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Scaling Our SaaS Gross sales Coaching Platform with Rockset

Trendy Snack-Sized Gross sales Coaching

At ConveYour, we offer automated gross sales coaching through the cloud. Our all-in-one SaaS platform brings a contemporary method to hiring and onboarding new gross sales recruits that maximizes coaching and retention.

Excessive gross sales employees churn is wasteful and dangerous for the underside line. Nevertheless, it may be minimized with personalised coaching that’s delivered repeatedly in bite-sized parts. By tailoring curricula for each gross sales recruit’s wants and a focus spans, we maximize engagement and scale back coaching time to allow them to hit the bottom working.

Such real-time personalization requires an information infrastructure that may immediately ingest and question huge quantities of person knowledge. And as our prospects and knowledge volumes grew, our authentic knowledge infrastructure couldn’t sustain.

It wasn’t till we found a real-time analytics database referred to as Rockset that we may lastly combination hundreds of thousands of occasion information in below a second and our prospects may work with precise time-stamped knowledge, not out-of-date info that was too stale to effectively support in gross sales coaching.


Our Enterprise Wants: Scalability, Concurrency and Low Ops

Constructed on the rules of microlearning, ConveYour delivers brief, handy classes and quizzes to gross sales recruits through textual content messages, whereas permitting our prospects to observe their progress at an in depth stage utilizing the above inside dashboard (above).

We all know how far they’re in that coaching video right down to the 15-second phase. And we all know which questions they obtained proper and mistaken on the most recent quiz – and might mechanically assign extra or fewer classes based mostly on that.

Greater than 100,000 gross sales reps have been skilled through ConveYour. Our microlearning method reduces trainee boredom, boosts studying outcomes and slashes employees churn. These are wins for any firm, however are particularly vital for direct sales-driven corporations that consistently rent new reps, lots of them contemporary graduates or new to gross sales.

Scale has at all times been our primary problem. We ship out hundreds of thousands of textual content messages to gross sales reps yearly. And we’re not simply monitoring the progress of gross sales recruits – we monitor each single interplay they’ve with our platform.

For instance, one buyer hires practically 8,000 gross sales reps a yr. Just lately, half of them went by a compliance coaching program deployed and managed by ConveYour. Monitoring the progress of a person rep as they progress by all 55 classes creates 50,000 knowledge factors. Multiply that by 4,000 reps, and also you get round 2 million items of occasion knowledge. And that’s only one program for one buyer.

To make insights obtainable on demand to firm gross sales managers, we needed to run the analytics in a batch first after which cache the outcomes. Managing the assorted caches was extraordinarily exhausting. Inevitably, some caches would get stale, resulting in outdated outcomes. And that might result in calls from our consumer gross sales managers sad that the compliance standing of their reps was incorrect.

As our prospects grew, so did our scalability wants. This was an amazing drawback to have. However it was nonetheless a giant drawback.


Different instances, caching wouldn’t lower it. We additionally wanted highly-concurrent, immediate queries. As an illustration, we constructed a CRM dashboard (above) that supplied real-time aggregated efficiency outcomes on 7,000 gross sales reps. This dashboard was utilized by a whole lot of center managers who couldn’t afford to attend for that info to come back in a weekly and even every day report. Sadly, as the quantity of knowledge and variety of supervisor customers grew, the dashboard’s responsiveness slowed.

Throwing extra knowledge servers may have helped. Nevertheless, our utilization can also be very seasonal: busiest within the fall, when firms carry on-board crops of contemporary graduates, and ebbing at different instances of the yr. So deploying everlasting infrastructure to accommodate spiky demand would have been costly and wasteful. We would have liked an information platform that would scale up and down as wanted.

Our remaining problem is our measurement. ConveYour has a crew of simply 5 builders. That’s a deliberate alternative. We’d a lot reasonably hold the crew small, agile and productive. However to unleash their interior 10x developer, we needed to maneuver to one of the best SaaS instruments – which we didn’t have.

Technical Challenges

Our authentic knowledge infrastructure was constructed round an on-premises MongoDB database that ingested and saved all person transaction knowledge. Linked to it through an ETL pipeline was a MySQL database working in Google Cloud that serves up each our massive ongoing workhorse queries and likewise the super-fast advert hoc queries of smaller datasets.

Neither database was chopping the mustard. Our “stay” CRM dashboard was more and more taking as much as six seconds to return outcomes, or it might simply merely day trip. This had a number of causes. There was the big however rising quantity of knowledge we have been amassing and having to research, in addition to the spikes in concurrent customers comparable to when managers checked their dashboards within the mornings or at lunch.

