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.

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.

Free download: Pricing Strategy Guide for Ecommerce

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.

Need help pricing competitively? Check out our free pricing guide

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.

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3 Challenges to Getting Product Data from Ecommerce Websites

Online retailers and ecommerce businesses know that there’s nothing more important than your product and your customer (and how your customer relates to your product).

Making sure that product information on your site is accurate and up-to-date is essential to that customer relationship. In fact, 42% of shoppers admit to returning products that were inaccurately described on a website, and more often than not, disappointment in incorrectly listed information results in lost loyalty.

That’s where having access to high-quality product data can come in handy. Product feeds can help keep that data organized and availed for review, so you can easily assess if there is information missing from your site that may be invaluable to your customer.

But aside from keeping your own product information up to date, product data is also valuable for many other facets of your business. It can help you purchase or curate products, compare competitor offerings, and even drive your marketing decisions.

The trouble, however, is that it can be notoriously difficult to collect, and unless you have the ability to gather that information quickly and comprehensively, it may not do you any good. Here’s what you should know.

Don’t miss: 5 More Things Retailers Can Do With Product Data

Why Product Data Is So Useful

Product data from ecommerce sites can be used for a variety of purposes throughout your company, both from internal and external sources. Here are just a few areas you can use product data to drive sales.

Sales strategy. Understanding your competitor’s strategy is important when developing your own. What are other brands selling that you’re not? What areas of the market are you covering that they’re not? Knowing what products are selling elsewhere helps you get a leg up on the competition and improve your product offering for better sales.

Pricing data. Product data allows you to find the cheapest sources of a product on the web and then resell or adjust your prices to stay competitive.

Curating other products. Many sites collect products from other retailers and feature them on their own pages (subscription boxes or resellers, for example) or to increase the number of products they sell on their own site. Curating those products from multiple sites that have their own suppliers and retailers with their own product data can make the whole process rather complex, however.

Affiliate marketing. Some sites might embed affiliate links in product reviews, monetize user-generated content with those links and then build product-focused inventories based on consumer response. In order to do all of that, you need product data. Product data can help build any affiliate sites or networks and help give the most accurate inventory information to marketers.

Product inventory management. Many ecommerce sites rely on manufacturers to provide data sets with specific product information, but collecting, organizing and managing that data can be difficult and time consuming. APIs and other product data scraping tools can help collect the most accurate data from suppliers and manufacturers to ensure that databases are complete.

There are plenty more things you can do with data once it’s collected, but the trick is that you need access to that data in the first place. Unfortunately, that data can be harder to gather than you might think.

Challenges of Scraping Product Data

There are a few challenges that may hinder your ability to use product data to inform your decisions and improve your own product offerings.

Challenge #1: Getting High-Quality Data

High-quality data drives business, from customer acquisition, sales, marketing and almost every touchpoint in the customer journey. Poor data can impact the decisions you make about your brand, your competition, and even your product offerings. The more comprehensive and accurate the data is, the higher the quality.

Quality data should contain all relevant product attributes for each individual product, including data fields like price, description, images, reviews, and so on.

When it comes to pulling product feeds or crawling ecommerce sites for product data, there are several obstacles that you might face. Websites may have badly formatted HTML code with little or no structural information, which may make it difficult to extract the exact data you want.

Authentication systems may also prevent your web scraper from having access to complete product feeds or tuck away important information behind paywalls, CAPTCHA codes or other barriers, leaving your results incomplete.

Additionally, some websites may be hostile to web scrapers and prevent you from extracting even basic data from their site. In this instance, you need advanced scraping techniques.

Challenge #2: Getting Properly Structured Data

Merchants may also receive incomplete product information from suppliers and populate it later on, after you’ve already scraped their site for product information, which would require you to re-scrape and reformat data for each unique site.

If you wanted to pull data from multiple channels, your web scraper would need to be able to identify and convert product information into readable data for every site you want to pull data from. Unfortunately, not all scrapers are up to the challenge.

Product prices can also change frequently, which results in stale data. This means that in order to get fresh data, you would need to scrape thousands of sites daily.

Challenge #3: Scaling Your Web Scraper

If you were going to pull data from multiple sites, or even thousands of sites at once (or even Amazon’s massive product database), you would either need to build a scraper for each specific site or build a scraper that can scrape multiple sites at once.

The problem with the first option is that it can be time consuming to build and maintain tens or even a hundred scrapers. Even Amazon with their hefty development team and budget doesn’t do that.

Building a robust scraper that can pull from multiple sources can also be difficult for many companies, however. In-house developers already have important tasks to handle and shouldn’t be burdened with creating and maintaining a web scraper on top of their responsibilities.

How Do You Overcome These Challenges?

