B2B: sales offers optimisation
The company operates at the B2B market. It sells the equipment, and every sale is quite a long process.
How well we perform in terms of margin and what actions should be undertaken to improve it. While first questions looks simple, it does not have simple answer for every day monitoring. Such analytics is usually available at the end of the year, when almost all opportunities are closed or shifted to the next FY. However, for course correction, the management needs in the monitoring of the situation on a weekly basis!
Analytics perspective: Where we are now?
- Business needs in one source of truth
- All figures are precise (at the level provided by databases)
- All summarized data points are back traceable to the level of order/ opportunity/ sales manager
- Business needs in useful tool
- All data in one place
- All data flows to the tool automatically, without manual interventions
- All values are actual
- All data is cleaned on the loading stage according to business rules
- Data must be secured and access levels must vary
Predictive perspective: What and how should be changed?
- Define parameters of sales process
- Set KPIs and project them to the future
- To show, how change in sales process parameters can influence KPIs
- To investigate, what parameter values provide highest KPIs in the future
If we look to opportunity-order-sale process a little deeper, we can notice that it consists of various steps:
In time perspective, we are always somewhere between start and end of multiple processes :
The process is actually taking time. Today (at the start of 2019) we have no recognised sales. Instead, we have only projected dates of sales, opportunities at the early stages and even not-yet-opened opportunities which all have impact till the end of FY.
For this project, we made a decision to apply Monte-Carlo simulation method to generate sale-process in various circumstances. The dashboard below allows us to see past, current state and the future - all in one place. The future is simulated in the assumption of no-change in four dimensions: we sell at the same markets, the same set of equipment devices, with the same level of complectation and at the same level of price uplift (margin, or PR - price realisation).
This approach allow us to run various scenarios: we are able to adjust any of four dimensions, or all of them, and new future vision will be generated (in assumption of some market resistance and competition level).
It is not only to tool for answering "what if we change", but also the recommendation tool: the AI actually shows you the optimal combination for these 4 dimensions by execution millions of scenarios:
It is also possible to look the projections to one of the axis (in the following example - expected margin):
