Customer analytics are the tools and processes used by developers and marketers to understand how your users are engaging with your application. This article discusses the three main components of customer analytics: customer demographics, interests and engagement.
To illustrate how customer analytics are used in the context of an application, we'll refer to an example app called All Things Sports, an app that provides latest sports news, allows users to purchase memorabilia and event tickets, and includes exciting mini-games.
Before you do anything else, you should gather basic demographic information about your customers. Demographic information includes personal information, such as the user's name, age and gender, as well as information about the user's location, device type, and operating system.
Some of this information—specifically, the device type, operating system, and geographic location—can be gathered from your application, or by third-party SDKs. You can use a sign-up process or in-app prompts to capture much of the remaining demographic information.
You can use this data for several purposes. In our All Things Sports app, we could send users in a certain region information about an upcoming game or event in their area. Or, perhaps our research indicates that users in a certain age range are highly likely to purchase a certain type of merchandise; when there's a special offer in that merchandise category, we could send users in that age range a notification.
Understanding your customers' preferences goes a long way in building a great product and delighting your customers. In our All Things Sports example, we could capture information about each user's favorite sports and teams, their preferred method for receiving score updates (such as SMS or mobile push, or not at all), and whether or not they like to attend live sporting events.
Capturing this data is beneficial for multiple reasons. First, it gives you a better understanding of your users' interests. If a large number of your customers are interested in live scores, you can enhance that aspect of your app with additional functionality.
Activation refers to the actions customers have to take when they sign in to your app for the first time. For example, in our All Things Sports app, users have to confirm their email address and, and can optionally specify one or more of their favorite sports teams. Our app uses the AWS Mobile toolkit to track the number of customers who started the sign-up process, the number who completed the sign-up process, the number who specified a favorite sports team, the number of users who allow the app to know their location, and their preferred communication channels. By collecting this information, we can learn things about the activation process for our app. For example, if a large majority of users do not specify a favorite team, it could indicate that there is a user experience issue that makes it difficult to select a team. If a large number of users opt not to allow the app to know their location, it could indicate that customers do not see a compelling reason for disclosing this information.
Retention metrics measure the likelihood that the user will continue to use your app. For the All Things Sports app, we'll collect data each time a user logs in, including the date and time, the device the user is using, and the user's location. By collecting this data, we'll be able to obtain the following important metrics:
- Daily Active Users (DAUs) – The number of unique users who sign in to the application in a day.
- Monthly Active Users (MAUs) – The number of unique users who sign in to the application in a given month or 30-day period.
- Sessions per DAU – The number of sessions divided by the total number of DAUs. Shows how many times per day the average user signs in to the application.
- D1 Retention – The number of unique users who open an app after one day, divided by the number who opened it on day 0. Useful for identifying retention issues related to first impressions of the app.
- D7 Retention – The number of unique users who open an app after seven days, divided by the number who opened it on day 0. Useful for identifying retention issues related to the early experience of using the app.
- Sticky Factor – The number of DAUs divided by the number of MAUs. Shows the percentage of monthly users who became daily users.
- Retention Rate – The number of users in a cohort who use the app in a time period, divided by the number of users in the same cohort who used the app in a previous time period. Shows the percentage of users you retained from one period to the other.
- Churn Rate – The result of subtracting the retention rate from 1. Shows the percentage of users lost from one period to another.
Conversion events occur when users make purchases made through the application. In our All Things Sports app, a conversion event happens every time a customer uses the app to purchase merchandise or event tickets. In this example, we'll record the user ID, the items purchased, the product categories from which the user made the purchase, the amount paid (as well as the currency and method of payment), and the page from which the user made the purchase. This information will help us calculate the following industry-standard metrics:
- Average Revenue per User (ARPU) – The total number of users in a given time period, divided by the total revenue generated in the same period.
- Average Revenue per Paying User (ARPPU) – The total number of users who made a purchase in a given time period, divided by the total amount of revenue in the same time period. Helpful for determining the
- Average Revenue per Daily Active User (ARPDAU) – The number of daily active users, divided by the total revenue generated by the application for that day. Helpful for understanding how incremental changes to the application impact conversion.
- Average Check – The total revenue for a given time period, divided by the total number of transactions for the same period. Reduces the impact of users who make multiple transactions within the analysis period.
- Payer Conversion Rate – The number of customers who made at least one purchase in a given time period, divided by the total number of unique users in the same period.
- Lifetime Value – A measure of the lifetime revenue generated by an average customer. Can be calculated using several formulas. A common formula is ARPU × (1/Churn rate). Useful when planning for marketing and user acquisition expenditures.
- Churned Payer – The percentage of users who previously made purchases from your app, but subsequently stopped using the app altogether.
Bringing it all together
When you collect metrics from these three categories, you can answer questions that salespeople and analysts might be interested in, such as "How many sign-ups did you have last month?" or "What is your average revenue per user?" But these questions might not answer the pressing questions that you need to answer in order to grow your business. For example, for our All Things Sports app, these questions might include the following:
- Are customers who use the live scores feature more likely to purchase event tickets?
- Which region generates the highest number of sign-ups?
- Which team's fans are the most likely to purchase merchandise?
- What is the most popular merchandise product category among males aged 18–24?
By bringing together activation, retention and conversion metrics into a single analytics platform, you can easily answer these questions, and many more.
Customer analytics with Amazon Pinpoint
Amazon Pinpoint can track customer attributes, device attributes, and application usage metrics, which can help you answer the important questions you need to answer in order to grow your business. It can also help you follow up with your customers, which can further increase your company's revenue. For example, if your analysis shows that customers are leaving items in their shopping carts and then abandoning your app altogether, you have Pinpoint send a follow-up email or push notification, which could lead to a conversion.
Amazon Pinpoint customer analytics helps create an all-encompassing view of your customers' attributes and behaviors, so that you can focus on making your product great.