The Art of Using Data to Make Decisions in Business
Flipping a coin, going with your gut, closing your eyes and begging the universe for guidance on how to proceed — these all have their place, sure. But using data to make decisions is a far more reliable approach in business.
Data driven decision making ensures that you make choices based on evidence about your customers, competitors, or whatever else you need to wrap your head around.
In this article, we’ll explain how web data can help you make informed decisions, how to go about collecting and analyzing that data, and how to overcome the challenges you may face in the process.
What is web data?
Web data is any publicly available information sourced from websites. Typically it’s gathered in a structured format that’s easy to analyze or put to use in other ways.
Examples of web data are lead phone numbers, prices of competitor products, or social media mentions of your brand.
Businesses use relevant data as the source material for the various analyses that inform business decisions across various topics, from pricing intelligence to customer and market research.
To collect this data efficiently, businesses often use a process called web scraping, which does so automatically and at scale.
Types of web data
Web data can be divided into three categories:
Web Content Data: Includes HTML, web pages, audio-visual data, images, text, numeral data, and more. This data type is the one that will matter most for data driven decision making in business.
Web Structure Data: Tells us about the relationships between various web pages on a website.
Web Usage Data: Captures how customers or users interact with a web page — e.g., data about the number of visitors to your blog last month.
Now that you know a bit about web data, let’s go over the different places where businesses commonly go to find and extract useful web data.
Sources of web data
There are as many sources of web data as there are publicly accessible websites. Almost every web page, from the homepage of a giant e-commerce store to a short blog post about the thrills of bird watching, contains web data.
Of course, the usefulness of that source varies from site to site and depends on your needs.
Below are some of the generally useful sources that businesses extract data from:
Product or Service Pages: Companies list valuable information about their pricing and features on these pages, making them helpful for competitor analysis.
Social Media Platforms: Platforms like Facebook or Twitter are loaded with valuable web data, especially about your buyers’ interests and issues.
Web Forums: Industry-related forums or general forums like Quora contain conversations between customers, which can be useful for understanding them.
Review Sites: Sites like G2 or Google reviews are filled with useful data about how customers feel about a product or service.
Databases: B2B database companies harvest web data and organize it in a filterable database to which customers can subscribe for access.
Online Marketplaces: E-commerce stores hold tons of product-related web data that can help with pricing intelligence and market research.
It’s hard to find the right sources and gather data from them manually. Scrolling through marketplaces and copying and pasting prices from Amazon to your Google Doc isn’t very efficient.
That’s why many businesses use a web data extraction service to collect reliable web data from millions of relevant web pages, either at once or on an ongoing basis.
The power of data driven decision making
If they were here today, the romantic poets would be horrified by the modern world’s dependence on data for our decision making.
But there’s no doubt that they would have to admit, perhaps reluctantly after several martinis, that it seems to be paying off financially.
That said, let’s now go over three ways data driven decision making can lead to business growth and a competitive advantage for your business.
Then we’ll show you some examples of how real companies are using web-data to make business decisions.
Benefits of using web data in decision making
Below are some of the major benefits you’ll get from including web data in your decision making process — greater belief in your plans, increased efficiency, and less analysis paralysis.
Believe in your plan (so you stick with them)
Doubt is one of the most common reasons that people, and businesses, fail to follow through with their plans and reach their business goals.
After a few weeks of execution, nasty thoughts inevitably creep in — “Will this even work?” “Am I going to get the outcome I want?” That’s just another delightful part of human nature.
The ones who finish the plan are those who can push through those doubts and keep on executing. It’s those who believe in the plan — who trust the process.
Brainwashing yourself with daily positive affirmations is one way to develop this trust. But, there’s another way, one that our surprisingly shrewd brains won’t as easily roll an eye at and discard.
The single best way to get yourself and your team to believe in a process is to have evidence that backs it up, proof that shows that the steps will lead to the desired outcome.
