Acme Data

𝟴 𝗪𝗮𝘆𝘀 𝗔𝗱𝗱𝗿𝗲𝘀𝘀 𝗩𝗲𝗿𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗣𝗮𝘆𝘀 𝗢𝗳𝗳

𝗥𝗲𝗮𝗹 𝗪𝗼𝗿𝗹𝗱 𝗗𝗮𝘁𝗮 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝗜𝘀𝘀𝘂𝗲𝘀 - #𝟴 𝗶𝗻 𝗧𝗵𝗶𝘀 𝗦𝗲𝗿𝗶𝗲𝘀

When discussing data quality challenges with clients, they frequently identify address verification as a top priority. Below is a list of some of the many reasons they give.

• 𝗖𝗼𝘀𝘁 𝗥𝗲𝗱𝘂𝗰𝘁𝗶𝗼𝗻

Returned, lost, or redirected shipments come with a high cost. DPV (Delivery Point Validation), that comes with Data Studio’s address verification, ensures that an address is valid and has a mailbox that will receive mail.

• 𝗜𝗻𝗰𝗿𝗲𝗮𝘀𝗲𝗱 𝗠𝗮𝗿𝗸𝗲𝘁𝗶𝗻𝗴 𝗖𝗮𝗺𝗽𝗮𝗶𝗴𝗻 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗲

Ensuring that materials reach the targeted recipients, higher response rates are achieved as well as higher engagement.

• 𝗠𝗮𝗶𝗹 𝗗𝗶𝘀𝗰𝗼𝘂𝗻𝘁𝘀

CASS Certified, that comes with Data Studio’s address verification, is required to qualify for mail automation and bulk mail discounts.

• 𝗦𝗮𝗹𝗲𝘀 𝗧𝗮𝘅 𝗖𝗮𝗹𝗰𝘂𝗹𝗮𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗖𝗼𝗺𝗽𝗹𝗶𝗮𝗻𝗰𝗲

Address verification is crucial for accurate sales tax calculation and compliance. It ensures tax is calculated based on the precise, validated shipping address—required for destination-based taxing—preventing overcharging or undercharging customers and reducing audit risks.

• 𝗧𝗲𝗿𝗿𝗶𝘁𝗼𝗿𝘆 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁

Verified addresses allow for better, more detailed territory analysis and segmentation. It enables CRM systems to automatically route leads and accounts to the correct sales representative, ensuring fair distribution and optimal coverage while preventing ownership disputes between sales reps.

• 𝗞𝗻𝗼𝘄 𝗬𝗼𝘂𝗿 𝗖𝘂𝘀𝘁𝗼𝗺𝗲𝗿 (𝗞𝗬𝗖) 𝗮𝗻𝗱 𝗔𝗻𝘁𝗶-𝗠𝗼𝗻𝗲𝘆 𝗟𝗮𝘂𝗻𝗱𝗲𝗿𝗶𝗻𝗴 (𝗔𝗠𝗟)

KYC and AML are mandatory verification process used by financial institutions and other regulated businesses to prevent crimes like money laundering and terrorist financing. Identity verification, including address verification, is a key step in KYC and AML processes.

• 𝗜𝗻𝗰𝗿𝗲𝗮𝘀𝗲𝗱 𝗖𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝗥𝗲𝗰𝗼𝗿𝗱 𝗠𝗮𝘁𝗰𝗵 𝗮𝗻𝗱 𝗠𝗲𝗿𝗴𝗲 𝗥𝗮𝘁𝗲𝘀

Verified addresses have complete address data and follow a common format. By improving the completeness and consistency of address data, match processes that rely significantly on address data points have much higher match and subsequent merge rates.

• 𝗜𝗺𝗽𝗿𝗼𝘃𝗲𝗱 𝗖𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝗘𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲 (𝗖𝗫)

CX is improved by eliminating frustration caused by failed deliveries. It creates a smoother, faster purchasing process that increases customer satisfaction and loyalty. It builds brand trust.

