{"id":9899,"date":"2017-10-19T11:31:25","date_gmt":"2017-10-19T09:31:25","guid":{"rendered":"https:\/\/chartmogul.com\/blog\/?p=9899"},"modified":"2023-08-10T17:11:17","modified_gmt":"2023-08-10T15:11:17","slug":"tips-tricks-segment-saas-metrics-product-usage","status":"publish","type":"post","link":"https:\/\/chartmogul.com\/blog\/tips-tricks-segment-saas-metrics-product-usage\/","title":{"rendered":"Tips & Tricks: Segment your SaaS metrics by product usage"},"content":{"rendered":"

Segmentation in ChartMogul is completely flexible, allowing you to get the view of your data you want to see. One key application of this is discovering how product usage impacts metrics such as Customer Retention Rate, Trial-to-paid conversion rate and Customer Lifetime Value (LTV).<\/p>\n

Segmenting your metrics by product usage enables you to measure the correlation between user actions in your product (e.g. feature usage, login frequency, etc) and subscription metrics such as MRR. This also\u00a0lets you compare how certain features or actions impact your conversion and retention rates.<\/p>\n

This article shows you how to achieve this, and is split into two\u00a0parts:<\/p>\n

    \n
  1. Tracking product usage<\/a><\/li>\n
  2. Segmenting metrics by product usage<\/a><\/li>\n<\/ol>\n

    Tracking\u00a0product usage<\/h1>\n

    Note: Unless you’re already tracking product usage\u00a0on your website, you may need a developer to help you set this up.<\/em><\/p>\n

    How to capture product usage data<\/h2>\n

    You can track product usage with a number of analytics tools such as Kissmetrics<\/a>, Intercom<\/a>, Mixpanel<\/a>\u00a0(and many more).<\/p>\n

    By adding product usage\u00a0attributes to customers in\u00a0ChartMogul, you can see the impact of product usage and features on metrics such as MRR, LTV, Customer Churn rate, and Net Cash Flow.<\/p>\n

    You may already be tracking customer product usage in other software such as\u00a0Segment<\/a>\u00a0or\u00a0Intercom<\/a>. In which case, you could use\u00a0Zapier<\/a>\u00a0to send product events directly to ChartMogul as customer attributes<\/a>.<\/p>\n

    \n

    Tip: Learn how to add attributes to your customers<\/a>\u00a0in the ChartMogul Help Center.<\/em><\/p>\n

    \n

    If you’re not already collecting customer-level product usage,\u00a0you’ll need some help from a developer to start tracking customer attributes. You can track product usage using Ahoy.js<\/a>, Purser.js<\/a>, or with a custom script. You can add attributes to customers using the ChartMogul\u00a0Enrichment API<\/a>.\u00a0You might find it useful to refer to the following article:\u00a0Tracking marketing attributes with Purser JS<\/a>.<\/p>\n

    Pushing the data to ChartMogul<\/h2>\n

    Switching to hypothetical mode, lets say\u00a0you run\u00a0a subscription video on-demand (VOD) platform like\u00a0Netflix and\u00a0you wanted to answer the following question:<\/p>\n

    Who is more likely to churn: Customers who watch House of Cards, or customers who watch Daredevil?<\/p><\/blockquote>\n

    To do this, you could track whether customers had watched either of the shows and push add an attribute into ChartMogul to represent that. For example, if a customer watched House of Cards, you could add the attribute ‘watched house of cards = true’<\/strong> to the customer in ChartMogul.<\/p>\n

    \"\"<\/p>\n

    Once you’ve added an attribute to a customer in ChartMogul, it will appear on their customer profile, and in the ‘All Filters’ drop-down menu:<\/p>\n

    \"\"<\/p>\n

    Segmenting metrics by\u00a0product usage<\/h1>\n

    Once you have added product\u00a0usage attributes\u00a0to your customers, it’s time to filter and segment your metrics in ChartMogul and get a view of your customers that’s specific to in-product actions.<\/p>\n

    Viewing trial-to-paid conversion rate by product action<\/strong><\/h2>\n

    You can analyze and improve your Trial-to-paid conversion rate by finding common in-app actions usually taken by leads who converted to paid. These actions are effectively conversion indicators you can use to optimize aspects of your product, like the onboarding experience.<\/p>\n

    Using the method described above<\/a>, add onboarding-related boolean attributes to your leads and customers, such as ‘created_account’<\/strong>, ‘invited_team_mates’<\/strong>, ‘imported_data’<\/strong>. These will be specific to your product. For example, Twitter might add the attributes ‘sent_first_tweet’<\/strong> and ‘followed_5_people’<\/strong> to their users.<\/p>\n

    To see if a product action leads to a higher trial-to-paid conversion rate, follow these steps:<\/p>\n

      \n
    1. In\u00a0ChartMogul<\/a>\u00a0go to your Trial-to-paid conversion rate chart<\/a><\/li>\n
    2. Click\u00a0Add filter\u00a0<\/strong>and select Invited team mates<\/strong>\u00a0> is > true<\/strong><\/li>\n
    3. Click\u00a0New segment<\/strong>\u00a0and then click\u00a0Add filter<\/strong> and select\u00a0Invited team mates<\/strong>\u00a0> is >\u00a0false<\/strong><\/li>\n<\/ol>\n

      In the example below we can see that leads that invited team members\u00a0were almost twice as likely to convert to paid. By adding onboarding attributes specific to your product, you can learn which actions are most frequently associated with leads that convert into paying customers.<\/p>\n

      \"\"<\/p>\n

      \n

      \"\"<\/h2>\n

      Viewing Customer Lifetime Value (LTV)\u00a0by\u00a0feature usage<\/strong><\/h2>\n

      You can see which features are associated with a higher LTV to drive future product development. If customers that use a particular feature set have a much higher LTV, this\u00a0might indicate that expanding on the feature\u00a0would increase revenue. Alternatively, driving\u00a0wider adoption of this feature set might increase the LTV for your wider customer base.<\/p>\n

      As before, using\u00a0the method described above<\/a>, add feature usage related\u00a0attributes to your customers. For example, Twitter might add the attributes\u00a0‘number of times tweeted’ (integer) and ‘heavy user of direct messages’ (boolean) to their\u00a0users.<\/p>\n

      To see if feature usage\u00a0is associated with a\u00a0higher LTV, follow these steps:<\/p>\n

        \n
      1. In\u00a0ChartMogul<\/a>\u00a0go to your\u00a0LTV<\/a>\u00a0chart<\/li>\n
      2. Click\u00a0Add filter\u00a0<\/strong>and select (e.g.) Number of times used stories\u00a0> greater than\u00a0>\u00a010<\/strong><\/li>\n
      3. Click\u00a0New segment<\/strong>\u00a0and then click\u00a0Add filter<\/strong> and select (e.g.) Number of times used stories\u00a0> equal to or less than\u00a0> 10<\/strong><\/li>\n<\/ol>\n

        You can also define whether a customer is a heavy user of stories as a boolean (true\/false) attribute.<\/p>\n

        \"\"<\/p>\n

        In the example below we can see that customers\u00a0that are heavy users of the Messenger feature have a higher LTV than\u00a0customers\u00a0who use the Stories feature. By driving for a higher feature adoption of the Messenger feature, it’s possible that the\u00a0LTV for your entire\u00a0customer base would increase.<\/p>\n

        \"\"<\/p>\n

        What we learned<\/h1>\n

        In the examples above, we’ve seen that:<\/p>\n