Using the Knowledge Graph to Segment Big Tech Investments By Industry

Every big tech investment is big news. If your firm raises a funding round with prestigious investors or is acquired, you better bet you’ll spread the news far and wide.

But where can you go for this information en masse? Even covering a handful of big investors over a handful of years can lead to a list of thousands of invested in firms. And a list of firms themselves isn’t that useful. Sure, some big names pop out. But how do you see what “plays” big tech is making?

That’s where our web-reading bots come in. By working through billions of web pages using NLP and machine vision, Diffbot’s Knowledge Graph is the largest public-web sourced database of organizations, articles, people, products, and events. For each entity — organization, articles, people, etc. — facts are vetted and accumulated to create a filterable, searchable database of “things.” So when we wanted to check out which industries big tech has invested in over the last decade, we knew right where to turn. No analyst middlepersons, just public web data structured into a market intel-rich format.

Big Tech Investment By Industry 2010-2021

Distribution of industries of organizations invested in by Facebook, Alphabet, Amazon, Microsoft, Apple, and Netflix from 2010 to July 2021. Firmographic data sourced from Diffbot’s Knowledge Graph.
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Generating B2B Sales Leads With Diffbot’s Knowledge Graph

Generation of leads is the single largest challenge for up to 85% of B2B marketers.

Simultaneously, marketing and sales dashboards are filled with ever more data. There are more ways to get in front of a potential lead than ever before. And nearly every org of interest has a digital footprint.

So what’s the deal? 🤔

Firmographic, demographic, technographic (components of quality market segmentation) data are spread across the web. And even once they’re pulled into our workflows they’re often siloed, still only semi-structured, or otherwise disconnected. Data brokers provide data that gets stale more quickly than quality curated web sources.

But the fact persists, all the lead generation data you typically need is spread across the public web.

You just needs someone (or something 🤖) to find, read, and structure this data.

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The 25 Most Covid-Safe Restaurants in San Francisco (According to NLP)

A few weeks ago, we ran reviews for a Michelin-reviewed restaurant through our Natural Language API. It was able to tell us what people liked or disliked about the restaurant, and even rank dishes by sentiment. In our analysis, we also noticed something curious. When our NL API pulled out the entity “Covid-19,” it wasn’t always paired with a negative sentiment.

When we mined back in to where these positive mentions of Covid-19 occurred in the reviews, we saw that our NL API appeared to be picking up on language in which restaurant reviewers felt a restaurant had handled Covid-19 well. In other words, when Covid-19 was determined to be part of a positive statement, it was because guests felt relatively safe. Or that the restaurant had come up with novel solutions for dealing with Covid-19.

With this in mind, we set to starting up another, larger analysis.

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How Employbl Saved 250 Hours Building Their Career-Matching Database

We started with about 1,000 companies in the Employbl database, mostly in the Bay Area. Now with Diffbot we can expand to other cities and add thousands of additional companies. 

Connor Leech – CEO @Employbl

Fixing tech starts with hiring. And fixing hiring is an information problem. That’s what Connor Leech, cofounder and CEO at Employbl discovered when creating a new talent marketplace meant to connect tech employees with the information-rich hiring marketplace they deserve.

Tech job seekers rely on a range of metrics to gauge the opportunity and stability of a potential employer.

While information like funding rounds, founders, team size, industry, and investors are often public, it can be hard to grab the myriad fields candidates value in a up-to-date format from around the web.

These difficulties are amplified by the fact that many tech startups are often “long tail” entities that also regularly change.

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What We Found Analyzing 300 Yelp Reviews of a Michelin Reviewed Restaurant with Natural Language Processing

Reviews are a veritable gold mine of data. They’re one of the few times when unsolicited customers lay out the best and the worst parts of using a product or service. And the relative richness of natural language can quickly point product or service providers in a nuanced direction more definitively than quantitative metrics like time on site, bounce rate, or sales numbers.

The flip side of this linguistic richness is that reviews are largely unstructured data. Beyond that, many reviews are written somewhat informally, making the task of decoding their meaning at scale even harder.

Restaurant reviews are known as being some of the richest of all reviews. They tend to document the entire experience: social interactions, location, décor, service, price, and food.

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What is Product Data?

what is product data

Every now and then it’s important to get back to basics and ask a question which seems obvious, because sometimes those questions have hidden depths. The question “What Is Product Data?” is one of those I recently asked myself, and it led me down a mini-rabbit hole.

