Product Intelligence. How it Helps Businesses Increase Revenue and Avoid Failure
In 2020, Quibi entered the streaming market with high hopes. They wanted to offer a unique service and capture a market share of the booming industry. But the company didn't conduct extensive product intelligence research. Instead, they focused on two user behavior trends: the popularity of smartphones and the demand for video content on streaming platforms. Quibi decided to exclusively target mobile users with short-form content created for smartphones. The strategy looked attractive enough to secure $1.75 billion in investments.
Six months later, Quibi failed. The owners ended up selling their content to a competitor for less than $100 million.
That happened because Quibi acted on incomplete customer data. They identified two trends but overlooked others. For instance, they failed to recognize the growing demand for longer TV episodes. Also, Quibi neglected customers' desire to watch videos on devices other than smartphones. As a result, they misinterpreted what users really wanted. This led to the service's failure.
Product intelligence aims to prevent failures like this by providing access to comprehensive product data. In this article, we'll explore how product intelligence software works and impacts business decisions. We will also share use cases when companies have successfully applied product analytics. Additionally, we will address the potential challenges of implementing a product analytics solution and provide practical tips to overcome them.
Let's get started.
What is Product Intelligence?
Product intelligence is a solution that enables businesses to collect, analyze, and visualize valuable customer data throughout the entire product journey.
On the one hand, this data is abundant. Users engage with a product through various channels. They browse listings, read reviews, make purchases, use the product, and share their customer opinion in social media comments. These numerous touchpoints continuously generate a wealth of data.
On the other hand, customer data holds immense value. Overlooking any aspect of it can result in outcomes similar to Quibi's. To make informed decisions and avoid pitfalls, businesses need to use the entirety of product intelligence data.
Doing this manually is impossible. That's why businesses rely on product intelligence software to learn about product performance.
“You can theoretically get hold of that data manually, but it’s really time-consuming on the scale we are operating and impossible in practice,” says Aurélien Jemma, CEO of Liwango and one of Zyte's clients.
How does product intelligence work? Let's explore a potential scenario.
How to Get Product Intelligence Done Step by Step
Imagine a small cosmetics company that sells its products on the Internet. The market is competitive, and big players offer high quality for low prices. The business recognizes thatpricing intelligence alone won't help them stand out. They have to find another way to differentiate themselves.
To do that right, the company needs detailed information about the market, its competitive landscape, and user behavior data. To gather this information, the company is planning to use a product analytics solution.
In a nutshell, here's how the solution would work with product intelligence data.
Step 1. Collecting user behavior data
The product intelligence software scans various sources of product usage data. It collects customer reviews and customer feedback from beauty forums, social media, and other relevant channels. Also, it explores customer experience on the company's website. Just be sure to anonymize the user behavior data so you don’t run afoul of any data protection laws.
Additionally, the platform monitors competitors' offers and stays updated on the latest news and trends in the cosmetics market.
Step 2. Bringing disparate data sources together
Customer data comes from various channels in different formats. The product intelligence software standardizes and integrates it into a unified format. Now, the company has a complete and accurate picture of what is happening in the market. Product teams can access it as a single source of information.
Step 3. Doing data analysis
Once data is collected and transformed into a readable format, the product intelligence solution undertakes a thorough marketing analysis. The more data the platform accumulates, the more precise its product analytics capabilities become.
Step 4. Presenting data in an easy-to-use way
When marketing analytics is completed, the platform proceeds to data visualization. It presents its findings in a comprehensive form, including reports, tables, and other visual tools.
Data visualization enables the business to navigate the data, apply filters, and uncover meaningful patterns. This way, product managers and product designers can zoom in on a specific area or get a view of the cosmetics market as a whole.
Step 5. Generating actionable insights
Now it's time to transform the data into valuable insights.
For instance, the product intelligence software may identify a growing demand for eco-friendly cosmetics, sustainable packaging, and the upcoming Mother's Day. The cosmetics company can use these insights to reconsider its product offerings, transition to biodegradable materials for its packaging, and run targeted sales campaigns.
As a result, the cosmetics company will improve key product metrics, enhance customer engagement, and foster brand loyalty.
How Product Intelligence Helps Access and Use Customer Data
Let's talk about the main benefits of product intelligence for businesses.
Understanding customer needs and preferences
Collecting and manually analyzing customer feedback would burden the company's employees with excessive workloads. Luckily, product intelligence comes to the rescue. It helps gather data on user interactions with the product. To do it quickly and efficiently, product intelligence software applies machine learning and natural language processing (NLP) text analysis algorithms.
Improving customer satisfaction
Many factors can spoil customer experience, ranging from difficulties during the ordering process to issues with product functionality. And a dissatisfied customer often translates to a lost customer. Now a business will spend more on new customer acquisition than it would have spent on old customer retention.
Product intelligence helps identify and address weak points in the customer journey. For instance, a company may discover that a significant number of customers abandon their shopping carts during the checkout process. To address this problem, they might consider implementing new features. As an example, they may add payment security logos, offer new payment methods, or reduce shipping costs. By taking these steps, companies can improve customer experience.
Identifying gaps in the market
Gaps in the market are hard to find; yet, they present a goldmine. Product intelligence helps uncover these hidden gems. When it has vast amounts of customer experience data, the platform can help detect underserved customer segments or unmet customer needs. Moreover, it can go beyond and identify emerging trends.
