Snowflake powers predictive modeling with LinkedIn Sales Insights
Data Cloud company Snowflake was encountering difficulties with data completeness and data integrity from other data sources. After integrating LinkedIn Sales Insights with their CRM, they saw a significant increase in data quality, the accuracy of predictive modeling, and strong results from their sales teams.
Inaccurate and incomplete data
Snowflake enables organizations around the world to mobilize their data with Snowflake’s Data Cloud.
Data scientists at Snowflake use predictive modeling to help their go-to-market teams prioritize target accounts. “Our modeling is used internally,” explains Yu Chen, Senior Data Scientist at Snowflake. “We create what we call an account propensity model, and our main target is the planning team. At the beginning of each year, our planning team will do account assignments and account prioritization by using the score we provide to them.”
The previous data sources that Snowflake had used were of poor quality and no consistency.
Snowflake needed a solution that provided accurate, complete, real-time data on accounts within their Ideal Customer Profile (ICP) to ensure their modeling delivered reliable outputs.
Integrating a wealth of insights
By integrating LinkedIn Sales Insights with their CRM, Snowflake validated and expanded upon the volume of data used in their modeling.
This influx of high-quality data has increased the reach of Snowflake’s modeling. “We have more data availability outside of the United States,” says Chen. “Because LinkedIn Sales Insights standardizes all languages into English, we are able to get data on global companies as well.”
According to David Gojo, Manager, Data Science, at Snowflake, the accuracy of modeling translates directly into a calculable increase in ROI: “There has been a 30% increase in data matching accuracy due to LSI’s unique and direct access to people powered data.”
More accurate modeling and higher conversion rates
Since adopting LinkedIn Sales Insights just over a year ago, Chen notes that the data science team at Snowflake has seen a significant increase in data accuracy. “After making important changes to our modeling process, including adding the LinkedIn Sales Insights data, the accuracy of our models improved by 14 percent.”
The high-quality data allowed the team to identify ideal target accounts with even greater precision. Tracking the performance of accounts, those identified as top-tier accounts through the predictive modeling had a 2x higher conversion rate than other accounts.
Better account prioritization
Gojo shares an example of the domino effect of quality data: “The sales team approached us and said, ‘We need more accounts!’ So we gave them about 7000 accounts not yet in our CRM and identified to be high value prospects through predictive modeling. What made the project so successful is that one salesperson managed to close a record deal for the team from one of those accounts ingested by the end of quarter, when a typical sales cycle averages 180 days.”
Snowflake saw a significant increase in data completeness with Sales Insights. Chen shares, “We found that with LinkedIn Sales Insights the match rate increased to 95% from 70%. It has become our source of truth when it comes to verifying data in our CRM.”
Gojo explained how significant the quality of integration with Sales Insights was for data enrichment in their CRM. “Just because a data source has an entity in their system, that doesn’t mean it matches ours. And I think this is the strongest feature Sales Insights has – its ability to connect and integrate with the system.”
LSI is also an important data source for us to ingest new accounts into our CRM. We used LSI together with companies’ personas info to define the target ICP in different territories.
“I think the strongest feature Sales Insights has is its ability to connect and integrate with CRM systems.”David GojoHead of Sales Data Science
“LinkedIn Sales Insights has become our source of truth when it comes to verifying data in our CRM.”Yu ChenSenior Data Scientist
Snowflake delivers the Data Cloud — a global network where thousands of organizations mobilize data with near-unlimited scale, concurrency, and performance. Snowflake’s platform is the engine that powers and provides access to the Data Cloud, creating a solution for data warehousing, data lakes, data engineering, data science, data application development, and data sharing.
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