The basic definition of a product is:

“A thing produced by labor, such as products of farm and factory; or the product of thought, time or space.”

When you think about it, that covers a lot of ground. By definition, a product can be almost anything you can imagine — from any item on the supermarket shelf, to an eBook, a house, or even just a theory.

So how do we at Diffbot pare down the definition from almost anything in the world, to a useful definition for someone interested in data?

What is a useful definition of a product in the context of data?

“A product is a physical or digital good, which has the attributes of existing, having a name, being tradable.” Beyond that, all bets are off.

So to frame that in the context of data, the universal attributes of a product are data attributes, like Identifier and Price.

There is, obviously, more to most product data than that. So how do you define a set of attributes (or taxonomy) that is useful, and defines all products as data? We’ve come up with a couple approaches to that question.

Approaches to defining a product as data:

1. Product Schema

One way people try to define product data is by imagining every possible product attribute, and then creating a massive set of predefined product types and the attributes each type is expected to have. Then they publish that as a schema. is an attempt at that exercise.

Their definition of a product is:

“Any offered product or service. For example: a pair of shoes; a concert ticket; the rental of a car; a haircut; or an episode of a TV show streamed online.”

They have tried to make a universal product taxonomy by setting out more than 30 attributes they think can cover any product — and even a set of additional attributes that can be used to add extra context to the product.

The primary aim of their schema for product data is to allow website owners to “markup” their website HTML more accurately. This method has had some success, with over one million websites using their product definition. Sadly, this is still less than 0.3% of all websites. works well for its intended purpose, and it does a good job at providing a framework to structure what a physical product is. But it also falls short on several fronts.

The downside of this approach is that by trying to fix a set number of attributes for all products, they exclude a vast amount of product data and limit the scope to only ever being a fraction of the data that could be available. Not only that but they require website creators to spend time adding this data themselves.

Take the example of a hard disk drive. It has some attributes that fit into’s product data definition, but it also has 10x more data that could be available to users. For instance, there is a full table of specifications that don’t fit into the premade definitions like these for the product.

WD Red 4TB NAS Hard Disk Drive – 5400 RPM Class SATA 6GB/S 64 MB Cache 3.5-Inch

The problem is that there are so many different data points a product could have, that you can never define a universal product spec for them all. So there needs to be another way to describe products as data.

2. AI Defined Product Data

The main problem with the “universal product data definition” is that someone has to try to foresee any and all combinations, and then formalize them into a spec.

The beauty of the AI approach is that it doesn’t approach product data with any preconceived ideas of what a product should look like. Instead, it looks at data in a way similar to how a human would approach it. Using AI, you can let the product itself define the data, rather than trying to make a product fit into your pre-made classifications. The process basically looks like this:

  • Load a product page
  • Look at all the structures, layouts, and images
  • Us AI, and computer vision techniques to decide what is relevant product data
  • Use the available data to define the product
  • Organize the data into a table like structure (or JSON file for experts)

You can use a smart product crawler like Diffbot to define any product data for any product.

Finally, we can define a what product is by using AI to look at the product is. So if we can now reliably define what a product is, and we can get all the data about what it is, what else do we need to know about product data?

3. Product Meta Data

Product metadata is the data about a product which is not necessarily a physical aspect of the item, but rather some intellectual information about it.It should also be considered product data. Product metadata may include:

  • Its location
  • Its availability
  • Its review score
  • What other products it is related to
  • Other products people also buy or view
  • Where it appears in keyword searches
  • How many sellers there are for the product
  • Is it one of a number of variations


Before getting any further down the rabbit hole of product semantics, data, knowledge graphs, Google product feeds and all the other many directions this question can take you, it’s time to stop and reconsider the original question.

What is Product Data?
Product data is all the information about a product which can be read, measured and structured into a usable format. There is no universal fixed schema that can cover all aspects of all products, but there are tools that can help you extract a product’s data and create dynamic definitions for you. No two products are the same, so we treat both the product and its data as individual items. We don’t put them into premade boxes. Instead, we understand that there are many data points shared between products, and there are more which are not.

As an individual or team interested in product data, the best thing you can do is use Diffbot’s AI to build datasets for you, with all the information, and then choose only the data you need.

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How Can Anyone Possibly Compete with Amazon?

