Bidding strategy is one of the important strategies in the early stages of a project’s lifecycle to shape a winning bid, and ultimately project success. The quality of decision making at the pre-contracts stage can enable a business to adapt quicker than competitors to the opportunities and threats presented in the market, and it can also unlock potential to develop other intangible competitive differentiators in the business identified through team dynamics, service methodologies, and technical expertise.
Data analytics is now used across entire companies to understand the metrics behind their bid pipeline and active opportunities, including the total dollar value of their bids submitted over a period of time compared to total bids won.
However, by combining financial and non-financial data both internal and external to the business, we can provide deeper insights into relational factors behind bid success and failure. By creating a bidding profile, companies can learn more about bid performance by team, by business line and by customer and they can use this data to also benchmark against their competitors to identify patterns and outliers to help them quickly adapt and make decisions to improve their competitive positions.
What data should bidders be tracking?
Getting started with analytics is about exploring the available data to generate new knowledge and insights. For bidding companies starting on a bid analytics journey, a good starting point is to consider defining some questions and then identifying the data that you know is relevant to answering them.
- Capturing number of incoming tender opportunities and creating a digital bid register (or bid board).
- How many bids progress from open market to qualification, to bid/no bid, to number submitted, and to won / loss?
- Bids submitted and won by value, and by volume
- Win/loss ratio (won bids compared to lost bids)
- Capture ratio (won bids divided by qualified submitted bids)
- Win ratio for customers new and incumbent
- Diversity of customers
- % bids going to core customers (total bids from core customers / total number of submitted bids)
- Opportunities month on month (increasing or declining? Are there seasonal trends?)
- Mark up (or profit margin) % influence on win ratio
- % bids across business lines.
Bid teams bring incredible value to organisations and quantitative analysis allows them to put hard numbers to their successes and opportunities. Adding qualitative analysis to understand reasons for won/loss outcomes can help the business to uncover opportunities for additional training, resources or alternative teaming which can influence an outcome.
As with anything strategic, it will be the people, not the technology, who make sense of the data and give it meaning. When embarking on a data analytics initiative, it is important to remember that business intelligence is the work of the people who apply it. The data warehouse is simply the enabler to support you in the process.