𝗗𝗮𝘁𝗮 𝗦𝘁𝘂𝗱𝗶𝗼 𝗯𝘆 𝗔𝗰𝗺𝗲 𝗗𝗮𝘁𝗮

Data Studio from Acme Data provides global address verification including DPV and NCOA, that integrates seamlessly with your enterprise applications and databases.

𝗜𝗳 𝘆𝗼𝘂 𝗰𝗼𝘂𝗹𝗱 𝘂𝘀𝗲 𝘀𝗼𝗺𝗲 𝗵𝗲𝗹𝗽 𝘄𝗶𝘁𝗵 𝗮𝗱𝗱𝗿𝗲𝘀𝘀 𝘃𝗲𝗿𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻, 𝗰𝗼𝗻𝘁𝗮𝗰𝘁 𝘂𝘀. 𝗪𝗲’𝗿𝗲 𝗲𝗮𝘀𝘆.

𝗖𝗮𝗻’𝘁 𝗙𝗶𝗻𝗱 𝗥𝗲𝘄𝗮𝗿𝗱𝘀 𝗜𝗗 𝗮𝘁 𝗖𝗵𝗲𝗰𝗸𝗼𝘂𝘁

𝗥𝗲𝗮𝗹 𝗪𝗼𝗿𝗹𝗱 𝗗𝗮𝘁𝗮 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝗜𝘀𝘀𝘂𝗲𝘀 - #𝟳 𝗶𝗻 𝗧𝗵𝗶𝘀 𝗦𝗲𝗿𝗶𝗲𝘀

Several times I have been to a retail store where I shop frequently, and when making a purchase, they can’t find my Rewards ID. They ask for a phone number, which I give them, but it doesn’t find my record. They ask for other phone numbers, and we go through a “try again” ritual. There are a number of options for solving this problem, but many do not create the optimal customer experience.

• I can put yet another app on my phone
• I can carry a rewards card in my wallet
• I can always use the same phone number for rewards accounts

You may be able to think of others.

𝗔𝗻𝗼𝘁𝗵𝗲𝗿 𝗔𝗽𝗽

I am getting app fatigue. Every business I work with tells me to download an app. It takes up storage and memory on my phone. I assume they might collect information that I don’t want them to collect. And there is no way I will read the fine print of the contract. It feels intrusive.

𝗥𝗲𝘄𝗮𝗿𝗱𝘀 𝗖𝗮𝗿𝗱

This actually works pretty well. It doesn’t have the privacy problem mentioned above. And it eliminates the “try again” ritual. But in the same way my phone is getting clogged with Apps, my wallet is filling up with rewards cards. And when a card cracks, I need to go through a process to get a replacement.

𝗦𝗮𝗺𝗲 𝗣𝗵𝗼𝗻𝗲 𝗡𝘂𝗺𝗯𝗲𝗿

Sometimes I might want a retailer to have my phone number, like the ones that deliver. Sometimes I give my rarely used landline as a throw away. When that phone rings I know it is someone who wants to sell me something. Now I am managing my rewards accounts, which is work that I definitely don’t want.

𝗖𝗿𝗲𝗮𝘁𝗶𝗻𝗴 𝘁𝗵𝗲 𝗢𝗽𝘁𝗶𝗺𝗮𝗹 𝗖𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝗘𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲

• What if a customer could simply tell you some basic information like “My last name is Jones and my area code is 415”, or just tell you that their last name is McGillicuddy?
• What if the customer could choose what pieces of information to give you, and the pieces could be different at any time?
• What if the person entering the information typed it in wrong but still found the correct record?

Those are questions our clients have put to us. The customer doesn’t have to download anything, carry a card or manage their rewards accounts.

𝗦𝗲𝗿𝘃𝗶𝗰𝗲 𝗠𝗮𝘁𝘁𝗲𝗿𝘀

Small items like this add up. Customers remember positive experiences. Customers remember better experiences. Customers are more likely to come back to businesses that provide a better customer experience.

𝗗𝗮𝘁𝗮 𝗦𝘁𝘂𝗱𝗶𝗼 𝗯𝘆 𝗔𝗰𝗺𝗲 𝗗𝗮𝘁𝗮

So this is where I tell you that Data Studio from Acme Data provides this better experience. Finding records is easy in Data Studio, in the user interface or with the API. It works at the cash register, on your website, and in the applications that run your business.