How does any ecommerce store compete with the veritable giant known as Amazon?

They do billions of dollars in sales every year. Five years ago they employed over 30,000 people, but now have over 110,000 employees. One-quarter of all office space in Seattle is dedicated to Amazon.

As of now, they are worth more than all major US department chains put together.

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But the most amazing thing about them is how they use data to improve the customer experience.

They utilize collaborative filtering engines (CFE) to analyze items that have been purchased, find products in shopping carts or wishlists, gather product reviews and connect it all to your search habits.

It’s like they know exactly what you want before you even know you want it. But this shouldn’t dissuade smaller retailers from sticking their toes in the water.

While Amazon may have access to a lot of data, smaller stores have access to the same data. They just may not know it.

Here’s how a small ecommerce store could potentially keep up with the likes of Amazon.

How Amazon Uses Data for Better Sales

Amazon was one of the first companies online to really use data to make the shopping experience as seamless as possible.

They have really leveraged data and AI over the years to create a customer experience that few other retailers can match.

Here are a few of the things that really set them apart when it comes to data:

Anticipatory Shipping Model

Amazon’s patented anticipatory shipping model uses big data to predict what products you’re likely to buy, when you may buy them and where you might need them shipped.

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According to the patent, their forecasting uses data from your prior Amazon activity to populate its predictions.

This includes things like:

  • Time spent on site
  • Duration of views
  • Links clicked and hovered over
  • Shopping cart activity
  • Wishlists

This predictive analysis allows them to anticipate needs, which in turn increases their sales and profit margin and reducing delivery time. They make money by knowing what you want before you do.

Supply Chain Optimization

Not only does Amazon predict your orders, they also use data to link with manufacturers to get you products faster.

Amazon uses data systems for choosing the warehouse closest to the vendor (or the customer) in order to drop shipping costs by an average of 10 to 40%.

They use graph theory to decide the best delivery times, routes and product groupings to lower shipping expenses as much as possible.

Price Optimization

Amazon also uses data for price optimization in order to attract more customers and increase profits (which they do by an average of 25% annually).

Prices are set according to your activity on the website, as well a bevy of other metrics like competitors’ pricing, product availability, item preferences, order history and so on.

Amazon also analyzes and updates their product prices every 10 minutes or so, which allows them to offer discounts and adjust prices as needed to drive more sales.

If all of this sounds impressive, it’s because it is. But it’s all made possible by the power of data. Without it, Amazon is just like any other store, really.

And data is also the key for smaller stores, too.

How Ecommerce Stores Can Keep the Pace

The only way to keep up with Amazon is to become what VentureBeat calls a “data-centric” company.

They describe a few key lessons that ecommerce stores can take from Amazon’s latest merger and their use of data for sales:

  1. Data will be needed to understand what drives consumer preferences and behavior
  2. Deep data gives you the competitive edge over other companies in your sphere of influence
  3. Depth and accuracy of your data will matter for effectiveness

The good news is that data is accessible to any retailer who knows how to get it.

Web scraping, for instance, allows you to gather data from competitor sites (including Amazon) for price comparisons and product details.

You can then use this information to offer discounts and optimize your prices in the same way that Amazon does.

Depending on the service you use, you can scrape this information as many times as you need to get the most accurate price data.

They will even track this information over time to find patterns.

Image source

You can also collect product reviews and ratings, as well as information from social media sites to offer insight into what your customers want, what you think they would buy again, or what they would skip.

If you scrape your own product data, you can figure out what they have already bought and offer product recommendations.

Almost anything that Amazon is doing with their data can be replicated by scraping data from the web.

In fact, Amazon does this all the time. If they want to know how their products are performing against BestBuy or Walmart, for example, they will crawl product catalogs from these two sites to find the gaps in their own catalog.

But the one thing that Amazon does well in terms of getting data is that they know how to use it once they have it.

This means getting the cleanest, most organized data you possibly can. You need product data that’s easily readable and decipherable, for example.

You also need the ability to gather new data from multiple sources as often as you need it. Amazon reviews their competitor data frequently enough to update their site every 10 minutes.

The fact of the matter is that you could be doing this, too.

The biggest thing that Amazon is doing that sets them apart in today’s market is using data to drive their purchasing, marketing, and sales decisions.

But the good news is that any company that can get their hands on data can do these things, too.

