Learn how to transform your bid organisation into a data-driven powerhouse in 6 steps.
With Mark de Bruijn
What is data-driven bid management?
Within bid management there is countless information that you can process into data. If you work according to the best practices in bid management, you keep track of that data and apply analysis techniques to it. The aim of data-driven bid management is to have a more targeted approach when competing for tenders and RFPs. Moreover, through the use of data, choices are no longer dependent on feelings; you can easily demonstrate why you are making them.
"We know everything by feeling. What we want is to substantiate that feeling with reports from our system."
- Lynn Schepers, Bid Team Manager - Daan
More data to process means more reliable oversight. There are a variety of data types you can use.
Different types of data in bid management
Public data
Public tenders are specifically interesting for data management. A lot of data needs to be made public.
For effective bid management, it is crucial that you are aware of that data.
Types of data you use are:
- Government announcements: information about contract requirements, specifications, deadlines and evaluation criteria
- Award decisions: winning tenderer, contract value and which work is to be done
- Market and industry reports: market trends, customer needs and competitive analysis
- Public regulations: bid requirements, evaluation criteria and contract terms
Performance data
Everyone who has ever competed for a tender receives data on their performance. You generally get a lot more data than with RFPs, including information about your competition.
You compare this with your proposal to find out what the difference is in expertise, experience, or price. This means you know why you win or lose.
Data you use for this is:
- Award decision: name of the winner, technical specifications of the offer, customer satisfaction scores, price, quality scores and other assessment criteria that led to selection.
- Rejection interview: context and information about the shortcomings of your submission. Sales' insight into the relationship of this information to what was previously experienced during customer conversations.
- Preliminary customer conversations: context and content about the customer, sales insights.
Qualification dates
By properly logging your bid/no-bid decisions, you will learn whether the way you assess your chances is accurate. This way you can clearly analyse from your no-bids what you need to improve on to attract more registrations.
If your product falls short, you communicate this to the product department in a data report. If customer contact proves not to be good enough, you prepare a report for sales.
In-Process Data
You will receive feedback from the contracting authority per text and quality criterion.
You know which text has been entered for which criterion. If that text does not score well, you look for the reason. Questions you can ask yourself are:
1. Is there something wrong with the text?
2. Does your organisation simply not offer the winning product?
3. Was there too little prior knowledge of the customer's wishes before you started writing?
The benefits of data-driven bid management
The entire commercial department can benefit from bid management data. A lot of information is required to be publicly available in tenders. As a result, bid management offers a reliable source for the strategic development of the entire organisation. It provides the opportunity to make better-informed decisions, increasing the chance of winning tenders and RFPs.
➡ Read how Daan works with Altura.
This is how you start with data-driven bid management in 6 steps
Mark de Bruijn is clear about it. Analysing data in your process is not something that only bid and tender management teams must do, but also the entire commercial department. It is important that you start with a clear vision of the goal you want to achieve with the data. Prepare your team well.
You should view data strategy as a commercial strategy and invest in it throughout the entire commercial organisation. Tenders are just as important as regular sales deals. Moreover, bringing the two together provides a complete picture of your environment and performance.
Step 1: Objective bids
Based on your organisation's objectives, you determine what will be important in your bid management data strategy. Use these objectives and create a roadmap for your process. Research data options to see if there are applications of data that help the organisation achieve its goals. By comparing those possibilities against the goals, you determine how you will use data.
In Mark's case, the goal was clear, but not simple: increase the turnover and market share of his organisation worldwide. He did this based on 3 core principles:
- Process: ensure a process that is used for every tender or RFP and is understandable to everyone.
- Data: Base decisions such as bid/no-bid, product developments, text development and training budget on performance benchmarks, innovation relevance indicators, scoring matrices, and performance analysis.
- Tools: Reapply texts in similar proposals, without having to rewrite. Maintains a single source of truth as described in best practices for your bid management.
Step 2: Vision of data and maturity
After step 1 you have a clear overview of your goals with data. This organisation-wide vision contains data points that are broader than tender management, but are a good basis on which to build your sales and tender strategy. This is important because there must also be support from management for positioning for a concrete implementation.
The more rigorously your organisation wants to approach the data process, the bigger you can think. Otherwise, you can try to start small to convince management of the value of data with your results.
Step 3: Action plan
Implementing data successfully depends very much on the way you do it. After you have determined your data vision based on the objective, you take practical measures to implement it. Make sure there is cohesion in the plan as a whole. But add department specific data-driven decisions and automation where this is possible.
- Strategy: ensure that there is a shared vision of procurement within the entire commercial department.
- Process: record the method you use to maintain your data management. Make sure everything is documented and that responsibilities are clearly divided. For a clear process, introduce phases into your tender approach and train your team to work with them.
- Team: make sure you have enough people who have tenders as a priority. These people must then receive support from management for the tenders that have been set as a target. There must be enough capacity available from contributors to your tenders from other departments.
- Knowledge: designate owners of knowledge bases that are available throughout the organisation. Those owners make sure that these sources remain up to date with the best information.
- Tools: choose tools that suit the size, industry and process within your organisation. Take advantage of the rise of AI for automation. Start small, but look for other possibilities to utilise tools, and take note, so that you can easily find them later. .
- Data: use data for the development of your own department, or communicate it internally for the organisation's strategy.
Step 4: Execution
Work with the owners of your data implementation and get started. It is important that this team is enthusiastic and consists of different disciplines. Manage the execution per department, phase, or process.
Step 5: Evaluation
During your implementation process, you will learn a lot from the new data and processes that are available. You will also see whether the course you have taken to achieve the organisational objectives is successful. The conclusions you draw from this are important for the data strategy that you implement within the commercial department.
Good implementation requires a clear evaluation. Therefore, make sure that your goals are set SMART. Evaluate what is going well and what could be improved. In consultation with colleagues, determine what the learnings are for the future. A learning that you may encounter, for example, is:
Evaluation: with a Google forms application you cannot properly transfer data to your business intelligence platform.
Learning: Google forms are not suitable for our process; we need another application for our use.
Adjustment: you switch to another data processing software with more options for your application.
Step 6: Adjustment
During adjustment, you reflect on the evaluation phase. Connecting actions to the learnings you determined. Make sure that you assess these actions clearly, by setting up structures to evaluate the results of these actions.
Review the objectives and data vision with which you started this project. See what next steps you can take. Then decide per department, phase or process where you will start with the next steps. Only then can you move forward in your data strategy.
Because a process change like this is drastic, it is again very important to obtain support from management or corporate leadership, so that you can position yourself with greater decisiveness.
Data as a result driver in your bid management
Data-driven bid management takes a more targeted approach to a bid submission. But to really apply data properly, you have to look beyond just bid management. A good data process needs an organisation-wide application. That is why it is important that you tailor your data strategy to the organisational objectives.
Not only because this gives you a clear basis to work from. But also because, when you have approval, you know that there is support and you are developing in the right direction.
.png)
