Table of Contents
What is Product Intelligence?
Product intelligence is the process of gathering and analyzing data related to a product in order to gain insights into how customers interact with it. It helps businesses identify opportunities for improvement, optimize features, and track usage trends.
Businesses use product intelligence to answer product-related questions, such as:
- What features are most popular?
- What is our new users’ time to first value?
- How can we make users more engaged with the product?
- Are there any bugs or UX issues we need to address?
- What can we do to reduce churn and improve satisfaction with the product?
SaaS companies collect product intelligence data by tracking usage (e.g., clickstream data, hours used per day), solicit customer feedback via surveys and focus groups, and test products to identify areas for improvement. Data from the product itself is collected and communicated to stakeholders automatically.
Product Intelligence vs. Business Intelligence
Although product intelligence and business intelligence have similarities, they are not the same. Business intelligence is focused on analyzing data to create a high-level overview of a company’s performance across all departments, whereas product intelligence focuses entirely on understanding customer behavior and usage of a product.
Business intelligence typically looks at things like sales volumes, marketing campaigns, customer segmentation, budgeting, forecasting, and pricing strategies. In some regards, it concerns product intelligence, but from the business side rather than the customer-focused perspective.
Product intelligence is more focused on customer engagement with a product or service. It looks at user behavior and usage patterns, feature preferences, customer feedback, qualitative metrics like user engagement, and quantitative metrics like time to first value and churn rate.
Since it’s all about product performance and analytics, it’s also the main driver of product innovation. By understanding what customers want and expect from their product, businesses are able to develop new features, refine existing ones, and roll out new “innovative” products according to their insights.
Importance of Product Intelligence
The business truisms “you cannot improve what you cannot measure” and “talk to your customers” might feel overstated. But it really is that simple.
To understand its importance, it helps to compare it to two popular strategic games: blackjack and chess.
- While blackjack is based on odds and mathematics, winning big means you got at least somewhat lucky. There’s no way to know what the dealer has, and the player acts first. If you play long enough, you’ll probably lose.
- Chess is based entirely on skill. Both players (and anyone watching) can see how the game will play out. That’s why grandmasters are able to come up with effective strategies that help them win.
With product intelligence, business is more like chess. Although it requires a bit of luck and plenty of failure, it’s outcome-driven and, in many ways, predictable.
Historical product data and direct insights from customers are like your pieces on the board. They show you where your business is headed and what you can do to win.
Product Intelligence Metrics
To measure product intelligence, businesses need to look at customer success, satisfaction, and engagement metrics. They also have to look at qualitative metrics, like customer challenges.
This is the most important metric for product intelligence. Customer success measures how successful customers are in achieving their goals using a product. It’s usually measured by time to value, lifetime value, retention rate, and customer churn.
- Churn rate — Churn rate is the percentage of customers who stop using a product or service within a certain period of time. A lower churn rate means that customers are sticking with the product and experiencing enough value from it to stay.
- Customer retention — The customer retention rate is the inverse of churn. It measures how many customers are retained within a certain period of time. A higher retention rate means that customers find enough value in the product to continue using it.
- Time to first value (TTFV) — Time to value looks at how quickly customers reach the first “aha” moment when using the product. Since customers are more likely to churn when they don’t know how to use the product, companies aim for a customer onboarding experience that’s both short and effective as possible.
- Customer lifetime value (CLV) — CLV describes the total amount of money a customer is expected to spend on a product or service over the course of their lifetime. When customers spend comparatively more over their time as a customer, it generally means they’re happier with the product.
Customer success metrics don’t necessarily tell businesses what they need to fix or how they can fix it. They do, however, give them a high-level overview of product-market fit and product quality.
Measuring customer challenges is a bit more difficult because they’re based on qualitative data. Companies usually track this information through customer surveys, focus groups, and feedback from customer support staff.
Helpdesk software helps a lot with this. When certain keywords or types of support queries appear frequently in the company’s support software, those are the features and areas customers are having difficulty with.
Surveys are easier to quantify since they’re organized into multiple choice questions. Focus groups are observational, but they help companies witness firsthand why customers are having certain problems.
The goal is to discover what features customers are struggling with and why they’re having difficulty understanding or using the product correctly. Companies can then use that information to make improvements and design more user-friendly products.
When a company develops an application or SaaS, they build user engagement monitoring into the program. User engagement metrics measure how people interact with the product, what they’re doing, and why.
Here are some of the most important user engagement metrics for digital products:
- Trial to paid conversion rate
- Freemium to paid conversion rate
- Average number of sessions per user
- Number of active/imactive users
- Activation rate
- Product/feature adoption rate
- Length of time customers spend using the product
- Usage rates for core features
This is mostly based on clickstream data and usage trends. It’s also benchmarked automatically when customers take certain actions or reach milestones, such as successfully completing the onboarding process.
Businesses measure customer satisfaction in a few different ways, but they all ultimately measure how happy customers are with the product.
The most common way businesses measure customer satisfaction is through CSAT surveys. CSAT stands for “customer satisfaction” — it’s a simple survey that asks customers to rate their experience from 1 to 5 (“4” and “5” indicating “Satisfied” or “Very Satisfied”).
The Net Promoter Score (NPS) is another popular measure of customer satisfaction. It breaks customers into three categories: Promoters, Passives, and Detractors. A customer’s status is determined by how likely they are to recommend the product to their friends and family.
When they want to examine their at-risk customers, they look at the customer health score. This metric combines customer satisfaction, engagement, and retention data to give businesses a comprehensive look at the health of their customer base. Most of the time, those who are at risk of churning are unhappy with the product in some way.