You don’t have to be the size of Amazon to do what Amazon does. You don’t need to be Jeff Bezos to drive sales.

You just need access to the right information.

Final Thoughts

You may not necessarily have the same influence that Amazon does in the marketplace, but there’s no reason why you can’t use Amazon’s best practices to gain an edge on your competitors.

Data is what powers Amazon’s sales, and that same data can be leveraged to power your sales, too.

The thing to remember is that you want to collect as much data as possible, but it needs to be clean, structured, and applicable to your services.

You don’t just want to use any data. You want to use the right data.

Ecommerce: Why You Need Web Data to Create Truly Competitive Pricing

Effective pricing is essential for successful sales, but what constitutes “effective pricing,” exactly?

Ideally, the best pricing strategy is one that maintains a good profit margin and keeps up with the competition, but knowing which product prices will maximize profit isn’t always easy.

[Tweet “Knowing which product prices will maximize profit isn’t always easy.”]

Pricing also changes over time, meaning that setting a certain price point one day may not make sense the next.

Customers today often compare product prices online before purchasing, leading to more competition between similar products and companies, and as a result, prices can shift dramatically over time as companies vie to stay on top.

So how do ecommerce businesses create pricing strategies that meet customer demand, beat the competition and still make them money? In short: A competitive pricing strategy powered by web data.

Web data can tell you what your competitors are doing, what customers are searching for, and how much power your products have at their current price points. Here’s what to know.

What Is Competitive Pricing Strategy?

There are several approaches that companies can take when it comes to pricing products:

  • Cost-plus pricing – A prefixed profit margin is added over the total cost of the product
  • Demand pricing – The price is set by establishing the optimal relationship between volume and price
  • Markup pricing – A percentage is added (as profit) over the wholesale price
  • Competitive pricing – Products are priced in accordance with what the competition is charging

In terms of a pricing strategy, competitive pricing has a lot of advantages. For one, it’s fairly simple, especially for retailers that have only a few direct competitors with similar products. In essence, your competition is doing the work for you (assuming they know how to navigate the market, of course).

It’s also lower risk than other pricing strategies. If you have a solid grasp on your market and your cost of production, you will almost always make a profit, which makes it an appealing option for retailers of every size.

But the trick with competitive pricing is that you absolutely need data in order to make the right decisions.

How Web Data Improves Pricing Strategies

Knowing exactly who your competitors are and what they’re charging for their products is essential. You also need to take into account the purchasing behavior of your customers as well as the performance of your own products.

Web data can support competitive pricing by allowing you to look at historical and current data from others in your marketplace and adjust to changing prices in near real time.

It can also help you:

  • Make more informed pricing decisions, widening the gap between you and your competitors
  • Adjust pricing by geography and product category
  • Enhance your product positioning in the marketplace

A retailer using web data would be able to collect millions of competitive price points in selected product categories and zip codes, giving them the edge over local competition or for specific products. They could also determine the ideal offer and pricing for certain products by gathering bidding information from sites like eBay or other auction houses, for example.

Web data extraction can help you gather important information to fuel the launch of a product, determine the best price for successful sales and define its niche in the market.

It can also help if you’re worried that competitors are pricing products too low. Knowing what they’re charging and what the product is worth can help you create a pricing strategy that matches your product’s value, too.

In some cases, you may also want to extract other information like product reviews and distribution channels so you know exactly what people think of your product (if they think it’s worth the price you charge) and where it’s being sold (is it cheaper on some channels over others?).

But to do all this, you need to gather a lot of data from a multitude of sources (even competitors you don’t know exist).

Gathering Product Pricing Data

The best way retailers can gather enough data is by using an automated data extraction and monitoring tool (Diffbot’s Product API is designed exactly for this job). Without a robust data extraction tool, you run the risk of:

  • Not getting enough data and limiting your pricing strategy to a handful of competitors (undercutting your profits)
  • Getting stale or inaccurate pricing information (competitor prices change constantly, yours need to change along with them)
  • Not getting a full history of product prices (if a product comes in price ranges or with quantity-based discounts, you want to know)

One of the biggest challenges with gathering product data is getting this data for hundreds or thousands of products from multiple sites daily. Not all data extraction tools can handle the job. Some scrapers may break, or in some cases may not be able to extract product information from certain sites due to formatting issues or other errors.