And where can you get that evidence? Data is a great place to start. Your findings from data analytics can supply your team with the best antidote to doubt: confidence.
Using data to make decisions about which initiatives to pursue and how to best pursue them will increase efficiency. It helps you accurately separate the opportunities from the time-sinks. Time is saved. Costs go down.
For example, imagine a company that wants to know how to best price their service. If they tried to figure this out manually, it might take weeks, and they still might come to the wrong decision.
It could take them several episodes of trial and error before landing on the perfect price. In the intervening months, they'll have missed out on a lot of profit.
If instead they used web data from thousands of websites and then analyzed it, they could arrive at an informed decision quickly.
Less Analysis Paralysis & Decision Fatigue
Have you ever spent what felt like an absurd amount of time on a food delivery app trying to figure out what to order for dinner?
After a long hard day it can be tough to find the perfect restaurant. There are so many factors to consider — the cost, delivery fees, what you’re in the mood for, the restaurant’s ratings.
It’s not a very comfortable feeling, sitting there hungry trying to make the optimal choice. Plus, it’s a big waste of time.
What you’re experiencing has a name — analysis paralysis — and it’s something that agitates and holds back not just tired and hungry patrons, but also savvy business professionals.
Fortunately, there are software platforms designed to help you collect and analyze web data to come to quick and informed decisions about business matters.
This way you can spend less time obsessively running the numbers in your head and more time making confident decisions on actionable insights. Using data to make decisions becomes simple.
In addition to saving you from the discomfort that attends bouts of analysis paralysis, you’ll also have less decision fatigue, something Steve Jobs avoided by wearing the same outfit every day.
This way you’ll have the energy to handle the hard questions that data tools can’t yet answer, like whether it’s Mexican or Thai food that’ll bring you the greatest satisfaction tonight.
Examples of how companies make data driven decisions
Big data is here to stay. Business leaders from top companies are using data to make decisions and drive business strategy.
Let’s explore some examples of how they’re doing this:
Conducting market research: Streaming services decide which tv shows and movies to buy and produce by analyzing subscriber data in terms of how viewers reacted to related shows.
Recommending products: E-commerce stores and streaming companies use data about each viewer’s history on the platform to identify their tastes so as to recommend content they’ll want to watch.
Identifying ideal store locations: Companies with brick-and-mortar stores analyze foot traffic patterns and the incomes of local residents to predict an area’s potential profitability.
Improving user experience: Companies that offer apps where customers can place orders often use data about past orders to identify which offerings each specific customer will be most likely to desire and, as a result, click and order.
Optimizing pricing: Some hotels collect and analyze data related to weather, cancellations, nearby events, and the global economy to identify the best prices for their rooms.
Though data is no crystal ball, it’s certainly helping companies make better decisions about pricing, marketing, product development, website and app design, and so many other aspects of running a business.
And as a result, these data-oriented businesses often gain a considerable competitive advantage in their industries.
How to collect and analyze web data
Collecting and analyzing web data to power your decision making may sound challenging, but with the help of the right tools it doesn’t have to be.
Below we’ll go through some of the best tools for handling this 2-phase process, as well as some best practices to help you efficiently get accurate results.
Tools for collecting web data
Tools for collecting web data can be divided into two categories: 1) DIY in-house scraping with Python, and 2) third-party web scraping software solutions.
DIY web scraping with Python
DIY projects require you to hire developers who use open-source Python packages like BeautifulSoup or Scrapy to build proprietary scrapers for you.
Doing it in-house allows you to focus on your exact scraping needs, but it also comes at a high cost.
You’ll have to pay for hundreds of hours of coding, invest in hardware/software and licenses, and maintain your software.
Plus, you’ll be on your own navigating the challenges of web scraping such as maintaining data privacy, complying with legal regulations, and avoiding IP bans.
Third-party web scraping software solutions
When you go this route you’re essentially paying for a subscription to use the company’s web scraping software to manage and automate your data scraping projects.