To get the most comprehensive data, you need to gather product data from more than one source – data feeds, APIs, and screen scraping from ecommerce sites. The more places you can pull data from, the more complete your data will be.

You will also need to be able to pull information frequently. The longer you wait to gather data, the more that data will change, especially in ecommerce.

Prices change, products are sold out and added on a daily basis, which means that if you want the highest quality data, you will need to pull that information as often as possible (at least once a day ideally).

You will also need to determine the best structure for your data (typically JSON or CSV, but it can vary) based on what your team needs. Whatever format you choose should be organized efficiently in case updates need to be made from fresh data pulls or you need to integrate your data with other software or programs.

The best way to handle each of these issues is to either build a robust web scraper that can handle all of these at once or to find a third party developer that has one available to you (which we do here). Otherwise you will need to address each of these issues individually to ensure you’re getting the best data available.

Here are 5 more surprising things you can do with product data

Final Thoughts

Unless you have high-quality data, you won’t be able to make the best decisions for your customers, but in order to get the highest quality data, you need a robust web scraper that can handle the challenges that come along the way.

Look for tools that give you the ability to refresh your product data feeds frequently (at least once a day or more), that give you structured data that helps you integrate that information quickly with other resources, and that can give you access to as many sites as you need.

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How Computer Vision Helps Get You Better Web Data

In 1966, AI pioneer Marvin Minsky instructed a graduate student to “connect a camera to a computer and have it describe what it sees.” Unfortunately, nothing much came of it at the time.

But it did trigger further research into the computer’s ability to replicate the human brain. More specifically, how the eyes see, how that information gets processed in the brain, and how the brain uses that information to make intelligent decisions.

The process of copying the human brain is incredibly complicated, however. Even a simple task, like catching a ball, involves intricate neural networks in the brain that are near impossible to replicate (so far).

But some processes are more successfully duplicated than others. For instance, just as the human eye has the ability to see the ball, computer vision enables machines to extract visual data in the same way.

It can also analyze and, in some cases, understand the relationship between the visual data it receives from images, making it the closest thing we have to a machine brain. While it’s not perfect at recreating the visual cortex or replicating the brain (yet), it still has some serious benefits for data users where it is in the process right now.

Don’t miss: 10 Innovative Ways Companies Are Using Computer Vision

Computer Vision and Artificial Intelligence

In order to understand exactly how valuable computer vision can be in gathering web data, you first need to understand what makes it unique – that is to say, what separates it from general AI.

According to Gum Gum VP Jon Stubley, AI is simply the use of computer systems to perform tasks and functions that usually require human intelligence. In other words, “getting machines to think and act like humans.”

Computer vision, on the other hand, describes the ability of machines to process and understand visual data; automating the type of tasks the human eye can do. Or, as Stubley puts it, “Computer vision is AI applied to the visual world.”

One thing that it does particularly well is gather structured or semi-structured data. This makes it extremely valuable for building databases or knowledge graphs, like the one Google uses to power its search engine, which is then used to build more intelligent systems and other AI applications.

Advantages of the Knowledge Graph

Knowledge graphs contain information about entities (an object that can be classified) and their relationships to one another (e.g. a Corolla is a type of car, a wheel is a part of a car, etc.).

Google uses their knowledge graph to recognize search queries as distinct entities, not just keywords. When you type in “car” it won’t just pull up images that are labeled as “car,” it will use computer vision to recognize items that look like cars, tag them as such, and feature them, too.

This can be helpful when searching for data, as it enables you to create targeted queries based on entities, not just keywords, giving you more comprehensive (and more accurate) results.

How Computer Vision Impacts Your Data

Computer vision also helps you identify web pages quickly, allowing you to strategically pull product information, images, videos, articles and other data without having to sort through unnecessary information.

Computer vision techniques enable you to accurately identify key parts of a website and extract those fields as structured data. This structured data then enables you to search for specific image types or text, or even specific people.

Computer vision also allows you to (among other things):

  • Analyze images – Using tagging, descriptions, and domain-specific models, it can identify content and label it accordingly, apply filters and settings, and separate images by type or even color scheme
  • Read text in images – It can recognize words even if they are embedded within images or otherwise unable to be extracted, copied or pasted into a text document (called OCR, or Optical Character Recognition)
  • Read handwriting – If information on a page is handwritten or an image of handwriting, it can also recognize and translate it into text (OCR)
  • Analyze video in real time – Computer vision enables you to extract frames from videos from any device for analysis

Certain ecommerce sites use computer vision to perform image analysis in their predictive analytics efforts to forecast what their customers will want next, for example. This can save an enormous amount of time when it comes to pulling, analyzing and using that data effectively.