How Companies Use Product Intelligence Data
Nowadays, every product is an entire experience. A customer’s relationship with that product reflects that. The main goal of product intelligence is to create a feedback loop where companies use compounding insights to iterate and innovate increasingly quickly.
Enhance the Customer Experience
It helps to look at modern products as experiences, rather than standalone products. Companies need to use product intelligence data to gain an understanding of what customers want and what it’s like to actually use the product.
Using this data, companies can:
- improve customer onboarding
- offer more tailored experiences (such as personalized recommendations)
- create better content that helps them get more out of the product
- optimize user experience
- identify customer pain points
- enhance support services
- better target product updates and feature releases
Improve Product Features and Quality
Product development teams specifically use intelligence to improve product experiences over time. To do this, they need to understand how customers are using their product and why.
Product intelligence provides the visibility needed to pinpoint feature issues, identify bugs, and measure user satisfaction with different features. Over time, they refine the most important features, add/remove some, and make their products work more effectively.
Innovate to Remain Competitive
“Innovating” doesn’t technically mean “creating something completely new” (although that tends to be how we look at it). In reality, innovation is the culmination of listening to customers and refining the product to the point where they get everything they need to out of it.
Most businesses don’t create something entirely new. They just create a better product for a certain market than the one(s) previously available.
The only way to do that is to understand what customers want and need from the product. On the backend, that means using product intelligence data to understand usage and engagement trends. Then, product teams tweak existing features, or make entirely new ones based on insights.
Product Intelligence Stakeholders
Although everyone in the company relies on product intelligence to some degree, sales, marketing, and product teams are the only people with their hands in it. They use it to make decisions about product features, pricing, and customer service.
Product managers are responsible for their product’s success and ongoing development. They’re the ones who pinpoint customers’ needs, examine how product changes fulfill larger business objectives, and define success for the product.
To rally their team and make data-driven decisions about future product development, product managers use product intelligence data to identify trends and understand the current state of their product.
Product designers rely on the direction from product managers and market trends to design the best user experience possible. They use data from customer feedback, experiments, surveys, and analytics to determine what works for users and refine their designs until they achieve the outcomes they’re looking for.
Marketing and Sales
Sales and marketing are concerned with product data because it helps them refine their messaging. If certain customer segments prioritize certain features, for example, they can make those central to their value proposition.
For salespeople, this might mean highlighting certain business problems or asking specific questions during cold outreach and sales demos. For marketers, it might mean slight changes to marketing copy or new marketing collateral altogether.
Methods for Collecting Product Intelligence
Product intelligence is comes from a multitude of data points, so there are lots of ways to collect it. Most methods include tracking customer actions and feedback, as well as testing products.
The Product Itself
The most significant source of product intelligence is the product itself. Of course, in-app tracking is only possible with digital products coded to do so.
Within an application, businesses can track:
- Clickstream data
- Milestones and accomplishments with the product
- Usage trends (frequency, time spent, etc.)
- How often users come back to the application
- Feedback on UX/UI problems
These data points help organizations understand the most important part of the customer journey: their actual day-to-day experience and how the product fits into that.
Most SaaS products and mobile/desktop applications also solicit surveys and in-app feedback from customers. They typically appear as random pop-ups or notifications, but they also appear throughout the product experience itself.
They’re usually quick surveys (no more than a few multiple choice questions). Sometimes, they will have a box where customers can explain their answers in more detail or add additional context.
Focus Groups & Beta Tests
Focus groups and beta tests are another way businesses can collect product intelligence data. Companies use focus groups to test out new features or gauge customer opinions on a particular feature/product. Beta testing, on the other hand, is used to identify any bugs or UX issues before releasing a product/feature/update.
Interviewing current customers can be a great way to gain insights into how specific customer segments use the product. It’s a good idea to interview high-value customers or those from crucial customer segments. DealHub, for example, would interview a long-term B2B SaaS customer, since that customer segment is a huge revenue driver for us.
Product Analytics Tools
There are lots of different tools a company might use to gather product intelligence. Most organization use sophisticated tools like Mixpanel or Amplitude. Depending on the type of product and data they’re looking for, companies might use a combination of different product analytics tools.
A lot of companies use social listening tools to capture and centralize users’ opinions and experiences with their product. These reviews typically come from third-party sites like G2 Crowd and Capterra. These reviews tend to be the most candid, so they often provide highly useful insights.
Types of Product Intelligence Tools
Really, companies gather product intelligence from all the activities related to their product. The most important part is what businesses do with this data — turning it into actionable insights that help them make decisions about their product.
There are several tools a company uses to capture valuable information about their product, how it’s used, and whether it’s been successful:
- Product analytics platforms
- Helpdesk and AI chatbot software
- Survey and feedback tools
- Social listening platforms
- A/B testing and heatmap tools
- Customer data platforms (CDPs)
- Databases and data warehouses
Each of these tools is specifically designed to capture and help analyze product intelligence data. Companies have to integrate them to gain a more holistic view of their product’s performance.
People Also Ask
What is the role of product intelligence?
The main goal of product intelligence is to inform product development decisions. It’s used to understand customer needs and wants, what features they’re using and not using, and how the product fits into their lives. Product intelligence helps teams make data-driven decisions about feature development, pricing strategies, and UX design.
What is an example of product intelligence?
Suppose a company typically takes one month to onboard its new customers. After making the process more interactive and simplifying the product, a reduction in time to first value would indicate that customers are finding the product easier to use.
After rolling out product updates, they look at onboarding milestones and product usage data. This is an example of product intelligence — if onboarding times decrease over the course of a few months, the company knows its updates were impactful. If not, their changes were unsuccessful.