A sought-after update to our API will allow you to create share links via new endpoints. A very useful feature for our clients/partners integrating with quintly via API

Shared interactive report links

One of our core shared features allows users to share dashboards in an interactive format. We always believed that when it comes to data, it is crucial to give the end viewer ability to zoom in on the information they are looking at. App users were able to take advantage of managing the links for quite a while, so we are happy that our API users can do it programmatically now too.

New API endpoints

To manage the dashboard share links, we made four new endpoints available: listing all shared links, creating, updating, and deleting share links. Creating and updating come with parameters, including timeframes, password protection, and expiration options. You can find the whole list of features in our API documentation. We have more exciting news coming, so stay tuned 🙇 .

We are happy to bring you a new powerful UI component - Inline grouping and tagging. This universal UI module will allow you to tag and group profiles on the fly, massively increasing your productivity within the tool.

Get organized

The inline component completely replaces the old mechanism of adding, removing, and modifying groups and tags. To access this component, click on the pencil icon next to profiles or posts. This will open a dialog window where you can quickly search for existing groups or tags and modify or remove them. We have unified the UI of this component for a more streamlined experience. You can still access and manage tags and groups in their respective sections within our platform.

Components

We have spent quite a bit of time reviewing the most used components and challenging how they are used. The goal of this exercise was always an improvement in how you will use our platform. Stay tuned for more updates.

📅  Dynamic date ranges.

We bring you a date selector on the dashboard level. We have made it available to choose from two types of date settings - fixed and rolling date. For a set timeframe - fixed is perfect. A rolling date range would be better if you want a more dynamic date range. Once set, the dashboard will remember this setting. Dynamic date ranges are available for interactive and scheduled reports.

😎  Improved onboarding flow.

We have simplified and improved our onboarding flow, allowing you to add profiles and authenticate them in quick steps. You are also able to invite new users directly from this flow. More streamlined, more self-serve, and will get you up and running in minutes :)

😀  Linkedin new reaction.

As you probably know, Linkedin has rolled out the “Funny” reaction. We have it implemented immediately, so this new reaction type is available in LinkedIn's Posts Table default metric.

📌  Data source section.

Minor update to our data source section: The column types (INTEGER, DATETIME, etc.) now link to a knowledge base article describing the data types in more detail.

💲 Ad break metrics for facebook.

We have added new ad break metrics for individual Facebook posts and videos from Insights. Ad break metrics cover impressions, earnings (in USD), and CPM (earnings per 1000 impressions) for advertisement in a Page's videos. These are the metrics.

For posts (Definition):

• post_video_ad_break_earnings
• post_video_ad_break_ad_impressions
• post_video_ad_break_ad_cpm

For videos (Definition at the bottom of the page):

• total_video_ad_break_earnings
• total_video_ad_break_ad_impressions
• Total_video_ad_break_ad_cpm

Those metrics have been added to our facebookInsightsOwnPosts and facebookInsightsOwnVideos, respectively. They are ready to use in custom metric builder and via API.

📥  Data Push tasks improvements.

We have made improvements to our data push engine. You can now access your Data Push tasks configuration using our API with a new endpoint. 

🔩  CSV importer for Migration assistant

We have finished an improved migration assistant script. It allows us to migrate public fan, follower, and subscriber counts from an old tool into quintly in various CSV formats.

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 😎 .

With the expansion of the scope of the text filters by adding the ability to exclude any or all text, you can now show the results that matter for you without the extra noise. When you create the exclusion on the text filtering, we will filter through the post content, returning only the results that do not contain specified words or phrases. This feature works on every dimension where text filters are applicable.

More complex filtering

To add the exclusion, click on the "+ Add filter". In the sidebar, in addition to already available options like including ALL or ANY tags and text combinations, there is new exclude section, where you can use the same logic to rule out ALL or ANY text appearance. You can also create tags based on those rules and even automate them. 

Use cases

We have covered the extensive use cases that post tagging and advanced filtering enables in this article, so you can see exclusion as a further refinement of your results and a new layer of flexibility in filtering them. This request came directly from you, our users, so thank you for your continuous support, and we hope you will find it helpful. I know we will.

One of our most significant features just got better. We have improved report error handling in the spirit of better communication with the end-user. If the scheduled report fails for any reason, we will notify you via email and our inbuilt notifications, allowing you to fix the issue quickly.

Meaningful knowledge

If a scheduled task fails to send or generate a report, we will send you a notification and email. We have spent some time making each of those notifications to be as informative and actionable as possible. You can easily access the dashboard that has been used as a base for the report and fix the issue quickly. If the report is based on a private dashboard, the notification will be sent to the dashboard owner. If it’s a shared dashboard, in addition to the dashboard owner, we will notify the account administrators.

Next steps

This improvement is the first step in our report overview initiative. We have added a new layer of transparency to the reports with this improvement. Stay tuned.

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.