AgilOne Updates Its Data Quality Engine
AgilOne, provider of a cloud-based predictive marketing platform, has updated its Data Quality Engine to let users collect and link data from multiple channels—online, offline, call center, and mobile—so retailers have a single view of the customer regardless of the channel used to shop.
With the enhanced Data Quality Engine, retailers can ensure the quality of millions of shopper profiles daily.
"Prior to working with AgilOne, we didn't have the ability to view our customer data across our North American power tool brands," said Sunny Mallavarapu at Bosch, in a statement. "AgilOne's Data Quality Engine lets us aggregate customer information from various data sources. For example, we can look at who has registered a product, signed up for a monthly newsletter, met with us at a tradeshow, or sent a tool in for service. Now that our data doesn't only reside in disparate systems any longer, we know we can trust the quality of the information for each customer profile and track each customer engagement."
The Data Quality Engine lets retailers do the following:
- Cleanse Data: At least once a day, retailers can cleanse customer data, including first names, addresses, phone numbers, and email addresses, to ensure that they have the correct information on file for each shopper.
- De-Dupe Data: Duplicate customer records are automatically combined at least once a day to ensure that only a single profile for each shopper exists.
- Augment Data: Retailers can add more information, such as census data, to customer profiles.
"If you can't verify the quality of your data, this can be death for a retailer," said Omer Artun, CEO of AgilOne, in a statement. "As a marketer, this can lead to a lot mistakes. For example, if you send an offer for kids clothing to someone that doesn't have children, it's a wasted effort and you may even irritate the recipient. If your goal is to keep customers shopping with you time and time again, then make sure your data is pure."
Related Articles
Empower Your Team to Deal with Data-Quality Issues
09 Jan 2015
Ignoring the warning signs could put your data at risk.