One of our clients' most requested features, Instagram Reels, is now available at quintly. Meta has announced that they are rolling out the Reels API endpoints starting from the 28th of June 2022. In a true quintly fashion, we are ready. Let's dive in.

Reels at quintly

Straight to the point, all your Instagram metrics will also start including Reels from today. No action is required from you, and you can very easily filter them by using the type column in your metrics. There is one additional column on the private level available with the Reels object whether Instagram shows the Reel on both the Feed and Reels tab or just Reels. 

You can access the Reels via two existing data sources - instagramOwnPosts and instagramInsightsOwnPosts. Reels will show as type “Reel”. It is also available in custom metrics, via API and Data Push tasks. 

Instagram data ready

We are thrilled to bring this update to you in less than 24h after the announcement - product team, you rock 🤘. This addition will allow you to include Reels into your social media marketing reporting easily.

Not a client yet? Book a personalized demo with us 👈 . 

Over the last couple of months, our team has been working on a massive data structure and process overhaul. The main task what to replace the last remaining real-time data sources. Why is it a big deal? Let's dive in.

Real-time VS persisted data sources.

Based on some data retrieved from social networks, a real-time API call was needed. A solution that worked quite well, but unfortunately, due to the volume of data, it was slow, we couldn't pass the data through our Data Layer in real-time, and building custom metrics was quite complicated due to the response format (JSON). It was especially problematic if there was an issue with the information delivery. Due to the real-time nature, it was tough to debug the origin of the case. We have decided to move the remaining real-time data sources within our Data Layer infrastructure, with the help of our import workers, effectively changing them to persisted data. Persisted what?

To keep it short, this type of information has been imported via our workers, treated by our Data Layer engine, and stored in our database, becoming easily accessible and queryable.

Data Layer

One of the pillars of quintly, Data Layer, is more than just a database storing information. It's a sophisticated and constantly evolving data curation engine to ensure that the information served to our clients is the most accurate and qualitative. Thanks to moving all of the remaining data sources within our Data Layer, we have achieved:

• Robustness: thanks to structuring the information the same way, you are free to combine the data sources the way you want with ease 
• Performance improvement, no more ad hoc parsers, we query already sanitized data from one source.
• Transparency: since we control the data on our end, we can track the whole journey and report on it better
• Data quality and consistency: as mentioned above, each data source will undergo treatment from Data Layer, performing various data quality checks and improvements

This iteration is also a huge deal for us internally. It will significantly reduce the time needed for the maintenance, bug tracking, and, most importantly, implementation of the new data sources.

Writing about the data quality and maintenance work involved is very hard. Still, this improvement is significant as it helps us continue providing the most sanitized data for all your analytical needs. Isn't that what we are all here for anyway?

The first quarter of 2022 brought a lot of bigger product updates mentioned here before. We have also worked on the more minor updates related to either iteration of feature release, customer feedback, or our discovery. We have put all of them below. 

Updates to our Data Push and API

👉 We have made it possible to fetch all of the metadata of all your data push tasks via /list-data-push-tasks API endpoint. It's the same information as the one from within our app in the Integrations > Data Push section. You can find more information in our API documentation.

👉 We also support pushing JSON format to AWS S3, supplementing the already existing CSV format. You can find information about the data format on the data push task detail page.

👉 One more minor update to those using our API and data push tasks. We will now return the primary key per data source. This is especially useful for data warehouses, where you would like to understand what set of data source columns identifies a row within.

Data sources and general improvements

👉 Small but powerful update to the profiles data source. It now includes a column containing the complete set of groups to which the profile belongs. It enables metrics that want to aggregate results per group and thus allows groups to be used as a kind of "label" for profiles.

👉 The expiry date is no longer mandatory when generating a new interactive link. You can share and access the interactive links indefinitely. 

👉 Finally, if you create a link for someone to authenticate the profile, you will be notified via email once the task has been completed.

Q2 and beyond

We hope that you liked this format and we will continue bringing more of those smaller updates to light in a quarterly fashion. We have quite a few big updates coming up too so watch this space.

We are happy to bring a significant data overhaul that we have done to time-series data sources. We give you fully accessible, per post, and filterable time-series data. Why is this huge? Let's find out.

Humble beginnings

The sheer amount of data queried in our platform at each given moment creates challenges when it comes to the loading times and performance. The time-series data was most affected, so we have decided to propose a solution - aggregation tables. In a nutshell, instead of getting information on the posts, we stored the critical metrics with timestamps in those aggregation tables. This was a perfect solution for many years, but when there is progress, there are welcomed changes and challenges - we have introduced powerful post tags and filters.   

