The analytical heart of your quote management software


“If you can’t measure it, you can’t improve it.”
— Peter Drucker

It wasn’t too long ago that your average sales manager had one metric for every proposal sent: was it signed or not? And while getting a yes or no answer is a good thing, getting to the why, when, and how of why it was signed or not is the only thing that will help you improve.

With quote management software, you not only get this info, but you get it in real-time.

In today’s post, we’ll @@take a deeper look at some metrics that move a quote from signed to unsigned@@, and how those metrics help you build repeatable processes that you can improve on over time.

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Know your measurements

Before you even begin to dig into the numbers, ask yourself what numbers you’re looking for. Are you looking to remove bottlenecks? Increase throughput? Identify top performers and/or weed out low performers?

Ask yourself if the improvements are going to be people-driven or process-driven. If you’re using quote management software already, you know that while it enables massive business process improvements — cleaner proposals, consistent product  and pricing configuration, and more — it’s also only ever as good as the information your people put into it.

So first ensure your sales reps are using it the same way and using it consistently. You can’t get a good sense of the metrics if it’s “garbage in, garbage out.”

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Mountain-high level analytics

When you dig into the numbers, you always dig down. Start at the 30,000 foot level of your overall pipeline: What percentage of ALL quotes close? What’s your average closing time? How often are you adding new accounts across your organization?

Knowing your enterprise-wide averages is critical to establishing a baseline for how likely the average quote is to be signed, and how long it typically takes. Your proposal management software typically provides this data via a dashboard that’s easily understood by sales leaders.

Once you have this high-level info, it’s time to get granular.

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Granular level analytics

Step one: you know what you want to measure and the team is using your proposal software consistently across the enterprise.

Step two: you’ve done a high-level analysis of where you are and established a baseline for proposal success rates and turnaround times.

Step three: using your quote management software to analyze individual performance.

Most salespeople are thrilled to have access to sales analytics: What worked for me on that last quote I closed? How can I roll that learning into my current pipeline? How do I improve?

Your solution should be able to provide reports on group productivity, agent productivity, and the details therein. And it’s this level of granular data that helps you spot and fix/remove bottlenecks. Because you can’t make overarching, “mountain-high level” fixes to your sales process without making improvements at the granular level.

The takeaway: know what you want to measure before you go in, analyze macrodata to establish baselines, and dig into the details to make improvements.

If you'd like to learn more about this topic or see IQX for yourself with a free demo, contact us.