>>> the costed action plan for revenue

Decisions in minutes, not in months.

>>> acceleratingchurn saves

Name your budget and your rules. Xplainable returns the best set of actions that fits, which lever, for whom, at what cost, and shows you why. Just ask in plain English in Claude.

Try it in the app >>>Request demo

[✓] free to start · no credit card

// costed-plan engineSTATUS:// LINKING

0xfaster than waiting on a data team

MCPSHOPIFY CONNECTORREST APIPYTHON SDKAUDIT TRAILSGOVERNED ACCESS

>>> deployed where your revenue data lives, your data stays yours

The shift01 / shift >>>

A dashboard tells you what happened. You still have to figure out what to do, and what you can afford.

// the old way

churn_dashboard.biexported 09:14

churn by segment

churn

12.4%▲

at_risk

$1.2M

auc

0.81

// now what? _

A dashboard you interpret, a data team you wait on, a prediction you still have to act on, with no idea what it’ll cost.

// xplainable

costed_plan.runready

plan spend$0

[✓] fits $30,000 · $400 left

ranked actionscost

01retention_call84 accts$14,200

02fee_waiver31 accts$9,400

03win_back_offer12 accts$6,000

>>> why: support_response +0.31 · tenure −0.22

Name your budget and get a ranked, costed plan that fits it. Who, which lever, at what cost, and why.

Pointed at the number you own02 / in practice >>>

Keep the customers worth keeping

[✓]A costed save plan ranked by expected value, never over budget

[✓]See what flips each customer, and by how much

[✓]Hand finance a plan it can sign off

Explore >>>

save_plan · churnready

of at-risk value
recoverable in budget

accounts at risk

value exposed

$1.2M

[✓] save plan $29,600 ≤ $30,000 cap

Without vs. with XplainableD / why >>>

the outputa score or a dashboard[✓]a costed action plan

your budgetyou guess the cost[✓]respected, fits your cap

the answerpredicts what will happen[✓]prescribes what to do

accesssql, notebooks, a data scientist[✓]just ask in Claude

audit trailscattered, hard to defend[✓]board-ready

From data to a costed plan, in four steps03 / under the hood >>>

connect & name budget

Connect Shopify or upload a CSV, then say it plainly. Works with the messy data you already have.

score & cost

The engine scores accounts and costs every lever, the same rigour our data scientists use.

explain

Each move cites its drivers, a what-if, and the constraint it respected, in plain language.

deploy & act

Approve the plan and act, over the API, in batch, or just by asking in Claude.

// screening moveCHECKING

candidate: 15% win-back offer

·discount ≤ 10%

·region: APAC

·price ∈ [99,159]

>>> checking rules…

Model your constraints

Encode your budget, your pricing rules, and the levers you’re allowed to pull. Off-limits stays off-limits.

// allocationRUNNING

spend$0 / $30,000

service credits

loyalty offers

win-back offer

OVER

>>> allocating…

Optimise within budget

Budget-constrained optimisation returns the highest-value plan that never breaks your cap.

// why thischurn .78

support_response

+31

tenure

-22

usage_drop

+19

nps_score

-9

>>> attributing…

Explain every move

Each recommendation cites its drivers, a what-if, and the constraint it respected, in plain language.

// audit logLIVE

>>> _

mcpgovernedauditsso

Built for trust

Governed access, audit trails, the open MCP standard, and a REST API + Python SDK under the hood.

Frequently asked questions04 / faq >>>

Recent posts05 / learn >>>

blog

Stop guessing. Start deciding.

Name your budget, ask your first question in Claude, and get a costed, explainable action plan that fits it.