How to Combat Data Quality Issues when Scraping eCommerce Data
Updated: Nov 22, 2021
Scraping eCommerce data to develop factual and reliable strategies for eCommerce businesses is becoming the need of the hour. How would you understand exactly how well your website – or your entire business – is performing if you don't have reliable data? Therefore, data must be central to your eCommerce strategy.
According to Statista, global data creation is expected to exceed 180 zettabytes within the next five years, up to 2025. The volume of data created as well as replicated reached new highs in 2020. The increase was more significant than expected due to competition induced by the COVID-19 pandemic, as more people were working and learning from home.
There is strong competition among eCommerce businesses, and you should come up with new innovative ideas to sustain. However, there is a catch: you have to come up with this idea faster than your competitors. This appears to become a little better with the help of Ecommerce data scraping because you have access to all sorts of statistics, customer preferences, and competitor plans. Thus, making it much easier for executives to make critical decisions based on an examination of the organized data once it has been evaluated.
But the key point to consider here is the quality of data! What Ecommerce data quality is acceptable? What is bad data? What is good data? How do you differentiate between both? Let's put some of these questions together!
What is bad data in eCommerce?
Nowadays, for eCommerce businesses to maintain their reputation & online existence and increase leads, quality data is a necessity. However, customer demand, shipping addresses, sales history, and marketing performance rely on precise data. As a result, missing or incorrect data can be detrimental to a business and result in unexpected losses.
Poor data quality refers to any incorrect or missing data about consumers, products, or stores, which leads to poor customer experiences and decreased revenue for eCommerce businesses. Examples of such bad data may include incorrect gender, wrong contact details, incorrect email addresses, incorrect shipping address, and so on.
Three types of bad data are wreaking havoc on your eCommerce business, and these are:
1. Bad Product Data quality
Assume a top retail company has a jeans description depicted on their e-store that differs from the actual product. Now assume that a consumer decides and purchases the same jeans, only to find that the jeans she received were not exactly what she had requested. Even if the inaccuracy was unintended, the client would feel tricked here. This is a pretty standard error made by many brands and can lead to blunders on a company's reputation. This is just one example of poor data quality in the context of Product Data. That is why product data must not only be accurate, current, complete, as well as detailed, but it must also be consistent for the client across all sales channels.
2. Bad Customer Data
Bad customer data refers to incorrect, outdated, and otherwise incomplete information customers provide when creating an account on any eCommerce store. This could include anything from misspelled customer names to phone numbers and email addresses. eCommerce requires customer data in order to provide personalized service.
Now taking the same example, imagine that the same jeans, despite conforming to its online specifications, are sold at a higher price than promoted, simply because the system cannot determine whether or not a customer is eligible for a rebate. This could have been due to various factors, such as incomplete or inaccurate buyer profile information, their collected discount vouchers or discount points, or the system's incapability to classify that customer eligibility for the discount consistently. Thus, inadequate audience data that includes incorrect or misdirected profile information such as inaccurate gender, incorrect country code, and so on can adversely affect customers' sentiments and the urge to purchase further.
3. Bad Shipping Data
Incorrect shipping information indicates incorrect credit card information, customer product codes, incorrect shipping addresses, and so on. A single failed shipment is more than just a single failed delivery. It has a significant effect on a retailer's balance sheet than one can imagine, including damaged brand reputation and decreased customer loyalty.
For example, suppose you bought the same jeans, paid for them, and are now awaiting your order. Unfortunately, your package has been delayed. Perhaps the reason was legitimate. However, you will be pretty disappointed, and even if you do not hear any reason, you will decide not to consider buying anything else from that store again. As a result, pay close attention to delivery details and delivery time.
How can bad data hurt your E-Commerce business?
1. Customer Return Rate:
If your rate of returning customers is declining, it usually means that your product lines do not align with buyers' needs or that your marketing strategy is not on target. Conduct A/B tests on deals and marketing messages to identify flaws. Examine your segmentation abilities as well.
