How data analytics is used in product 🤔
Products are pretty COMPLEX !!! Building the right products not only delights users but also brings value long into the future.
We are familiar with how building products are challenging. The product teams always try their best to ship the high performing products. Yet in today’s world where products are quickly uprooted by newer ones requires an intimate understanding of their target customers.
To have awareness of how the customers are and will be using the product becomes crucial, so that the informed decisions can be made on future releases and iterations.
This is where the process of product analytics comes to rescue.
Product Analytics What’s that ??
Product analytics is a process of analyzing how customers (users) engage with product or service. This enables to track and visualize the customer behavior and engagement data.
Organization uses product analysis to optimize customer/user experience, to identify the challenges faced by them, to make improvements in the product. The analysis requires certain kind of data. The data shall include but not limited to — average time user spends on product, most used features within products, user demographics and product market performance.
With good product analytics we can identify trends, user behavior, user engagement and their whole flow of journey with product.
Lets look at some important product analytics examples 👇🏼
💥 Trend Analysis
It is a type of reporting that looks at trends over time. It helps organization know whether feature adoption is increasing or decreasing with time. This helps us to evaluate and improve the user journey by analyzing trends and conversions.
💥 Cohort Analysis
Cohort analysis takes data from a given subset and sort it into related groups named as cohorts. The idea is that these cohorts share similar characteristics such as time, geography or size. This is helpful in analyzing the customer/user behavior within the segments/cohorts and then can tailor the offers to specific cohorts.
💥 Retention Analysis
Retention analysis helps you understand how many of your customers return to your product over a period of time. We can use the data to develop retention strategies and enhance marketing efforts to improve customer lifetime value.
💥 Churn Analysis
Churn analysis helps shows how many people are sticking or leaving the product. This type of analysis looks across time periods to determine customer retention trends and losses incurred. The goal is to run analysis regularly to check on churn and eventually reduce it.
💥 Conversion Analysis
In conversion analysis we interpret data related to any and all conversions. Conversions drives every business revenue generation and product growth. Analyzing conversion data helps us understand how business is performing on whole.
💥 User Experience Analysis
Customer experience analysis looks at end to end things, from the initial marketing efforts through ongoing retention and product loyalty. Connection with customer throughout their journey with product is crucial. The data having customer feedback and user journey event logs is helpful to draw certain analyses on customer experience.
Who uses Product Analytics within organizations ?
There are many teams and within those teams that can benefit from a product analytics solution including: Leadership, Product management, Customer success, marketing and also engineering.
In conclusion, Product Analytics expose the reality of how users use the product, and even use particular features. It gives a good picture on how you are currently doing and, in future how can you build better products.
🌀 Look at some wonderful product analytics tools : Google Analytics, Pendo, Amplitude and Heap.
🌀 I work in product management space and have used Google Analytics❣ and Pendo❣
PS: In the article i have used user and customer interchangeably. Does it matter ? Comment below 👇🏼 👇🏼
References : The whole Product community
👋🏼 👋🏼 Until next time 👋🏼 👋🏼