Because it works on structured data, computer vision also gives you cleaner data that you can then use to build applications, inform your marketing decisions. You can quickly see patterns in data sets and identify entities that you may have otherwise missed.

Learn more about what you can do with computer vision here

Final Thoughts

Computer vision is a field that continues to grow at a rapid pace alongside AI as a whole. One of its biggest boons is the ability to power databases of knowledge that power search engines. The more that machines learn to recognize entities on sites and in images, the more accurate the results are.

But more importantly, computer vision can be used to drive better results when data is extracted from the Web, enabling users to pull accurate, structured data from any site without sacrificing quality and accuracy in the process.

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Here’s Why You Need to Clean Your Marketing Data Regularly

Data is becoming increasingly valuable to marketers.

In fact, 63% of marketers report spending more on data-driven marketing and advertising last year, and 53% said that “a demand to deliver more relevant communications/be more ‘customer-centric’” is among the most important factors driving their investment in data-driven marketing.

Data-driven marketing allows organizations to quickly respond to shifts in customer dynamics – to see why customers are buying certain products or leaving for a competitor, for instance – and can help improve marketing ROI.

But data can only lead to results if it’s clean, meaning that if you have data that’s corrupt, inaccurate, or otherwise stale, it’s not going to help you make marketing decisions (or at the very least, your decisions won’t be as powerful as they could be).

This is partly why data cleansing – the process of regularly removing outdated and inaccurate data – is so important, but there’s more to the story than you might think.

Here’s why you shouldn’t neglect to clean your data if you want to use it to power your business.

Download our FREE Data Cleansing Best Practices cheat sheet

Why Clean Marketing Data Is Important

Marketing data is most often used to give marketers a glimpse into customer personas, behaviors, attitudes, and purchasing decisions.

Typically, companies will have databases of customer (or potential customer) data that can be used to generate personalized communications in order to promote a particular product or service.

Outdated, inaccurate, or duplicated data can lead to outdated and inaccurate marketing – imagine tailoring a marketing campaign for customers that purchased a product several years ago that no longer need it. This, in turn, leads to missed opportunities, loss of sales and an imprecise customer persona.

That’s partly why cleaning your data – scrubbing it of those inaccuracies – is so important:

Clean data also helps you integrate your strategies across multiple departments. When different teams work with separate sets of data, they’re creating strategies based on incomplete information or a fragmented customer view. Consistently cleaning your data allows all departments to work effectively toward the same end goal.

It’s important to note that data cleansing can be done either before or after it’s in your database, but it’s best if data is cleansed before being entered into a database so that everyone is working from the same optimized data set.

What Makes Data “Clean,” Exactly?

But what exactly does clean data look like? There are certain qualifiers that must be met for data to be considered truly clean (in other words, high quality). This criteria includes:

  • Validity – Data must be measurable as “accurate” or “inaccurate.” For example, values in a column must be a certain type of data (like numerical) or certain data may be required in certain fields.
  • Accuracy – Customer information is current and as up-to-date as possible. It’s often difficult to achieve full data accuracy, but it should have the most current information as much as humanly possible.
  • Completeness – All data fields are filled in.
  • Consistency – Data sets should be consistent, but there may be times where you have duplicate data and you don’t know which values are correct. Clean data contains no duplicate information.
  • Uniformity – Data values should be consistent. If you’re in the Pacific Time Zone, for example, your time zones will all be PT, or if you track weight, each unit of measure is consistent throughout the data set.

Your data should also have minimal errors – the stray symbol here, spelling error there – and be well organized within the file so that information is easy to access. Clean data means that data is current, easy to process and as accurate as possible.

How to Clean Your Data

While some companies have processes for regularly updating their database, not all have plans in place for cleansing that data.

The data cleansing process typically involves identifying duplicate, incomplete or missing data and then removing those duplicates, appending incomplete data where possible and deleting errors or inconsistencies.

There are usually a few steps involved:

  • Data audit – If your data hasn’t already been cleansed before it enters your database, you will need to sift through your current data to find any discrepancies.
  • Workflow specification – The data cleansing process is determined by constraints set by your team (so the program you run knows what type of data to look for). If there’s data that falls outside of those constraints, you need to define what and how to fix it.
  • Workflow execution – After the cleansing workflow is specified, it can be executed.
  • Post-processing – After the workflow execution stage, the data is looked over to verify correctness. Any data that was not or could not be corrected during the workflow execution stage is done manually, when possible. From here, you repeat the process again to make sure nothing was left behind or overlooked to ensure fully cleansed data.

When done correctly, successful data cleaning should detect and remove errors and consistencies and provide you with the most accurate data sets possible. Some companies choose to clean their data in-house, while others outsource the process to third party vendors.

