Data quality for retailers

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Did you know?
How bad data affects retailers?
Retailers are uncertain about their product data accuracy

High return rates
Inconsistencies and errors in product attributes can mislead shoppers into buying wrong items, leading to high product returns.

Lack of customer personalization
Incomplete or inconsistent data can limit personalization to tailor campaigns for new purchases, cart abandonment, etc.

Late shipping orders
Missing street names, apartment numbers and unverified addresses can lead to missed or delayed orders, increasing customer complaints.

Poor campaign response
Low email sender reputation and deliverability and missed direct mail deliveries due to dirty email and phone data.

Inefficient inventory planning
Silos in point of sale and inventory data can cause large quantities of slow-moving inventory or out-of-stock situations.

Missed sales opportunities
No single customer view leads to little or no opportunity for segmentation and find lucrative revenue opportunities.
Solution
DataMatch Enterprise – Achieve a single customer view

Customer Stories
See what retailers are saying...

With DataMatch™, we were able to really increase the number of people we communicated with this year.



The main benefit of DataMatch Enterprise™ was the fuzzy logic and synthetic matching. It was just something I couldn’t reproduce myself.



Kingfisher was able to populate their online store and give their customers more accurate search results with standardized product attributes, subsequentially increasing their online sales dramatically.


Business Benefits
What’s in it for you?
Enrich customer experience
Optimize customer in-store and ecommerce experience with better listings and clear categories, and eliminating duplicates.
Ensure faster deliveries
Standardize addresses with prebuilt USPS database and pinpoint delivery coordinates by ZIP+4 values for faster and precise shipping orders.
Streamline inventory levels
Utilize clean data to make accurate forecasts for estimating stock ordering levels in both peak and off-peak demand levels.
Enhance email campaigns
Increase sender reputation, inbox placement rate, open to click ratio metrics of email campaigns by discarding outdated and incorrect addresses.
Uncover profitable segments
Identify lucrative cross-sell, up-sell opportunities through consistent and reliable data on customer purchase history, average order value, and other signals.
Gain a single customer view
Break down data silos to track customer data across various touchpoints for omni-channel marketing, segmentation, and retention purposes.
Want to know more?
Check out DME resources

Merging Data from Multiple Sources – Challenges and Solutions
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The Truth About Data as a Service (DaaS): Why It All Breaks Without Data Matching
Everyone’s Talking About DaaS, Few Are Ready for It The concept of Data as a Service (DaaS) is having its moment. On paper, it’s easy

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