We use Melissa Data Matchup for SSIS to de-duplicate our customer data on a daily basis so that we were able to reduce marketing costs and increase the quality of communication with customers.

It replaced a weekly primitive custom de-duplication (record level) matching process.

Its survivor-ship logic handles very complex column-level rules efficiently providing us with a first-time for a single version of truth for our customer data. It's inherent intelligence into name and address parsing provides a very accurate exact match with no false positives and no unexpected false negatives. We are continually impressed by its sophistication and ease of use. The tool does not requires a middle tier or specialized staff like every other tool on the market.

We used to use a third-party product that had to be installed and maintained onsite. Since we switched to Melissa Data web services, we do not need to maintain those servers and/or software. Also, we get the most up-to-date addresses from USPS.

When we first started using Melissa Data products, we needed it for cleaning up parcel addresses and adding Zip+4 to its latitude and longitude coordinates. At the time, we were actually using the products for verifying the addresses and cleaning up cities and zip codes so our clients could use the information to send out mailings. Over the last few years, we have changed our direction a little bit. We no longer use the product for verifying or changing any of the address information we get from the state or property appraiser offices. We only use it now for parsing the addresses into street name, suffix, direction, unit, etc. to make our data easier for our clients to search on these separate fields. If the street name that comes from the property appraiser’s office is misspelled, we no longer use Melissa Data to clean it up. We just parse. There are reasons for why we do this that would take longer to explain, but wanted to give you an idea of what we are doing. We do append the lat/long info for any additions or changes that we get when we get the new tax roll updates each year.

Since we have changed directions and we are no longer using Melissa Data to clean up our address information, there are free tools that we can use to do the same thing. We can also write our own parsing routines ourselves, if necessary. The reason we have not looked at other tools yet is because this takes time for our team to change the processes that we already have in place. However, the long-term benefit is we would save this money that we are spending every year on Melissa Data products. We would just have an initial cost of rewriting everything the first year. Also, we can get most of the lat/long information we need from the county parcel maps that we already get for free. We also have another solution to handle the properties that are not necessarily in the maps data (because of timing or other issues). We are looking into this. 

Just giving honest feedback. We have no gripes with the products. We just have to look at ways to keep our own costs down.

Before we used DQSand an algorithm for matching and only updated valid address information quarterly. This caused returned mailing and upset customers. This suite has monthly updates to ensure we are up to date with recent new home construction and gives us the ability to modify the matching rules to our needs. We have seen a reduction of calls and returned mail.

I was referring to two different components: Contact Verify and MatchUp.
Contact Verify: Our company runs campaigns for our membership and needs accurate address information. We previously "cleansed" and matched the incoming customer addresses but did not necessarily know if the address is valid until we ran our quarterly National Change Of Address process. Melissa Data's contact verify component is updated monthly with national addresses. When the address runs through the component the output is not only "cleansed" but tells if the address is valid (Cass certified) or if there is some other issue with the address.

MatchUp: The matching component has rules that can be modified to look at address or combo of columns to match.

We do hundreds of thousands to millions of customer deliveries. By using Melissa Data, we are able to scrub and verify, then better validate the end customer's address to ensure a more consistent delivery of products. 

We mainly communicate with our customers via email, so we primarily use it to find a phone number so we can contact them more efficiently. This allows us to talk to them and resolve their issues much more quickly.

We have, however benefited in additional ways from the service, in that our customer database is now significantly more accurate and reliable. This is particularly important when it comes to both cost savings and cost generating measures. There have been tangible benefits in combating fraudulent transactions. The information from Melissa Data is fed straight into our fraud system. This creates efficiency but also removes the need for manual address checks. Similarly, through more accurate data, our marketing department has been able to increase delivery and conversion rates through email direct marketing initiatives.

By validating and parsing the addresses our customers submit to us, we have reduced the number of addressing errors encountered during our processing.

Reduced direct mail errors

It gives me an assessed value of the property in question. My partner and I are property investors, and it's good to get an assessed value. Other databases that we extract information from don't always give us that. So we're able to get an assessed value to cull out properties that we're not interested in.

Better and standardized addresses in our systems.

Allows us to identify cell phones before dialing, and giving us data about callers.

Ability to validate addresses, make corrections to address. Also, if a person has moved, use the new address to send the mail and thus reduce the postage cost and reduce return mails.

It saves a huge amount of time. Before using this service, we used a vendor that manually ran our lists through this NCOA list, which might have taken one to three business days to return the file. This was a huge bottleneck in our process, and the data returned was not always accurate. After switching to Melissa Data’s SmartMover, the process has been reduced to between ten minutes and three hours, depending on the amount of records sent.

When we plugin the contact verify component in the ETL from Source Systems, it will greatly help in standardizing and cleansing the source data and help keep the downstream systems clean.

Gives us the ability to offer an additional resource that other companies do not.

I was able to dedupe millions of records in the past, and append the most recent email.

We have only been using this for about two months, but it has sped up our processing significantly. It makes data mining easy and fast. We don't have to spend an entire month gathering correct information on leads. With LWO, all we need is a list of home addresses, and in minutes we have names and phone numbers to increase our chance of these leads becoming customers.

• Data appends helped improve our Customer reach.
• Improved Address and Email Quality.

I have only just started to use it at my current company so no improvements yet. In my previous role, we used Listware to reduce returned mail and reduce mailing costs.

We had a project where we had purchased data files. We ran a standard name, address, and zip code, internal dedupe between the different files we had, and we were able to quickly notify our vendor that they had tens of thousands of duplications that they were not even aware of, that we would have ended up paying for if we had not used the MatchUp software on the front end.

I was contacted by a products distribution company about using their technology. I was able to answer their questions, and then refer them to Melissa Data for further product support.

Helped us to correct all the invalid addresses in our DB.

Previously, we had to rely on the driver of the delivery to mark a shipment as residential. If they forgot or were not sure, the delivery would not be marked and we would potentially lose revenue from the residential delivery.

Helps our organization provide accurate address information to our customers for direct mailing (household) and other campaigns they want to do.

NCOA address verification was a requirement from USPS to send out the mailers. This was the only option that charged per address, which was extremely helpful since we are a small non-profit school. We didn't need the 10,000+ list verification services that the other providers offered.

Our organization does not use it directly. We use it in our product that we sell to other businesses.

Our membership addresses are kept up to date.

We have been able to avoid costly duplication of data and effort. Additionally, verifying has increased the accuracy of our target marketing efforts.

We now have less errors on catalog address labels.

Decreased charges incurred from UPS for incomplete or bad addresses.

NCOA processing is now quick and easy. No waiting for the list to come back, no calling, and no worrying if there are enough credits available.

Allows us to delete and correct incorrect data to make the searching of our applicant tracking system more consistent and relevant.

It cuts down significantly on time in trying to match names to addresses. I can do in a few hours what would otherwise take days to accomplish.

The customers' addresses are now complete, correct and follow one consistent format. This ensures the delivery of services/products, and all data to be consistent and valid.

Decreased chance of incorrect shipping addresses and, thus, returned packages.

It's an enabling technology for us.

 identify valid, and better formatted, data which improves the data we store in our database