You may also need your extraction tool to gather other information, like product reviews, so you can price based on product performance. You want to choose a tool that has the capacity to handle large quantities of data from multiple sites if you want to stay truly competitive.

But getting that data is only half the battle. The other half is knowing how to use that data to create the best pricing strategy. Once you have access to pricing data from your competitors, you need to analyze it, automate it, and monitor its performance over time to ensure that your prices are actually beating the competition.

Analyzing the data. Once you have the data, you need to know how to interpret it to create a meaningful strategy. Good analytics can help companies identify factors that are often overlooked, like economic situations or product preferences that might change how pricing is set.

Automate the data extraction. Again, data needs to be fresh in order to stay competitive. Automated data extraction can pull current data from a multitude of sites at once. Automating also makes it easier to replicate and tweak data pulls so it’s not necessary to start from scratch every time.

Monitor product performance. The right extraction tool will help you monitor not only competitor pricing, but also your own product performance to see how those prices are performing for your target audience. This will help you make better pricing decisions that widen the gap between you and your competition.

Final Thoughts

When it comes to pricing, the more data you have, the better your strategy will be, the higher your profit margins will be and the more value you’ll provide for your customers.

Look for data extraction tools that can handle the challenges of pulling product data from websites. You want something that can give you fresh data on a regular basis and give you a complete view of a competitor’s pricing strategy so you know how to make better decisions.

Keep in mind that collecting the data is only part of the equation. You should also be looking at internal data – your historical sales and trends, promotions, your brand and product’s perceived value, and so on – as well as external data to create the best strategy for your business.

3 Ways Retailers Can Use Web Scraping to Improve Conversions

Conversions are the lifeblood of any online store, but they’re not always easy to come by. According to Econsultancy, only about 22% of businesses are satisfied with their conversion rates.

There’s a lot that goes into improving your conversion rates, however. You have to consider things like website design, checkout processes, site speed, SEO and a plethora of other things to really turn site visitors into customers.

But the biggest obstacles to conversions are often associated with the products themselves – What are you selling? What does it cost? Who’s talking about it? – as well as the marketplace – Who else is selling something similar? What are they charging? How can we beat their price?

[Tweet “The key here is data: You need to know what your competition is doing.”]

The key here is data: You need to know what your competition is doing. You need to be informed about what else is happening around the web in order to make the best decisions about your store and you need to understand how your own site and products are performing.

Unfortunately, there are thousands of other marketplaces out there, which means thousands of potential competitors, thousands of user generated reviews, and millions of products you need to be aware of.

What ecommerce store owner has the time to track all of that data? No one, really. But the good news is that there is a way to gather and analyze that data quickly and painlessly to improve your conversions.

The solution? Web scraping. Here are a few ways it can be used help you boost conversions.

[content_upgrade cu_id=”753″]Don’t miss: 5 Ways Amazon Uses Data to Beat Their Competition[content_upgrade_button]Click Here[/content_upgrade_button][/content_upgrade]

Product Details and Prices

Pricing is considered to be one of the four (or five) pillars of marketing, and studies show that 61% of online shoppers visit retailer or manufacturer websites to compare prices before making a purchase decision.

In fact, comparison shopping is becoming a large part of the overall online shopping experience. Comparison shopping engines exist to gather product information, including price, descriptions, shopping options, product guarantees, and so on, so that shoppers can weigh their options and choose the best deals.

These engines work by gathering product details either through web scraping (data is scoured from thousands of sites) or by having retailers submit their own product details, typically in a CSV (Comma-Separated Value) file.

While retailers can certainly build their own comparison sites or list comparisons on their own landing pages, they can also use comparative data gathered through web scraping to simply develop a better and more competitive pricing strategy.

Amazon, for example, might want to know how their products are performing against other online retailers, whether other online stores have products that Amazon doesn’t sell, or whether there are deals, promos or other price changes that may affect their own sales.

By monitoring and tracking competitor prices and product details (through web scraping tools, of course, since they track millions of products each year) they’re able to provide the best deals for shoppers.

Web scraping can also help with things like shipping times, number of selling, product availability, recommended products and other data that can help retailers create the best marketing strategy for better conversions.


Like price comparisons, retailers also use reviews and ratings to set product prices, determine which items to sell or shelf and gauge how well their brand is doing as whole.

While it’s fairly easy to jump on Yelp and see what people are saying about your business, the problem is that people aren’t just using Yelp to talk about you. In fact, studies show that social media reviews (and peer reviews in general) are becoming an essential component to the buyer journey.

