YoY revenue lift, same sales team
Tier-2 Financial Services Operator, Brisbane
Score every lead and account by likelihood to convert, then push the ranked list into the CRM your reps already open.
What changes once this is in
40% YoY revenue on the same sales effort
Validated at a tier-2 financial services operator in Brisbane. Human effort re-allocated to the leads most likely to convert. No headcount added. The Managing Director called it a fundamental change in how the business operates.
80 to 95% less time on list-building
Managers stop spending Fridays in Excel. The 95% time reduction measured at the Brisbane financial services operator freed the team to coach reps and review pipeline quality, not reconcile rows.
Top-quartile effort on top-quartile accounts
Your best reps already do this by instinct. The engine gives the rest of the team the same lens. Average-rep conversion rates move 20 to 40% on the same call volume.
Defensible ROI in the first quarter
The model either lifts conversion against the pre-agreed baseline or it does not. You measure it with the same numbers your CFO already trusts. No vanity metrics, no attribution arguments.
A Sales Prioritisation Engine scores every lead, account, and open opportunity in your CRM against the signals that actually predict conversion in your business. The ranked list lands in the tools your reps already use, so effort shifts to the top quartile without new dashboards or training. Built for mid-market sales teams working spreadsheets and gut feel today.
What usually breaks
Friday afternoon. The sales manager opens the CRM, exports 3,200 open leads to Excel, and starts sorting. Postcode, last contact date, a hunch about which brokers pay. By Monday the reps have a list. By Wednesday half of them are working different ones anyway.
This is how most mid-market sales teams prioritise. Not because the people are lazy, but because nobody has ever given them a better instrument than a spreadsheet. The CRM knows who every customer is. It does not know who is worth calling this week. That gap costs real money. The best reps pick well by instinct and hit target. The average rep works the list top to bottom and misses by 15%. The difference is not effort. It is prioritisation.
The problem compounds. Managers spend their week building target lists instead of coaching. New starters take 18 months to learn the patterns senior reps already know. And the pipeline the board sees every month is the output of a process nobody can quite explain.
How this lands inside your operation
A model scores every lead, account, and open opportunity in your CRM weekly. The score reflects the signals that actually predict conversion for your book: recency, product mix, regional trends, the things your best reps already weigh without naming. Reps see the ranked list inside Salesforce, HubSpot, or Dynamics the moment they log in on Monday. No new tool. No new login.
Sales managers see the same list rolled up by territory, with the score trend for each account over time. Conversations shift from "who should I call?" to "why did this one move?". The model learns from every closed deal, so the signals get sharper quarter on quarter. Validated at a tier-2 financial services operator in Brisbane: 40% YoY revenue on the same sales team.
This is for you if…
- Mid-market sales teams, $20M to $500M revenue, 100 to 2,000 staff, running a CRM that reps mostly fill in.
- Heads of sales who know their best reps outperform the average by 2x and want the gap closed.
- Finance services, B2B distribution, SaaS, industrial sales, and dealer groups with a repeatable sales motion and at least two years of closed-deal history.
- Businesses where list-building currently eats a day a week of manager time.
This probably isn't the right fit if…
- Deal-centric sales motions with a dozen opportunities a year and a named account list. The model needs volume.
- Teams whose CRM is empty or so inconsistent it cannot be cleaned. Fix the data first, then score it.
- Organisations unwilling to measure the before-and-after honestly against a baseline.
The scoring inputs
The model is trained on your data, so the exact weights land on your book. The table below shows the signal categories typical for a mid-market sales prioritisation engine, with indicative weights from the financial services build. Weights shift by industry: product-mix signals dominate in B2B distribution, while recency signals lead in wealth and broking.
| Signal | Typical weight | Why it matters |
|---|---|---|
| Recency of last purchase or interaction | 15% | Stronger predictor in B2B than B2C. Decays fast after 90 days. |
| Product or service mix held today | 14% | Current wallet share predicts next-best-action better than demographic fit. |
| Historical close rate of the assigned rep | 12% | Reps with higher close rates get slightly up-weighted accounts they already own. |
| Industry or segment conversion rate | 11% | Baseline expectation before account-level signals move the score. |
| Recency and volume of rep outreach | 10% | Overworked accounts and ghosted accounts both score down. |
| Regional demand and seasonality | 9% | Postcode-level trend catches local market shifts the head office misses. |
| Deal-size band | 9% | Controls for the fact that large deals convert slower but worth more per hour. |
| Recent service or support activity | 8% | Tickets, complaints, and renewals materially change the probability either way. |
| Payment behaviour on existing book | 7% | A strong late-payment signal is usually a disqualifier, not a soft negative. |
| External firmographic signals | 5% | ABN register, employee count changes, credit events. Useful as tiebreakers, not drivers. |
The engagement, step by step
- 1
Two-week discovery: sit with your top reps, map the signals they already use, pressure-test the business case against a baseline.
- 2
Pull five years of closed-deal history from the CRM and finance system into the AIS, cleaned and joined.
- 3
Train a conversion model against the signals that actually predict outcomes in your book, not generic benchmarks.
- 4
Back-test the model on the last 12 months of deals, publish the accuracy, and agree a go-live threshold with sales leadership.
- 5
Push ranked scores into your CRM so reps see priority accounts the moment they log in on Monday.
- 6
Instrument the feedback loop: every accepted, declined, or closed lead feeds back into the next weekly re-score.
- 7
Review the model monthly for the first quarter, then quarterly once it holds, with the original senior consultants on call.
The capabilities behind this
Advanced Analytics & ML
Predictive models, recommendation systems, forecasting, and MLOps, delivered with a ruthless ROI focus.
Learn moreAIS
The operating layer your AI, automation, and analytics run on. Deployed in two weeks.
Learn moreInsight-to-Action Systems
Close the gap between what your data knows and what your business does. The architecture from insight through decision to action.
Learn moreWhat this looks like in the field
Tier-2 Financial Services Operator
Financial Services
“The prediction engine has fundamentally changed how we operate our business.”
- Human effort re-allocated to leads most likely to convert — 40% YoY revenue increase
- Staff morale boost from a more effective, less grinding sales process
- 95% reduction in time spent on spreadsheet analytics to prioritise targets
Frequently asked
You might also care about
Churn & Retention Intelligence
Know which customers are leaving 30 to 90 days before they do, and what to do about each one.
10–25%
reduction in voluntary churn within 12 months
Cross-Sell & Next-Best-Action Recommendations
One recommended action per customer, grounded in their own data, delivered where your reps already work.
5-15%
lift in cross-sell revenue per customer contact
Dynamic Pricing & Margin Recovery
Recover 1-4% of gross margin from the pricing decisions you already make, without losing a single unit of volume.
1-4%
gross margin recovered without volume loss
Let's talk about Sales Prioritisation Engine.
30-minute call, no slides, no obligation. We'll tell you plainly whether this is the right fit for what you're trying to do.
Cookie preferences
We use essential cookies to run the site. With your permission, we also use analytics cookies to understand what content helps visitors make better data and AI decisions.