If outsourced, it’s important to provide your data-cleansing vendors with the constraints of your data sets so they know which data to look for and where discrepancies may be hiding.

Of course, if you’re regularly collecting data from an external source, you want to make sure that data is clean before it comes into your database so you have the most accurate data from the start.

This is why we’ve developed programs like our Knowledge Graph, which enables us to create clean data sets when we gather data from multiple sources. This keeps our records as accurate (and useful) as possible.

Make sure you’re following these Data Cleansing Best Practices

Final Thoughts

It’s important to remember that data cleansing isn’t a one-time process, since data is constantly in flux.

It’s estimated that around 2% of marketing data becomes stale every month, so you want to make sure that the data you’re bringing in is as accurate as possible (to minimize the amount of cleansing you have to do later) and that you clean your data regularly to maximize your marketing efforts.

Continuous cleansing of data is necessary for accuracy and timeliness, and for ensuring that every department has access to clean, accurate and comprehensive data.

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What’s the Difference Between Web Scraping and Diffbot?

Web scraping is one of the best techniques for extracting important data from websites to use in your business or applications, but not all data is created equal and not all web scraping tools can get you the data you need.

Collecting data from the web isn’t necessarily the hard part. Web scraping techniques utilize web crawlers, which are essentially just programs or automated scripts that collect various bits of data from different sources.

Any developer can build a relatively simple web scraper for their own use, and there are certainly companies out there that have their own web crawlers to gather data for them (Amazon is a big one).

But the web scraping process isn’t always straightforward, and there are many considerations that cause scapers to break or become less efficient. So while there are plenty of web crawlers out there that can get you some of the data you need, not all can produce results.

Here’s what you need to know.

Don’t Miss: 9 Things Diffbot Does That Others Don’t

Getting Enough (of the Right) Data

There are actually plenty of ways you can get data from the web without using a web crawler. For instance, many sites have official APIs that will pull data for you. For example, Twitter has one here. If you wanted to know how many people were mentioning you on Twitter, you could use the API to gather that data without too much effort.

The problem, however, is that your options when using site-specific API are somewhat limited; you can only get information from one site at a time, and some APIs (like Twitter) are rate limited, meaning that you have to pay fees to access more information.

In order to make data useful, you need a lot of it. That’s where more generic web crawlers come in handy; they can be programmed to pull data from numerous sites (hundreds, thousands, even millions) if you know what data you’re looking for.

The key is that you have to know what data you’re looking for. Your average web crawler can pull data, but it can’t always give you structured data.

If you were looking to pull news articles or blog posts from multiple websites, for example, any web scraper could pull that content for you. But it would also pull ads, navigation, and a variety of other data you don’t want. It would then be your job to sort through that data for the content you do want.

If you want to pull the most accurate data, what you really need is a tool that can extract clean text from news articles and blog posts without extraneous data in the mix.

This is precisely why Diffbot has tools like our Article API (which does the above) as well as a variety of other specific APIs (like Product, Video, and Image and Page extraction) that can get you the right data from hundreds of thousands of websites automatically with zero configuration.

How Structure Affects Your Outcome

You also have to worry about the quality of the data you’re getting, especially if you’re trying to extract a lot of it from hundreds or thousands of sources.

Apps, programs and even analysis tools – or anything you would be feeding data to – for the most part rely on highly structured data, which means that the way your data is delivered is important.

Web crawlers can pull data from the web, but not all of them can give you structured data, or at least high-quality structured data.

Think of it like this: You could go to a website, find a table of information that’s relevant to your needs, and then copy it and paste it into an Excel file. It’s a time-consuming process, which a web scraper could handle for you en masse, and much faster than you could do it by hand.

But what it can’t do is handle websites that don’t already have that information formatted perfectly, like sites with badly formatted HTML code with little to no underlying structure, for example.

Sites with CAPTCHA codes, pay walls, or other authentication systems may be difficult to pull data from with a simple scraper. Session-based sites that track users with cookies, those that have server admins that block access to non-servers, or those that have a lack of complete item listings or poor search features can all wreak havoc when it comes to getting well-organized data.

While a simple web crawler can give you structured data, it can’t handle complexities or abnormalities that pop up when browsing thousands of sites at once. This means that no matter how powerful it is you’re still not getting all the data you could possibly get.

That’s why Diffbot works so well; we’re built for complexities.


Our APIs can be tweaked for complicated scenarios, and we have several other features, like entity tagging that can find the right data sources from poorly structured sites.

We offer proxying for difficult-to-reach sites that block traditional crawlers, as well as automatic ban detection and automatic retries, making it easier to get data from difficult sites. Our infrastructure is based on gigablast, which we’ve open sourced.

