Quantifying Successful Salesforce Adoption

A Salesforce Adoption Scoring Algorithm and Case Example

In my first article, I outlined practical steps leaders should take to measure and drive Salesforce adoption. Here, I’ll give an example of an adoption scoring algorithm to simplify the measurement of adoption across an organization. It’s important to have a single metric (Adoption Score) that captures your definition of successful Salesforce usage across your teams.

4 Steps to Calculating your Adoption Score

Step 1: Decide on a few metrics that measure desired Salesforce behaviors

Want to see more follow up activities from your sales team? Look at number of calls per week or days between client connections. Accelerate opportunity close times by evaluating how fast a Salesforce opportunity moves through your pipeline.

Step 2: Define a relative priority for each metric

All KPIs aren’t created equal. If you value close times twice as much as the number of emails your team sends each week, give it a higher priority in your algorithm. With 5 KPIs with equal weighting, each KPI is scored 20 out of 100. Redistribute the 100 points to the KPIs in other ways to increase or decrease their priority.

Step 3: Set up Salesforce to do the math

Create a report to collect and calculate the scores based on your KPIs and relative priorities. This is easy to do with a custom report and some calculated fields.  Run the report in a test environment and do a gut check on the results: Does the report highlight your top performers? Make some tweaks if necessary and make the report available to your leaders.

Step 4: Build a wall of fame!

Using a dashboard, evaluate and rank sales team members using their performance against the calculated adoption score: top performers at the top, bottom performers at (you guessed it) the bottom.  Let the competitive nature of your sellers take over and watch the dashboard change over time.

Example: What does “adopted” look like for us?

Our Sales Cloud business case assumes we’ll increase revenue by 10% a year and cut our sales close times by 20%, from an average of 10 weeks down to 8 weeks. What seller behaviors are needed to hit these measures? We need to meet with our target customers more frequently, on average once each week. In addition, we should see our salespeople moving deals from Sales Stage 2 (Needs Analysis) to Stage 3 (Propose) in 15 days or less.

We’ll use three metrics to measure adoption:

1. Calls per month with a target of 4
2. Average duration from the start of Needs Analysis to the start of Propose in days, measured on a running 6-month basis, with a target of 15 days
3. Open pipeline in the Propose stage or greater, with a target of $200,000

Here’s how our algorithm works:

In this example, the ‘Points Available’ column drives the relative priority of each metric: more points mean a higher priority.

Now let’s see how our team performed using this Adoption Score. Here, we’re using a mocked-up Salesforce report using data from the prior month:

Notice that although Amit had a lower pipeline, his hard work meeting with his clients and quickly moving deals through the pipeline results in a higher score than Amy despite her pipeline value. It’s important to run some real data through your algorithm to confirm that the calculated scores align with your definition of success. If you look at this example and think that Amy should have earned a higher score because her pipeline is greater, the relative priority of Pipeline versus the other KPIs needs adjusting. 

Look out for part 3 of this series where I will demonstrate how to create this dashboard in your own Salesforce instance.