10 Ways Bad Data Hurts an Ecommerce Business

Inaccurate or missing data can impact an ecommerce business in often surprising ways.

Data is essential for ecommerce businesses. Order quantities, shipping addresses, sales history, marketing performance — all rely on accurate data. Thus missing or inaccurate data can harm a business. Here are 10 ways that poor data can reduce ecommerce performance.

Impact of Poor or Missing Data

Return customers. If the rate of repeat customers is decreasing, it typically indicates that your product offerings do not align with customers, or the marketing message is not on point. It could also be due to the inability to segment customers properly or contacting them at the right time. To correct, conduct A/B tests on offers and marketing messages. Also, review segmentation abilities.

Upsells. Upsells generally indicate the accuracy of your analytics. If an upsell rate decreases, you presumably are not pairing the right products. This is typically a data problem, in my experience, of either not analyzing purchases promptly or with segmentation. If you cannot easily segment shoppers, use similar product categories for upsell offers.

Website traffic. Declining traffic could indicate a drop in your organic search rankings, which could point to a keyword optimization problem. It could also indicate a lack of key data on product descriptions, which are therefore missing from search engines.

Undelivered shipments. Undelivered or missing order shipments are typically caused by address changes or typos. One way to catch typos is to use automated address verification software for all shipments. For subscription-based orders, run a change of address quarterly or semi-annually.

Email bounce rate. A gradually increasing email bounce rate is common in an aged list. To fix, generate more subscribers than bounces! A sudden increase in bounces usually indicates a problem with new subscribers, such as from purchased lists or unclear signup campaigns.

Customer service call volume. Increasing customer-service call volume could stem from poor data. Examples can include:

• Missing or weak product descriptions.
• Wrong shipping estimates.
• Inability for customers to update their info. Instead of having an automated way to update shipping details, email addresses, and phone numbers, customers are forced to call in.

Average call length. Like call volume, an increase in customer-service call length could indicate bad data, such as duplicate records, misspelled names, shipping errors, and incorrect product descriptions.

Reports not matching. Ecommerce managers often receive multiple performance reports, such as weekly revenue, product shipments, profit margins, inventory levels, and similar. If those reports are inconsistent, your system integration could have a problem. This could lead to poor decisions. For example, marketing personnel could run a promotion on a product without realizing that sale price results in a loss, not a profit. Small differences in interdepartmental reports — 1 to 2 percent — are typically acceptable.

Length of tasks. Employees work at different speeds. However, an employee who takes longer than usual to complete a task could be struggling with data. For example, it may take 10 extra minutes to launch a product on Amazon because data is missing. One way to avoid the delay data is to have an honest conversation with employees about efficiency and data gaps.

Declining marketplace sales. Companies that sell on marketplaces such as Amazon or Etsy can experience low or declining sales if product information is missing. This occasionally happens, in my experience, with merchants that upload product info automatically via software. I’ve seen missing color choices, sizes, or shipment options.