𝗜𝗳 𝘆𝗼𝘂 𝗰𝗼𝘂𝗹𝗱 𝘂𝘀𝗲 𝘀𝗼𝗺𝗲 𝗵𝗲𝗹𝗽 𝗰𝗿𝗲𝗮𝘁𝗶𝗻𝗴 𝗼𝗽𝘁𝗶𝗺𝗮𝗹 𝗰𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝗲𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲𝘀, 𝗰𝗼𝗻𝘁𝗮𝗰𝘁 𝘂𝘀. 𝗪𝗲’𝗿𝗲 𝗲𝗮𝘀𝘆.

𝗜𝗻𝘁𝗲𝗿𝗽𝗿𝗲𝘁𝗮𝗯𝗹𝗲 𝘃𝘀. 𝗘𝘅𝗽𝗹𝗮𝗶𝗻𝗮𝗯𝗹𝗲 𝗔𝗜: 𝗪𝗵𝗼 𝗦𝗵𝗼𝘂𝗹𝗱 𝗬𝗼𝘂 𝗧𝗿𝘂𝘀𝘁 𝗳𝗼𝗿 𝗠𝗮𝘁𝗰𝗵𝗶𝗻𝗴?

𝗥𝗲𝗮𝗹 𝗪𝗼𝗿𝗹𝗱 𝗗𝗮𝘁𝗮 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝗜𝘀𝘀𝘂𝗲𝘀 - #𝟲 𝗶𝗻 𝗧𝗵𝗶𝘀 𝗦𝗲𝗿𝗶𝗲𝘀

Many of our clients are large enterprises that deal with significant volumes of customer records, often millions. If you have a million customer records and a duplicate rate of 20%, that means 200,000 of your customer records are duplicates. Some simple math quickly informs you that it is cost prohibitive to match and merge that volume using only manual efforts.

What is needed, and what our customers require, is a system that can positively identify and automatically merge the largest number of duplicate records without user intervention. That is where AI comes in. We can use AI to positively identify the largest number of duplicate records that can then be automatically merged.

That takes a lot of trust. If the AI positively identifies 3/4 of the duplicates, and you trust the AI, that means that it will merge 3/4 of your duplicate records without any human supervision. In the case where the customer has 1,000,000 records and a 20% duplicate rate, and assuming they are all pairs, 150,000 of the 200,000 duplicate records will automatically be merged as non-survivors. 150,000 records will no longer exist. Get that wrong and there will be trouble.

𝗜𝗻𝘁𝗲𝗿𝗽𝗿𝗲𝘁𝗮𝗯𝗹𝗲 𝗔𝗜

In the world of AI, the concept of interpretability is a big deal. It means that users inherently understand a model's internal mechanics, that they comprehend the logic beforehand. Interpretable AI is built to be transparent from the start.

𝗘𝘅𝗽𝗹𝗮𝗶𝗻𝗮𝗯𝗹𝗲 𝗔𝗜

Explainable AI involves post-hoc techniques to explain black box model decisions after decisions have been made, after it merged your records. Black box is the opposite of transparent.

𝗟𝗮𝗯𝗼𝗿𝗶𝗻𝗴 𝗧𝗵𝗲 𝗣𝗼𝗶𝗻𝘁

• Interpretable = Transparency
• Explainable = Justification

Our customers do not want to have to justify their actions.

𝗖𝗮𝘀𝗲 𝗜𝗻 𝗣𝗼𝗶𝗻𝘁

A very large retailer built a duplicate detection engine using an AI algorithm provided by one of the “Big Three” cloud providers. This algorithm was purpose built for duplicate detection. They trained the model and ran the model against their database and found that the results were unusable. The algorithm matched many, many records that were not duplicates.

There were discussions about how the model would probably do better given higher volumes of data. Those discussions didn’t sway anyone and the company abandoned the model.

That’s when they called us.

