Today Twilio is being used by 1000s of companies to enhance the human experience by creating better custom communications solutions. Twilio helps developers develop state-of-the-art digital communication solutions for almost all the digital channels like text, SMS, email, call, etc.
Though Twilio is meant for improving the human experience, sometimes unexpected changes reverse this whole objective. Instead of a better experience, users get the opposite because something got messed up and you didn't know about it soon enough.
Cliff to the rescue!
This integration with Twilio will help you track the unexpected changes that might act as a blocker to a better experience and let you know as soon as they occur. Now whenever there is an unexpected change in metrics like number of SMS sent, number of calls, etc. cliff will notify you of the abnormal change that caused this and will help you resolve the issue by providing a proper "root cause analysis".
Now stay in control of what is happening within your customer communication solutions and improve the overall experience!
Stripe is a simple way to process online payments. If you use Stripe as your payment option, you have an awesome product handling your payments.
But sometimes unexpected events can hinder the payment process and make your customers bounce from the checkout process. Now this can be due to several reasons. You might be able to eventually determine the cause, but until then you are losing money as most customers won't come back to try again!
This is where cliff comes into the picture. The new Stripe integration helps you monitor your online payments with complete reliability. Now you will get notified as soon as something unexpected occurs in your payment process along with the "root cause" for occurrence.
With this update you can stay on top of unexpected issues in your payment process and reduce losses by resolving those issues super fast.
Do you want to know whenever important business metrics in your HubSpot CRM have abnormalities, that too in in real-time? You want to instantly know:
If Marketing metrics like CTR and OR have gone significantly high/low.
If Sales metrics like sales volume have an unexpected spike/dip.
If Customer metrics like Ticket volume by customers have had a spike/dip.
Our new HubSpot integration allows you to do just that. Now you can monitor every business metric that you visualise on your Hubspot CRM and identify anomalous events every time they occur.
Cliff's integration with HubSpot CRM gives you the power to stay truly in control of business metrics in your CRM!
One of the most common feedback that we got from the early beta users is the need to "reuse" their existing definitions of metrics and dimensions that are defined in their DBT.
Well now, doing that got much easier! With the new DBT integration, you don't need to write separate queries in Cliff for creating streams. Integrate your DBT Cloud account with Cliff and you can re-use the already stored queries in your git repositories in Cliff for creating streams.
With this update you get -
Quick stream creation with queries stored in your DBT git repository. Support is currently offered for git repositories hosted on GitLab. GitHub and Bitbucket support coming soon.
Create metrics in Cliff.ai as per your desired schedule or use the schedule information from your DBT jobs.
With this new update, you can now import queries straight from your DBT git repository into Cliff to create streams and start monitoring right away.
Added a button to the "Stream schedule" tab in the stream details page which allows the users to re-run the data collection process.
Fixed an issue with the content of the stream delete confirmation modal.
Fixed an issue where incorrect stream count was shown on the data streams page.
Often you see a spike/dip in your business metrics and the immediate question that pops up in your mind is - "why?". Why did that metric change the way it changed? What were the key factors that impacted that change?
Typically when you see such changes in your metrics, you'd scan through multiple dashboards or go to your data team to write queries on your data warehouse to understand the root cause of that change. This entire process can be very time-consuming. Depending on your scale, if it is large then it means that you'd need to scan through a million combinations to understand those changes.
For example, let's say you're monitoring
Revenue per country, per city, and per product category (which are called dimensions). One day, you see that there is an unexpected dip in your revenue. Here are the few questions that might immediately come to your mind:
Is this dip in revenue being driven by a particular country or city or product category?
How's this change in revenue spread across these dimensions?
How did revenue change for
premium users vs
Let's say if 80% of your revenue comes from the USA - what were the changes in revenue for the USA?
And the list can go on and on.
This is where the Root Cause Analysis functionality of Cliff.ai comes into action. 👏
Understand why a metric changed between two time intervals
The Time Comparison feature help you understand exactly this. Select the time range for which you want to understand the change for the metric and Cliff.ai automatically scans through millions of possible explanations for these changes and helps you surface the most relevant segments that impacted this change
Understand how a particular segment behaved vs another segment
Let's say you see that revenue for
UK is 30% higher than that of
USA for a particular time range and you want to understand what were the other factors that contributed to this difference. Is it due to a particular customer segment or demographics - you can get answers to this in a click by Group Comparison feature.
Understand metric behaviour between two points of time.