Nevertheless, the most important motive was merely that MySQL isn’t designed for high-speed analytics. If we didn’t have the suitable indexes already constructed, or the SQL question wasn’t optimized, the MySQL question would inevitably drag or day trip. Worse, it might bleed over and damage the question efficiency of different prospects and customers.

My crew was spending a median of ten hours per week monitoring, managing and fixing SQL queries and indexes, simply to keep away from having the database crash.

It obtained so dangerous that any time I noticed a brand new question hit MySQL, my blood stress would shoot up.

Drawbacks of Various Options: Snowflake, ClickHouse, Redshift

We checked out many potential options. To scale, we considered creating extra MongoDB slaves, however determined it might be throwing cash at an issue with out fixing it.

We additionally tried out Snowflake and appreciated some features of their answer. Nevertheless, the one large gap I couldn’t fill was the dearth of real-time knowledge ingestion. We merely couldn’t afford to attend an hour for knowledge to go from S3 into Snowflake.

We additionally checked out ClickHouse, however discovered too many tradeoffs, particularly on the storage facet. As an append-only knowledge retailer, ClickHouse writes knowledge immutably. Deleting or updating previously-written knowledge turns into a prolonged batch course of. And from expertise, we all know we have to backfill occasions and take away contacts on a regular basis. After we do, we don’t need to run any reviews and have these contacts nonetheless displaying up. Once more, it’s not real-time analytics if you happen to can’t ingest, delete and replace knowledge in actual time.

We additionally tried however rejected Amazon Redshift for being ineffective with smaller datasets, and too labor-intensive normally.

Scaling with Rockset

By way of YouTube, I discovered about Rockset. Rockset has one of the best of each worlds. It might probably write knowledge rapidly like a MongoDB or different transactional database, however can also be actually actually quick at complicated queries.

We deployed Rockset in December 2021. It took only one week. Whereas MongoDB remained our database of report, we started streaming knowledge to each Rockset and MySQL and utilizing each to serve up queries.

Our expertise with Rockset has been unimaginable. First is its pace at knowledge ingestion. As a result of Rockset is a mutable database, updating and backfilling knowledge is tremendous quick. Having the ability to delete and rewrite knowledge in real-time issues loads for me. If a contact will get eliminated and I do a JOIN instantly afterward, I don’t need that contact to indicate up in any reviews.

Rockset’s serverless mannequin can also be an enormous boon. The way in which Rockset’s compute and storage independently and mechanically grows or shrinks reduces the IT burden for my small crew. There’s simply zero database upkeep and 0 worries.

Rockset additionally makes my builders tremendous productive, with the easy-to-use UI and Write API and SQL assist. And options like Converged Index and automated question optimization get rid of the necessity to spend precious engineering time on question efficiency. Each question runs quick out of the field. Our common question latency has shrunk from six seconds to 300 milliseconds. And that’s true for small datasets and enormous ones, as much as 15 million occasions in considered one of our collections. We’ve lower the variety of question errors and timed-out queries to zero.

I now not fear that giving entry to a brand new developer will crash the database for all customers. Worst case situation, a foul question will merely eat extra RAM. However it should. Nonetheless. Simply. Work. That’s an enormous weight off my shoulders. And I don’t should play database gatekeeper anymore.

Additionally, Rockset’s real-time efficiency means we now not should cope with batch analytics and off caches. Now, we will combination 2 million occasion information in lower than a second. Our prospects can have a look at the precise time-stamped knowledge, not some out-of-date spinoff.

We additionally use Rockset for our inside reporting, ingesting and analyzing our digital server utilization with our internet hosting supplier, Digital Ocean (watch this brief video). Utilizing a Cloudflare Employee, we recurrently sync our Digital Ocean Droplets right into a Rockset assortment for simple reporting round value and community topology. It is a a lot simpler technique to perceive our utilization and efficiency than utilizing Digital Ocean’s native console.

Our expertise with Rockset has been so good that we are actually within the midst of a full migration from MySQL to Rockset. Older knowledge is being backfilled from MySQL into Rockset, whereas all endpoints and queries in MySQL are slowly-but-surely being shifted over to Rockset.

If in case you have a rising technology-based enterprise like ours and want easy-to-manage real-time analytics with immediate scalability that makes your builders super-productive, then I like to recommend you try Rockset.



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