Poor-quality data drains marketing resources and limits marketing effectiveness, according to a new report.

High-quality data about consumers, products and other retailers has helped Amazon.com Inc. garner an outsized share of the retail industry.

Amazon, No. 1 in the Internet Retailer 2019 Top 500, accounted for 36.8% of U.S. online sales last year, according to Internet Retailer estimates. That’s up from 34.1% the previous year thanks in part to the retail giant’s ability to collect and leverage a massive trove of data that it gathers on its website and app, via its Prime loyalty program, its Alexa virtual assistant and home automation services, and its Ring internet-connected security devices.

For example, Amazon integrated Alexa into its AmazonBasics Microwave and offers consumers who order microwave popcorn on Amazon a 10% discount when they allow the retailer to automatically reorder popcorn when their supplies run low. That approach has helped Amazon gather valuable data on consumers’ consumption patterns while also helping it capture sales that might otherwise take place elsewhere.

In an increasingly competitive retail environment, high-quality data, such as the data Amazon gathers, are among the most valuable commodities in a marketer’s tool kit, in part, because it isn’t easy to come by, according to a new report.


61% of marketers rate the quality of their own data as “excellent,” according to the Forrester Consulting report “Why Marketers Can’t Ignore Data Quality,” which  digital marketing vendor Marketing Evolution commissioned. However, marketers are far less confident about the data they receive from their vendors (26% consider their second-party data as “excellent”) or outside data collection  firms such as Acxiom LLC (17% rate their third-party data as “excellent.”)

That lack of confidence can pose problems because a marketing model relies on the data that it is built upon. That helps explain why 37% of respondents said poor data quality contributed to wasted marketing spend, 35% said it produced inaccurate targeting, 30% said it lost customers, 29% said it reduced productivity, 28% said it resulted in a poor customer experience and 24% said it resulted in inaccurate marketing performance results. (Respondents could select more than one response.)

Put another way, poor-quality data drains marketing resources and limits marketing effectiveness, the report says. Respondents estimate that 21 cents out of every media dollar they spent last year was wasted due to poor data quality. That translates to a $1.2 million to $16.5 million average annual loss for the midsize and enterprise companies that participated in the study.


While it is neither realistic nor necessary for marketers to attain perfect data, the report argues they should seek to improve their data as much as possible across seven crucial areas:

    • Timeliness: Data comes from sources that are up to date.
    • Completeness: All expected attributes are provided in the data set.
    • Consistency: A common taxonomy across platforms, channels and campaigns.
    • Relevance: Data relates directly to the analysis.
    • Transparency: The sources of the data are easy to trace and identify.
    • Accuracy: Data reflects the true actions of customers or marketers.
    • Representativeness: The data accurately reflects the marketplace or the advertiser’s target audience.

Only 33% of marketers have complete confidence that their data is timely, which outpaces the six other categories.

Only 9% of respondents are “mostly” or “completely” confident that their data meets their standards across the seven areas.


Retailers’ ongoing struggles to track consumers as they interact with consumers across devices and channels contributes to those challenges. For example, 30.9% of retailers cannot track consumers across devices and another 38.2% can only track some consumers, some of the time, according to a July Internet Retailer digital marketing survey of 115 retailers.