Product analytics is at the core of the success of a product-based business. It is all about how the users are using the product.
Product analytics allows you to understand how a customer feels about a product. It can then be used by product teams to develop improved digital experiences for the customers. Product analytics provides critical information to optimize performance, diagnose problems, and correlate customer activity with long-term value.
“Is the product working?” “Where is the drop-off happening from the ideal flow?” “What is the most important feature?” “What drives customer success?” These are some examples of the type of questions that can be answered using the simplest form of product analytics.
Why is product analytics important?
Earlier, companies depended heavily upon guesswork and experience when creating and marketing their products and services. However, with the advent of analytics, businesses have learnt, or learning, to optimize their decisions based on data and insights.
Product analytics enables you to understand how the item concerned is performing by providing detailed insights. Companies can understand if customers like the product or not. More importantly, product analytics helps in understanding the problems in your product, if any.
Businesses can utilise this method to address issues such as low consumption or poor conversion rate by providing insights. Companies can then use these insights to improve their product. With proper usage of product analytics, companies can develop better ideas and improve customer satisfaction.
Products analytics can answer various kinds of questions, including details on trends, analyzing feature adoption and customer engagement. Since there are various types of tools to perform, different functions, companies need to keep their goals in mind. Choosing the correct tool will get use the best results. Sometimes, you may need to use more than one tool to achieve your goal.
Tools to use
Here are few tools that companies use:
- Cohort analysis: This enables a company to break its users into different segments, or cohorts, based on similar characteristics. Doing so allows the companies to identify customers who are high-value or those who can become one. It also helps them understand how customers react to different products and ascertain how to retain them.
- Retention analysis: This helps in understanding how many of the customers are returning to the product over a period of time as determined by the analyst. While cohort analysis shows you how the users are interacting with a product, retention analysis displays the aggregate retention rate for a period of time.
- Trends analysis: This is one of the most commonly used analysis methods. It assists in visualizing whether the adoption rate of a new feature is increasing or decreasing over time.
- Churn Analysis: This helps in figuring out how to fix the churn rate – the measurement of the number of individuals or items moving out of a collective group over a specific period. Other analyses methods can show the companies how many individuals are moving out of a group. But the Churn analysis reveals why people are dropping out, giving out an idea what problem needs to be fixed.
If you want to learn more about marketing analytics, you can read this excerpt from the book: Behind Every Good Decision – How Anyone Can Use Business Analytics to Turn Data into Profitable Insight.
If you are in Product or part of a Business Unit (BU)
The Chief Product Officer (CPO) or head of a Business Unit faces similar challenges while leading the product organization or a Business Unit. Again, he or she can use many of the same techniques and strategies.
|1. Identify new products and features for the various customer segments—understand consumer needs per segment and deliver targeted products:|
||Sizing and Estimation|
|2. Prioritize which product features to include. This can be determined by understanding the expected business impact||Sizing and Estimation|
|3. Optimize the customer experience to increase product usage by motivating consumers to take action like buying a product or signing up|
In the future, in the next set of series beyond the 101, we will talk about predictive analytics, machine learning, and deep learning for product analytics. But it’s important to note, that is not where you begin.
In the next blog in this 101 series, we will look at Customer Analytics.