𝗗𝗮𝘁𝗮 𝗦𝘁𝘂𝗱𝗶𝗼 𝗯𝘆 𝗔𝗰𝗺𝗲 𝗗𝗮𝘁𝗮

Our customers want to know why records are merged and they want to know before it happens. They want clarity around what the AI is doing, what decisions it is making and how the AI is making those decisions. Only then can they trust.

Data Studio’s AI is interpretable. It puts you in control.

𝗜𝗳 𝘆𝗼𝘂 𝗰𝗼𝘂𝗹𝗱 𝘂𝘀𝗲 𝘀𝗼𝗺𝗲 𝗵𝗲𝗹𝗽 𝘄𝗶𝘁𝗵 𝗱𝗮𝘁𝗮 𝗾𝘂𝗮𝗹𝗶𝘁𝘆, 𝗰𝗼𝗻𝘁𝗮𝗰𝘁 𝘂𝘀. 𝗪𝗲’𝗿𝗲 𝗲𝗮𝘀𝘆.

𝗛𝗼𝘄 𝗬𝗼𝘂 𝗖𝗮𝗻 𝗠𝗲𝗮𝘀𝘂𝗿𝗲 𝘁𝗵𝗲 𝗩𝗮𝗹𝘂𝗲 𝗼𝗳 𝗬𝗼𝘂𝗿 𝗗𝗮𝘁𝗮 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻

𝗥𝗲𝗮𝗹 𝗪𝗼𝗿𝗹𝗱 𝗗𝗮𝘁𝗮 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝗜𝘀𝘀𝘂𝗲𝘀 - #𝟱 𝗶𝗻 𝗧𝗵𝗶𝘀 𝗦𝗲𝗿𝗶𝗲𝘀

Our customers want to know exactly what value they are getting out of Data Studio, Acme Data’s data quality solution. They want specific metrics, nothing vague.

𝗢𝗻𝗲 𝗠𝗲𝘁𝗿𝗶𝗰 𝗧𝗵𝗲𝘆 𝗪𝗲𝗿𝗲𝗻’𝘁 𝗦𝘂𝗿𝗲 𝗢𝗳

• 𝗜𝗻𝗰𝗿𝗲𝗮𝘀𝗲𝗱 𝗖𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝗦𝗮𝘁𝗶𝘀𝗳𝗮𝗰𝘁𝗶𝗼𝗻 – While they could do surveys or measure NPS, or hear anecdotal information from employees, they found it difficult to directly attribute higher customer satisfaction to higher quality data. While that might seem inherently true, it might have limited value when trying to justify a purchase.

