By department · Sales
A forecast that shifts is rarely caused by the sales people and usually by the definitions: when is a deal a deal, and what counts as a lead. With one set of definitions and a view of the stage where deals stall, the pipeline becomes predictable instead of optimistic.
We reduce the pipeline to one set of definitions, make visible where deals stall, and turn that into management information the sales team actually uses.
The work itself is described under business intelligence and data analysis.
All three routes apply to sales. New Data Student puts an analyst on the pipeline: clean up hygiene, make the stages unambiguous and build a forecast that is reproducible: work that is clear and mainly needs doing. New Data Squad comes in when it first has to be worked out why the forecast keeps moving: is it the definition of a stage, is it behaviour, or is it pricing. And New Data Search finds the sales analyst you want in the team permanently, on your own payroll.
Usually not because the market moves but because the stages are not unambiguous: two account managers put the same deal in a different stage, and then the total does not add up. The second cause is hygiene: deals that stay open because nobody closes them. Both can be measured before anything about the process changes.
Yes, and that is usually where it starts. Building a forecast on data that is wrong produces a tidier version of the same problem. We first look at what is in the CRM, what is missing and what contradicts itself, and only then at the reporting.
Then the New Data Scan is the cheapest first step: one day on site, fixed price, and you end with a roadmap for 30, 60 and 90 days with an owner for each step. No obligation to continue. That is deliberate: if nothing worthwhile comes out of it, you hear that too.
These are indicative figures from earlier work and not a promise. What it delivers for you depends on where you are now.