The aggregation tables were a great solution at the time. Still, with the introduction of tags and filters, it was impossible to add this functionality to those tables as the aggregation concept works on the number of posts and key metrics and not on the individual posts themselves. That meant that metrics based on aggregation tables could not take advantage of the advanced post filtering when needed. 

Over the last few months, we have improved our data infrastructure, which allowed us to revisit the topic of how to support filters for time-series data. Today we can happily say that we finalized it.  

What does it mean to you?

Simply put, any metric based on post data can now be further filtered down by our advanced filters in real-time. Would you like to see how tagged posts, hashtags or mentions did over time? You have a social media campaign running, and you want to do competitive benchmarking on top-level KPIs? Now you can. In terms of functionality, nothing changes. You can still use and build the same metrics, with the difference that popular metrics like "Key Metrics Table" and "Number of Posts", if there are filters applied, the total aggregated results will show only the filtered posts where requested. This change will allow you to build even more robust dashboards that can start with the most generic KPIs, drilling down to post level to tell the full zoomed-in story. It is worth mentioning that this also applies to our auto-tagging filter.

There are a few small prints that we need to list below.

• This change only applies to post related metrics as it operates on the post titles and descriptions, so followers and user metrics won't be affected.
• Filtering by tag won't be available for User Generated Content (members posts on your timeline), but the post text filter will. Our system will recognize this for you and present it accordingly.
• Due to the sheer amount of data that has to be parsed ad hoc, in a very rare scenario (we are talking hundreds of profiles with thousands of posts) that the generation of the result might take more than 20 seconds

What the future holds

This update was an important missing piece to provide you with genuinely consistent data. It also standardizes all the processes within quintly and removes unnecessary maintenance. Our advanced filters will only get more powerful over time (we have a few things planned), so this alignment is really welcomed. Most importantly, this improvement allows you, our users, to zoom in and filter out the noise where it matters the most. We cannot wait to see what you come up with and how you will use it. That's our biggest win.

We are happy to announce that you are now able to analyze Twitter polls. This is a welcome addition to the already extensive Twitter data sources. Let’s dive in.

Opinions as data metric

In Twitter’s own words: “Twitter Polls allow you to weigh in on questions posed by other people on Twitter. You can also easily create your own poll and see the results instantly”. Polls have been heavily used by brands and media companies alike and are the best source of instant question validation. We have decided to include polls as a part of our private-level twitterAnalyticsOwnTweets data source. We have added a few columns, including:

In a true quintly fashion, we have accompanied this new addition with new default metrics (chart and table) to get you started.

What’s next for Twitter?

We will continue to work on adding those extra data points to our data sources. Our private-level data sources are already running on Twitter V2 API, but we are working on moving all public data requests to the new version as well. Not only is it the newest, most supported version, but also comes with brand new metadata. This change will enable new metrics currently not available, exciting 😎 .

Being one of the most sought-after topics, data availability is crucial for our customers. The most vital part is to know how much historical data is available when adding new profiles. As a part of our data transparency initiative, we are happy to deliver another update helping you understand the availability of historical data within quintly.

Why is it important?

In a nutshell, social networks won't return audience data via API at any specific time. It means that we cannot get the number of followers that the account had, for example, exactly one year ago. Some platforms have even the in-app analytics limited to the last ninety days only (Instagram, for example). To tackle those limits, here at quintly, we take daily snapshots of account vitals and store them as soon as the profile is added.

The challenge

Since the audience data is publicly available to anyone, we also make it available to any other user who starts to track a specific profile. If the account was added before yours, you could benefit from already existing audience data. The challenge is to transparently show our users how much historical data is available.

Transparency at the center

We have decided to create visualization bars to indicate how much information we contain on a specific profile within one full calendar year. This graphical cue will let you quickly identify how much, time-wise, information is available. It’s straightforward, three bars, two colors, four possibilities: 

• All gray bars - there is no data available. Tracking starts now
• One green bar (low) - less than six months of data
• Two green bars (medium) - more than six months of continuous data
• Three green bars (high) - more than a year of continuous data

Thanks to those hints, you can quickly determine how much information you will have available from the start, even though you just added the profile. It is worth mentioning that we have also introduced a Migration Assistant, an import tool that will help you move your existing audience data into quintly for historical reasons. 

Over the last couple of months, we have introduced new API endpoints based on common use cases. Those changes include improvements around spaces, users, and available data sources. Let’s dive in.

/list-spaces - this endpoint is related to businesses that are managing isolated instances for their quintly accounts. It returns all of the managed spaces within the organization. 

