3 analyses to define sales kpisWhat separates the modern sales leader from others? Well, for one there’s a data mindset. In my last blog post, I stressed the importance of this. Great sales organizations treat sales as a science and do everything possible to eliminate guesswork.

Part of that, of course, includes defining your sales team’s key performance indicators (KPIs). Obviously there’s a lot to consider in doing this. If you have not performed these 3 analyses yet, they’re a great place to start. Each will help inform your sales strategy, so you can ensure that both you and your team are focused on the right activities.

3 Analyses to Help Define Your Sales KPIs:

1. Customer Cohort Analysis

We all know that not all customers are the same. By dividing your current customer base into cohorts based on various metrics (such as industry and annual revenue) and then analyzing product usage and customer lifetime numbers across those groups, you can quickly and precisely determine the customers that extract the most value from your product or services.

Then once you find your customer sweet spot, or the most valuable cohorts, tailor your sales KPIs to leverage it. For example, let’s say the media industry offers you significant value. Instead of measuring conversations with prospects as one of your sales KPIs, measure conversations with prospects from the media industry. It’s a simple change that will optimize new business wins.

2. Churn/Retention Analysis

Why do companies churn? How can we effectively retain more customers over a longer period of time? These are questions every sales leader needs to ask, and they’re questions a churn/retention analysis can help answer. Start here by looking at usage information and contract details to identify trends in churn and retention rates.

This information can be immensely crucial and especially during high-pressure times like end of quarter. For example, use historical churn data to choose which current customers to focus on for upsells and renewals. Then, of course, adjust your sales KPIs accordingly to get your team focused on these high-value accounts. Rather than treating every customer the same when it comes to touches for upsells and renewals, using a data approach can improve efficiencies and retention rates without heavy sales expense.*

(*Side note: This is really important to understand. Using big data on average only puts 1% of your revenue at risk with the upside of a 40% cost reduction over time. There’s a big reward, with very little extra expense, for companies that use data creatively to drive analysis and make decisions.)

3. Pricing & Return on Investment Analysis

30% of pricing decisions fall short of the best price a company can offer. Why? Most sales organizations can’t effectively prove ROI on products. How can you justify charging a price if you can’t quantify the return that consumers will get from your product(s)?

In the same way that data can help find the “sweet spot” for customer demographics, it can also help find the sweet spot for your pricing. Look at past deal sizes and compare these sales metrics with usage statistics as well as customer lifetime data. When companies conduct analysis that combines all of these data points, they can stop assuming pricing makes sense and start justifying it with cold hard facts.

Combined with specific sales metrics that revolve around proving ROI, a compelling sales argument very easily develops from there. Imagine your reps being able to walk into their next sales pitch with the ability to back up your pricing using data analysis and ROI numbers. THAT is the power of data for the modern-day sales team.

And of course, when you update pricing, you should update any sales KPIs that include it. It might even be a good idea to implement a short-term KPI that measures something like sales proposals sent, to ensure your team focuses on getting familiar with that new pricing model.

What other analyses have you performed that should be considered when defining sales KPIs? We’d love to see your comments below.

(Source: “Big Data, Analytics, and the Future of Marketing & Sales” – McKinsey & Company)

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