A build-to-order scoring engine for Zoho CRM that trains on your own won and lost history, then writes conversion probability, score decay and a next-best-action back onto every Lead and Deal. Built to order by ECOSIRE for Zoho One (build-to-order) — indicative price from $499.00 USD; request a quote for a scoped proposal.
A build-to-order scoring engine for Zoho CRM that trains on your own won and lost history, then writes conversion probability, score decay and a next-best-action back onto every Lead and Deal.
Built to order
Zoho CRM's built-in scoring is rule-based. Someone in a workshop decided a job title is worth twenty points and an opened email is worth five, and those numbers have not been revisited since. The result looks quantitative but is not: the weights encode a guess, not what your pipeline actually did. Two leads sit at 65 points and one of them was never going to buy.
Meanwhile the answer is already in your org. You have closed Deals with stage histories, source attribution, industry, region, deal size and cycle time. You have Leads that went nowhere and Leads that converted in nine days. That history is a training set, and nobody is using it.
The second problem is time. A score frozen at creation says nothing about a lead that has gone quiet for six weeks. Without decay, your reps work a list sorted by a number that stopped being true. And a score on its own does not tell anyone what to do — a rep looking at 82 still has to decide whether to call, send a quote, or route it to a senior closer.
This is a build-to-order engine. Nothing is pre-trained and nothing downloads instantly. We build it against your data, in your Zoho org, after a scoping call and a fixed quote.
We pull your historical Leads, Contacts, Deals and related activity through the Zoho CRM REST API — including Deal stage history, Lead Source, Industry, Rating, campaign attribution where you use Zoho Campaigns or Marketing Automation, and engagement signals such as Call, Task, Meeting and Email counts from the Activities modules. Where the business runs on Zoho Books, we can include commercial signals that matter for a returning buyer: prior invoice value, payment behaviour, or an existing customer flag drawn from the Books organization.
Before any modelling we produce a data-readiness report. It tells you plainly how many closed outcomes you actually have, which fields are too sparse to use, and where labels are ambiguous — for example Deals parked in a stage that means "lost" to your team but is not marked Closed Lost. If your history is too thin to support a model, we say so and propose a calibrated rule-based interim instead of selling you a model that fits noise.
We train a supervised classifier on your labelled outcomes and hold out a time-based validation split, so the model is judged on periods it never saw rather than on a random shuffle that leaks the future. You get the evaluation honestly: discrimination, calibration, and which segments the model is weak on. The output is a probability between zero and one, calibrated so a 0.30 lead really does convert about three times in ten in your business — not an arbitrary points total dressed up as a percentage.
A lead's probability is recalculated on a schedule and on meaningful events. A decay function reduces the score as time passes without engagement, with the shape of the curve fitted to your observed sales cycle rather than a default half-life. New activity — an inbound call logged in Calls, a replied email, a Meeting booked, a quote sent — refreshes the score. Sales sees a list that reflects today.
Probability alone does not change behaviour, so the engine writes an action recommendation alongside it: call now, send pricing, nurture, route to senior owner, or disqualify for review. Recommendations are derived from what historically moved comparable leads forward, and each carries the top contributing factors so a rep can see why. Custom fields on the Lead and Deal hold the probability, band, decay-adjusted score, recommended action, top factors and last-scored timestamp — ordinary CRM fields, so they work in list views, filters, Kanban, workflow rules, assignment rules, Blueprint transitions and Zoho Analytics.
Scores reach your org through Deluge custom functions and scheduled functions calling the scoring service, with a Zoho Flow path where you prefer that orchestration. Bulk backfill runs through the Bulk Write API rather than record-by-record updates, so an initial scoring of a large database does not exhaust your API credits. We build API-limit awareness into the scheduler and document the consumption we expect at your volume.
Models drift. We build a monitoring job that compares predicted probability against realised conversion by cohort and raises a flag when calibration slips. Retraining is a documented, repeatable procedure — you own the pipeline and the artefacts, and you can commission a retrain or run it yourself.
Sales organisations with at least a year of clean closed-won and closed-lost history and enough inbound volume that prioritisation is a real constraint. Marketing teams held accountable for lead quality who need a defensible measure rather than an argument. Revenue leaders who want routing and SLA rules driven by likelihood rather than by whoever shouted first.
Scoping call. We look at your CRM structure, historical volume and label quality, which modules and fields are in play, your definition of a qualified lead, and how you want recommendations to appear to reps.
Fixed quote. You get a written scope covering the data-readiness report, the model class, the fields we will create, the scoring cadence, and the price and lead time. If readiness comes back weak, the scope changes before you are billed for a model.
Build. Typical lead time is two to four weeks from approval, depending on data volume, how much cleaning the history needs, and the number of modules in scope.
Install in test, then production. We deploy to a sandbox or non-production org, backfill scores there and review the distribution with you against leads your team already has an opinion about. Only then do we move to production, with the ability to disable the write-back cleanly.
Support. Handover includes training for administrators and a session with the sales team on how to read the score, the decay and the recommendation. A defect-fix and tuning window follows, with duration stated in the quote.
We will not promise a lift percentage. Any vendor quoting you an uplift figure before seeing your data is guessing. We will not train on a few dozen outcomes and call it AI. And we will not hide the model behind a black box — you receive the feature list, the evaluation, and the retraining procedure, because a score nobody can explain is a score nobody will trust.
A short call to confirm the workflow, your platform version and where the integration boundaries sit.
You receive a written scope and a fixed price. Nothing is built until you approve it.
We develop against a copy of your configuration and test it there. Typically two to four weeks.
We install on your instance, hand over the source, and support it for twelve months.
Has more inbound leads than capacity and no defensible way to decide which ones the senior closers work. A calibrated probability with decay gives a queue that reflects likely revenue today rather than a points total set in a workshop last year.
Is judged on lead quality but can only argue about it, because the existing score was designed to make marketing look good. Model-driven scoring trained on real closed outcomes turns the quality debate into a measurement both sides can check.
Maintains scoring rules that nobody can justify and that break whenever a new source or region appears. Scores delivered as native fields with a documented retraining path replace unmaintainable rule sprawl with something they can operate and explain.
| Criterion | ECOSIRE | Custom Build | Competitor |
|---|---|---|---|
| Trained on your own closed-won and closed-lost history | Included | Included | Partial support |
| Calibrated conversion probability rather than a points total | Included |
From $499.00
Starting point — quoted to your scope
| Partial support |
| Score decay fitted to your observed sales cycle | Included | Partial support | Not included |
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| Next-best-action with contributing factors on the record | Included | Partial support | Not included |
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| Data-readiness report before any modelling is billed | Included | Not included | Not included |
|---|
| Scores as native fields usable in views, Blueprint and Analytics | Included | Partial support | Partial support |
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| Bulk backfill via Bulk Write API with documented credit consumption | Included | Partial support | Not included |
|---|
| Drift monitoring plus a retraining pipeline you own | Included | Partial support | Not included |
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