Facebook, for instance, influences around 52% of all consumers’ online and offline purchases, 42% of online shoppers find recommendations from friends and family to be the most influential and 69% said they wouldn’t consider a product unless it had a review.

Reviews and ratings are also important for things like SEO and can be a factor in building lifelong customer trust and encouraging repeat purchases.

But tracking down every single mention on social media manually would be nearly impossible. If your products were also listed on bigger commerce sites like Amazon, there would also be reviews there you might not be able to track down. And there are a variety of other comparison sites that may also gather reviews you might otherwise never see.

That’s where web scraping will be your most valuable tool for collecting this data. Web scraping allows you to capture statistically relevant reviews from social media and other review sites. You can also scrape product reviews and ratings from thousands of sites at once without any effort on your part.

Real Time Data

The world of ecommerce is fast-paced, products can come on and off the market quickly and retailers can see huge spikes or dips in their conversions overnight, which is why having real-time information about your shop’s performance is essential.

Real-time data can help you see if there are areas of your site with high traffic but low conversions, for instance, in which case you might have a problem with your CTA or checkout process.

It can help you determine which internal and external links are getting the most attention or alternatively which are broken and need to be fixed. It can even help you monitor all types of user activity so you can see exactly where your conversions are slowing.

Having fresh, up-to-date information is key to making the best strategic decisions, but as with gathering social media reviews or product details, the process for tracking every competitor (or even your own website) at every minute of the day, every day, just isn’t practical.

Again, this is where web scraping comes into play. Web scraping can get you real-time data, and, most importantly, the information will be accurate. You won’t be working from stale inventories or month-old reviews.

You will know exactly what your audience is saying, what products they’re talking about and looking at, and how you rank compared to your competition, all within a matter of minutes. And if you’re an online retailer, you know just how valuable every minute is.

[content_upgrade cu_id=”753″]Curious how Amazon uses their data? Here are 5 things they do with it[content_upgrade_button]Click Here[/content_upgrade_button][/content_upgrade]

Final Thoughts

The key to using web scraping for ecommerce is to make sure you have a tool that can really do things quickly and accurately. Product details can change quickly, inventory can be moved around in a matter of seconds, and thousands of reviews are posted every minute (26,380 reviews are posted on Yelp every 60 seconds).

It’s almost impossible to gather all of that information manually, and even if you could, the time and energy it would take wouldn’t be worth the effort. But by utilizing web scraping as a resource to pull and sort that data for you, you can save an enormous amount of time.

The data you gather can be used to help you make inventory and marketing decisions, which can in turn affect the overall profitability of your store. And having this data on hand will also help you stay in the competition with those in your marketplace and allow you to keep up with the bigger retailers, too.

3 Ways Web Data Can Help You Beat Your Competition

Web data is everywhere and growing by the minute. An estimated 2.5 quintillion bytes of data is produced every day, and that number is predicted to jump to 40 zettabytes by 2020.

But what are people using that data for, exactly?

In 2016 alone there were 2.4 million searches generated on Google every minute, 700,000 Facebook logins, and over $200,000 worth of sales made on Amazon, to name a few. Experts have estimated that 90% of all the data in the world today was produced in the last few years.

Depending on your industry and end goals, there are plenty of practical applications for data to help power your business and set you apart in your marketplace. Here are a few of the biggest ways that web data can do that.

[content_upgrade cu_id=”705″]Don’t miss: 5 Insights Web Data Can Give You About Your Competitors[content_upgrade_button]Click Here[/content_upgrade_button][/content_upgrade]

Product and Price Comparison

In the world of retail, web data helps you understand your customers and improve your advertising. One of the main uses of data for eCommerce companies is to monitor the movements of direct competitors to improve their own shopping experience.

[Tweet “Web data helps you understand your customers and improve your advertising.”]

You can use web scrapers to extract product data from other eCommerce stores for their prices, descriptions, images, and customer reviews, for example, and analyze that data for either affiliation or comparison. You can track the stock availability and prices of products to ensure that you’re carrying something that your rivals are not.

You could even create your own web crawler to extract product feeds, images, prices, and other details from multiple sites at once to create your own price comparison site. Or you could use that information to predict the best products for your own shoppers.

