Like many companies, Snowflake uses Outreach as a sales execution platform to help our sales teams improve prospecting efforts and efficiently follow up on leads. For Snowflake sales reps, Outreach is the central repository for almost all inbound and outbound communications with current and potential customers. For the sales development representative (SDR) leadership team, it’s an immensely valuable source of insights for sales enablement and automation. Outreach data, available via Snowflake Marketplace, contains a huge amount of useful information including lead scoring, topics that are resonating with audiences, where sales reps spend the most time, which accounts are open to conversations and more. However, that data must be ingested into our Snowflake instance before it can be used to measure engagement or help SDR managers coach their reps — and the existing ingestion process had some pain points when it came to data transformation and API calls. To improve go-to-market (GTM) efficiency, Snowflake created a bi-directional data share with Outreach that provides consistent access to the current version of all our customer engagement data. In this blog, we’ll take a look at how Snowflake is using data sharing to benefit our SDR teams and marketing data analysts. For a more in-depth exploration, plus advice from Snowflake’s Travis Henry, Director of Sales Development Ops and Enablement, and Ryan Huang, Senior Marketing Data Analyst, register for our Snowflake on Snowflake webinar on boosting market efficiency by leveraging data from Outreach.
Most marketing data stacks have data coming in from multiple sources, including sales engagement platforms like Outreach as well as advertising data, web and mobile event data, CRM systems, internal databases and more. Each of these sources may store data differently. In a traditional marketing data stack, you have to perform several steps before you can analyze your data: transform it, move it into your data warehouse, perform security reviews, get it back into a tabular format and find a place to store it that doesn’t break your budget. But that’s not all. If you’re ingesting the data via an API call, you may be rate limited, resulting in data that’s less timely than you would prefer. The whole process can become a costly, inefficient and ultimately inflexible way to get the valuable insights your sales development team replies upon. Snowflake teams had been using Outreach for some time and making API calls to request data. To increase flexibility and improve run time, the Snowflake team shifted to accessing Outreach data through a bi-directional data share enabled by Snowflake Secure Data Sharing. Now, the Snowflake teams have a direct view into their Outreach data. They always have access to the most current data, because Outreach’s updates are made and distributed automatically. Privacy is maintained because only the recipient account (Snowflake) can discover and access the data. There’s no need to worry about API latency because there are no API calls, and storage remains on the providers’ side because there’s no need to move data.
The smooth, seamless ingestion of data from Outreach into Snowflake via data sharing allows the Snowflake data science team to easily combine it with Salesforce, Marketo and other data within Snowflake and start querying and aggregating the data right away. The SDR and data analyst teams are seeing significant benefits in many areas, particularly lead scoring, account scoring and optimizing SDR effectiveness and efficiency.
Before the direct share with Outreach, Snowflake had a rules-based lead scoring process. It started in Marketo, which assigned scores based on the lead’s actions, then pushed to Salesforce and Outreach. However, if a conversion strategy changed, the Marketing Operations team had to manually update the logic to fit the new structure. Now that Snowflake gets data directly from Outreach, our data analysts have automated the entire flow and it runs every two hours. Leads enter the system and get a score, the score is pushed to Salesforce and Marketo and used to inform predictions, and the SDR team can pick up that information to use in their daily applications. Outcome data goes back into the system to train the model. The underlying model needs few adjustments because it is constantly updated based on the data being poured into it. An automated process watches for shifts or discrepancies in the model outputs and raises alerts if something needs troubleshooting. The frequent, consistent delivery of updated lead scores and account scores means reps can take action more quickly than under the previous once-a-day refresh schedule. Now, if a lead with a medium-level score signs up for the Data Cloud Summit — a big data point for our sales team — in the morning, the model is able to raise their score to a high level and deliver the newly hot lead to a rep by the time they’re back from lunch.
For the SDR leadership team, having information that’s trusted, accessible and available in near real time means they can make truly data-driven management decisions. Front-line sales or SDR managers who are responsible for building and coaching the SDR teams now have a more complete picture of team performance, a view built on aggregated data from multiple systems that powers BI and analytics visibility. SDR managers can identify bottlenecks for teams that are struggling or which SDRs are following up on specific marketing campaigns, and use that insight to support and guide their sales reps, helping them become better at their jobs. The ability to run reports at scale and quickly get metrics in front of managers also helps our SDR team gauge the relevancy of its communications and make informed decisions on business and content processes. For example, reports on post-event followup communications can spark ideas for operational improvements like A/B testing various components of follow-up messages and prospecting sequences or creating different approaches for in-person events versus virtual events.