𝗠𝗲𝘁𝗿𝗶𝗰𝘀 𝗧𝗵𝗲𝘆 𝗗𝗶𝗱 𝗟𝗶𝗸𝗲

o 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗲 𝗥𝗮𝘁𝗲𝘀: While they found it difficult to attribute more sales to higher quality data, they could definitively measure response rates from campaigns before and after data cleansing.
o 𝗣𝗼𝘀𝘁𝗮𝗹 𝗖𝗼𝘀𝘁𝘀: They were able to measure the money saved on postage and physical mail by not mailing to undeliverable or duplicate records
o 𝗖𝗮𝗹𝗹 𝗛𝗮𝗻𝗱𝗹𝗲 𝗧𝗶𝗺𝗲: One customer measured that our accurate and reliable “search before create” reduced their average customer support call handle time by 45 seconds (at 3 million calls per year)
o 𝗗𝘂𝗽𝗹𝗶𝗰𝗮𝘁𝗲 𝗥𝗲𝗰𝗼𝗿𝗱 𝗦𝘂𝗽𝗽𝗿𝗲𝘀𝘀𝗶𝗼𝗻: The number of “search before create” calls that found an existing record that was selected as opposed to creating a new one
o 𝗥𝗲𝗰𝗼𝗿𝗱𝘀 𝗠𝗲𝗿𝗴𝗲𝗱: The number of duplicate records merged
o 𝗔𝗱𝗱𝗿𝗲𝘀𝘀𝗲𝘀 𝗩𝗮𝗹𝗶𝗱𝗮𝘁𝗲𝗱: The number of addresses validated against the USPS, Canada Post and global address databases
o 𝗗𝗲𝗹𝗶𝘃𝗲𝗿𝘆 𝗣𝗼𝗶𝗻𝘁 𝗩𝗮𝗹𝗶𝗱𝗮𝘁𝗶𝗼𝗻 (𝗗𝗣𝗩) 𝗥𝗮𝘁𝗲: The number of addresses positively identified as having a mailbox that would accept mail
o 𝗠𝗶𝘀𝘀𝗶𝗻𝗴 𝗔𝗱𝗱𝗿𝗲𝘀𝘀 𝗖𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁 𝗙𝗶𝘅 𝗥𝗮𝘁𝗲: The percentage of records where missing data (e.g., apartment numbers, ZIP+4) was appended
o 𝗜𝗻𝘃𝗮𝗹𝗶𝗱 𝗔𝗱𝗱𝗿𝗲𝘀𝘀 %: The number of addresses deemed invalid by postal data
o 𝗨𝗽𝗱𝗮𝘁𝗲𝗱 𝗠𝗼𝘃𝗲𝗿 𝗔𝗱𝗱𝗿𝗲𝘀𝘀 %: The percentage of records that matched against the 48-month NCOA database of moves

That’s a lot of metrics and there are more.

They like being able to see these statistical totals and percentages, the month to month trends (going up or down), as well as the effects on new data being entered.

This information can help you justify an initial data quality purchase or maintain an existing one.

𝗜𝗳 𝘆𝗼𝘂 𝗰𝗼𝘂𝗹𝗱 𝘂𝘀𝗲 𝘀𝗼𝗺𝗲 𝗵𝗲𝗹𝗽 𝘄𝗶𝘁𝗵 𝗱𝗮𝘁𝗮 𝗾𝘂𝗮𝗹𝗶𝘁𝘆, 𝗰𝗼𝗻𝘁𝗮𝗰𝘁 𝘂𝘀. 𝗪𝗲’𝗿𝗲 𝗲𝗮𝘀𝘆.

𝗡𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗖𝗵𝗮𝗻𝗴𝗲 𝗼𝗳 𝗔𝗱𝗱𝗿𝗲𝘀𝘀 (𝗡𝗖𝗢𝗔) 𝗳𝗼𝗿 𝗖𝗼𝗺𝗽𝗹𝗶𝗮𝗻𝗰𝗲, 𝗮𝗻𝗱 𝘁𝗵𝗲 𝗕𝗲𝘀𝘁 𝗗𝗮𝘁𝗮

𝗥𝗲𝗮𝗹 𝗪𝗼𝗿𝗹𝗱 𝗗𝗮𝘁𝗮 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝗜𝘀𝘀𝘂𝗲𝘀 - #𝟰 𝗶𝗻 𝗧𝗵𝗶𝘀 𝗦𝗲𝗿𝗶𝗲𝘀

A large and very well known manufacturer of consumer durables ( #1 is their space) has to deal with recalls from time to time. Product issues can have a number of unwanted outcomes:

• 𝗛𝗮𝗿𝗺 𝘁𝗼 𝗰𝗼𝗻𝘀𝘂𝗺𝗲𝗿𝘀
• 𝗟𝗮𝘄𝘀𝘂𝗶𝘁𝘀
• 𝗟𝗲𝗴𝗮𝗹 𝗻𝗼𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗿𝗲𝗾𝘂𝗶𝗿𝗲𝗺𝗲𝗻𝘁𝘀

When they called us, and due to a potential fire hazard, they had a court order to notify anyone who had purchased a specific product. They were also required to show the court the specific actions they took to demonstrate “best efforts”.

They used Data Studio’s 𝗡𝗖𝗢𝗔 product to update stale addresses in their database with mover data provided by the USPS. The tight integration between Data Studio and their CRM system made this process seamless and easy. Taking things one step further, Data Studio provides a full audit trail of changes. They could see what addresses were updated and they could provide this audit data to the court to demonstrate compliance.

