CAPITAL CORP. SYDNEY

73 Ocean Street, New South Wales 2000, SYDNEY

Contact Person: Callum S Ansell
E: callum.aus@capital.com
P: (02) 8252 5319

WILD KEY CAPITAL

22 Guild Street, NW8 2UP,
LONDON

Contact Person: Matilda O Dunn
E: matilda.uk@capital.com
P: 070 8652 7276

LECHMERE CAPITAL

Genslerstraße 9, Berlin Schöneberg 10829, BERLIN

Contact Person: Thorsten S Kohl
E: thorsten.bl@capital.com
P: 030 62 91 92

Transparency in Data Onboarding

Data Onboarding, Data Quality, Digital Advertising, Research, Statistics

In today’s digital age, information is only a click away. Social media platforms like Facebook and Instagram entice individuals to increasingly feel the need to share everything. Whether it’s political views, or the need to share you’re heading to the gym, people are living their lives completely out in the open.

This expectation for transparency has extended beyond personal interactions and is now a reality in business. The topic of transparency has never been more important to the business environment.

Why?
The answer is simple: transparency conveys trust.

There’s a misconception out there about transparency. Too often, companies see transparency only as a tool to be used when owning up to a mistake or righting a wrong. This approach is shortsighted and isn’t an effective way to build trust. Clients will be far more forgiving of mistakes if a company has a history of being forthright with all interactions, not just the negative ones.

Transparency implies openness, and accountability. Operating in a transparent manner means operating in such a way that it is easy for others to see what actions are performed.

So, why has this industry accepted the notion of anything less than transparency? Why doesn’t a client know what their data looks like prior to onboarding and post onboarding? Why doesn’t the client understand what kind of data they are providing? And more importantly, why aren’t clients getting insights to their data after onboarding?

As this industry matures and grows, transparent results are a necessity in making proper strategic marketing decisions. Marketers want their customer data onboarded accurately and need to understand the following:

• What data was onboarded in a deterministic way vs. a probabilistic way
• What data is household-based vs. individual-based
• What is the recency and frequency of the results
• How many devices are associated per record

Does it make sense to include a consumer into a segment if you know that the cookie is probabilistic and has been seen once in 90 days? Is that a true targetable customer?

Digital marketing is becoming more and more sophisticated and data is driving decisions and results. The trusted firms connecting consumers to devices need to make sure that all data is of high quality and is accurate. They must also deliver transparent reports to verify the matches. Marketers also need to know the status of the data that they provided (quality, NCOA, duplications, errors).

Ideally, an onboarder should be able to process a client’s data so that it is of the highest quality prior to matching the data to any device: The nature of data is that it is constantly changing, the population changes millions of times daily, for instance:

  • There were 3.9 million babies born in 1999 – This year they are targetable 18 year olds.
  • There were 2.7 million deaths in 2015 alone – They need to be removed.
  • There were 124 million households in 2015 of which only 81 million were families – Which household should be targeted?
  • The average household size in 2015 was 2.54 – Wouldn’t you want to know more about that household?

The point is, that in order to develop an effective marketing strategy that targets individuals (not households) and produces accurate results, the data being utilized needs to be accurate and be shared openly with the client.

There are many ways to onboard a customer today (offline to online, online to online, and online to offline). Once the data has been processed for onboarding and successfully matched, the client should fully understand exactly what data was matched. It is the transparency of these results that will allow for better planning, strategy, and analysis. For instance, transparent reports should include:

Pre-Onboarding Data Results:
• Address standardization
• Email append rate
• Attribute append rate
• Record and device duplication removal
• Deceased removal
• Unusable email and postal records
• Net usable records

Post-Onboarding Report:
• Individual match rate
• Household match rate
• Probabilistic match rate
• Deterministic match rate
• Cache clearers
• Frequency
• 30-60-90 day recency
• Segments & quantities

Brands and data providers should not settle for a simple match percentage. Instead, they should demand to know the results so they can better market to their existing customers and strategize to attract new ones. No longer will a one line match rate do. TRANSPARENCY should now and always be the new normal in data onboarding.