ID

Recommendations

- Email

Can be provided in either SHA256 hexadecimal format or raw data. Must be in a single column. 

If you use SHA256 format, ensure all email addresses are normalized in lowercase with leading/trailing white spaces removed before you convert to SHA256 format.

- First name & last name

Provided in raw format

All words lowercase

Forename and Surname should be separated into individual columns

- Postcode

- Zipcode

Always provided in raw format

For US address please provide zip5 or Zip9

PH37 1AB (Example)

- Phone number

Always provided in raw format

Valid phone number in valid E.164 format

When activating to Meta you need a second column that includes the country so please ensure your dataset has that information if you plan to use the Meta destination

- IP address

There is no format enforcement but most clients provide this in IPv4

e.g. 116.61.80.61, 30.161.132.202, 137.143.254.196, 62.158.243.253

Attribute

Recommendations

- Age

Leverage one of the most useful existing groupings such as 5 or 10 year bins. The platform will automatically give you the option to view this representation if you input this in a different format like DOB.

DOB must always provided in raw format. Must be in three columns, each with a separate input value for "yyyy", "mm", and "dd". If your DOB is in a single column (for example, YYYY-MM-DD, DD-MM-YYYY), you can use transformations to convert them into three columns.

- Cost- Price- Revenue

The platform does not recognize currency symbols or currency as a data format. Due to our focus on privacy, you cannot perform calculations that you would usually perform on currency figures

We recommend:

• Use range buckets as well, but strategise and think about what is most interesting for you to do with that data, how your customers purchase the product, the individual product you’re adding data about, and the type of analysis that you want to do on the platform
• Full numbers or ranges are also great for greater than/lower than filtering

If your analysis is going to be heavily into cost efficiencies please speak to our team to get a more tailored recommendation

- Ratios or percentages E.g. share of wallet

The platform does not recognize % signs or percentages as a data format.

We recommend:

• Include ranges (e.g. 0-5%) - this pre-bucketing helps with generating usable insights
• Full numbers or ranges are also great for greater than/lower than filtering

- Dates- Timestamps

The platform does not recognize dates as a format. Due to our focus on privacy, we don’t allow to use dates in the usual way as it would aid in re-identifying individuals.

• Option 1 (string): You could import as a string when it’s a static value (‘show me all the users where date and time is X X X X’)
• Option 2 (integer): If they are variable and you care about the integer range - think about whether you care about the time, would just date be sufficient (measurement might need both but when did someone purchase a ticket? Date is probably enough)
• Option 3 (integer): Need an integer because you cannot only use OR conditions and want a range - distill down to dates but still need integer range
  • Day | month | year in 3 separate columns - might not need year if you’ve only been with us for under a year
  • When someone needs dates and times - PP (cannot put full timestamp other than passing it through directly)

We recommend that you keep only the necessary information. For example, it might be enough to have just months or months and years to create actionable insights, or you might care about the date that something

- Interests- Characteristics- Content consumption- Contextual

Using multi-value attributes is more column volume efficient and creates more interesting insights, by grouping similar interests or interest fields into one column. E.g. instead of one column per type of sport, create a ‘favorite sports’ column where you can list the sport or sports.

It can be challenging to provide relevant insights without going to the highest level of granularity which makes analysis more cumbersome. We suggest you include just two levels of granularity, for example:Favourite sports > type of sport (multi-value) > sports clubTV channel > Type of program > program titleMusic > Music genre > Artist

You may also wish to upload your content organized following a common mendia language such as IAB categories, if you’re using these already as it can reduce the data preparation needed on your side. Following these categories (names and format) can create more transferable insights for the brands (e.g. they can activate the insight programmatically or as part of a media plan)