𝗕𝗲𝘆𝗼𝗻𝗱 𝗖𝗼𝗺𝗽𝗹𝗶𝗮𝗻𝗰𝗲

Having the most current information about their customers also improved the yield of their marketing efforts. Some of their products have consumables that are highly profitable. By having the correct addresses, the company knew that marketing offers were sure to make it to their customers.

𝗙𝗶𝘅𝗶𝗻𝗴 𝗕𝗮𝗱 𝗔𝗱𝗱𝗿𝗲𝘀𝘀𝗲𝘀 𝗜𝘀 𝗦𝗼 𝗘𝗮𝘀𝘆

With 𝗗𝗮𝘁𝗮 𝗦𝘁𝘂𝗱𝗶𝗼, 𝗔𝗰𝗺𝗲 𝗗𝗮𝘁𝗮’𝘀 𝗱𝗮𝘁𝗮 𝗾𝘂𝗮𝗹𝗶𝘁𝘆 𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻, address verification is implemented in less than 5 minutes. If that sounds hard to believe, make us prove it. It’s a good investment of one phone call.

If you could use some help cleansing and enriching your data, 𝗰𝗼𝗻𝘁𝗮𝗰𝘁 𝘂𝘀. We’re easy.

𝗪𝗵𝗲𝗻 𝗚𝗼𝗼𝗱 𝗠𝗮𝘁𝗰𝗵𝗶𝗻𝗴 𝗜𝘀𝗻’𝘁 𝗚𝗼𝗼𝗱 𝗘𝗻𝗼𝘂𝗴𝗵

𝗥𝗲𝗮𝗹 𝗪𝗼𝗿𝗹𝗱 𝗗𝗮𝘁𝗮 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝗜𝘀𝘀𝘂𝗲𝘀 - #𝟯 𝗶𝗻 𝗧𝗵𝗶𝘀 𝗦𝗲𝗿𝗶𝗲𝘀

One of our clients, a well known software company, had a CRM system that contained more than 12 million customer records. They had purchased a popular, mid-tier deduping solution. The fact that the mid-tier solution was widely adopted by other companies gave the client confidence that they were making a good choice.

Post implementation, they experienced some undesirable outcomes.

𝗟𝗼𝘄 𝗬𝗶𝗲𝗹𝗱, 𝗕𝗶𝗴 𝗣𝗿𝗼𝗯𝗹𝗲𝗺𝘀

With 2,000 support reps incentivized to close calls quickly, speed was everything. When customers called for support, reps used the "search before create" feature to locate existing records. They found that too often this search feature did not find matching records and was slow to respond. The support reps learned that creating new customer records was faster than searching for existing ones. The result was that the client ended up with an army of support reps creating duplicate records.

Compounding the problem was the fact that the product registration web page used the same matching logic. If the system couldn't positively identify a returning customer during registration, it simply created a new customer record. Between the call center and the registration page, the company was generating duplicate records at scale.

And then they called us.

𝗧𝗵𝗲 𝗞𝗲𝘆 𝘁𝗼 𝗠𝗶𝗻𝗶𝗺𝗶𝘇𝗶𝗻𝗴 𝗗𝘂𝗽𝗹𝗶𝗰𝗮𝘁𝗲 𝗥𝗲𝗰𝗼𝗿𝗱𝘀

When the client first engaged us, we found that roughly 25% of the customer records in their CRM system were duplicates (~3 million). For this volume for records, manual intervention was not practical. For any significant volume of records, it is essential that matching processes positively identify the largest number of duplicate records. Positive ID is required for the automated merging of duplicate records.

Precision positive identification also maximizes the accuracy of the search before create function. Only when search before create works very well do users then adopt and rely on it.

𝗕𝗲𝘁𝘁𝗲𝗿 𝗠𝗮𝘁𝗰𝗵𝗶𝗻𝗴 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗲𝘀 𝗛𝗶𝗴𝗵𝗲𝗿 𝗥𝗢𝗜

Data Studio, Acme Data’s data quality solution, identifies more duplicate records with greater precision than competing products—and implements in minutes.

