Find out who your best users are, how they interact with your product, how to retain them and attract more of them.
For example, ecommerce companies may better benefit from Amplitude software to analyze user behavior than other options. Companies who offer products such as web apps or mobile apps, or ecommerce companies who make most of their revenue from digital properties can get the most from product analytics, but even they have to set up different analytics tools.
Use product analytics to look for answers to business questions around two challenges: acquiring users at low-cost and retaining users.
For example, ask questions such as:
- What marketing channels are driving the best users (not just the most)?
- Are users getting stuck somewhere in our onboarding funnel
- What’s the percentage of users that signs up and completes critical actions?
- How many of those users are returning on a daily, weekly or monthly basis?
- What attributes do our best users share?
Track a user from the moment they first learn about you to the moment they become a customer to answer these questions.
Gather sufficient data to be able to extract valid conclusions - wait until you get the first 100 users before investing in advanced analytics tools.
For example, secure 100+ B2B users (companies) or 2000+ B2C users (consumers), be active on multiple marketing channels and already spend $1000+ per month on user acquisition before proceeding with analytics tool investments. Focus on qualitative data like interviews and surveys to get the answers you’re looking for when you can’t secure the minimum benchmarks mentioned above.
Run a business analysis to identify parts of your business where you’re underperforming, which could be improved with data use.
- Focus on areas where you’re performing below industry averages. For example, perhaps your onboarding funnel has a poor conversion rate or your user retention is too low. This is where data can help you.
- Set a crystal clear objective. For example, we want to figure out how to convert more of our free users to paid users to make the most of the data analysis.
Create a tracking plan in a spreadsheet before writing any code.
- Use event-driven analytics tools such as Mixpanel, Amplitude, Snowplow and Intercom to collect data. For example, playing a song would be an event for Spotify.
- Use properties alongside any event. For example, if a user uploaded a picture, you might also want to know what kind of picture it was (JPG, PNG, GIF) or what size the picture was (500px by 500px).
- Enter event and properties data in the spreadsheet.
Use a combination of 2-3 different tools such as Google Analytics, Mixpanel or Amplitude to analyze marketing traffic, channels, and user behavior, and a tool like Segment to manage them.
For example, a typical stack of tools would look something like this: Segment.com + Google Analytics + Mixpanel OR Amplitude OR Snowplow OR Heap Analytics.
- Segment to simplify the implementation process of multiple tools and send data to multiple tools simultaneously.
- Google Analytics to analyze marketing traffic, look at marketing channels and what users did before signing up.
- Mixpanel or Amplitude to understand user behavior after sign up (e.g., if they are coming back to use your product). Amplitude is a better fit for ecommerce and cross-platform products companies.
- Snowplow to manage large amounts of data across multiple teams and channels, especially when mining real-time event data.
- Heap Analytics when you lack development resources and your products are mostly on the web.
Use a funnel type report in tools like Mixpanel to understand the different steps that you want your users to take in a particular journey, such as product onboarding or an ecommerce sales funnel.
For example, the Mixpanel funnel report (or an equivalent) would look at the different actions needed to complete an onboarding, from signing up for the product, to taking a critical action which would mark them as Onboarded.
Use cohort analysis to follow the percentage of users who came back and used your product after day 1, day 2 - assess whether you’re successful at getting new users or if those who already signed up return for your product.
Most product analytics support cohort analysis reports. The key thing to keep in mind here is how you define active users. Make sure that you’re focusing on key activities and not just opening the app.
Segment your event data into valuable insights, and use messaging software to send event-based notifications.
For example, look at the Signup completed segment of your product data to see how users signed up. If 98% of them used email instead of social media, consider removing the social options altogether to improve the signup rate. Use tools such as Intercom to tailor your communication with signed-up users. Set up your messages to only be sent if users have taken or not taken a certain action. For example, user sign-up, or having completed the first of five steps after sign-up.
Choose easy naming conventions, educate your team on analytics, and consistently maintain data to avoid long-term problems.
- Choose logical and easy-to-understand names for events and properties set in products analytics. For example, if you have an event that tracks when users make purchases, call that event purchase.
- Give your entire team an analytics crash course and follow up with everyone individually as they start to generate their reports.
- Set up a Slack channel where anyone can post questions around the analytics tracking. For example, What events to use? or How to create a specific report in an analytics tool?
- Keep your team active when it comes to maintaining data accuracy.
- Anticipate tracking errors after a new product release or major product changes.