Searchlab Revenue Optimization
This is diagnostic work on your existing funnel. We measure conversion at every stage, time in stage, sales velocity and win rate by segment, then rebuild the specific steps that are costing you deals. No new channels, just better economics on the pipeline you already generate.
Optimisation starts with arithmetic. Sales velocity is opportunity count multiplied by average deal value multiplied by win rate, divided by sales cycle length. Four inputs, and each one has a different fix. Teams that chase more leads when the actual constraint is a 41 day proposal stage spend money to make the problem larger. We calculate the baseline first so effort goes to the binding constraint.
Then we look at stage exit criteria. Most pipelines have stages defined by rep sentiment rather than buyer behaviour. Discovery becomes anything after a first call. Proposal becomes anything with a document attached. We rewrite each stage around an observable buyer action, such as budget confirmed by an economic buyer or a security review scheduled, so stage position predicts something.
Conversion by stage exposes the leak. If lead to MQL is 22 percent and demo to proposal is 61 percent but proposal to close is 14 percent, the problem is pricing, competitive position or a missing champion, not lead volume. We segment those rates by source, deal size, industry and rep to separate a structural problem from an individual coaching problem.
Fixes get tested rather than announced. That means one change at a time where volume allows, a defined measurement window, and a written before and after. Typical interventions include tightening qualification criteria, rebuilding the discovery call structure, adding a mutual action plan to late stage deals, or removing a stage that adds delay without adding information.
Everything in Revenue Optimization
Funnel conversion model
Stage by stage conversion rates for the trailing four quarters, segmented by source, deal size and rep, with the baseline documented for later comparison.
Sales velocity calculation
Your current velocity broken into its four inputs, with a sensitivity view showing which input moves revenue most for a realistic change.
Stage exit criteria document
Rewritten definitions for every pipeline stage, each tied to an observable buyer action, an owner and a maximum time in stage.
Leakage diagnosis
A ranked list of where deals are lost or stall, with the evidence behind each finding and an estimate of the pipeline value affected.
Qualification framework
A qualification standard fitted to your sales motion, whether that is MEDDICC, BANT or a hybrid, with the CRM fields to capture it.
Time in stage monitoring
Automated flags on deals exceeding their stage threshold, routed to the deal owner and surfaced in the weekly pipeline review.
Win and loss review process
A structured template and cadence for capturing why deals close or die, coded into reportable reasons instead of free text notes.
Intervention test plan
A sequenced list of process changes with the metric each one targets, the measurement window and the criteria for keeping or reverting it.
The process
Baseline the funnel
We pull historical deal data, calculate conversion and time in stage for each step, and compute current sales velocity so every later change has a comparison point.
Diagnose the constraint
Segmented analysis plus interviews with reps and a sample of lost deals isolates whether the drag is qualification, pricing, stalled mid funnel or late stage decision risk.
Rebuild the weak stage
We rewrite the criteria, scripts, fields and checkpoints for the specific stage causing the leak, then update the CRM and brief the team on the new standard.
Measure and iterate
Changes run for a defined window, results are compared against baseline, and we keep, adjust or revert. Then we move to the next constraint in the ranked list.
Questions about Revenue Optimization
How much historical data do you need?
Four quarters is ideal because it covers seasonality and gives enough closed deals for segmentation to mean something. Two quarters is workable for faster moving sales cycles. If your pipeline is very low volume, we lean more on qualitative deal reviews and treat the quantitative read as directional.
Is this useful if our sales cycle is long?
Yes, though the feedback loop is slower. With a nine month cycle you cannot wait for closed won data to judge a change, so we track leading indicators instead: stage progression rate, meeting to next step conversion and time in stage. Those move within weeks and predict the later outcome.
Do you retrain the sales team?
We brief and enable rather than run a full sales training programme. That means walking the team through the new stage criteria, qualification standard and call structure, plus a written playbook. If a deeper skills gap shows up in the diagnosis, we will say so rather than paper over it with process.
What if the problem turns out to be the product or price?
Then we report that. Loss reason analysis frequently points at pricing, a missing feature or a competitor advantage rather than at process. Process work cannot fix a positioning problem, and pretending otherwise wastes a quarter. You get the finding with the evidence behind it.
Ready to talk about Revenue Optimization?
Tell us where you are stuck. We reply within the hour on WhatsApp, usually sooner.
Or email Searchlabtools@gmail.com