Why Simple Crawlers Aren’t Enough

There are many other issues with your average web crawler as well, including things like maintenance and stale data.

You can design a web crawler for specific purposes, like pulling clean text from a single blog or pulling product listings from an ecommerce site. But in order to get the sheer amount of data you need, you have to run your crawler multiple times, across thousands or more sites, and you have to adjust for every complex site as needed.

This can work fine for smaller operations, like if you wanted to crawl your own ecommerce site to generate a product database, for instance.

If you wanted to do this on multiple sites, or even on a single site as large as Amazon (which boasts nearly 500 million products and rising), you would have to run your crawler every minute of every day across multiple clusters of servers in order to get any fresh, usable data.

Should your crawler break, encounter a site that it can’t handle, or simply need an update to gather new data (or maybe you’re using multiple crawlers to gather different types of data), you’re facing countless hours of upkeep and coding.

That’s one of the biggest things that separates Diffbot from your average web scraping: we do the grunt work for you. Our programs are quick, easy to use (any developer can run a complex crawl in a matter of seconds).

As we said, any developer can build a web scraper. That’s not really the problem. The problem is that not every developer can (or should) spend most of their time running, operating, and optimizing a crawler. There are endless important tasks that developers are paid to do, and babysitting web data shouldn’t be one of them.

Here’s a rundown of what makes Diffbot so different and why it matters to you

Final Thoughts

There are certainly instances where a basic web scraper will get the job done, and not every company needs something robust to gather the data they need.

However, knowing that the more data you have (especially if that data is fresh, well-structured and contains the information you want) the better your results will be, there is something to be said for having a third party vendor on your side.

And just because you can build a web crawler doesn’t mean you should have to. Developers work hard building complex programs and apps for businesses, and they should focus on their craft instead of spending energy scraping the web.

Let me tell you from personal experience, writing and maintaining a web scraper is the bane of most developer’s existence. Now no one is forced to draw the short straw.

That’s why Diffbot exists.

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What Every Business Should Know About Deep Learning

Over the last few years, deep learning – a branch of AI inspired by the structure of the human brain – has seeped its way into the technology we use on a daily basis.

Facebook uses deep learning to generate sophisticated algorithms that can be applied to sharable data like text, pictures, and videos. Google uses it in its voice and image recognition algorithms.

Researchers at MIT use it to predict the future. Amazon uses it to improve their smart speaker, Alexa, and Microsoft is using it to create speech software for Skype that can easily translate languages.

Some of the most powerful tech companies in the world have been taking advantage of deep learning for a while now, but you don’t have to be Amazon or Microsoft to benefit from deep learning.

In fact, many businesses can use deep learning in combination with their own data as well as data scraped from the web to power their operations.

Get 12 More Practical Uses for AI and Deep Learning Here

How Does Deep Learning Work?

The term “deep learning” is relatively new, but the concept has been around for some time, especially in how it relates to AI, Big Data, and analytics. Deep learning is, in essence, machine perception (though it shouldn’t be confused with Machine Learning).

With deep learning, data is fed through neural networks in order to analyze and classify it, creating smarter algorithms. The more data a machine receives, the more it learns about how to process and apply that data.

Chris Nicholson, co-founder of, describes it as a way machines can interpret sensory data the same way humans do:

“Two main ways we interpret things are by naming what we sense; e.g. we hear a sound as we say ourselves ‘That’s my daughter’s voice.’ Or we see a haze of photons and we say ‘That’s my mother’s face.’ If we don’t have names for things, we can still recognize similarities and dissimilarities. […] Now imagine that, with deep learning, you can classify, cluster or predict anything you have data about: images, video, sound, text and DNA, time series (touch, stock markets, economic tables, the weather).That is, anything that humans can sense and that our technology can digitize. You have multiplied your ability to analyze what’s happening in the world by many times. With deep learning, we are basically giving society the ability to behave much more intelligently, by accurately interpreting what’s happening in the world around us with software.”

He points out that in order for this process to work, machines need training. He adds that to train a neural network to learn the same way the human brain does, it needs feedback that tells it when it gets something right and when it gets it wrong. If an algorithm fails to recognize a face in an image, for example, it will need to adjust.

The more data that a machine receives and the more training it has to recognize the right input, the smarter (and more useful it will be).

Deep Learning and Structured Data

In order to train machines to use data more intelligently, they need a lot of it. More specifically, they need a lot of structured data.

Zeeshan Zia, Senior Data Scientist at Microsoft, describes the need for structured data to power deep learning machines as akin to the human brain processing structure:

“In the pictures that almost always fall on our retinas, near-by pixels are typically close to each other in value, with sudden edges separating different regions. This is structure! […] When you talk to your friends, it’s not random gobble-de-gook. [You’re] using commonly known sounds (corresponding to certain words) spaced by a natural range of blank space.”

