Analyze assists data
Using data from the Assists dashboard you can get a broad perspective on how each of your marketing channels impact one another.
If you are running CTV to mobile campaigns, understanding the role of assists is particularly important because CTV impressions tend to be higher up the funnel and drive users to down-funnel channels via which they convert.
All of the widgets on the Assists dashboard can be used to draw important insights and inform your decision making. Below you can find recommendations on how to act on the data you see reported in four of the more complex widgets.
An install can benefit from more than one assisting engagement. This means that a network can provide multiple engagements for each install they assist. Filter this widget to see the difference between average clicks and impressions per assisted install.
What to look out for:
If you see a very high average number of clicks per install this could indicate click spamming. Take a closer look to see whether you are receiving fraudulent data.
If you see a medium-to-high number of assisting clicks per install, you may want to investigate why users are bouncing and not converting. For example, is there a disconnect between the creative copy and app store listing, and is the targeting correct?
If you see a high average number of impressions per install, you may want to speak with the network about the number of impressions served. Ask them to improve your targeting or implement a lower daily impression cap to avoid wasting ad spend or degrading the user experience.
If you see a low average number of impressions per install, this is a good sign of campaign performance. Using a minimal number of ads, you're converting users. We'd recommend increasing your budget for this channel.
This widget gives you precise insights by offering a complete breakdown of your data to all campaign levels. Use this data granularity to assess key budget and creative changes.
For even more flexibility with your reporting data, you can open this widget as a Datascape report. This allows you to break the data down to the assisting engagement type, and modify the filter settings as you require. To open the widget as a report, select (Open as report).
What to look out for:
- If you see that Network A is assisting Network B, you can drill-down to see exactly which campaigns and creatives are proving to be the most effective.
- If you see that a specific creative plays an important role in driving conversions (even for another network), consider increasing the budget for that ad. You can also replicate successful ad formats or campaign targeting across other campaigns or networks.
- If you see that a creative is heavily assisting itself, this could indicate a high bounce rate and you may want to invest in creatives that convert better.
- To see how efficiently Network A is assisting Network B, try opening a Quick Report and looking at the average number of assisting engagements between those two networks. With this, you can further see how scaling an assisting network would impact other specific networks' performance.
You can use this widget as a monitoring tool to see how many assisting engagements are provided by different networks over time. For example, compare how different assisting networks perform against one another. What's more, if you launch a new network you can measure its impact earlier - as it may begin assisting other networks before it drives attributions itself.
What to look out for:
- If a network shows assisting engagements trending upwards, look up what networks it is assisting. The scenario to look for is that growth in assisting engagements drives install growth for the assisted networks. If this is not the case, you may want to reduce the budget of the assisting network, particularly if the network is assisting itself.
- See how the line trends: do the number of assisting engagements change incrementally or in sudden jumps? Compare changes with the dates of campaigns you've run, and see if there is any overlap with the numbers of assisting installs.
- Create a Quick Report to see in one view the assisting engagement trends in comparison to overall install trends. This can show you whether assisting engagements are leading to more installs, in which case scaling assisting networks makes sense.
Use this widget for insight into how users engage with ads prior to install. Judge the impact different networks have by comparing the average number of clicks or impressions they serve to converting users.
What to look out for:
- A high performing network will show a low number of assisting impressions. This means that they are reaching converting users faster and spending less.
- You can clearly see the average total assisting engagements, average assisting clicks and average assisting impressions in one view. With this data, you can determine whether to work on lowering your bounce rate, improve targeting, cap impressions further, or scale or downsize budgets.
With self-attributing networks, Adjust uses API calls rather than links to send information to the network. Adjust sends SANs every app session our SDK reports. If the network recognizes the activity, they claim the attribution (called self-attribution) by sharing the details from the last ad engagement on their side. Adjust then uses the engagement data we have from all networks (including non-SANs) to attribute the install to the last known engagement source.
Since this attribution flow with SANs works differently to other channels, Adjust has different levels of visibility into the various areas of assist data.
Full visibility into other channels assisting SANs
With the Assists dashboard, you can see the full extent to which SANs are being assisted by other networks. This lets you understand the comparative performance of other networks in producing engagements which resulted in a conversion on a SAN platform.
No visibility into SANs self-assisting
Adjust cannot report the extent to which SANs assist themselves. Since we only receive attribution claims from SANs, we don't know what happens on their platform prior to this. As a result, Adjust cannot report on other engagements that may have assisted the attribution.
Limited visibility into SANs assisting other channels
Adjust does not always accept attribution claims from SANs. When another network provides a stronger engagement, we attribute them instead. In these cases where we reject the self-attributed network's attribution claim, we report this as assist data.
A self-assist is when a network provides an assisting engagement for an install they are attributed to. For example, a user who engages with multiple ads on ironSource before installing the app may be attributed to ironSource for both the install and assisting engagements. These engagements are self-assists.
Why are you seeing a lot of self-assists?
It's normal to see that a large number of assisting engagements are self-assisting. If you consider how users are targeted online, it is not uncommon to see the same ad displayed on the same channel to a user multiple times.
Self-assists can be a positive sign. Users who have seen multiple ads before they install an app will:
- Have greater brand awareness
- Be better informed about what the app actually does
However, if you see a particularly large number of self-assists, you may need to consider:
- Over-exposure and brand fatigue
How to recognize and act on self-assists
To see the extent to which a network is self-assisting, use the Assisting engagements by assisting channel and Assisted install engagement metrics widgets.
These can help you to define whether a network is creating brand awareness or merely spamming audiences, particularly by looking at the average number of 'engagements per assisted install'.
You can also use the Campaign breakdown for assisted installs widget to check whether there is a mix of campaigns, ad groups and creatives being used. If this is not the case, it's a sign users are being served the same ad over and over again.
Using this analysis, you can decide whether to reduce or increase the budget for a network. Any time you make a change, monitor how this affects the overall performance of that channel. For example, it may go up or down, or you may see that just the level of self-assisting goes down.
Attribution type filter only relates to the attributed engagement, not to the assisting engagements.
- If you select
clickas the only attribution type, you will only see engagements that assisted clicks.
- If you select
impressionas the only attribution type, you will only see engagements that assisted impressions.
It's likely that applying the attribution type filter to clicks will hardly have an impact, because nearly all attributed engagements are clicks. If you apply the filter for impressions, you will see severely limited amounts of data.