For the past 2 years, digital marketing has been all about one thing - accurate data and tracking. But the terminology around it can be hard to follow.

As a D2C performance marketer, you’re probably looking at data across channels - not just ad platforms like Meta, Google, TikTok, but also email marketing, SMS marketing, and more. Multiple 1st-party pixel applications like TripleWhale, Northbeam, Socioh, Rockerbox, etc. have come up to help consolidate these views and help you scale your spend profitably.

I’ve put together some definitions in relatively simple, jargon-free language. So the next time, you're not sure what the terms used by your attribution solution mean, check out this <low-highlight>quick-and-easy guide to advanced attribution terminology. <low-highlight>

(Want a refresher on basic ad terminology? Check out our Meta Analytics Glossary 2024.)

Measurement Metrics

First-party pixel

The first-party pixel (or cookie) is a piece of code added to your website that tracks your site visitors’ actions. It is the most accurate way to track your conversions across traffic channels based on user clicks & UTMs, and can be used to allocate your marketing spend effectively across campaigns and channels.

All attribution software offer first-party tracking. However, Socioh is the only one that has a free first-party pixel available for all Shopify users.

First-party vs in-platform reporting

First-party data‍

First-party data is the data that is collected by your first-party pixel.

‍In-platform data‍

The data and analytics provided by the platform on which you are advertising like Meta, Google, Tiktok, Twitter, Pinterest, Youtube etc.

Click-based vs View-based 

Click-through Attribution‍

This only takes into account actual clicks on your ad when calculating metrics like revenue earned or ROAS (Return on Ad Spend).

View-through Attribution‍

This also takes into account impressions of your ads where the shopper ‘viewed’ your ad even if they did not ‘click’ on it. The assumption is that ‘viewing’ the ad was part of the customer journey and helped the shopper make the final purchase decision
Note: View-through attribution can not be captured by your first-party pixel and is tracked only through in-platform reporting.

<purple-low-highlight>Example:<purple-low-highlight> your ad was served to a shopper. The shopper did not click the ad, but then went to your website directly and made a purchase. This ad would not get any credit for the sale on the ‘click-through’ basis, but would get credit in a ‘view-through’ basis.

The most common attribution windows are 1-day click, 1-day view, 7-day click, and 28-day click.

Attribution Window vs Conversion Window

Attribution Window

The period of time during which an ad would get credit for the sale. This duration could be click-based or view-based.

So a 1-day view, 7-day click attribution window would give credit to campaigns that the buyer ‘viewed’ less than 24 hours or ‘clicked on’ less than 7 days before the sale. Lifetime attribution would credit every single campaign that had ever ‘touched’ the buyer prior to the sale.‍

Conversion / Optimization Window

This is defined by the advertiser as the period of time that it typically takes a shopper to convert for that account. Based on this, the ad platform’s algorithm will optimize for shoppers who are likely to convert during that time. Less expensive impulse purchases will typically have a shorter conversion window than big ticket items like appliances, furniture, or luxury goods.

<purple-low-highlight>Example:<purple-low-highlight> For a fashion jewelry brand, the advertiser may specify a 1-day click conversion window as an interested shopper is unlikely to spend too long making a decision to purchase. The platform will then look for people who are likely to buy immediately.

For a high-end jewelry brand, a 7-day click window may be more realistic as this is an expensive purchase and the shopper may need to think about it.

For a premier furniture brand, a 28-day conversion window may be the most suitable.

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Single-touch vs Multi-touch

Single-touch

Single touch attribution means that a 100% of the credit of this sale will go to ONE of the channels or campaigns. This could be first-touch or last-touch (explained below).

Multi-touch‍

In multi-touch attribution all the touchpoints leading up to the sale are given credit for the sale. These could be linear or weighted (explained below).

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First touch vs last touch

First touch

The full credit for the sale is given to the campaign that had the first click or interaction with the buyer.

Last touch

The full credit for the sale is given to the campaign that had the last (non-direct) click or interaction with the buyer prior to the sale. This does not include a direct visit to your website.

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Linear vs Weighted

Linear

Each touchpoint gets equal credit for the transaction. 

