Definition: Discrepancy

Discrepancy Definition

Discrepancy means an unexpected (or unexplainable) difference between two sets of information. Unfortunately, in digital marketing, a discrepancy is always to be expected between any two sets of analytics platforms.


What does a discrepancy mean?

A discrepancy between the results between different analytics platforms does not mean much in and of itself. In fact, it is generally to be expected, as all analytics programs work slightly differently and so will record different results.

You would expect discrepancies between platforms to stay roughly steady. If the discrepancy changes dramatically then you should start to be concerned.

For example, if you are tracking links the difference between results from Google Analytics and is 10% every month, but then shoots up to 20% then you should investigate. In this way, a discrepancy can be useful.


What causes a discrepancy

Discrepancies are to be expected in digital marketing because, although they often use the same terminology to describe a stat (eg “Page View”) different analytics programs will often measure these items slightly differently.

For example, one platform may measure when a page starts to load while another will measure when a page finishes loading. If all goes well, these two platforms will record the same thing. However, with such a huge combination of websites, browsers, computers, etc there is huge scope for things going wrong.

This means that while all tracking may work for most users, it is very unlikely it will work perfectly for all users. A user may leave the page before one set of tracking loads, or one set of tracking may be blocked. When these small issues are measured across a large number of users, the difference in stats will become large.


Technical Information

If you’re still wondering why a discrepancy is occurring between your analytics platforms, there are simply too many reasons to list them all. It is certainly not the case that there is always someone to blame. Many times a discrepancy occurs simply due to a unique set of circumstances.

In general, you would expect discrepancies between different analytics platforms to remain somewhat stable. This doesn’t mean that month-on-month you should see exactly the same discrepancies occurring, but there should be a stable range (5-10% for example). However, if you start seeing this range dramatically change, then there is likely a problem that needs addressing.

Here is a list of some (but not all) reasons why discrepancies might occur:

  • A user leaves before a page fully loads
  • One or more set(s) of tracking is incorrectly implemented
  • Tracking doesn’t work on some devices/browsers
  • A changing element of the site (eg advertising) affects tracking, meaning it is disrupted sometimes but not others
  • Geo-targeting means some element of tracking is occasionally disrupted
  • An update to some part of the system has broken some tracking
  • A large increase in traffic to a site has overwhelmed servers
  • Ad-blockers
  • Two sets of tracking interact oddly
  • A tracking service is being blocked by some ISPs
  • A large image (or another element) means a page loads slowly and times-out
  • An analytics platform is sampling data and making educated guesses as opposed to measuring every user


Advice For Website Owners

Choose an analytics platform and stick with it when reporting. If you report Google Analytics one month, make sure you use it again next month. Otherwise, you are adding in a level of randomness for no reason.

The platform you choose should be accurate enough that you can consider it your ‘single point of truth’. No platform is 100% accurate, but consistency is the key here.

When selling advertising, again use consistent stats for reporting. In most cases the advertiser or ad network will have a preferred method (often Comscore) for reporting, so make sure you consistently use that.

The advertiser’s stats will also be inconsistent with yours. You should ensure that you get reports sent over from the advertiser throughout campaigns (from within one week of launch). On top of this make sure you agree beforehand which (and whose) stats you are using to record information. This will help you make sure you do not think you are hitting all your targets, only to find out the advertiser is refusing to pay for anything as their analytics don’t work.

In the USA it is standard to use advertisers reporting for payments, but in the UK it is still mostly standard to use publisher or ad network stats. There are exceptions to the rule in both cases.

Advertisers will, of course, want to use their stats, and agreeing to do so can help you close deals. However, if you do agree to this, you should plan in at least a 20% buffer zone (as in sell 20% less inventory than you think you have). Keep checking their stats against yours throughout any campaign and adjust delivery accordingly. This is the only way to make sure you don’t under-deliver.

You should note that conversion data is especially prone to discrepancies. This is because the advertiser will be de-duping data, whereas you will not. This means the advertiser will almost always be recording fewer conversions than you are. Sharing reporting is therefore especially important in CPA campaigns.


Advice For Ad Buyers

Ask for regularly scheduled reports from anywhere you advertise to minimise discrepancies.

It is, of course, advantageous for you to insist that any campaigns you run are paid out based on your own third party reporting. This can be a large concession for website owners/ad networks so treat it as such – you are essentially asking for up to a 20% bump in inventory. This is because while your reporting may more accurately record how many times your ad is shown or clicked on, it will likely have lower numbers than the publisher records. This is because your tracking pixels will load after the publishers in most cases.

It should be noted that demanding to use your own statistics for billing will also hamper publishers optimising efforts. If they are optimising towards clicks, for example, they won’t be able to optimise towards the clicks you record, only the ones that they do. To try and keep campaigns running as well as possible, you should definitely provide as much reporting as you are comfortable with. This includes broad stats from other people you are advertising with (if you are happy with that) as this helps create competition between the publishers you are working with.

It may seem like getting this extra inventory is a nice bonus, but if you take advantage of the system too much it will backfire. Publishers and ad networks will be measuring their own eCPMs to check if your campaigns are worth it. Trying to pull a fast one on the people you work with (especially if they provide good performance) is not a good business strategy.

Please note: conversion data is especially prone to discrepancies, as you will be de-duping data, whereas the publisher will not. Therefore sharing reports is especially important, as you want the publisher to be working towards as many conversions as possible.


How Digital Stat Discrepancies Happen Infographic

How Digital Stat Discrepancies HappenClick to enlarge


Other names for discrepancy (synonyms)

These are some other words people might use to mean discrepancy:

  • Variance
  • Difference
  • Disparity
  • Deviation



Discrepancy Definition


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