Data That Drives

Leveraging proposal analytics to propel business value

Businesses, people, and connected devices create a staggering amount of data every day. Companies are racing to implement technology that can glean insights from this data and put those insights to work in their businesses. Proposal teams in particular have a substantial opportunity to use analytics to refine and inform nearly every aspect of the RFP and proposal creation process—and ultimately to help close more deals, in a more efficient way.

When companies use proposal automation software, they also collect data that has the power to shed new light on the proposal creation process and its outcome. Time spent on proposals, win/loss rates, frequency of content updates, and other levers are all valuable metrics that can be fed back into the process, educating sales and proposal teams on which prospects they have the best shot at converting. At the same time, it is easy for proposal teams to get bogged down focusing on the operational data points, such as how long a proposal took or the overall win rate. While these metrics are insightful, proposal teams should take a step back and work to pinpoint which metrics really matter from a strategic standpoint. It’s important to understand which data insights can be used to inform strategic and revenue-generating initiatives. This is one area where proposal teams can provide value within their companies.

When companies use proposal automation software, they also collect data that has the power to shed new light on the proposal creation process and its outcome.

Currently Tapped Data Insights

According to a recent survey commissioned by Qvidian, 88 percent of respondents—which included proposal and RFP writers as well as sales operations and enablement professionals from large companies—use data insights from their proposal automation software to inform their internal processes and strategies.

Respondents reported using data analytics in the following ways:

  • 65 percent consider win/loss data from previous efforts most of the time when determining whether or not to bid on a specific RFP; only 10 percent of respondents never do.
  • 62 percent are using—and finding value in—tracking the time it takes to create proposals and RFPs.
  • 56 percent are tracking the number of completed responses within the RFPs and proposals.

However, many teams aren’t fully taking advantage of the breadth of data insights their platforms could deliver. The following are ways that proposal teams can use data insights not only to shape their RFPs and proposals, but also to inform strategic decisions across departments.

The Power of Selective Bidding

With more and more RFPs coming across their desks, proposal teams are stretched thin. Team members are expected to complete proposals faster than ever before—and at any cost. This shows no sign of slowing down; in fact, 30 percent of respondents from the same survey feel that the proposal turnaround time has tightened since 2016.

For smaller companies hoping to grow quickly, failing to bid on a RFP is sometimes not an option. However, some organizations don’t realize that it’s not worthwhile or cost-effective to respond to all proposals. In some cases, completing an RFP can actually cost more money than the organization would generate if it ultimately won the business. More obviously, completing an RFP requires many different resources within an organization. Being selective and targeting those prospects that offer a high chance of winning and will also be profitable customers is a smarter strategy. Companies no longer need to rely on a gut feeling to decide which RFPs to answer.

Teams can use past intelligence to predict likely outcomes in competitive situations. For example, before beginning the bid process, proposal teams can take a look at historical data around RFPs and proposals that were developed, and then determine the win rates for similar proposals. By incorporating data from customer relationship management (CRM) systems that point to the outcome of previous proposals, teams have the opportunity to make an educated decision on whether or not the prospect is worth pursuing. Some factors to consider include who the key stakeholders were (i.e. company size, industry, etc.), what the deal characteristics were, and what content the team used to respond to the RFP.

If history points to a lost deal, it probably makes sense to save internal resources to pursue opportunities that are more prone to pan out.

Building and Inspiring a ‘Dream Team’

In many cases, proposal team makeup is a critical part of the deal response strategy. Data analytics can be used to determine which teams and individuals have a winning record so that strong people can be assigned to important pitches. There is also an opportunity to analyze individual team member expertise and understand who is historically stronger with different types of proposals for various industries, products, and services, allowing team makeup to be even more strategic and thoughtful.

Analytics can be used to track team member performance, helping managers identify star performers and those who need extra attention and support. Within their proposal automation software, managers can drill down into metrics such as how often individuals refresh content (content is typically updated by subject matter experts, or SMEs), productivity in terms of volume of contributions to proposals, adherence to deadlines, and ability to collaborate efficiently with SMEs.

