The 2022 Guide to Unstructured Data: How Businesses Can Unlock the Power of Deep Insights

Unstructured data is driving AI innovation. After all, around 90% of all data is unstructured, as reported by MIT. At the same time, a Deloitte survey uncovered that less than one-fifth of companies are able to take advantage of such data.

In this post, we'll cover what unstructured data is, the different types of unstructured data, how to deliver unstructured data insights to internal teams (in any industry), trends in unstructured data, and how to create an unstructured data strategy. Without further ado, let’s dive in.

What is Unstructured Data?

Unstructured data is any kind of raw data that doesn't fit into a structured format like a spreadsheet or database table. It can be text, images, audio, video, or anything else you can think of that isn’t in a standard tabular format. Unstructured data covers everything from product reviews on Amazon to stock market trends to social media posts.

The majority of the world's information is unstructured in nature. For example, according to Statista statistics, there are nearly 2 billion websites online. There are also millions of social media posts, articles, product reviews, and more published online every day. For instance, this single Amazon reviews dataset contains over 230 million product reviews.

This means that companies need ways to access this information for their own use cases as well as for innovation purposes (e.g., product discovery).

Traditional methods for accessing unstructured data include manual processes like scanning documents with optical character recognition (OCR) software for transcribing speech with remote teams. These processes are time-consuming and labor-intensive, which means they're usually only used by large organizations with dedicated teams who have the resources needed to invest in them. 

Fortunately, we now live in an age where machine learning has made it possible for computers to read unstructured text just as humans do—and sometimes better! This opens up new possibilities for businesses that want access to this valuable source of information but don't necessarily have the resources available to dedicate full-time staff members.

What Are the Types of Unstructured Data?

Unstructured data types include contact center recordings, chatbot messages, social media posts, emails, data lakes, YouTube videos, team meetings, and more. Let’s explore each of these, and how data scientists and product teams can use them to gain insights.

Contact center data

Contact centers are a great example of where unstructured data is used in the real world. When you call a customer service line, you're talking to a person who has to listen to your concerns and then respond with the appropriate information. In order to optimize the experience for customers, contact centers need to store and process all that information.

The challenge is that this information can come in many different forms — emails, chat transcripts, voice recordings, and so on. A lot of organizations outsource their contact center operators, which further complicates the process of extracting insights.

The most common contact center data type is unstructured recordings, which are files that contain the contact center agent's and customer’s voice. These files can be transcribed and stored in a database for later use, but they're usually stored in an unstructured audio file format like MP3 or AAC.

Social media data

Social media is another great source of massive amounts of real-world unstructured data. When your customers or leads post things on social media, you want to be able to access that information so you can engage with them and provide the best possible customer experience.

For example, if a customer posts a photo of their new product on Instagram, you might want to post a comment or respond directly to them in order to show your appreciation for their purchase. Social media data is usually stored as JSON files (or similar formats) which can be accessed using APIs.

Additionally, customers often share product and service reviews on social media. These reviews are extremely valuable for your business, so you'll want to make sure that you're capturing them in a way that makes it easy for your team to access later. By aggregating reviews and analyzing sentiment across different product and service attributes, product teams can better understand where to focus their efforts.

Email data

Emails are another example of unstructured data. When you receive an email, it doesn't necessarily look like a spreadsheet or database table — it's just text. But emails contain valuable information that can be used to improve customer engagement and market your products/services.

For example, if you're a travel company and you receive emails from a customer who says they have a flight coming up in two weeks, you might want to reach out to them directly to see if they need any last-minute accommodation or assistance with their trip. This is where customer relationship management (CRM) tools come in handy, because they allow teams to store and manage all their customers' information in one place.

At scale, this data can reveal customer sticking points and areas for improvement.

You'll also want to capture the date and time of when the emails are sent so you can analyze trends over time. For example, if there's a particular time of day when your emails get the best response rate, you can use this insight to optimize your marketing efforts accordingly. 

Chatbot chats

Chatbots are another common example of where unstructured data is used in the real world. When customers communicate with your company through chat, you'll want to store that information so that you can analyze it later. 

For example, if customers communicate through a Facebook Messenger bot, you'll want to store the chat transcripts so that you can respond to customers more efficiently in the future, while extracting insights. This is also a great way to engage with customers and build a relationship with them over time, which is especially valuable for B2B businesses.

The same goes for customer support chats — if your customers need assistance, you'll want to store their messages so that you can provide the best possible customer experience in the future.

Data lakes

Data lakes are a great way to store and analyze unstructured data. They're essentially big data repositories that can be used by teams across an organization for various purposes. For example, if you work at a company like LinkedIn, you might use a data lake to store all your customers' contact information.