If the end users of your CRM, Analytics and AI systems rave about the quality of your data, you should stick with it. If not, you should put us to the test.

𝗜𝗳 𝘆𝗼𝘂 𝗰𝗼𝘂𝗹𝗱 𝘂𝘀𝗲 𝘀𝗼𝗺𝗲 𝗵𝗲𝗹𝗽 𝘄𝗶𝘁𝗵 𝗱𝗮𝘁𝗮 𝗾𝘂𝗮𝗹𝗶𝘁𝘆, 𝗰𝗼𝗻𝘁𝗮𝗰𝘁 𝘂𝘀. 𝗪𝗲’𝗿𝗲 𝗲𝗮𝘀𝘆.

𝗛𝗼𝘄 𝗮 𝗠𝗲𝗱𝗶𝗰𝗮𝗹 𝗖𝗼𝗺𝗽𝗮𝗻𝘆 𝗨𝘀𝗲𝘀 𝗔𝗱𝗱𝗿𝗲𝘀𝘀 𝗩𝗲𝗿𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝘁𝗼 𝗘𝗹𝗶𝗺𝗶𝗻𝗮𝘁𝗲 𝗥𝗲𝘁𝘂𝗿𝗻𝗲𝗱 𝗠𝗮𝗶𝗹 & 𝗕𝗼𝗼𝘀𝘁 𝗥𝗢𝗜

𝗥𝗲𝗮𝗹 𝗪𝗼𝗿𝗹𝗱 𝗗𝗮𝘁𝗮 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝗜𝘀𝘀𝘂𝗲𝘀 - #𝟮 𝗶𝗻 𝗧𝗵𝗶𝘀 𝗦𝗲𝗿𝗶𝗲𝘀

One of our clients, a well known company in the medical space, had two rooms full of returned mailers. This was back when companies would send DVDs in the mail. People would call to inquire about the company’s specialized offerings, and the company would mail them a DVD. The DVD mailers cost about $8 each. The company did a good volume of business (they are global and number one in their space) and too often the packages were returned by the post office. This was because the “send to” addresses had problems.

The packages accumulated because reprocessing them took time and effort. They were stored in these rooms with the idea that the company would eventually reprocess them, but they didn’t like having rooms full of returned mailers.

That’s when they called us.

𝗦𝗼𝗹𝘃𝗶𝗻𝗴 𝘁𝗵𝗲 𝗣𝗿𝗼𝗯𝗹𝗲𝗺

The address information was entered by potential customers on the company website or taken over the phone by the company’s support reps. The data that was entered often had problems.

The solution was to implement Data Studio’s address verification in a way that addressed the whole problem. Working closely with the client, this is what we did:

• Implemented batch address verification to clean all the records in their CRM system (Salesforce)
• Implemented incremental address verification (running every minute) to keep them clean
• Configured real time address auto complete in their Salesforce screens and on their website

This multi-point plan ensured that every address was scrutinized for accuracy. Addresses are not only verified for accuracy, they are often corrected or enriched with accurate data.

And the address verification has additional features.

𝗗𝗲𝗹𝗶𝘃𝗲𝗿𝘆 𝗣𝗼𝗶𝗻𝘁 𝗩𝗮𝗹𝗶𝗱𝗮𝘁𝗶𝗼𝗻 (𝗗𝗣𝗩)

This not only verifies that an address is correct, it also flags if the address has a mailbox that can receive a package. This was key to eliminating returned mail.

𝗡𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗖𝗵𝗮𝗻𝗴𝗲 𝗼𝗳 𝗔𝗱𝗱𝗿𝗲𝘀𝘀 (𝗡𝗖𝗢𝗔)

This automatically updates addresses for people and business that have moved.

𝗧𝗵𝗲 𝗥𝗲𝘀𝘂𝗹𝘁

The returned mail issue and the associated costs were entirely eliminated. And they converted more prospects into paying customers because virtually all of the prospects now received the promotional mailer.