In the same way that the human brain processes the natural structure of language, images, and sounds, deep learning allows a machine to make judgments based on structured data.

But the key is that it has to be structured. The more structured data a company has access to, the better the outcome will be.

How Businesses Can Use Deep Learning

What are those outcomes, exactly? As mentioned, there are several practical applications that businesses can use deep learning for, even if they’re not tech giants like Amazon.

A few practical applications for companies include things like chatbots, news aggregators, image or document tagging, and ecommerce recommendations.

Build a chatbot. A chatbot is a digital agent that responds (as humans do) to common questions. They can be particularly helpful for ecommerce companies looking to expand or improve on their customer service, as the chatbot can respond to customers through a website or even Facebook Messenger. Companies can provide excellent service without having to hire additional manpower to run their customer service operations.

Create a news aggregator. With advanced natural language processing techniques like deep learning (among others), you can filter out news stories based on sentiment and then present it back to the readers. This can help you become a thought-leader in your industry or, at the very least, keep you updated on what’s happening in your industry around the world.

Tag images and flag documents. Image tagging is one of the first real breakthroughs to come from deep learning. Unlike text, which can be easier to classify, images can be harder to understand for machines because they require more pixel data. Thankfully, deep learning is improving and companies can use image tagging for things like social media marketing or even finding and flagging PDF documents or other images internally.

Generate product recommendations. Recommendation systems are a near essential for ecommerce businesses, especially with the sheer number of products on the market. Online retailers can use deep learning to automatically populate accurate product recommendations, which can increase the purchasing power of your site.

Identify your brand (or competitors) online. Ditto Labs uses deep learning to identify company brands and logos in photos posted to social media. You can also use image recognition to find out who’s talking about you online. Indico, for example, uses it to identify positive or negative sentiments about their business as well as topics and other keywords.

While those things may seem like small potatoes compared to Facebook or Amazon, keep in mind that deep learning will give you an advantage over your competitors no matter what, even if you’re not one of the “big guys.”

What else can you do with deep learning? Here are 12 more examples

Final Thoughts

There is a lot to be said about the benefits of deep learning, and so far we’ve only scratched the surface of what it’s capable of. As machine learning and AI progress, deep learning will become more and more relevant in terms of its applications for business.

For those wanting to take advantage of deep learning to gain an edge over the competition, it’s important to consider a few things. First, you will need to have access to a lot of structured data (which you can get with web scraping techniques like ours).

Second, you will need to know what you can do with it. Deep learning has a lot of practical applications, so the sky is the limit. Figure out what you really need from your business – do you want to search for brands online or do you really need a product recommender? – so that you know what kind of deep learning process you need to engage in.

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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?

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.

Don’t miss: 5 Ways Amazon Uses Data to Beat Their Competition

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.

Curious how Amazon uses their data? Here are 5 things they do with it

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.

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How to Be Your Company’s Data Scientist (Without Actually Being One)

Data Scientist In Their Natural Habitat

Harvard Business Review recently dubbed data scientist the “sexiest job of the 21st Century” for its growing importance in relation to big data. But what exactly is a data scientist, what do they do, and why does it matter for your business?

In the simplest terms, data scientists analyze big data to determine the best applications for it. Their role is similar to that of a Chief Data Officer, but how they gather and analyze that data differs greatly.

While a CDO often focuses on the “big picture” of data – internal data policies, procedures, standards and guidelines, and so on – a data scientist (or Chief Data Scientist) deals specifically with unstructured data on a massive scale, and uses statistics and mathematics to find practical applications for it.

Though the role of data scientist, and data science in general, is necessary for businesses looking to understand the complexities of big data and gain an edge over their competitors, not every business can afford to hire one.

But even if you’re not ready to onboard a data scientist, that doesn’t mean you can’t reap the benefits of data science. Almost any company can take advantage of data science to boost the power of data for their business.

Here’s what you need to know.

Don’t miss: The “Data Scientist” Web Scraper Checklist

What Is Data Science?

It’s important to understand that data science and big data are not the same thing.

“Big Data” is a buzzword that many companies are starting to use, but it’s an umbrella term for many different types of data and applications for it. While data science falls under that umbrella, it has its own purpose.

Big Data is any data that can be analyzed for insights and that can help businesses make better decisions. It can include unstructured, structured, internal or external data, or any combination thereof. It’s essentially an umbrella term for all the data a company uses to make strategic moves.

Data science, on the other hand, comprises the processes related to cleansing, preparing and analyzing that data. It gives value to Big Data, allowing organizations to take noisy or irrelevant information and turn it into something relevant and useful.