These web scraping tools empower your developers to create scraper programs quickly, while also providing them with access to experts, ban protection features, and other useful tools. Often, little to no coding is required.
Some companies, like Zyte, also offer a web scraping service, which will handle the various steps of web data extraction for you. That includes finding, extracting, cleaning, and formatting datasets.
Regardless of how you choose to work with them, also ensure that the data is high quality and that your project is legally compliant.
Overall, working with a third-party vendor is a good option for those who want to collect data efficiently and gain access to a wide range of helpful features.
Techniques for analyzing web data
Using data to make decisions involves two big steps. After you’ve collected your data, you need to analyze it for insights and trends that’ll help you make smarter business decisions.
Analyzing raw data is a complex, multi-stage process. So it’s strongly encouraged that you leverage some sort of software to help you conduct your analyses.
Below are three techniques involving software.
Manual data analysis with Microsoft Excel
Microsoft Excel is a powerful and affordable tool for analyzing web data and answering questions about your business.
Data analysts can use functions, graphs, arrays, and pivot tables to organize and draw conclusions about their data.
But, when it comes to larger and more complex data analysis projects, it can be rather time-consuming. Plus, users need to be trained in Excel to produce insights from the data.
Data analysis with reporting tools
Most business software platforms, from CRMs and accounting software to smaller point solutions, offer some sort of reporting functionality that allows you to analyze the data within the platform and present it in a digestible way.
Most also allow integrations that enable you to enrich their databases with the information you've scraped from the web.
It’s a good idea to get comfortable using these reporting features as your assistant business analyst so you make data driven decisions.
Automated data analysis with business intelligence software
Business intelligence solutions enable you to analyze data from various external sources (e.g., web-scraped data) and internal sources (e.g., the separate software platforms you use).
With business intelligence software, you can knock down data silos and combine your different databases into one source of truth, giving you a holistic perspective of your data.
This makes your data analysis findings more accurate and substantial.
These tools also automate or simplify a lot of the data analysis process, so using data to make decisions will become less time-intensive.
Best practices for data collection and analysis
Below are some best practices to follow when collecting and analyzing web data.
Begin with a specific question: Define the question that if answered will assist your decision making process. This question will guide your data collection process and analysis.
Collect relevant data: Identify the types of websites that house the data required to answer the question, then extract data from these sites.
Track your sources: Keeping a record of your sources enables you to identify sources that provided faulty or incomplete data so you can avoid using them again.
Always clean the data: To use the data for analysis, you first have to clean the database, removing duplicates and errors, structuring the data, and enriching the gaps.
Visualize your conclusions: After the analysis, show your conclusions to stakeholders using tables, graphs, and other data visualization graphics. This is a great way to share insights in a digestible way.
If you follow these tips, you’ll be on the right track toward effectively using data to make decisions.
Challenges of using web data
Using data to make decisions poses many challenges for businesses, from poor-quality data to confirmation bias.
Let’s go over some of the most common challenges businesses face and some ways to overcome them.
Common challenges in collecting and analyzing web data
Dealing with data quality issues
Inaccurate or incomplete data can cause faulty conclusions, which can then lead to bad decisions and ineffective plans. In other words, bad data is misleading information.
Therefore, when collecting your web data, it’s critical that you take steps to verify, clean, and enrich it to ensure it’s appropriate for your analysis.
But this process can be time-consuming, not to mention subject to human error. So many companies use a web data extraction tool that handles these steps for them at scale.
Avoiding IP address bans
A website can ban your IP address, thus blocking your scraper from collecting any data from their site.
Websites do this for various reasons:
You’ve violated their terms of service.
They want to reduce the load on their servers.
They’re preventing spam.
It’s challenging to determine why any website banned you. They don’t send you a nice email outlining their reasons for doing it. Sometimes it’ll happen even if you’re playing by the site’s rules.
