Assisted decisions: yes or no?
How to improve sales managers' decisions? What is the potential of data analytics in the decision-making process?
Imagine a world where several sales managers gather in a meeting. One by one they are being asked to support a million dollar deal, with limited time to decide whether the deal is feasible, if it fits with the growth strategy and if the offered price is appropriate.
Sounds pretty stressful, right?
It is however a reality and that is why one of my client decided to improve the sales process and release pressure on his team by using data analytics.
How an algorithm can release the pressure
In the past, the client's deals were decided by looking at a single cell on a spreadsheet. Meetings were chaotic, without context or information on previous deals. Due to missing context, numerous deals where repeatedly discussed and the number of client proposals was ever growing. Today with the algorithm put in place, the decision making is strongly improved, and the sales meetings are more structured and less repetitive.
Moving away from excel sheets
The dashboard indicates which deals need a yes against which period of time. The client’s specific KPI’s are reported and an analysis on the historical data feeds the ‘win’-opportunity. By making this information transparent, the context of the decisions is no longer steered by a single excel sheet cell. The dashboard and algorithm support sales managers in their estimations on winning or losing the deal – whilst being able to spot the patterns of the past. This implementation improves the transparency on the sales pipeline, the quality of the deals and the meetings by:
- Setting priorities
- Assisting the decision making process (based on historical data, you can decide to approve or ignore a deal)
- Giving context (decisions are no longer based on a single cell)
In the future we will be assisted in our decision-making, everywhere
The future is looking bright: not only will my clients' sales meetings be more structured and based on precise and relevant information, the dashboard approach also provides the opportunity to make future meetings more efficient: by only discussing outliers in approving calls, 50 minutes per meeting are saved.
Even more impressive are the savings a company can make thanks to better business and sales decisions being made through these data-driven dashboard insights. Losing deals be saved, as well as efforts and resources that would disappear in less-attractive deals. What remains are the efforts, deals and investments to be made in the most attractive deals – resulting in high win rates, higher profits and lower sales costs.
The beauty of this model is that it is applicable in any sort of business: from sales meetings to court meetings and law suits, from investment proposals to doctors’ appointments and surgeries.
In my next post I’ll explain why data partnerships are the new way to go.
This is one article in a series of publications on your organizations’ Data Analytics strategy. The series, called ‘Moving beyond point solutions and pilots in Data Analytics’ addresses several challenges. These are challenges that organizations most experience when they want to upgrade their Data Analytics from point solutions to strategic, data-driven and coherent governed activities.
This series has been written by my team at Atos Consulting: Carline Nauta, Erik Schroten, Tom Konings, Gerli Meijerink and Sjoerd Rieske.