Product teams can use data lakes to explore customer behavior and make decisions about product innovation. HR teams use data lakes to uncover insights about the company's employees. IT teams use data lakes to monitor infrastructure, security, performance, and other aspects of operations.

Data lakes are also useful because they can be easily scaled up or down depending on the amount of data stored in them. This makes them extremely cost-effective—an advantage for companies that have large amounts of data but limited technical resources. Data can be stored in these repositories in various forms, including unstructured text files, images, video files, audio files, and so on. 

They're also often connected to external systems so that they can fetch additional information needed for analysis.

Review videos

YouTube has become one of the most popular places for people to share their opinions on products. For example, in a 2 year period, Google recorded that over 50,000 years of YouTube product review videos were watched on mobile alone. Meanwhile, the topic #TikTokMadeMeBuyIt has nearly 6 billion views on TikTok, as of writing, showing that TikTok is driving massive amounts of sales and product interest.

When someone creates a video about a product or service, they're essentially sharing their opinion with the world. And when people watch these videos, they might be interested in trying out the product or service because of what they see in the video. This is why it's so important to capture all your customer reviews — not only do they help you understand your customers better, but they also help you engage with them and build relationships over time.

Product calls on Zoom, Hangouts, and more

Most product innovation teams regularly share their thoughts and ideas in online meeting platforms like Zoom. This is a great way for teams to collaborate and share information with each other, but if meetings aren’t stored and analyzed, you can miss out on valuable insights.

This is why data analysis and AI are important for these teams. They can explore the data, identify patterns and trends, and come up with new ideas for product innovations.

How to Deliver Unstructured Data Insights to Internal Teams

Now that we've looked at some of the top unstructured data types, let's look at how to deliver insights to internal teams, including marketing, sales, and customer support.

Marketing

Let’s start with delivering unstructured data insights to marketing teams to optimize their KPIs.

Marketing teams often have a lot of data that they don't know what to do with. They might have tons of customer reviews and social media posts, but it's not always easy for them to make sense of it all. By delivering this data to marketing teams in an easy-to-use way, they can free up their time so they can focus on strategic tasks instead.

For example, market research is a notoriously time-consuming and labor-intensive process. Market research teams have to manually transcribe phone calls, analyze survey results, and so on. By using market reports and unstructured data APIs like those from Commerce.AI, teams can free up their time.

With Commerce.AI, marketing teams can amplify positive responses, address negative feedback and product recalls, and gauge positive and negative changes in products and categories to prioritize your objectives, in addition to solving product risk areas and escalations. 

Let's look at each of those areas. 

Customer support, product reviews, and social media are great sources of information. But they can also be a source of frustration if the information is negative or doesn't meet your expectations. By using Commerce.AI, you can gather customer feedback in one place so that teams can address the concerns raised before they become major problems for your business. 

Commerce.AI’s APIs provide a way for teams to identify customer sentiment and provide insights into how to improve products and services based on real-time data captured directly from customers.

The insight provides valuable context around changes needed within products/services as well as risk areas which can help prioritize objectives or speed up time-to-market. 

Teams can also gauge the impact of product improvements over time across different channels like email marketing campaigns, social media posts, and so on, thus giving them valuable insights into how their efforts are being perceived by their target audience.

Sales

Unstructured data insights are a great way to boost your sales team's productivity. When you have actionable insights, your sales teams can take the appropriate actions to close deals more efficiently.

For one, Commerce.AI highlights what product and service attributes have the most positive customer sentiment, which lets salespeople prioritize their efforts when it comes to finding customers. 

And with the ability to analyze unstructured data, such as customer reviews on Amazon or social media mentions of your competitors, your sales teams can gain a better understanding of what customers really want and need — which helps them make more informed sales calls and ultimately close more deals. 

This is particularly important in a world where consumers have an abundance of options, and they're always on the lookout for new products and services.

Customer Support

Customer support teams are often the first point of contact for your customers. They're responsible for helping your customers solve their problems, and they need to be able to access all the information they need in order to do their jobs effectively.

If you have customer support teams that are siloed from each other, it can be hard for them to collaborate on solving a customer's problem. This is where unstructured data comes into play. By delivering insights from unstructured data sources like chat transcripts or product reviews directly to customer support teams, you'll be able to help your customers more efficiently and provide better service overall.

This helps optimize KPIs all the way from Time to Resolution to Average Response Time, which is especially valuable for companies that provide customer support as a core part of their business. Nowadays, customers expect timely, accurate responses to their questions, and they'll quickly turn to another company if they don't get the service they need.