Think of Big Data as a proverbial haystack in which you’re searching for a needle. Even if you know what needle you’re looking for (what value you want from the data), you still have to sort through a pile of irrelevant information to get it.

Data science is the machine that can sort through the hay to find the needle. In fact, it not only helps you find the needle, it turns all the hay into needles. It can tell you what value all the needles have so you know that you’re using the right one.

This makes data science essential for any business looking to actually use the data they gather. But how do you incorporate it into your business, exactly? What if you don’t have a data scientist to help?

How to Leverage Data Science

Typically, a data scientist’s job is to collect large amounts of data and put it into a more usable format. They look for patterns, spot trends that can help a business’s bottom line, and then communicate those patterns and trends to both the IT department and C-Level management.

One of the biggest tools that data scientists use to do all of this is web scraping.

They will use web scraping (or web crawler) programs – often built from scratch – to extract unstructured data from websites, and then manually structure it so it can be stored and analyzed for various purposes.

This process is often extremely time-consuming, however, and requires a deep knowledge of programming languages along with that of machine learning, mathematics and statistics in order to draw out the right results. And that’s usually why companies hire data scientists: they need a dedicated person to do the heavy lifting.

But you don’t necessarily have to hire a data scientist to get similar results.

Many companies that don’t have the resources or ability to hire a full-blown data scientist are taking advantage of web scraping tools (like us) to sort and analyze that data themselves.

This means that almost anyone within an organization (especially those with programming knowledge or an understanding of data, like an IT leader or CDO) can collect and analyze data like a data scientist, even if they’re not one.

Tips for Being a “Data Scientist”

But how do you get the most value if you’re just using a web scraping tool in place of an actual data scientist? Here are a few things to keep in mind.

1. Know what data is important

Data scientists can usually tell you what data is valuable and what data is just hay in the haystack. Before you choose or build a web scraping tool, you’ll need to understand which data you actually need.

An ecommerce company looking to gather product information from their competitors, for example, may want product URLs but not URLs from a blog or miscellaneous page. Your web scraper should be able to tell the difference.

Make a list of goals that you want to achieve so you know what data can be pulled. Focus on solving problems that have real and immediate business value.

2. Make sure your data gathering is easy

If you’re not hiring a data scientist to pull and analyze your data, you may find that the process is rather time-consuming. Your web scraper should be able to pull data fairly effortlessly on your part, otherwise, it’s not much of a time saver.

You also want to make sure that it can pull data as often as you need it. Data can become stale very quickly, so scraping or crawling for new data will be an important part of the process.

3. Leverage external data

Both internal data and external data have value, but external data (user-generated data from social media, competitors, partners, etc.) can provide you with a bigger picture.

External data can give you real-time updates on industry insights, customer activity, and product trends that you may miss with internal data alone.

Again, you will have to make sure that you’re pulling the right kind of external data, however. Data scientists focus on cleansing unstructured data to make it more manageable, so your web scraper should be able to do that without much hassle on your end.

Here’s what your web scraper should do to equal a data scientist

Final Thoughts

Of course, having a dedicated data scientist who really understands the math, statistics, and coding involved with data science is a huge benefit. But if that’s not possible for your business, having access to data science tools – like web scraping – will help bridge the gap.

Just be sure that the tool you choose is comprehensive enough to cover the roles that a data scientist would normally fill.

You will want to ensure that your web scraper can pull the exact data you need, as often as you need it, and that it’s cleansed (organized) in a way that you can understand. Your web scraper “data scientist” should bring as little stress to your organization as possible.

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Why Data-Driven Content Marketing Doesn’t Always Mean Blogging

94% of small businesses, 93% of B2Bs, and 77% of B2Cs use content marketing in some form or another.

Yet many are still struggling to create content that actually boost their rankings. Why? Well, for one, they’re not using data to power their content.

Data offers insights into what customers are reading about, where they’re going to get their information, and what type of content they’re consuming (and believe it or not, it’s more than just blogs). Without understanding these variables, any content strategy is merely a guessing game at best.

If you’re serious about improving your SEO using data-driven content marketing, or you just want to understand how to use data to drive better marketing results, here’s what you need to know.

Download your copy of our Data-Driven Content Marketing Roadmap

Why Data-Driven Content Is Important

The amount of content being published on the web each day is staggering. It’s reported that each second around 1,400 blogs are posted, 277,000 tweets are sent, 2,460,000 Facebook posts are shared, 26,380 users review something on Yelp, and 4,000,000 search queries are received by Google.

But how much of this content is actually impacting its intended audience?

