this moment.
After the close