What Industries Use Unstructured Data?

All industries nowadays can benefit from unstructured data, given that it can help companies solve real-world problems. Here are just a few examples of industries that use unstructured data:

• Automotive
• Appliances
• Apps Reviews
• Banking and Insurance
• B2B
• Consumer Goods
• Electronics
• Finance and Investment
• Food & Beverages
• Health & Beauty
• Media / Publishing
• Restaurants
• Toys & Games
• Travel & Hospitality

Let's dive deep into each of these areas, starting with how automotive firms can gain value from unstructured data, through the lens of product innovation.

How Automotive Firms Can Use Unstructured Data

Unstructured data is a great source of product innovation ideas for automotive firms. For example, if you're an automaker and you want to improve your product line, you can look at unstructured data sources like YouTube for product reviews and chat transcripts to find ways to improve your products.

Let's say that car enthusiasts post video reviews of a different brand's car on YouTube. You could then use the transcripts of those video reviews to identify key phrases or words in the review. This would allow you to create better recommendations for future customers based on what they've said about their experiences with other brands in the past. 

You could also look at chat transcripts from automotive forums to gain insights into how customers feel about certain features or technologies. By mining this information, you'll be able to identify trends among customers and make more informed decisions about which features/technologies are most important for your company's long-term success. 

For example, if you're an automaker and many people mention safety features in their chat transcripts, you could use that information to prioritize the development of new safety features for your vehicles.

How Appliances Firms Can Use Unstructured Data

Appliance firms use unstructured data for innovation across all areas, from market research to competitive intelligence. Let's say that you work at a company that makes home appliances. You could look at chat transcripts from appliance forums like Applianceblog or Houzz to gain insights into what features customers are most interested in when it comes to purchasing new appliances.

For example, if many people on Houzz mention that they're interested in energy-efficient appliances, you could use that information to prioritize the development of new products with energy efficiency as a key feature. Or suppose that, in many customer support tickets, people mention that they want more options when it comes to purchasing appliances. You could use this information to prioritize the development of new product lines like appliances with multiple options for purchase.

How Apps Review Firms Can Use Unstructured Data For Innovation

Apps review firms use unstructured data to help them gain a competitive advantage by providing customers with the most relevant information. For example, if you work at an app reviewer firm like PCMag, you could look at analyses from the Google Play Store, App Store, or even Product Hunt to gain insights into what features customers are most interested in when it comes to purchasing new apps.

For example, perhaps many people on Product Hunt mention that they're interested in a certain feature in a particular app, such as new AI-based dating apps, or gaming apps with AR features.

How Banking and Insurance Firms Can Use Unstructured Data For Innovation

Banking and insurance firms can gain value from unstructured data by using it for competitive intelligence. For example, suppose that you work at a bank that provides home loans. You could look at chat transcripts from banking forums like Wall Street Oasis to gain insights into what customers are most interested in when it comes to purchasing a home loan.

For example, if many people mention that they want more options when it comes to selecting their loan type (i.e., fixed or variable), you could use this information to prioritize the development of new product lines like adjustable-rate mortgages (ARMs).

How B2B Firms Use Unstructured Data

When it comes to B2B firms, there are many ways that they can use unstructured data to improve their products and services. Let's say that you work at a company that provides HR software solutions. You could look at product reviews on sites like G2Crowd or Forrester to identify pain points in the market so that you can inform your roadmap for new features/products so that customers will have what they need when it comes time to purchase your software. 

For example, if many people on G2Crowd mention that they'd prefer an email-based onboarding process instead of a web-based one, then your company could work to include more emails in the onboarding process for new customers when they purchase your software. This might seem insignificant now, but having just this one feature could help increase customer satisfaction with your product immensely.

How Consumer Goods Firms Use Unstructured Data

Consumer goods firms use unstructured data for both market research and competitive intelligence. For example, if you work at a consumer goods firm like Procter & Gamble or Unilever, you could look at chat transcripts from consumer forums like Quora to gain insights into how customers are interacting with your products. 

For example, if many people on Quora mention that they're looking for coupons when purchasing an item, that information could be used to inform the development of new product lines like consumable items that come with coupons attached. 

Or suppose that many people on Quora ask for more options when it comes time to purchase an item. That would be great information to have because it allows you to develop more options so customers can customize their purchases as much as possible.

How Electronics Firms Can Use Unstructured Data

Electronics firms can use unstructured data in many areas, including market research and competitive intelligence. Let's say that you work at an electronics company and you want to gain insights into how your customers prefer to buy electronics. You could look at chat transcripts from forums like Electronics Point to find out what features most people request when it comes to purchasing electronic devices.