Fortunately, there are ways to bypass IP bans, such as by using a proxy rotation strategy or by placing delays between requests.
Struggling with poor data skills in your company
Someone within each department needs to know how to actually use their tools and analyze data if each team is going to productively use data to make decisions.
Many companies overlook the importance of expertise in this regard, relying on employees to already be familiar with how data works, or to learn it on-the-fly.
But this isn’t working. Accenture found in a survey that a mere 50 percent of middle management respondents feel that all or most employees have the right data abilities.
Falling prey to confirmation bias
When employees aren’t properly trained in the fundamentals of data analysis they might make mistakes in their calculations, predictions, and conclusions.
One of the most common causes of such mistakes is confirmation bias. This is when you start with a hypothesis and then only collect and interpret evidence in a way that confirms that hypothesis.
For example, if a CRO feels strongly that their pricing is too low, they might go out and collect data only about companies with prices higher than theirs.
This of course is not a very scientific way to go about collecting and analyzing data. Once again, training and education is the best way to prevent this tendency and other cognitive biases in your company.
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Strategies for overcoming these challenges
While discussing challenges of using web data we briefly mentioned some solutions — web scraping tools, proxy rotation strategy, etc.,
Let’s now explore these and other strategies a bit more in-depth.
Use a proxy rotation strategy to avoid bans
It’s common for websites to ban an IP address for making too many requests to their website. This is a big problem for web scrapers, who need that data.
Proxy rotation is a popular solution to this dilemma. It allows you to cloak your IP address with a new disguise every time you make a request to that website.
In more technical terms, the rotating proxy server automatically pulls a new IP address from its large pool of proxies for each connection request.
Invest in a web scraping API
A web scraping tool like Zyte API helps your team efficiently extract data from websites of all complexity levels while avoiding IP bans.
It also combines all the tools you need for avoiding IP bans under one API:
Data proxy centers
Advanced session manager
These days, proxy management alone is at times insufficient to avoid IP bans, so it pays to have a Swiss army knife to protect your web scraping projects and the actionable insights they produce.
Offer data analysis training and courses
“Data literacy has always been a requirement in successful organizations. It's just that data illiteracy is more obvious now — or data illiteracy just causes more damage now than it used to,” — Miro Kazakoff, MIT Sloan Lecturer
Having data literacy means you’re able to effectively read data, work with data, analyze data, and argue with data.
Companies need to provide training and resources so staff is empowered to make data driven decisions in their domain of control.
This doesn’t have to be a huge expense. There are plenty of online courses out there designed to help professionals learn how to leverage data.
For example, Coursera offers various online courses for data analysis in different aspects of business — operation analytics, customer analytics, etc.,
You can also hire a data specialist who can serve partly as a mentor to the rest of your team, educating them on the tools and techniques necessary to do the data analytic tasks that would support decision making in their specific role.
Get a business intelligence solution for sound analysis
Business intelligence software makes working with data easier for your team at every stage of the data analysis process.
These tools typically provide features that can automate data preparation, analysis, interpretation, reporting, and visualization.
It does the heavy lifting for data interpretation and provides your team with key insights and trends so that anyone, no matter how tech-savvy, can reap the benefits of data driven decision making.
The most powerful companies in the world understand that effective business decision making leads to organizational success.
And they know that effective decision making starts with harnessing data to answer critical questions.
When you start basing your choices off of real data, some great things happen:
You’re more likely to follow through with your plans
Your company becomes more efficient
You waste less time in analysis paralysis
Of course, the data driven decisions you make are only as sound as the quality of the data you use and the accuracy of your analysis.
Therefore, it helps to have a standardized process and a robust set of tools for collecting reliable web data and analyzing it accurately.
Training your team on the basics of data literacy will also have a profound effect on your business goal of shifting to a data driven culture.
Lastly, one of the biggest challenges you’ll face when using data to make decisions is avoiding IP bans when collecting web data. For that, check out how Zyte API can help you out.