Every website uses a database to store all sorts of information, including images and text, and eCommerce websites are not an exception. However, unlike other websites, eCommerce sites often end up having a huge database.
That’s quite normal given the constant changes regarding listed products, adding new products, comets, and reviews. So, how is data cleansing for eCommerce relevant?
Over time an eCommerce site’s database will become flooded with unusable, inaccurate, and irrelevant data. Data cleansing can help you get to sort out the database and have a clear and accurate set of information for analysis. You probably know about this and other benefits since you are cleaning the database for your store.
However, before you continue, you should know a couple of things that can make data cleansing for eCommerce even more efficient.
Turn Data Cleansing for eCommerce into a Long-Term Effort
Data cleansing for e-commerce is very popular. It helps businesses of all sizes develop and improve customer segmentation, get access to a single customer view, avoid issues with GDPR and CASL, run cost-effective operation, and increase overall ROI.
One of the most important things you should understand about data cleansing for eCommerce is that it has to be a long-term effort to deliver consistent results.
Don’t leave your database unattended as it significantly affects your data quality. Poor data quality only generates costs. In fact, according to an IBM study, poor data quality resulted in over 3 trillion dollars in costs in the US only.
Data quality should always be one of your main goals not only because of its benefits but also because it generates costs exponentially. It also plays a role in all your eCommerce business processes ranging from marketing and sales to compliance and operations.
Work on Your Data Quality Plan
Do you have a data quality plan? If you do feel free to skip to the next tip, but if you don’t, you need to develop one immediately. Cleaning your eCommerce data on the fly is a bad practice. You can’t approach data cleansing without having clear goals and plans.
We are not talking about your personal or your team’s expectations here. What we have in mind are actual goals. How can you define goals? Ask yourself what you want from your data. Most often, the answers are going to be healthy, accurate, and up-to-date.
Healthy, accurate, and up-to-date should be your data quality key performance indicators (KPIs). Once you have defined your KPIs, you should identify the causes of incorrect data and do all in your power to address them.
This action will put you in a unique position of knowing and understanding the root cause of the problems related to your data. You will be able to custom-tailor your approach, reduce the errors, and make data cleaning faster and more efficient.
Standardize Ecommerce Data Entry
What is eCommerce data entry? Every time you access your shop to update your products, add images, descriptions, and features lists, you enter data.
The same goes for your customers who come to leave reviews and rate your products. If any of this information is inaccurate, you will have dirty data on your hands. You can’t use it to make business decisions.
The point of entry should be one of your primary concerns. Your data doesn’t just appear. It has to be input by a human or pulled from another online or offline resource. Identify all your points of entry, especially for the most critical data.
Standardize the data entry, and you will significantly improve the quality of your data. For instance, all of the information will be standardized before getting stored in the database. Define the best practices and standardization protocols and compile them into a Standard Operating Procedure.
Make sure your entire team is up to date with it.
Achieve Data Accuracy Verification in Real-Time
When handling large data sets, it is important to make sure that all the entries are accurate. There are some tools you can use to verify data accuracy in real-time. At least for some data sets such as email addresses in your contact list.
This phase of data cleaning is perhaps the most important as it ensures all the data entries in your database are accurate. You should consider adding redundancies to be 100% sure your data is correct. For instance, you can use automation tools and human agents to go through all your database entries.
Once you achieve data accuracy and implement standardized data entry practices, you will future-proof your eCommerce database. You will significantly reduce the errors and duplicates and make your future data cleansing efforts less time-consuming.
Get Rid of the Duplicates Early On
Duplicate data entries in your eCommerce database can potentially cause you a lot of troubles, especially if you use CRM software. You can end up listing the same products over and over while updating only one of the listings.
Your marketing team can waste valuable resources by sending duplicate emails and ultimately spamming customers, ruining the experience they have with your brand, and giving inaccurate reports and analytics.
When cleansing data, always make sure to check for duplicates. Keep the most recent and updated entries and delete the duplicates. If you are doing data cleansing for the first time, the chances are that there will be a lot of duplicates. Take your time, and delete one by one.
A database without data dupes provides accurate insights.
Reduce White Space In Your Database
Every data entry in your database should be complete, both in terms of products and customers. You can’t afford to have empty fields, so-called white space, in your database table. It will help you run a better operation and reduce certain risks.
When it comes to products, your complete data entries will ensure that your customers get all the information they need when buying online. On the other hand, you have to know where your customers are coming from to be able to comply with the regulations in their country of origin, such as CASL or GDPR.
Completing your product data entries is something you will have to do manually. But, to complete your customers’ entries, you can use automated tools to pull and compile data from social media websites.
While practicing data cleansing for an eCommerce store is a good practice, you should be aware of several details to improve the process overall.
Standardizing eCommerce data entry, reducing white space, removing duplicates, and devising a data quality plan will help you refine your data cleansing initiative and achieve long-term results.