For example, if several people on Electronics Point mention in their posts that they prefer pre-owned purchases instead of new items whenever possible, this might signal a shift in consumer preferences toward more pre-owned products. You could use this information as input into product development so that you can create more pre-owned options for future customers.

How Finance and Investment Firms Can Use Unstructured Data

Finance and investment firms can use unstructured data for a variety of purposes, such as market research and customer engagement. For example, you could look at chat transcripts from financial forums like the Morningstar Community to gain insights into what people are talking about in the market and current events.

For example, if many people on the Morningstar Community's message boards mention that they're interested in cryptocurrencies or blockchain technology, then that would be an interesting topic for your analysis because those technologies are currently very hot topics. You could then use this information to inform your product decisions.

How Food & Beverage Firms Can Use Unstructured Data

Food & beverage firms can use unstructured data to improve their products, increase market share, reduce waste, and even save money. For example, suppose that you work at a company that makes bottled water. You could look at forum posts or social media for insights into what people are looking for in new brands of bottled water.

For example, if many people mention that they want bottles that come in interesting flavors or are recycled or sustainably sourced, you could use this information to prioritize the development of new product lines like beverages with those attributes.

How Health & Beauty Firms Can Use Unstructured Data

Health & beauty firms use unstructured data for a wide variety of purposes, including product development. For example, suppose that you work at a health & beauty company and you need to develop new products that can help your customers fight cellulite. 

You could look at chat transcripts from online forums like Women's Health or Skincare Talk to gain insights into what types of questions customers ask the most when it comes time to purchase new skincare products. 

You could also review product reviews on Amazon or YouTube to gain insight into what customers are saying about certain products. This way, you'll be able to determine which products are working well for customers and which ones aren't. These insights will help inform the development of new products in the market.

How Media/Publishing Firms Can Use Unstructured Data

Media and publishing firms use unstructured data for everything from market research to finding new authors, given that it can help them gain insights into the needs of their readers. One area where media has historically struggled is in understanding what its audience wants.

Traditional strategies for gaining insights into reader preferences typically involve survey responses, focus groups, or in-person observation—all of which are expensive and time-consuming. Unstructured data can give you a real-time look at your target audiences through social media platforms like Facebook and Twitter, which are free to use. 

For example, if you're a publisher who publishes books on topics related to video game development, you could search Twitter for the keywords "video game development.” This will show you people who mention these terms on Twitter — including people who follow those accounts. 

You can then drill down further by following each account to see what they tweet about specifically when they mention video game development. This information gives you an idea of what topics your readers find most interesting so that you can plan future content accordingly. 

With AI, you can analyze this information at scale, and across many data sources at the same time, no matter what area you’re working in.

How Restaurants Can Use Unstructured Data

Restaurant chains can benefit from using unstructured data, particularly for market research and competitive intelligence. Let's say you work at a chain of family-style restaurants. You could look at chat transcripts from restaurant review sites like Yelp to gain insights into what features customers are most interested in when it comes to purchasing new restaurants.

For example, if many people on Yelp mention that they're interested in high-end restaurants with live music as part of the experience, you could use that information to inform the development of a new wing for your next expansion project so that you can offer more options for customers who want those types of restaurants as part of their dining experience. 

The same goes for competitive intelligence—if many people on Yelp mention that they prefer local cuisine when looking for a new restaurant, you could use this information to inform the development of an expansion project focused on expanding into markets where local cuisine is popular. 

How Toys & Games Firms Can Use Unstructured Data

Toys and games firms also use unstructured data for innovation across all areas, from market research to competitive intelligence. Let's say that you work at a company that makes toys and games. You could look at chat transcripts from gaming forums like PlayStation Forum to gain insights into what features customers are most interested in when it comes to purchasing new toys and games.

For example, suppose that many people mention that they're interested in virtual reality (VR) kits for their controllers. You could then use this information to inform the development of new VR kits for your controllers.

How Travel Firms Can Use Unstructured Data

When it comes to travel, unstructured data has a similar value proposition as it does in many other industries: it provides companies with product innovation ideas. For example, if you work at a travel company and you want to improve your product line, you can look at chat transcripts from travel forums like TripAdvisor to gain insights into what features customers are most interested in when purchasing airfare or hotels.

For example, suppose that many people on TripAdvisor complain about having to pay baggage fees when they check their luggage on business flights. You could use this information to inform changes to your pricing model for business travelers so that they don't have to pay baggage fees after all! 

