Rockset is a real-time analytics database that serves low latency, high concurrency queries at scale, enabling developers to build real-time applications without the operational burden of creating complex data pipelines or pre-defined schema.
With a growing customer base across industries such as e-commerce, healthcare, finance, and more, Rockset was looking for ways to make client-facing teams more effective and efficient in their engagements. All the way from the first demo through continued customer support, Rockset saw an opportunity to optimize the cycle:
With Superblocks, Rockset was able to build a series of applications that enable GTM teams like Sales Engineers and Account Executives to move faster and deliver more value to their customers.
The Org Insights Application provides a comprehensive view of customer health, including billing information, product usage patterns, infrastructure and performance metrics, related sales opportunities, as well as open support tickets. Previously, these datasets were isolated in Rockset analytics collections and third party APIs like Salesforce and Freshdesk, making it difficult for Sales Engineers and Account Executives to make sense of all the important metrics and events happening inside a customer account.
Now, with Charts and Tables that can be filtered to any customer and timeframe, Sales Engineers can proactively spot spikes or dips in key metrics like query volume and latency down to the user level. Through clickable Table cells and Buttons, they can also respond to that user’s related support ticket in Freshdesk, investigate their sever metrics in Grafana, or manage org limits in an iFrame.
Similarly, the Sales team can track cost and user activity trends, helping them identify expansion opportunities in an account. They are also one click away from the opportunity in Salesforce, saving valuable time spent navigating between tools and inputting queries manually.
Zooming out, the Consumption Cross Org Application provides higher level stats for Sales and Leadership to see the top and bottom accounts in terms of credit consumption and growth metrics. With a simple dropdown selection, an Account Executive can filter multiple graphs to surface information relating to only their accounts, identify among those accounts who are the top candidates for renewal based on consumption rates, and click into any bar or pie chart slice for a more detailed breakdown of costs and usage on a per org level within the account. Without a dedicated BI team, Leadership can also view a snapshot of usage across the business.
Since real-time analytics is core to Rockset’s product, it’s critical that their demo highlights this in a way that’s easy to grasp and resonates with prospects. To accomplish this, Sales Engineers streamed live Twitter posts into Rockset and used Superblocks as a frontend to display the tweets and metadata. The Superblocks Tables and Charts automatically refresh every few seconds with Timers, while under the hood, Rockset continues ingesting the live data and indexing it for querying within seconds. The Sales Engineering demo story now includes a powerful visual component that showcases Rockset’s ability to deliver exceptionally fresh data.
The Superblocks official Rockset integration was an obvious plus for the Rockset team. With nothing more than a Rockset API key, they were able to programmatically access Rockset resources from Superblocks by writing SQL directly in the Application API editor or copying existing queries over from the Rockset console.
In addition to visualizing customer data with Applications, Rockset can also send recurring reports to any stakeholders or automate tasks using Scheduled Jobs. For example, one job queries analytics data, formats it into HTML with Python, and sends an email to Finance and Leadership so they can stay up to date on customer contract consumption. Another handles the scaling up and down of the demo environment to optimize costs.
Rockset will continue automating processes to allow their teams to be more proactive with customers. For example, they plan to use Scheduled Jobs to check customer usage stats and alert Slack channels for anomalies and unexpected vi switches. In addition, they are building tools to track KPIs on new feature usage and exploring ways to augment support tooling with OpenAI.