What are the problems when merging MMP data and SKAdNetwork data?

App Store A/B Testing
buy reviews for app

Entrepreneurs rely on measurement to find out whether or not or not a marketing campaign has been profitable. Undoubtedly, iOS 14.5+ and dealing with SKAdNetwork has posed vital challenges and a shakeup to technique and processes. Entrepreneurs and builders have reframed their strategy to measurement to finest work with a mixture AppTrackingTransparency (ATT) opted-in knowledge and aggregated SKAdNetwork knowledge concurrently for campaigns on iOS — which is the brand new actuality of attribution on the platform.

 

Table of Content

  1. Deduplication: Earlier than even contemplating combining SKAdNetwork set up knowledge with ATT degree set up knowledge, a deduplication that takes out the installs from one of many sources which can be attributed in each knowledge units should happen. Because the SKAdNetwork knowledge set is aggregated and never on a tool degree, the deduplication will be tried by introducing a dimension that splits the system degree attributed installs from the non attributed. That is an early idea of deduplication that may eat no less than one little bit of your conversion values and won’t remedy all the opposite issues when eager about the way to merge ATT degree and SKAdNetwork knowledge.
  2. Randomized set up dates: The set up date acquired from SKAdNetwork installs is at all times randomized and isn’t clearly identifiable. Because of this SKAdNetwork installs will be legitimate for wherever between 0 and 48 hours earlier than it’s acquired, and complicates the flexibility to take away them from an information set.
  3. Google associated installs: The set up dates of SKAdNetwork installs from Google are much more sophisticated to work with. By making use of a degree of modeling, Google makes an attempt to find out the date of the associated advert interplay (clicks or impressions) and hyperlink it to an set up. As Google is among the greatest self attributing networks and a part of most channel mixes, this closely impacts the flexibility to merge knowledge.
  4. Attribution strategies: SKAdNetwork and device-level attribution work in a different way, and it’s extremely probably distribution throughout channels can also be considerably totally different between the 2. Because of this it’s nonetheless anticipated to have duplicates for some channels when aggregating, even when following deduplication as talked about above. For instance, some system degree installs in multi-touch advertising eventualities attributed to Fb may get attributed to Twitter through SKAdNetwork.
  5. Randomization at conversion worth degree: There are a certain amount of conversion values set to be null for SKAdNetwork. As the data concerning whether or not the set up is attributed, or not attributed, is packaged into the values itself, when this worth is nulled, the set up can’t be recognized as attributed or not attributed. The distribution of null values shouldn’t be essentially linear throughout all conversion values and is determined by the quantity of installs per marketing campaign ID. This implies we can’t merely extrapolate on a proportion of null values vs. installs with a conversion worth. There can simply be as much as 40% null values throughout all SKAdNetwork installs. To achieve a excessive sufficient degree of installs to beat this threshold and have a sufficiently big knowledge set to work with, a comparatively massive marketing campaign spend is required. As a result of many entrepreneurs are already grappling with the challenges surrounding the privateness modifications, we don’t advocate splitting funds throughout a number of campaigns until you count on to succeed in this excessive threshold of installs. This could closely influence deduplication and is one other key motive to be cautious when merging device-level and SKAdNetwork knowledge.
  6. Variations by nation/area: For entrepreneurs working throughout a number of nations and languages, the nation dimension is normally extraordinarily vital, as prices and efficiency can fluctuate closely from market to market. For SKAdNetwork installs, there’s typically no nation data accessible — one more reason why mixing knowledge units collectively, after which later going by way of to aim to interrupt them down by nation will be very inefficient and inaccurate.
  7. Efficiency inaccuracies: Along with the inaccuracies recognized above in relation to put in numbers, there’s the issue of not figuring out the efficiency of further installs that may be added when merging knowledge. Attending to a legitimate complete value per channel may roughly work primarily based on extrapolation and by ignoring the issues outlined above, however as quickly as you get into marketing campaign degree, the place entrepreneurs normally make their choices, it’s rendered roughly unusable. Getting associated efficiency metrics equivalent to day 7 and day 30 income for a report right down to marketing campaign degree would even be inaccurate.

Comments are closed, but trackbacks and pingbacks are open.