Find opportunities to optimize and activities that correlate with customer success for the metrics you’re optimizing for.
There’s no one-size-fits-all answer for picking your success metrics, what you choose needs to be determined based on what your product is and what your model is. For example, Facebook’s success metrics could be the frequency of user posts or engagement with others’ content because they work on an ads-based model. An ecommerce site might determine its success based on revenue per visitor (RPV), whereas a SaaS tool might use monthly active users or weekly active teams (for a collaborative tool like a CRM).
Examples of common success metrics include:
- Revenue from purchases.
- New user signups.
- Leads generated in the last 28 days.
Take a group of users for a certain period, like the past month, and add them to a spreadsheet, along with your success metrics and other business metrics you are tracking.
Add each user as a row and success metrics or other business metrics as columns. Adding other business metrics you are tracking makes it easier to find out which ones correlate with the success metric and determine what drives it.
Use tools like customer surveys, one-on-one interviews, focus groups, and exit polls to collect qualitative data on your users and add this data to your spreadsheet.
Qualitative data helps you get a sense of the value users get from your product, what keeps them coming back, and what they like about it.
Compare data for users that converted with data for users that didn’t convert to find out what actions they took and identify striking and obvious correlations.
For example, you might find that everyone who converted in the last 12 months did X, Y, and Z before they converted.
Use tools like Data Robot and Amplitude Analytics to dig deeper and find hidden correlations.
Upload your spreadsheet with your inputs and the variables you want to predict (your success metrics) to Data Robot and build prediction models that show univariate analysis for all variables. Using Data Robot can help you identify correlations like people who shop between 11am and 12pm, use Safari, and visit a certain page are more likely to convert.
Amplitude Analytics works similarly and can track activities that correlate to success metrics. For example, the image below comes from AppCues, who used Amplitude Analytics when analyzing Pingdom and discovered that receiving 6 or more texts positively correlated to long-term retention.