TLDR: If you believe in Net Promoter Score® (NPS) as a key measure of customer loyalty and future purchase intent (as I do), then you’re naturally drawn to analyzing your survey results. But are we doing it all wrong?
Update: the vendor landscape described here has evolved considerably since writing this. But the basic concepts still apply.
In a prior blog, I talked about ways to drive revenue growth with product usage data. Including reducing churn, spotting up-sell opportunities, increasing trial conversions etc.
In a follow-up blog, I discussed the four dimensions of product usage that you need to analyze:
- Frequency: how often a user interacts with your product
- Features: which features are used
- Volumetrics: how often a feature is used, or a data record is created
- Configuration settings: ways a user configures the app to suit their needs
By now you’re probably interested to unlock the value of product usage data for your company. Which begs the question: where do you get this data in the first place?
There are three sources of usage data. Generally speaking, usage data comes from:
- Clickstream data
- Log events
- Database queries
We’ll review each in succession.
Clickstream data is generated by an end-user’s interaction with your product interface. For example, logging in to a web browser. Or performing an action in your mobile app.
This type of usage data can help you understand frequency of use (usually but not always) and coarse-grained feature usage, depending on how you “tag” usage events from the browser.
Several products are good sources of clickstream data for your browser-based app:
An aside: why not Google Analytics for product usage? Unlike these other tools, Google’s terms of service prohibit you from collecting user-specific data of any sort. Thus, it becomes very difficult to understand which user or even company is accessing your application and how. Stick to the products above.
Getting usage data for Mobile apps can come from several of the packages above, plus some that are purpose-built for mobile:
- Flurry. Note that Flurry monetizes by driving ad placements, so it’s not for everyone
Pros of clickstream data
- Easy to deploy
- Little to no engineering team involvement
- Good for basic engagement metrics
Cons of clickstream data
- Not detailed enough to reveal important features and user segments
- Brittle to maintain
Depending on how well your engineers have instrumented your server-side code, they may be generating usage data as log events. For example, your Web or App Servers might be generating Apache logs that contain details about the user’s actions, especially feature usage.
Pros of log data
- Log events can be very specific and accurate in depicting feature usage, compared to page-level clickstream data from a browser
Cons of log data
- Log events need to be parsed, which can be challenging if you’re doing it for yourself in a database or Excel file
- Log events can contain types of events that aren’t meaningful to you, because they describe system behaviors not user behaviors (think error logs)
- Your engineers need to be involved to do a good job of log instrumentation
On the server side of your application is some sort of database (such as MySQL, Hadoop, MongoDB, etc.). Each user action may have a corresponding “transaction” or record in the database that forms a picture of usage. For example, if a user started a new “Project” in your Project Management app, then that record was created in the database at a specific time by a specific user.
These records can be queried from your database to produce events or counts of events (such as daily summaries).
Other functions of your application may behave as “set it and forget it” where your app is automating processes without requiring a user action each time. In this case, usage events are generated even though the user hasn’t logged in lately. Database queries may be the only means to collect this type of usage event.
Pros of database queries
- The most comprehensive picture of usage
Cons of database queries
- Your Engineering or Operations team has to retrieve the data for you
There’s no “silver bullet” to getting usage data.
In many companies, you have easy access to one type of usage data and not the others. And, no single source of usage data depicts your application’s usage in a comprehensive way.
Think of it as a journey. It’s best to get started with the data on hand. As you learn to make sense of it, and drive business results, you’re armed with the justification to get other types of usage data. Sometimes this means further instrumenting your product. In other cases, it’s simply about getting another team to help you access their data.
But the journey is well worth the effort, because usage data is the foundation of understanding the health of your customer.
Customer analytics for SaaS companies is about reducing churn and increasing lifetime value of your existing customers. While every company looks at acquisition metrics, SaaS vendors must do more because the customer lifecycle begins at the point of web conversion.
A customer that enrolls in a trial or subscription is beginning their relationship with your company. However, your SaaS offering will need to be constantly delivering the value your customer is seeking or you will fail at customer retention.
How do you know if you’re meeting customer expectations?
There is a recent body of research called behavioral economics. In a nutshell, it states that what a customer does, versus what they say, is a truer measure of their satisfaction and intent. This means that a customer who says they are happy in a survey might not actually be happy. Even worse is the customer who never tells you they’re unhappy, and they simply churn.
Customer analytics unlocks the power of this research by enabling SaaS vendors to understand their customers’ actions, focusing on product usage combined with everything else you can know.
How to get started? Make an inventory of all of the data you have about your customers, and ways it can be used.
- Traffic and volume metrics: Attributing customers to the channel that they came through will enable you to find patterns of valuable or less valuable customers.
- Product usage: Logins, modules used, processes run, usage frequency. This tells if your customers are using your product and what parts of it they are engaging with.
- Sales CRM: (when you have a sales-assisted acquisition model)
- Support CRM: Issues your customers needed help with. Use it to spot your customer’s frustration with your business.
- Purchase & product plans: What your customer purchased and how much they paid. Identifies customer tiers, potential up sells, upcoming renewals and payment status.
- NPS ® (Net Promoter Score ℠): Surveys taken on a subset of your customers to gauge satisfaction. Allows your customers to tell you qualitatively vs. quantitatively if they are happy with your service.
- User comments: In emails, on support forums, or even Tweets. A second data source of customer sentiment that can be tied with NPS and Support CRM.
- Customer intelligence: Key changes within your customer’s organization that could signal potential changes in the stability of your relationship.
Most SaaS companies review metrics from each of these sources independently. Customer Analytics happens when we analyze our customers’ data across multiple touch points. This reveals valuable insights you would have no way identifying if the data remained apart.
Look across all of your customer data, and consider what it means across the lifecycle. The result? Better products and increased revenue.