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Bring Your Product to a Higher Level with Product Analytics
Product is the key to an organization's business success, while a good product can attract customers, innovation helps you keep your customer engaged with high satisfaction.
However, when it comes to product updates, you may need to figure out “What are the actions before conversion?”, “Where are the drop-off?”, “Is my feature popular in all my markets?” Since these questions can help you improve the quality of products for customers, you cannot completely answer them fast without product insights.
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Why should you not neglect Product Analytics?
Product analytics refers to capturing and analyzing quantitative data through embedded tools that record how users interact with a product. This type of usage data can include the most frequently accessed features of a product, the drop-off points, and a map of each user’s journey through the product.
Every product's goal is to solve a problem and make life easier for customers. Using product analytics helps raise the knowledge of your customer journey and their behavior to ensure the product is used effectively with the minimum point of friction. However, it’s also the product team's responsibility to listen to what customers say and make the product more useful.
Considering the benefits of these analytics, the State of Product Analytics Report in 2020 evaluates significant improvements that will impact your Cost Per Action (CPA) and Lifetime Value (LTV) by using self-service Product Analytics are:
- Improvement in conversion rates: 32%
- Improvement in user engagement: 31%
- Improvement in user retention: 30%
Organizations interviewed have shown significant benefits from product analytics over three years.
- Increased ROI by improving customer LTV: 298%
- Productivity savings with self-service capabilities (faster funnel, retention, segmentation analysis): 465M
- A minimum cost of an alternative approach: creating and maintaining an in-house data analytics solution: 1.1M+
Product analytics provides critical information to optimize performance, prioritize product roadmap, diagnose problems in the customer journey, and correlate customer activity with long-term value customers. Approaching product analytics service providers is one of the possible ways to stop the dependencies with your data team, which sometimes can be the bottleneck. Also, they can determine the optimal pricing for a solution that aligns with your business model and growth strategy.
What should you expect from your Product Analytics?
Here are some advantages that product analytics can bring to your business:
- Fast to Value: To have insight quick and self-service deep-dive
- Prioritize: identify what is working or not as your application feature
- Unblock your team: No data team bottleneck; wait for them for the new report just for exploration or impact of new features.
- Measure Impact: measure the interest and behavior after new releases
- Detailed insights: understand how the features perform
- Know your users: Companies can know if customers like the product or not. More importantly, product analytics helps in understanding the problems that break the customer journey that creates drop-offs or lack of engagement in your product, if any.
Moreover, the proper product analysis will bring your product towards the successful value aha-moment, such as:
- "a document signed in less than 24 hours" - DocuSign,
- "Start a new conversation" - Rakuten Viber,
- "Client book trip / Partner accepts booking" - Vrbo.
Which reports can be used for Product Analytics?
Product analytics can answer various kinds of questions, including details on trends, analyzing feature adoption, and customer engagement. Since multiple types of reports exist to perform different functions, companies must keep their goals in mind. Choosing the correct feature will get us the best results.
Here are a few reports that companies can 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 high-value customers 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 understand how many customers return to the product over time, as determined by the analyst. While cohort analysis shows how the users interact with a product, retention analysis displays the aggregate retention rate for a while.
- 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 analysis 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 an idea of what problem needs to be fixed.
The above analytics reports are used in usual cases. However, when experimentation is performed, or signal insight is provided, the data is deposited into an ad-hoc account, and it may take your data team a while to respond:
- Experiment analysis: This will help you to control your A/B testing, you can design a template report and make some deep dive easily to understand what is not working in your beta features. Typically, this report is ad-hoc and time-consuming, and as with all ad-hoc reports from the data team, it contains accuracy risks.
- Signal Analysis: every product have a goal, but do you know what your customer doing the most before achieving that goal? Often the way that your customer converts your goal is often wrongly ignored, as it is important to understand what the most frequent features are leading to that conversion.
Many types of reports can be done as templates, but deep-dive ad-hoc reports often give a burden on your data team and often end up with quality issues. Therefore, using Product Analytics solutions can provide you with a data model and strong data semantics with a lexicon feature.
How can Product Analytics support your business growth?
Now you have complete analytics to determine what is working and what is not, as well as how customers are navigating your application. But is that sufficient to improve your Monthly Active Usage (MUA)?
That is wrong to think that acquisition for growth is only based on spending, but the MAU and viral factors are crucial too. The viral factor depends on customer satisfaction. That is where the quality of your application is; therefore, your product must be exceptional.
Besides, MAU is driven by engagement; your growth manager can help with a campaign to transform the terrible or unsmooth experience into a delightful experience.
So, the recommendation here is to find out where are your points of friction for your customer and where they are dropping off. Analyzing it and fixing it will improve your customer experience.
What the business will need is a strategy on the use case, which can be easily acquired from utilizing product management analytics. These analytics helps optimize the process so customers can have more meaningful experiences. Moreover, if it happens to have bugs, you can also find the group of users impacted before they complain and make a commercial gesture.
Just think about Product analytics!
Anything that connects customers to value is the product experience. Product analytics ensures that the customer needs are met with your product and that your product generates more significant revenue and profits.
KMS Solutions has long been recognized as a trusted partner in providing Product analytics solutions and other services to enterprise organizations across industries. We also keep up with the latest technologies to ensure the solutions for clients are innovative and suitable for the market and regulation changes.
Written by Tuan Nguyen, Data & Analytics solutions engineer @KMS Solutions, Inc.