Being analytics-driven is the only way for an e-commerce business to succeed in the
hyper-competitive market. Product data extraction and analytics from competitor websites is a KPI, or key performance indicator companies should measure.
Most small and medium e-commerce businesses are not very tech savvy. They lack the technical resources to set up an in-house team to extract the data and find insights out of it.
So what should they do?
They can use their resources smartly, by getting data feeds from a company like Datahut and for analytics, they can use tools like looker or thoughtspot. This choice helps them become analytics-driven without having a big team of engineers. Sweet, isn’t it?
Let’s see what kind of data companies needs to extract from the competitor websites.
Fields we need to extract
Image ( URL or the image itself )
Average star rating
Preparing the data
Now, you have the data in a tabular format, so how do you use it?
If I were you, I’d put the data in a cloud data storage. Amazon / Azure / Google cloud platform are good choices.
Then I’ll connect my analytics tool with the data. Tools like Thoughtspot and looker have native ways to connect to data sources on Amazon / Azure or Google cloud platform.
Once the data is connected and imported into the tool – we are good to go.
Here are a few important use cases of product data analytics:
Margins are shrinking in the e-commerce industry. Brands want to understand how price competitive they are. The only way to know is by analyzing the pricing data of their competitors and then comparing it with their prices.
Historical pricing data or pricing data gathered about a particular product over a period of time can provide deep insights into the competitors pricing strategy. Once you have enough data – you can reverse engineer and find answers to the critical questions like:
How frequently they are changing the pricing?
Which categories get more pricing fluctuations?
Where do I stand in the benchmarking of prices?
Which competitor is trying to beat me in a pricing war?
And many more.
The marketing teams at e-commerce businesses spend a ton of money on advertisements and other modes of promotions almost blindly without having data to back it up.
You need data to justify your marketing/promotion decisions, and web scraping is the way to do it.
Understanding the promotional strategy of the competitor is what you can understand by scraping competitor data. Extract the promotional information of your competitors daily and try to find patterns in it.
Timing your promotions is the key to its success, and knowing your competitor’s promotional strategy is a huge advantage. For example, running a promotional campaign when the competitor’s product is out of stock makes a perfect sense.
How do you know it? From the product data we scraped.
What does the competitor have that I don’t?
What do I have the competitor does not?
Assortment analytics answers these two questions on a high level. You need to have more varieties to give your shoppers more and better choices. That’s an excellent way to get repeated business. The most significant outcome of a well-executed assortment analytics plan is actionable insights on how to reduce customer acquisition cost ( CAC) and an increased customer lifetime value (CLTV).
The best way to understand the weaknesses and strengths of your competition is by analyzing their reviews. You should analyze reviews of your brand as well.
Find the weaknesses of your competition and exploit them to increase your market share.
Challenges of Scraping Product Data
There are a few challenges that may harm your ability to use product data to improve your product offerings.
Challenge1: Quality issues
Bad data is bad for business. It can negatively impact the decisions you make about your brand and your competition. Any analytics requires high-quality data; otherwise, the insights obtained will be totally wrong. When you’re extracting data from e-commerce websites – you need a robust quality assurance protocol in place.
Challenge2: Structure of the product data
The structure of the data you get from different sources is a big headache. Usually, you start with a basic data structure and tweak it as you scrape data from more websites. Finding a unified data structure is a very challenging problem when you have to extract data from 100 plus websites.
Web scraping at a scale is a different story. IF you want to extract 100K records daily – it is relatively easy. However, if you need 10 million records per day – the story is entirely different.
You need a scalable platform, a data warehouse and a lot of tools to deal with challenges at scale if you were going to extract data from multiple sites ( say 100 ). Building a scalable web scraper in-house that can pull from multiple sources can be difficult.
Challenge 4: Anti scraping technologies
Anti scraping technologies block, slow down or trick web scrapers into crashing. At scale – you need to have a reliable infrastructure to deal with anti-scraping technologies.
Small and medium-sized e-commerce businesses can compete with big boys using accurate competition data; they don’t need to have a big technical team or significant investments to get it done.
Regarding how you obtain the data, You should decide what your priorities are. Spending time writing web scrapers are not worth it If you are running an e-commerce business fulltime. In that case, it is best to hire a third party to do it for you.
If you want to do it in-house – build a team of competent engineers and use an opensource framework.
Wish to extract product data efficiently? Contact Datahut, your web scraping expert.