Most businesses that dip their toes into content marketing do a decent job of creating content, but few actually track their content’s metrics or assess their overall ROI. This is partly due to a lack of understanding about how data affects content.

Being able to monitor trends, collect data about platform engagement, and track review or news sites for pertinent information is essential to developing content that actually reaches people.

Data can help answer questions like, “Who are we selling to?” “What topics resonate with them?” and “Who and where are they getting their information from?”

Popular retailer Kohl’s, for example, uses both internal and external data to track customer behavior in-store and then creates targeted email campaigns based on their findings. They also analyze their online data to find relevant content to send to customers in multiple mediums.

Without data, marketing to a mass audience is a shot in the dark. You may hit on relevant content, but you’ll never be able to repeat your results. Data helps pinpoint real-time user activity so you can narrow down your marketing efforts in meaningful ways.

But just because data is essential to seeing results doesn’t mean every company knows how to successfully use it to drive their content..

How Companies Use Data-Driven Content Marketing

One of the biggest driving factors for content creation is SEO. Search engines like Google and Bing often reward websites that publish dynamic content with better rankings. This is partially why so many companies are now using frequent, fresh content to improve their online visibility.

So where does data fit into the process?

Some digital marketers use web crawlers and other data scraping techniques to scan competitors’ sites and analyze trending topics. Typically, they’ll scrape specific company pages or even social media networks to see what people are saying about certain topics, companies and/or products and then create content relevant to those trends.

If they find a mention related to their business, industry or product, they might extract data like related posts, comments, URLs, hashtags, and even tweets and number of likes. They will then use that data to generate relevant content.

They will even use data to perform content audits on their own sites for better ROI. Kissmetrics actually uses data scraping to create a list of all of their own blog’s content to determine which topics rank the best and which titles and keywords have the highest click-through rates. They also use that data to build a list of the leading influencers in their industry.

Data scraping can also be used for content related things like:

  • Getting product reviews from retailers to determine customer pain points
  • Finding news sites to curate content for your own website visitors
  • Crawling sites for statistics for general marketing research

While the application of data-driven marketing is vast, the real question for many businesses still remains the same: How will we use this data to create content and what type of content should we create?

For the majority of businesses, the output of this data is blogging. Surveys show that 76% of B2C businesses have a blog and/or participate in frequent writing-related content marketing efforts. While blogging is a significant generator of SEO and inbound leads, it’s not the only thing that can reach customers.

In fact, limiting yourself to only blogging means you restrict the power of data to drive your content, and in turn, cap your ability to see real results.

Why Data Needs More Than Blogs

Over half of all B2C businesses consider creating engaging content a significant obstacle to growing their business, but the content itself might not be the problem – it could be the way content is being presented.

82% of B2C marketers produce videos as part of their content marketing strategy, 61% rate digital newsletters as an effective tool parallel to blogging, 40% of small businesses use webinars and/or webcasts as a part of their marketing strategy, and 90% of B2C marketers use social media in their content marketing programs, making it the most popular platform to publish content.

Studies show that when B2B buyers do research to make buying decisions, they actually look for white papers, case studies, and webinars more often than blogs. And retail customers more often use online reviews and social media than blogs to determine whether or not they’ll buy from you.

Some companies are taking advantage of this by incorporating other forms of data-driven content on their sites to generate leads.

Intuit, for example, tracks data about the financial habits of users in different age ranges in order to create shareable infographics, like this one comparing the financial habits of Millennials versus Gen Xers.

True data-driven content marketing should give you a solid understanding of your target customers, and it’s very possible that your customers are using social media to share videos and infographics instead of reading your blog. It’s also very possible that your target customers are looking at review sites instead of browsing Google for related articles.

That’s why the data collection process is so important to content creation. Without understanding data, you can’t predict which content or medium will have the most impact on your ROI. But you can use data to pinpoint your audience’s habits and prioritize your content development accordingly.

And if you’re looking to stay ahead of your competitors, you’ll need that data to tell you what’s working and what’s not.

How can data drive your content? Grab your free roadmap here

Final Thoughts

Gathering as much information as possible about your customers’ online and offline behaviors, reading patterns, and even which platforms they use is essential to your content development.

If you’re focusing on creating content in order to improve your SEO or to generate leads, you need data to drive the creation process, or at the very least, to evaluate the success of your content’s performance so you know the real value of your investment.

Data should also inform your chosen delivery method, and it’s important to remember that blogging is not the be-all and end-all for content marketing. If your data shows you that another method is more effective, trust those results. Don’t follow the trends just because everyone else is doing it. Do it because the data tells you it works.

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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.

Don’t miss: 5 Insights Web Data Can Give You About Your Competitors

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.

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.

Here are 5 more insights web data can give you about the competition

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.

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