This is why artificial intelligence is becoming extremely important for B2B SaaS products like HelpScout: they provide B2B companies with the ability to automate much of the analysis required. This helps B2B businesses build highly personalized experiences for their customers by learning about their customers' preferences over time. 

5 Trends in Unstructured Data

Unstructured data is booming right now because of all the ways that it can help companies reach customers in new and innovative ways. Here are a few trends to watch when it comes to unstructured data:

• AI surge
• Business adoption
• Cloud ubiquity
• Data explosion
• Enterprise adoption

Let’s dive into each of these areas.

Trend 1: AI Surge

AI is helping to drive the growth of unstructured data by automating processes that used to be time-consuming, error-prone, or even impossible. Consider natural language processing (NLP), which allows computers to understand what humans are saying.

We’re on the verge of an AI explosion where advances in NLP will enable computers to converse with us in human language and perform tasks that were previously thought impossible for a machine to do on its own. This is already having widespread implications across all industries, but NLP is especially applicable to unstructured data because it enables machines to read, analyze, and interpret text without relying on pre-existing knowledge or context.

Trend 2: Business Adoption

Business adoption is also playing an important role in spurring the growth of unstructured data. 

Today, many companies are realizing that they need to embrace unstructured data if they want to stay competitive. For example, salespeople sometimes use sales calls as opportunities to build rapport with customers over the phone or via email—or both! But these interactions often go undocumented; after all, does everyone always keep detailed notes during a call? By harnessing the power of AI systems, businesses can now leverage this valuable customer interaction information and transform it into actionable insights.

Trend 3: Cloud Ubiquity

Cloud ubiquity is another factor driving the growth of unstructured data. The cloud has democratized access to computing resources by making them available on-demand from anywhere around the world at any time—and that includes enormous amounts of storage space and processing power.

This means that businesses can now leverage powerful computational resources without having to invest heavily in hardware infrastructure—even if they’re just getting started with their analytics programs.

Trend 4: Data Explosion

The fourth trend contributing to the growth of unstructured data is an exponential one: The amount of publicly available information available online continues to grow exponentially each year. It used to take years for new scientific research findings or economic statistics to trickle down into mainstream media coverage—but today you can find out everything from the solar eclipse’s weather forecast to the value of a rare coin in seconds.

This data explosion has led to a gold rush mentality where companies are racing to collect as much unstructured data as possible and analyze it using AI algorithms. But there’s a catch: The sheer volume of data being collected far outstrips our ability to comprehend it all on our own. 

That’s why AI systems are so valuable for processing this information—they can help reduce the massive amount of available data down into actionable insights that can be easily understood by humans.

Trend 5: Enterprise Adoption 

The final trend contributing to the growth of unstructured data is enterprise adoption, which is largely responsible for fueling the rise of big data in general. Businesses have finally come around to realizing that big data offers compelling opportunities for exploiting their vast troves of structured and unstructured information on everything from customer preferences to market trends. 

When you think about how many different types of information businesses collect every day—from financial transactions and product sales records, to social media posts and email correspondence—it becomes clear that big data can help them gain unprecedented visibility into their operations, identify new revenue streams, optimize processes, spot emerging competitors or threats, and so forth.

By adopting enterprise-grade tools designed specifically for managing large volumes of structured and unstructured information across multiple platforms, businesses are able to better leverage these opportunities while mitigating risk amidst this unprecedented amount of available information.

How to Create an Unstructured Data Strategy

Now that you know what unstructured data is and why it's so valuable, let's talk about how you can build an unstructured data strategy for your business.

To begin with, you'll need to assess your organization's KPIs and define the goals you want to achieve with your unstructured data strategy. This will help you determine which metrics you should focus on as part of your overall business success.

Once you've figured out what success looks like, it's time to figure out who will be responsible for delivering that success. In most cases, this will fall to a team leader or manager. The role of the team leader is to set up the process and define roles and responsibilities within it so that everyone knows exactly what they need to do in order for the strategy to succeed.

Next, it's time to figure out where all your data is coming from and how you'll collect it in the first place. This can include setting up new systems for collecting data if necessary, but can also be automated with tools like Commerce.AI.

Once you have your data in a system, you'll need to figure out how you'll analyze it and use it to create value for your business. This includes defining what metrics you want to track and how often, as well as any additional information that might be helpful when solving the customer support issue at hand.

Finally, once you have all that figured out, then you can start delivering the insights that will help your organization achieve its goals more effectively. Check out our case studies to see how leading product and service firms have used Commerce.AI for unstructured data-fueled innovation.