Perhaps an individual has a strong track record in developing winning content for a particular line of business. This is something a manager would want to recognize and encourage. On the flip side, not keeping content up to date can derail the quality of a proposal, making it vital to pinpoint the culprits.

Identifying Core and Winning Content

By automating the RFP and proposal process, ideally, writers are freed up to spend more time on creating quality, message-rich, and up-to-date content. There is an opportunity to take this a step further and use data analytics to identify content that will resonate most with the prospect and increase chances the deal will be won. Analytics can help determine which content is used the most and which content is not used at all, helping proposal teams and SMEs to create a relevant and up-to-date content library. Proposal writers can examine which content was most frequently used in winning proposals, such as various iterations of product or service differentiation statements. Writers can discover whether case study information is a key ingredient of winning proposals and how much custom content is typically required.

One large company, a leader in the financial technology space, used analytics to pinpoint which content was most valuable to the organization and therefore warranted regular review. The company had collected content over the years, thinking more content would provide more value. The company was not curating the content, however, deciding which was most useful and successful in proposals. Often, it had multiple content elements for the same topic. By running analytics reports, the company was able to determine which content records were used by proposal managers and how many times. The results were telling. Out of a library of 96,000 records, 53,000 records created more than a year prior had never been used. Unfortunately, the company had just completed a focused effort to maintain those never-used records.

Running more analytics, the company discovered that only 4,000 of its records had been used more than five times. This was its core content. By focusing on this content, the company was able to reduce the content management workload by more than 90 percent, improving quality and efficiency.

Creating a Cross-Department Feedback Loop

Data analytics also empowers proposal teams to work across departments. They can loop in colleagues in sales and marketing by providing them with information on which messages are—and are not—resonating with audiences in different geographic locations and industries. Proposal teams can engage sales teams in data insights, giving them visibility into winning content, as well as proactively discussing which messages resonated most during conversations with a prospect.

When marketing has a hand in ensuring RFP and proposal content is strong, analytics can allow teams to focus their resources on making the most-used key pieces of content shine.

“There is a great deal of power in data points that are seemingly so simple, such as the usage of a ‘record,’ also known as an individual piece of content in the library,” said Leif Ueland, director of sales operations for Questar Assessment Inc., a K–12 assessment solutions provider focused on building a bridge between learning and accountability. “I worked for a large health care company where we had over 10,000 records in our proposal content library, but there was a very small group of core records (less than a dozen) that were consistently used hundreds of times per year. This was a data point that was very interesting to the marketing department and the sort of information that can motivate a marketing team to commit resources to make sure the necessary work happens to ensure the text and graphics in those outlier FAQs really is exceptional.”

Going beyond sales and marketing, ideally proposal-related data can also be fed back to product management and development. For instance, product teams could look at RFPs to determine gaps between current offerings and those inquired about by the prospects in order to shape future product road maps.

When marketing has a hand in ensuring RFP and proposal content is strong, analytics can allow teams to focus their resources on making the most-used key pieces of content shine.

The Future of Data Insights

Machine learning, where software becomes more intelligent over time and helps the user predict outcomes based on historical data, has already started to significantly enhance the capabilities of proposal automation software. As software becomes more advanced, it will help teams be more strategic and proactive in their prospect outreach, marketing, and lead-generation activities. Users could look at RFP data in aggregate to understand patterns and trends in the questions being asked by the companies that send the RFPs and develop outbound marketing campaigns to promote their capabilities in these areas.

Measuring the true cost of an RFP response is not an exact science today. Proposal automation software will soon make this easier, taking into account aspects of the process like the number of contributors and questions and the amount of time spent on the document, to offer a more concrete dollar amount.

Ultimately, smart use of data analytics doesn’t involve just looking backward. Rather, it entails use of historical instances to look forward and select not just an RFP or a proposal strategy but also the individuals involved in its creation. Proposal teams who do so are truly elevating the task to be a strategic, revenue-generating function—proving themselves indispensable to their businesses.


Lewie Miller is president and CEO of Qvidian, a provider of cloud-based RFP and proposal automation software. He has more than 30 years of sales and marketing experience, as well as operating executive expertise.

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