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What is eCommerce Product Data Cleansing?
eCommerce product data cleansing is the process of correcting and standardizing product attributes. Instead of displaying erroneous information, a sweep clean reflects an accurate and timely feed.
Having a meticulously curated listing with market-friendly descriptions and customer-friendly attributes is not enough.
Every eCommerce can match the effort on this front.
To make a real difference in sales, you need to actively clean the data and eliminate any erroneous content from your product descriptions
- Inconsistencies and errors in eCommerce product data descriptions boost bounce rates
- By cleansing your data, you have a better chance of increasing conversions and revenue
- In a sea of competitive D2C and eCommerce stores, don’t let erroneous and incomplete data add to visitor confusion
- Tidy up and organize your data with professional eCommerce Product Data Cleansing services.
Stop Bleeding Sales With Inconsistent, Incomplete & Confusing Data: Use Acelerar's eCommerce Product Data Cleansing Services
eCommerce stores have a notorious habit of piling up irrelevant data and inaccurate information. Unlike brick and mortar stores, they do not have the luxury of explaining.
Every eCommerce store needs regular product data cleansing.
eCommerce product data cleansing services bring consistency and accuracy to the feed through the elimination of incorrect, redundant, incomplete, and non-standard data. This enables customers to make well-informed purchase decisions and minimizes product returns.
Top Signs of a Healthy Product Feed
A healthy product feed is consistent across the store’s data format, structure, and product attributes. All product entries should follow the same pattern, which ensures uniformity and ease of processing.
Here are the top signs of a healthy product feed:
A cleansed feed has product attributes that are uniform and set in the same format. The standardized feed makes it easier for customers to locate your products. It also allows search engines to sort, filter, and analyze the product database.
Product data cleansing ensures that the feed isn’t missing crucial product attributes. It also fills the gaps and closes channel requirements for each product. The product feeds with gaps and incomplete data are immediately filtered out and sent to cure.
eCommerce product data is constantly evolving. This muddies the datasets furthermore. Outdated product information contributes to customer bounce. Cleansed data feed ensure the data on display is timely and accurate.
Product feeds should be educational, informational, and digestible. All of this aids in the buyer’s decision process. Data cleansing removes unnecessary HTML, duplicate words, consecutive white spaces, improper spelling, faulty grammar, and other structure issues.
eCommerce Product Data Cleansing with Acelerar
At Acelerar, we treat each product page as the website’s primary landing page. Our unparalleled software and trained professionals optimize for maximum data filtration and rectification without breaking the bank.
We help in:
- Removal of duplicate and redundant data from the site to produce a streamlined and efficient product database.
- Structuring product information through meticulous reorganization of product information.
- Enhancing product discoverability by linking relevant keywords to specific product attributes.
- Loading product database to support upselling and cross-selling.
- Adding informative data and product descriptions through graphics, images, videos, and other forms of media.
Acelerar Is Leading The Clean Product Feed Revolution
If your eCommerce store is struggling with inconsistent feeds, missing product information, poor data organization, low visibility and declining conversions, then give us a call.
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Frequently Asked Questions
A professional eCommerce product data cleansing service is carried out in five steps:
Step 1: Evaluate onsite data and remove irrelevant and duplicated data sets.
Step 2: Fix structural errors such as typos, incorrect capitalization, etc.
Step 3: Identity outliers such as improper data-entry.
Step 4: Fill in the missing data.
Step 5: Check the categorization of products.