/list-users - returns all of the users, including the spaces that they belong to, whether they are an admin, and the user's timezone.

/list-data-sources - will list all of the available metadata for our data sources across all social networks. This includes all columns, type, and whether it is public or private 

Further, we have introduced new and updated existing endpoints to help you manage private statistics connections more effectively via the API, giving you full control over the private stats use cases activated for individual profiles.

/list-private-stats-use-cases - lists all of the available private stats use cases. It is especially useful in order to understand which use cases you require. Also needed for the next endpoint.

/activate-private-stats-use-cases - by passing the userId, spaceId, profileId and useCaseId, you can quickly activate private statistics use cases for specific profiles.

/generate-private-statistics-authentication-link - the final step after activation is to generate the authentication link for someone to authenticate the required use cases. You can do via this endpoint for yourself or your customer.

/deactivate-private-stats-use-cases - in order to deactivate specific use cases you can use this endpoint. It takes similar parameters as /activate-private-stats-use-cases.

Those changes add an extra layer of management to our API, allowing our API users to handle many more scenarios within their connected applications. You can find the complete documentation for all endpoints on https://api.quintly.com.

Instagram announced it is combining IGTV and feed videos into one format - Instagram Video. With this update, we want to explain how this affects data in quintly.

IGTV data sources not showing new videos

From October 5th onwards, Instagram's APIs classify all new videos as regular feed posts. So far, some of those were marked as IGTV. At quintly we have two separate data sources holding IGTV data, one owning public and another holding private data. Videos created on or after October 5th won't be returned by these data sources anymore. It is expected to see no more IGTV posts from that date onwards.

For historic access, we keep these data sources active for some more weeks. We publish a separate announcement once there is a definite deprecation date.

Videos are now included within regular post data sources

Instead, all videos created on or after October 5th are now included within our regular Instagram posts data sources. Also, here this change applies to both the public and the private data.

What steps do I need to take?

No immediate action is required. IGTV metrics based on the IGTV data sources will stop to show new IGTV videos. You might want to remove these metrics once your analysis completely moves beyond October 5th. On the other side, regular post metrics will show all videos created on or after October 5th. In general, this is an excellent simplification as IGTV videos are not considered a separate format anymore.

We are happy to announce the ability to push data to AWS S3 using our data push feature. This adds another option when building your own data warehouse/data lake.

What is the data push feature?

Part of our mission is to offer simple ways to take your social media data out of quintly to avoid data silos and enable analysis across several sources. Our data push feature handles scenarios where you want to build your data warehouse/data lake. Our system proactively pushes structured data to you on a defined schedule, eliminating extra work on your end.

How does it differentiate from the API or other connectors?

Our API provides a generic interface to take data out of quintly. The data push feature is an extra layer on top of our API. It takes away all of the development work required to connect to and maintain quintly's API. Once activated, it will proactively push the data you need to your data warehouse/data lake on a defined schedule. We make sure nothing goes wrong and provide transparency into each of the data push tasks performed. 

If you want to analyze data on the spot and are not interested in building your own data warehouse/data lake, recently we have expanded our integrations library by adding a native Google Data Studio connector.

Both approaches have pros and cons, but in the true nature of flexibility, we strive to cover each use case.

Where can data be pushed?

With this update, we can push data to an AWS S3 bucket and a Google BigQuery database, covering two popular services offered by AWS and Google Cloud. We look forward to add more destinations very soon.

How to get started?

If you are interested in pushing data to your data warehouse/data lake, please contact our customer service at support@quintly.com or via chat in-app.

Today we are excited to release our new Google Data Studio connector, enabling you to easily integrate quintly data in Google Data Studio. Google Data Studio is a popular tool used to analyze and report across various data sources.

What is this all about?

Measuring the impact of your marketing initiatives requires avoiding data silos and enabling analysis across various sources, Social Media data being one of them. A common use case is connecting your Social Media data with Web Tracking or Sales data.

We understand that analyses spanning several sources cannot be done in quintly alone, and this is why we strongly believe in offering multiple ways to integrate quintly data in third-party solutions. While our API provides a generic interface to take data out, it comes with extra integration effort which is unsuitable for all cases. This is why we have decided to develop native integrations for commonly used business intelligence tools to simplify the integration. Our Google Data Studio connector marks the beginning of a series of new connectors to be developed by our team.

How to get started?

Log into Google Data Studio and find our Google Data Studio connector here. Click "quintly" and you will start right with creating your first data source in Google Data Studio using quintly data.

More connectors coming soon

You can expect us to offer additional connectors for popular business intelligence solutions soon. Next in line is a Tableau connector that we expect to finish in the coming weeks.