Businesses can use these insights to anticipate and surpass customer expectations. By being pioneers in the market, they gain a serious competitive advantage.
Developing products that meet customer needs
Once a business has data about what a customer wants, it's time for product innovation. Data collected by product intelligence software enables companies to form hypotheses, conduct tests, validate their ideas, and add new product features. Sometimes, businesses may even transform the entire development process to stay ahead of their competitors.
Real-Life Examples: Product Analytics Tools in Action
Now, let's take a look at a couple of successful examples of a product intelligence process.
Use Case One: Intuit and its strategic approach to product key performance indicators
Intuit from the US creates personal finance and tax preparation software. One of their products is QuickBooks aimed at small businesses. The tool automates routine tasks like bookkeeping, invoicing, and time tracking.
In the past, when a customer was billing a client or scheduling a meeting, they had to manually transfer data from QuickBooks to other products that would use it. That was inconvenient and increased the risk of human error. As a result, it spoiled the customer experience.
Thanks to their product intelligence software, Intuit tracked down this issue. Also, they found out that a majority of their customers used Google tools like Calendar and Gmail in their daily work. That's why Intuit decided to integrate G Suite into QuickBooks.
Now, all Intuit's customers need to do is install a special plugin. Once installed, the plugin automatically transfers all the data from G Suite to QuickBooks. This seamless process saves small businesses the effort and time associated with manual data transfer.
Use Case Two: StarKist and the power of consumer opinion
StarKist, a manufacturer of canned tuna and fish products in the US, has been a major player in the market for decades. However, with changing customer habits, they recognized the need to adapt and introduce new features.
To stay ahead of the curve, StarKist invested considerable effort in product intelligence, including customer feedback analysis and competitive research. Their product analytics revealed a growing interest in healthier food options for a more active lifestyle.
Using these insights, StarKist underwent a rebranding process. This time, they placed a strong emphasis on promoting healthy eating habits in their product development. The company introduced Tear, Eat, & Go tuna pouches for convenient on-the-go snacking.
As a result of their proactive approach, StarKist has seen a 75% increase in the pouch category. Now pouches comprise 40% of Starkist's sales. Their commitment to staying relevant in the competitive market has paid off.
Challenges in Implementing Product Intelligence
We have already discussed the risks that arise when a business relies on incomplete information in its product analytics. Now, let's explore why product intelligence software may provide incomplete data.
Problems with antibot tools
When a service wants to access a lot of websites, it always faces the challenges of blocks and bans. Many tools end up being blocked at this stage. The platform can't access data from these websites which makes data collection incomplete.
Too many elements to manage
To make a system work, a business may use tools from various manufacturers. It helps create a solution the company needs.
But in practice, it may be hard to manage all the tools effectively. Product development teams may spend a ton of time managing proxies, updating user agents, and dealing with other technical issues.
No timely data updates
Data collected by product managers is valuable as long as it's relevant. A product intelligence solution that doesn't update data in near real-time doesn't serve its purpose.
Data operating in silos
A product may have thousands of touchpoints where a user interacts with it. But this data exists all over different places. If product teams and others working with product analytics data can't access all the information they need with a click, they may fail to take the right informed decisions.
How Businesses Can Tackle Product Intelligence Solution Inefficiencies
At Zyte, we'd advise you to pay attention to the following characteristics when you're choosing a product intelligence solution.
Accessing comprehensive data
To obtain high-quality data, a business needs to access various sources, including pricing, market trends, and competitor strategies. That's why it's important to ensure that your product intelligence solution has the capability to effectively gather and analyze such extensive data.
When assessing a product intelligence platform, it is always beneficial to examine its user base. If you find that news aggregators, marketplaces, and other entities that heavily rely on extensive data are utilizing the platform, it indicates that the product has the capacity to crawl thousands of websites at scale.
Using effective web scraping
Web scraping helps product intelligence software extract data from various online sources. That's why it is worthwhile to investigate the scraping techniques employed by the solution.
A successful product intelligence solution uses tools such as rotating proxies and user agents, to bypass antibot measures. This enables the tool to collect actionable data from a wide range of sources and get a comprehensive view of the market landscape.
Implementing data integration and transformation
To merge data from multiple sources, you need robust data integration tools and techniques. Make sure to explore how your product intelligence platform uses data cleansing, normalization, and other tools to structure unstructured data.
Adopting integrated product intelligence solutions
When selecting a product intelligence platform, consider opting for integrated and easy-to-use software that offers a comprehensive set of product analytics tools. That will simplify management processes and save the effort of product designers and product managers.
Product intelligence aims to equip businesses with comprehensive behavioral data. Companies use product analytics to make informed data-driven decisions and improve product performance.
High-quality product intelligence solutions access the latest usage data, track key performance indicators and provide actionable insights. As a result, product designers and modern product teams can bridge the gap between what a business thinks about product performance and how the product performs in reality. Product intelligence enables companies to align reality with the intended user experience and drive successful product development.
Make sure to explore more about product intelligence tools and how to make them work effectively. For example, in our blog, we regularly share strategies to improve web scraping for an enterprise. Take a look if you want to learn how to make your product intelligence solution collect more product data in a legal and secure way.