Amazon, for instance, uses web crawlers to extract product information from competing online marketplaces to identify brands that are listing products elsewhere, but not on Amazon. They then use this data to reach out to those brands and encourage them to list the products on Amazon’s marketplace as well.

But you don’t have to be in retail to take advantage of data for price comparison. Many travel companies will track prices from other airlines’ websites in real time to give consumers the best fare options. Expedia uses web scraping to pull information from Global Distribution Services and then gives that data directly to their customers for comparison.

Web data can also be used for things like comparing shipping times, number of sellers, availability, or identical products for price matching. These insights can help any business create actionable steps to better serve their customers.

And pricing and product optimization techniques are good for the bottom line, as even a 5% reduction in variable costs can improve gross profit margins by 30% or more.

Online Reputation Management

Web data can do more than give you transactional insights. You can also observe intentions by monitoring customer values and opinions about your business.

Companies from any industry can use web data to monitor their online reputation and presence using behavioral data. You can track customer-generated reviews as well as things like user reaction on social media channels.

Online reviews can be a powerful tool for growth in particular. One study found that 88% of consumers trust online reviews as much as personal recommendations and 72% of consumers say positive reviews make them trust businesses more. Millennials especially trust user-generated content 50% more than other media. It’s essential that companies stay on top of their online reviews in order to target those customer segments.

Through web scraping, you could crawl various sites to compile and analyze reviews, ratings, and comments for better insight into what customers really think about your business. Web data could also reveal trending topics and demographic facts about your users that you might otherwise miss, like gender, age group, or geolocation.

If you can understand customers on a more individual level, you can segment and target customers more accurately. You can better understand their preferences, needs, and current priorities.

But in order to do that, you need access to their online behavior, shopping history, social conversations to know whether or not your marketing is effective. Focused targeting needs specific predictive models built through web data extraction.

By using data to narrow in on customer needs, you can launch products that are different than others being sold, explore new markets, and understand gaps in the current marketplace.

This enables you to stay ahead of the competition and give customers a more personalized experience and creates brand loyalty. You can quickly set yourself apart as a company who understands what they want better than anyone else by including customer behavior toward and beliefs about your company and your competitors in your product development.

Lead Generation

Once you have an understanding of what your customers really want, you can use that data to create targeted ads, improve your SEO, and generate leads.

Many marketing teams use data to chase leads on their own, but often that data comes from stale lists that don’t always reflect real-time information. In fact, one CSO Insight study reported that 42% of sales reps feel they don’t have the right information before making a call, and another study found that 35% of those surveyed said the biggest barrier to lead generation success is the lack of quality data.

SearchCRM site editor Tim Ehrens notes that using poor data can affect sales:

“Customer data management often falls to the bottom of the priority list. Organizations get bogged down with more pressing issues, such as cutting costs or keeping daily operations running. But relying on poor-quality customer data almost always frustrates customers — and many of them take their business elsewhere.”

Compiling high-quality prospect lists using web data, on the other hand, can help businesses deliver campaigns directed at more qualified leads, which can turn users into buyers. This process can be especially time-consuming to do by hand. Instead, you could simply scrape leads from the online directory of your industry trade organization and use that data to proactively reach out to potential prospects.

You can also use data to improve how you appear on search engines, generating leads through a boost in organic traffic. Using data markups, you could index your content in such a way that gives users a better chance of finding you.

Kissmetrics explains it this way:

“[The markup] tells the search engine what that content means. For example, let’s say the word “Neil Patel” appears on an article. The search engine sees this, and produces a SERP entry with “Neil Patel.” However, if I put the right schema markup around the name ‘Neil Patel,’ I’ve just told that search engine that ‘Neil Patel’ is the author of the article, not just a couple random words. The search engine then provides results that display better information for the user who was searching for ‘Neil Patel.’”

By using web data to define who you are as a company and better understand your customers, you can improve your advertising efforts and your ability to be found in ways that automatically set you apart from the competition.

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Final Thoughts

With the power of the Internet, and the continued growth of data, business leaders looking to propel their business forward will need to better understand their market and their customers.

As more data is generated, the higher the need will be for companies to collect that data quickly and use it to better serve their audience.

As data-innovator Arthur Nielsen once said, “The price of light is less than the cost of darkness.” In short, data impacts your bottom line. If you can find a way to use it to power your business, you will set yourself apart in the market and gain the edge over the competition.