𝗔𝗰𝗰𝘂𝗿𝗮𝘁𝗲 𝗔𝗱𝗱𝗿𝗲𝘀𝘀 𝗗𝗮𝘁𝗮 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗲𝘀 𝗛𝗶𝗴𝗵𝗲𝗿 𝗥𝗢𝗜

Data Studio, Acme Data’s data quality solution, provides global address verification that maximizes the value of your data. And it is implemented in minutes.

If the end users of your CRM, Analytics and AI systems rave about the quality of your data, you should stick with it. If not, contact us. We’re easy.

𝗛𝗼𝘄 𝗗𝘂𝗽𝗹𝗶𝗰𝗮𝘁𝗲 𝗥𝗲𝗰𝗼𝗿𝗱𝘀 𝗞𝗶𝗹𝗹 𝘁𝗵𝗲 𝟯𝟲𝟬 𝗗𝗲𝗴𝗿𝗲𝗲 𝗩𝗶𝗲𝘄

𝗥𝗲𝗮𝗹 𝗪𝗼𝗿𝗹𝗱 𝗗𝗮𝘁𝗮 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝗜𝘀𝘀𝘂𝗲𝘀 - #𝟭 𝗶𝗻 𝗧𝗵𝗶𝘀 𝗦𝗲𝗿𝗶𝗲𝘀

One of our clients, a large software company, wanted to upsell their customers. They knew that if a customer had purchased one of their products (Product A) that they were an excellent candidate for purchasing a companion product (Product B). So they pulled a list of customers that fit this profile from their CRM system and ran a campaign targeting these customers.

Soon after, they started getting a lot of negative feedback. The problem was that a significant number of the customers who were targeted had already purchased Product B. This happened because the client’s CRM system had duplicate customer records (one associated with Product A and a duplicate associated with Product B). They didn’t have the promised 360-degree view that their CRM system was supposed to provide.

𝗗𝗶𝗱𝗻’𝘁 𝗠𝗮𝗸𝗲 𝗧𝗵𝗲𝗶𝗿 𝗡𝘂𝗺𝗯𝗲𝗿𝘀/𝗔𝗹𝗶𝗲𝗻𝗮𝘁𝗲𝗱 𝗖𝘂𝘀𝘁𝗼𝗺𝗲𝗿𝘀

This company wasted time and resources on an inefficient campaign that didn’t produce the revenue they had projected. Perhaps more importantly, they alienated their customers. The customers learned that the company didn’t know what products they had purchased, didn’t have good records.

And there were more problems. Customers would call for support and were told they did not have support. The support reps didn’t see the support contract the customers had purchased because it was under a different, duplicate record. This did not go over well.

And then they called us.

𝗕𝘂𝘁 𝗙𝗶𝘅𝗶𝗻𝗴 𝗗𝗮𝘁𝗮 𝗜𝘀 𝗗𝗶𝗳𝗳𝗶𝗰𝘂𝗹𝘁 𝗮𝗻𝗱 𝗧𝗮𝗸𝗲𝘀 𝗮 𝗟𝗼𝗻𝗴 𝗧𝗶𝗺𝗲

It doesn’t have to be. 𝗗𝗮𝘁𝗮 𝗦𝘁𝘂𝗱𝗶𝗼, Acme Data’s data quality solution, delivers the powerful functionality that large enterprises require, that mid-tier solutions do not. And it 𝗶𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝘀 𝗶𝗻 𝗺𝗶𝗻𝘂𝘁𝗲𝘀. If that sounds unbelievable, good. It means we’re doing something significant, something better. You should put us to the test.

𝗜𝗳 𝘆𝗼𝘂 𝗰𝗼𝘂𝗹𝗱 𝘂𝘀𝗲 𝘀𝗼𝗺𝗲 𝗵𝗲𝗹𝗽 𝘄𝗶𝘁𝗵 𝗱𝗮𝘁𝗮 𝗾𝘂𝗮𝗹𝗶𝘁𝘆, 𝗰𝗼𝗻𝘁𝗮𝗰𝘁 𝘂𝘀. 𝗪𝗲’𝗿𝗲 𝗲𝗮𝘀𝘆.