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Weighted

Each touchpoint is assigned a certain ‘weight’ or importance based on factors like time since visit, level of engagement etc. The ‘weight’ given to each touchpoint will differ from app to app as each has its own way of calculating this metric. 

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Cash vs Accrual

There are two ways to report multi-touch attribution. Depending on the selected method, you will see different numbers.

Cash 

A multi-touch attribution model where each touchpoint gets credit for the sale on the day of the final sale. How far back this goes will depend on the attribution window chosen by the advertiser. For instance, if you choose a 7-day attribution window, then any click or interaction more than 7 days back will not get any credit.

<purple-low-highlight>Example:<purple-low-highlight> An ad was viewed on 3 different channels on 3 different days, but the final purchase was on the 10th of March. All 3 channels/ads get credit for the ad on the day of the purchase.

Accrual

A multi-touch attribution model where each touchpoint gets credit for the final sale on the day of the click or interaction (not on the day of the final sale). Again, the number of touchpoints will depend on the attribution window selected.

<purple-low-highlight>Example:<purple-low-highlight> In the same case as above, all 3 channels/ads will get backdated credit for the day the ad was clicked (the 10th). So the revenue/ROAS of the Meta ad will increase on the 10th even though the click happened on the 8th. This is called conversion lift.

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Conversion Lift

Conversion lift measures the percentage increase in ROAS a specified number of days after your ad was clicked. In the screenshot below, the number in green is the conversion lift.

Projected Conversion lift predicts the anticipated increase in ROAS of the ad after a specified number of days. 

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Attributed vs influenced 

This is a Socioh-specific metric. 

Attributed

Metrics based on the revenue that can be attributed directly to Meta. This is a smaller percentage of the total ROAS that is influenced by Meta. Socioh’s proprietary weighted model is based on multiple factors including time since visit, level of engagement, etc.

Influenced 

Metrics based on revenue from all the orders that Meta ads have touched. This does not mean that this channel is the only contributor to this ROAS.

Business & Customer Metrics

MER and nMER

MER (Marketing Efficiency Ratio) or Blended ROAS

This measures how your marketing spend is affecting your store revenue. This is an overarching store-level metric, and is not typically used to guide decisions at the ad or campaign level. However, it does help you ensure that each new dollar you spend on acquisition is adding to new revenue.

<blue-low-highlight> MER = Total Revenue / Total Ad Spend<blue-low-highlight>

nMER or aMER (Marketing Efficiency Ratio for New (n) or Acquired (a) customers)

This measures how effective your marketing spend is in generating new customer revenue.

<blue-low-highlight> nMER = New Customer Revenue / Total Ad Spend<blue-low-highlight>

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AOV and nAOV

AOV (Average Order Value)

The average dollar value of transactions credited to this campaign.

<blue-low-highlight> AOV = Revenue from this campaign / Total Number of Transactions credited to this campaign <blue-low-highlight>

nAOV (Average Order Value of new customers)

The average dollar value of purchases from new customers credited to this campaign.

<blue-low-highlight> nAOV = Revenue from New Customers / Total Number of Transactions credited to this campaign <blue-low-highlight>

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CAC and nCAC

CAC  (Customer Acquisition Cost)

This measures how much it costs to get one customer to purchase from this ad. This is also known as Cost per Acquisition or Cost per Conversion. A good way to look at this is to measure your CAC against your AOV - your average order value should be greater than your average CAC. Simply put, the amount a customer spends with you should be more than the cost of acquiring them.

<blue-low-highlight> CAC = Ad Spend on this campaign / Number of Customers <blue-low-highlight>

nCAC  (Cost of New Customer Acquisition)

This measures how much it costs to get one new customer to convert. 

<blue-low-highlight>nCAC = Ad Spend on this campaign / Number of New Customers <blue-low-highlight>

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Here are some additional resources that you might find useful:

• An in-depth video by Socioh’s founder & CEO, Karan Jassar, answering all your questions about first-party attribution

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Is there anything that I’ve missed that you would like to me to add? Please leave a comment and I’d be happy to make a loom for you or send you an explanation.