Users use hundreds of apps daily and every app keeps a record of its users. Companies track user behavior to in-app user behavior. This practice generates a lot of product usage data. And today, product usage data is of great significance as it allows companies to keep a check on user activities.
Companies can use product usage data to improvise and optimize their products, contact leads and help them to upgrade their plans, etc. Analyzing product usage data provides enables companies to keep track of their leads who are using their products.
In this article, you will learn about Product Usage Data and why it is paramount to operationalize. You will also read about the ways to operationalize your product usage data.
Table of Contents
- What is Product Usage Data?
- What is Operationalizing Product Usage Data?
- Ways to Operationalize Product Usage Data
What is Product Usage Data?
Each app, website, or digital product being used by a user generates tons of data that is product usage data. This data gets transferred to the companies and provides quantifiable information about the end-users, their behavior, how much time they spend on the product, etc.
Companies break down this data to optimize their products, rebrand them, identify the flaws, etc. Generally, product usage data can include data related to usage frequency, time spent, churn rate, bug reports, customer retention, and much more. Analyzing Product Usage Data regularly is a good practice, especially when new changes or updates have been rolled out.
What is Operationalizing Product Usage Data?
Whenever a user uses a digital product, the product usage data is sent to the company servers. There the data is collected, processed, analyzed, and visualized in isolation from the rest of the company’s data. Operationalizing Product Usage Data means using this data with the company’s daily operations.
This data is then made available to different stakeholders and users of the organization based on their needs. Different departments such as Sales and Marketing, business executives, customer support services, distributors, and partners use this data to run daily activities and analyses.
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Ways to Operationalize Product Usage Data
In this section, you read different ways of how you can operationalize your product usage data. A few ways are listed below:
- Enable Sales Team to Prioritize Leads Based on Purchase Intent
- Improving the User Experience
- Deploy Appropriate Messaging Across Customer Journey
- Alerting Teams When Customers are at Risk of Churning
Enable Sales Team to Prioritize Leads Based on Purchase Intent
You should ask your sales team to pull data from product usage data gathered and sort them for users with the highest buying intent. Analyze the behavioral data of the potential customers. Contacting users with purchase intent is the heart of any outbound sales process.
It is pretty challenging to rely on the accuracy of the data and analyze it but it gives the best chance to book up a sale. Salespeople of every company prioritize their activities, sales, and expectations for selling products to organizations based on various factors. They mainly prioritize their leads based on standard criteria listed below:
- Type of industry
- Number of Employees
- Contact or Lead’s role in the company.
Sales representatives are more likely to be successful when following such order of hierarchy in dealing with the customers. But these are just the basic signals that support the sales. How Sales teams can determine which of the leads are the most valuable ones that are likely to convert to your paying customers?
This is where product usage data comes into play. The buying intent data gives you so much information and helps you distinguish the potential and non-potential customers. It helps you evaluate which customers are just searching for some topic to read or test and which customers are searching for the solution that you can solve.
You can prioritize your leads based on user behavior using product usage data. By sorting your leads from the behavior data of all the free trial users then it will make sense to prioritize leads who are closer to the point of purchase.
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Hevo Activate, the Reverse-ETL platform, automates the manual data enrichment job, directly syncing data to the destination of your choice in real-time. Unify customer & product data from data warehouses such as Amazon Redshift, Snowflake, and BigQuery to SaaS Software such as Salesforce, Google Sheets, HubSpot, and Intercom, etc., in a hassle-free & automated manner.
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- On-Demand Sync: Hevo Activate gives users the option to resume the sync or run sync now to execute data sync as per their demand.
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- Live Support: The Hevo Activate team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.
Improving the User Experience
The best and most widely used feature of your product is the pinpoints that will attract most of the customers. With the help of product usage data, you track, monitor, and identify how the users are using your product, which features they are more into, and at what frequency they are using it. All this product usage data will eventually help in acquiring more users.
Analyzing product usage data can help you identify the frequently used feature and also detect the pain points where users feel problems in the user journey to create a better user experience. It will also help in streamlining the onboarding flow.
By analyzing the product usage data and behavior of the leads online, you can generate a map of what touchpoints correlate with higher conversions. It helps in improvising the current features, graphical interface, and other aspects that will acquire more customers.
Deploy Appropriate Messaging Across Customer Journey
One of the challenges B2B companies face is sending the right message at right time at every stage of the customer journey. Using product usage data, companies can analyze their users in real-time by stage and usage patterns.
Once you integrate your CRM and Marketing tools with the product usage data then you can rapidly send appropriate E-Mails and notifications to their customers at every stage in their customer journey and enrich their user experience.
When Sales team-facing customers have access to the product usage data, they can make sense of how different leads are engaging and not engaging with your product. Product usage data allow companies to deliver richer and more contextual customer experiences. This can potentially lead to a higher retention rate and customer delight.
Alerting Teams When Customers are at Risk of Churning
For every business, retaining customers is as important as acquiring new customers to grow your business. An increase in customer retention significantly increases companies’ profits multifold. Usually, as the business grows rapidly it becomes harder to keep a check on old customers.
With the help of product usage data, you can keep track of the customers that haven’t logged in for a while and go for follow-ups. It is a great concern that your valuable customers who are giving you business weren’t logging in or their usage is dropping since the last week.
Identifying their concerns and resolving them can help companies retain their customers. Keeping your customers is cheaper than acquiring new customers, so reducing the churn rate significantly benefits your business.
In this article, you learned about product usage data and why it is essential, and how it helps companies get some useful insights about their customers. You also read about different ways to operationalize your product usage data.
Product usage data is the user behavior and how and when users engage with the digital product. Analyzing product usage data is of great significance as it helps sales teams to engage with the right customers, helps IT teams to optimize their apps, and helps companies to monitor user activities.Visit our Website to Explore Hevo
Hevo Activate will automate your data transfer process, hence allowing you to focus on other aspects of your business like analytics, customer management, etc. This platform allows you to transfer data from Cloud-based Data Warehouses like Redshift, BigQuery, and Snowflake to various Business Applications. It will provide you with a hassle-free data syncing experience and make your work life much easier.
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