5 Ways to Derive Actionable Insights from Sales Proposal Automation

There’s no question that we’re in the throes of a data-driven world. Relevant data is what sales and marketing departments rely on to understand customer buying patterns, determine the likelihood of customer churn and make sound business decisions. Yet organizations often overlook readily available data gathered from their sales proposal process that can provide insight to help them achieve peak performance.

Automating sales proposals has helped companies easily keep their content current so that it can be shared and leveraged across teams. It also has allowed companies to quickly respond to numerous and increasingly complex RFPs, in addition to helping them set standards in terms of a consistent look and feel for their sales proposals. But what happens once the RFP response is out the door?

Analytics can help organizations retrieve valuable insights from their experience to better inform future efforts. Using data-driven analytics, sales and proposal teams can improve the process, allowing them to grade their success rates, manage content, accurately predict completion times for RFPs, determine the appropriate mix of resources and more accurately determine the impact of their efforts. 

Below are five ways you can use analytics to continuously improve processes, so your team can deliver more winning proposals in an efficient, strategic and metrics-driven manner.

1. Manage content. Sales proposal content must hit the mark and resonate with each unique customer prospect. Building an analytics dashboard, whether on your own or through a prebuilt content analytics tool can help you customize, assess and refine your content by helping you visualize what is performing well, identify outdated content and show gaps where specific content may be needed. By automatically building in regular content review cycles — whether its monthly, quarterly or any other timeframe — you can be sure your content captures the most-up-to-date product information, pricing changes and other relevant data.
2. Prioritize RFP deadlines. If you find that you’re frequently up against RFP deadlines, it can sorely impact how well you respond to them. Analytics can help you determine if the problem lies with unrealistic timelines, late input from team members or overly ambitious RFP goals. Analytics can allow you to set up automated status alerts to quickly ascertain which RFPs are in the queue, which are in danger of missing deadlines and which are awaiting action pending completion. If RFP response efforts are shared across the team, this insight can help make decisions on which RFPs to prioritize and where support may be needed to complete them in a timely manner.
3. Uncover what you need to succeed through data analytics. If you’re experiencing less-than-stellar win rates, dig deep to see where the problem could lie. Using analytics to uncover patterns and see what worked well in the past, as well as what has changed, can help you get to the cause of the issue. For example, did you have the right team in place? Was your content relevant to the prospect, demonstrating your domain expertise? Was it a busy time for your company when the sales proposal was created? Examining all of these factors can help you determine the problem and fine-tune your team resources, content or strategy for future bids.
4. Use data insights to assign the best team for the job. The best RFP results come when the most qualified team responds to it. However, many RFPs have been lost because the assigned team members are simply overburdened and don’t have the time needed to give the project the attention it deserves. Analyzing your data can show you the track records, domain expertise and workloads of specific team members so you can assemble the right team for the job. You also can identify specific team members who may have been responsible for too many RFPs and are becoming overburdened, so you can reassign projects accordingly.
5. Estimate cost allocations and prove your team’s value with metrics. Estimating how long a future RFP will take, which (and how many) team members are needed to make it successful and how much it costs to submit the proposal can be challenging if there is no visibility into past metrics. This information helps you create cohesive plans for future RFPs and also helps justify your investments in automation and process improvements to the C-suite and key stakeholders. Key metrics you will need to assess include: how long it takes to respond to an RFP, the number of people involved in the process and the average hourly rate for each person involved.

Everything is a learning process and the sales proposal is no exception. Analytics is fast becoming the wise counsel behind sales proposal automation for organizations worldwide. By using data-driven analytics, sales teams can learn from past RFPs (both the winners and the losers) to ensure continuous process improvement along with more efficient management of resources, more predictable outcomes, and the solid metrics that can provide a scorecard of your successes and help land the next big win.

Toby Murdock, general manager, Upland Software, has extensive experience helping companies of all sizes turn business ideas into marketing content and strategy that resonates with customers throughout the buyer journey. Prior to joining Upland Software, Murdock was CEO and co-founder of Kapost, a B2B content operations platform, where he led a team of more than 100 employees, serving Fortune 500 companies in a variety of industries.