Upsells are a good indicator of the reliability of your analytics. If your upsell rate falls, you are not bonding with the right products. This is usually an eCommerce data scraping issue, such as not analyzing purchases quickly or with segmentation. For upsell deals, use similar product groupings and product types to improve.
3. Website traffic:
Dropping traffic could imply a drop in having sound organic search rankings, resulting in problems with keyword optimization. It could also suggest a lack of relevant data on product details, thus not being indexed by search engines.
4. Undelivered shipments:
Address modifications or typos are the most common causes of undelivered or having trouble finding order shipments. Using automatic data address verification software overall shipments is an excellent way to catch typing errors and improve Ecommerce data quality. Subscription-based orders should have a change of address run periodically or semi-annually.
5. Email bounce rate:
In an obsolete list, an email bounce rate that gradually increases is typical. To correct this, increase the number of subscribers over the number of bounces! A spike in bounces typically means a problem with the new subscribers, such as those from purchased lists and unclear signup promotions.
6. Customer service call volume:
Poor data could be the cause of an increase in customer service call volume. Here are some examples:
Product descriptions are either missing or inadequate.
Shipping estimates were incorrect.
Customers are unable to update their information.
Customers are compelled to call rather than giving an option to use an automated system to update delivery details, email addresses, and contact information.
How good quality eCommerce data can impact your online retail business
1. Product recommendations
If an online retailer's data is of high quality, evaluating when users visit the website, where people visiting are located, additional details about their tastes and likes become more precise. To attain the optimum knowledge of their customers' navigation or shopping history, retailers can use the best quality data from all across the distributed platforms and decide what exactly to offer.
2. Improved Deliveries:
The online retail industry has become hyper-competitive, necessitating supersonic delivery of commodities – and this is essential. The accuracy of the customer's shipping address and other details ensures the timely delivery of goods purchased. Inaccurate data or a lack of data will lead to orders being delivered to the wrong address or a failed delivery. You better remember that your customers will appreciate it if their order arrives sooner. However, if you do not deliver on time, they will never buy from you again.
3. Inventory management:
Incorrectly entered historical customer data could result in inaccurate analytics and ultimately to inventory waste and money waste. Quality customer data makes sure that your online business has a sufficient supply of the desired products. Cleansed data allows your retail outlet to anticipate what your customers will buy and how their preferences will change depending on the season, adjusting your inventory accordingly.
How to maintain data quality when scraping eCommerce data
1. Automated monitoring system
Websites are updated more regularly than you might think. Most of these changes may dissolve the crawler or might even lead to scraping inadequate and inaccurate data. Thus, you need a fully automated monitoring system to keep track of all crawling jobs taking place on your servers. This monitoring system tracks the scraped data for inconsistencies and errors continuously. It can look for three types of problems:
Data validation errors
Inconsistencies in volume
2. High-end servers
The reliability of the servers determines how smoothly the crawling occurs and, as a result, impacts the eCommerce data quality. As a result, we must use high-end servers to run the crawlers. This will prevent crawlers from failing due to a sudden high load on servers.
3. Data cleansing
The crawled data may contain unnecessary extra elements such as HTML tags. In that sense, this information can be described as crude. The cleansing system does an excellent job of removing these elements and thoroughly cleaning up the data.
Structuring gives the data an appropriate, machine-readable syntax, which makes it suitable for databases and analytics systems. When the data has been structured, it is ready for consumption, either by uploading it into a database or just by plugging it into an analytics system.
Whether you own a start-up or a billion-dollar brand, quality data is like breathing for your business to ensure survival. Accuracy, Relevancy, Completeness, Timeliness, and Consistency are the five pillars of quality data that can ensure a rise in conversion rate for your business! Thus, make sure you choose the right web scraping service that knows what you want and how he/she can maintain the data consistency over time to help you grow.
Given the importance of eCommerce data scraping, a customized web scraping service is the one that can provide you with a competitive advantage. Datahut, a web scraping service provider, handles the difficult work while you focus on becoming your organization's superhero. But, of course, it is entirely up to you whether to begin large or small; all we recognize is that we are eager to get you started! Contact datahut to learn more.