The Point Comparison feature of RCA helps you compare and analyse metric values for two points in time (first and last selected point of time). Let's say you want to compare sales between two dates in a week or month. You want to see what change has occurred in individual segments between these two points in time that resulted in a change in metric values. You can do this by the Point Comparison feature of RCA within seconds.
Understand what segments have contributed in what way to alter a metric's behaviour
Let's say you want to understand what individual factors(dimensions) have contributed to spike sales by 40% from "Aug 10 to Aug 15 " for the USA. Now you can get to know these factors by selecting the Segment Analysis feature of RCA. You can view the drill-down of these factors that have caused the spike in sales and their individual impact on sales metric for the USA.
RCA feature allows you to have an in-depth analysis of the root cause of an incident within your metrics. It provides you means to -
A better context to analyse your business and operations metrics.
A faster speed to resolution
Now users can click, drag and zoom into the metric graphs.
Users are shown a list of properties for their Segment integration; this list is populated from the list of events that have already been sent to cliff from Segment.
We are glad to announce the new MySQL database integration. With this update, you can now stream data from your MySQL database into cliff.ai and start monitoring for anomalous events right away.
To get started, go to "Integrations" and select MySQL from options within the database category.
You can also enter your custom queries through the SQL query builder for a highly custom metric stream.
Exploring your metrics at the finest dimension granularity just got easier with the brand new metrics filter. Now you can filter different measures and dimensions directly from the metric filter in the sidebar. Get relevant default filters automatically based on the stream you select. No need to remember exact filters to apply anymore, just select the stream and get default filter options to choose from.
It's easy, quick & time-saving.
Added new advanced settings in the stream creation process, where the user can configure the minimum and maximum bounds for the anomaly detection bands
Made slight modifications to the "Getting Started" page's UI.
Our new SQL Query Builder is ready to roll. Now you can create custom metrics directly from your data warehouse in a familiar SQL - like editor. Just write the query, preview the results, and you're good to go!
This opens up two very big possibilities:
You can create metrics on the go. Combine different metrics to create new custom metrics as per your requirements. You no longer need to rely solely on metrics present inside your data warehouse.
The new SQL Query Builder also allows you to collaborate with your team members. You can save, copy and share queries with your team members to collaborate better
Once you define the query and preview the results, you can just select which fields correspond to:
and your metrics are ready to be monitored. As simple as that.
Example - Let's say you're an e-commerce firm and you have an orders table in your data warehouse where you store all the confirmed orders. Now, let's say you want to monitor the total number of orders that you receive on a daily basis and you want to break it down by product category and geography. All you'd need to do is write this simple query in Query Editor and that's it.
SELECT COUNT(*) as orders, category, geography order_date::date FROM orders GROUP BY category, geography, order_date::date
This will create a data stream for monitoring your orders. Further, you can share this query with your team members or can save it for future use. It will save you time & effort.
Keep an eye for a future "dbt" update on SQL query builder. This update will let you import your dbt queries into Cliff.ai. No need for rewriting queries and creating dashboards for monitoring your metrics.
Fixed the problem of "error alert" that popped up every time on the alert details page.
New users are now redirected to the getting-started page.
Fixed an issue where decimal values were not displayed properly in the metrics chart.
With the new Hotkeys, working with Cliff.ai is now way easier & effortless. Hotkeys are the keyboard shortcuts that can be used to directly navigate between various components or perform any action within a component through the comfort of your keyboard.
You can view all key combinations (hotkeys) by using the combination
CTRL + / on Windows/Linux and
⌘ + / on Mac.
As every module on Cliff.ai is now accessible through Hotkeys, user interaction time for accessing a module & performing actions on that module has reduced drastically.
Fixed an issue of filter in metrics view, where even after removal it would not actually remove it.
Creating and updating channels are now more convenient with searchable dropdowns and better UI.
Now, whenever a connection to a source created by you gets broken for any reason, Cliff.ai will automatically pause all the streams related to that connection and mark the connection status as
broken. Later on, if you want you can update the connection and resume all the streams that were affected.
For example, let's say you connected a database with username and passwords as credentials to Cliff and now due to some reason, you changed your database password. This will result in an
broken connection on the connection list for this particular integration. Now instead of generating false alerts for streams with no data flow, cliff will pause all the streams related to the broken connection. With an update option, you can update the connection as soon as you find the broken connection or at a later time. It's up to you.
Channels now show anomalies from the last data point extracted to the past 14 days as previously it was just past 14 days.
Improved alert graphs with formatted values and legends.