If you’re seeing differences between your Instagram Insights data and the results you see in your Supermetrics reports, there are a few steps you can take. These differences — which are often referred to as discrepancies — can occur for different reasons in different types of data. Follow the steps below for the type of query you’re having trouble with.
Not all Instagram users define their demographic dimensions (country, city, age, or gender, for example) in the account. This means that when you split your followers by these dimensions, the total number of accounts with these dimensions might not equal your total follower count.
Say you have 10 followers. 5 of them have defined their city in their account, and 5 haven’t. Your total follower count will be 10. Your followers split by the demographic dimension “city” will only be 5, as only 5 followers have defined this dimension.
If a query pulls media count data at the same time as data from another media-related field, incorrect data could be displayed in Looker Studio (formerly Data Studio) scorecards connected to that query.
To solve this, add a filter that excludes a certain media type. Set the filter to exclude a type that doesn’t exist (or just a random string). Looker Studio will read this as a dimension in the query, which will mean that you get the correct results in your scorecard.
In most cases, the Instagram Insights API only tracks a metric's organically generated values. With the exception of the Profile reach and Profile impressions metrics, the API doesn't report interactions on ads that contain media objects.
For that reason, post interaction data can appear incorrectly, as it might be made up of a total value that includes both paid and organic values.
We recommend using Facebook Ads data to track promoted or paid metrics for Instagram.
Created more than 2 years ago
The Instagram Insights API might remove data that's more than 2 years old. If that happens, it’s no longer possible for Supermetrics to query the removed data.
Generated before an account was converted to a business account
The Instagram Insights API only offers data on posts and content created after an account was converted into a business account.
For this reason, Supermetrics can't pull data on anything managed by an account before the date when it became a business account.
Instagram calculates unique impressions insight value independently. For that reason, total reach might not always exactly equal the sum of your organic and paid reach metrics.
When summing daily reach values, counts are checked for uniqueness daily, but not for the whole period.
Reach values are only available for 1-day, 7-day, or 28-day date ranges. Date ranges outside of these won’t return data.
Stories are only available in the Instagram Insights API for 24 hours. To access this data more than 24 hours after it was created, you should store Story data as historical data.
Stories are only available for 24 hours, so special steps need to be taken to capture them before they disappear from the system. If you're using Supermetrics for Google Sheets, storing this data as historical data will let you access it for more than 24 hours.
Additionally, Instagram Insights won't return any data on Stories that have less than 5 views.
Our Instagram Insights connector can only access organic data. To get ad data from boosted posts, you need to use our Facebook Ads connector.
To get only the Instagram ad data, filter your query with “Publisher platform” equals “instagram”.
This applies to customers using our Instagram Insights data source connector in data warehouse transfers.
Due to the logic of how the Instagram Insights data source connector works, mixing lifetime metrics with incremental metrics can cause situations that look like data duplication or data loss when used with our data warehouse destinations.
This applies to queries that include lifetime metrics, and it allows users to mix lifetime metrics (such as profile followers) with incremental metrics (such as new followers or impressions). This can lead to confusion, as the data source connector will always add a row with the current value for the lifetime metric, even if you are querying for a historical date range that does not include today.
Metric types in Instagram Insights
For Instagram Insights, the metrics in the Account lifetime category are lifetime metrics. The metrics in the Photo & Video, Video, Carousel, and Reels categories should be treated as lifetime metrics. The Account insights category contains incremental metrics. The Story category behaves like lifetime metrics, but they only have data for 24 hours after publication.
For example, if you query data for October 15, 2023, you'll get two rows of data: one with the date 2023-10-15 and the value for new followers, and another row with the date 2023-10-31 (the current date) and the value for profile followers.
This can cause data duplication if you're using a refresh window greater than 1, as the connector will insert a new row for the lifetime metric every day with date = today. For example, if the refresh window is 2, there will be 2 rows with the lifetime metric that have date = today. Also, if you run a transfer or backfill more than once in a given day, you might end up with multiple figures for profile followers for the same date, in case the number of followers has changed between the transfers or backfills.
It can also lead to data loss if you are running backfills, as the backfill will overwrite the historical data with the current value for the lifetime metric. For example, if you run a backfill for 2023-10-15, the backfill will scan your data warehouse tables or data lake files for that date and replace the data with the current results. Thus, you could be replacing the profile followers with date = 2023-10-15 with profile followers with date = today.
We're working on a fix for this issue in our systems, and we'll release a patch as soon as possible.
In the meantime, follow the steps below to avoid data duplication and data loss.
- Create separate custom table groups for your Instagram Insights data. One table group for only incremental metrics, one table group for only lifetime metrics, and one table group for posts/media metrics. Make sure to include the dimension Date in the table group for lifetime metrics.
- Set the refresh window for the lifetime metrics transfer to 1, so that each daily transfer does not overwrite the data from the previous day. This allows you to build up a historical view of your lifetime metrics over time.
- Don't run backfills for the lifetime metrics transfer for any other day than today, and only run backfills if your daily transfer has failed. Otherwise, there is a risk of data duplication. If you run a backfill for a specific date that's not today, you'll likely overwrite historical data that's stored in your data warehouse or data lake.