Zack Blois, Senior Manager, Platforms and Automation
Empowers revenue team to rapidly match leads to accounts and route to sales 3x faster.
Enables revenue team to holistically field inbound automation requests to automate manual processes and close operational gaps.
Positions revenue team to further enable sales with virtual sales assistants and long-term sales database management.
Chicago-based Jellyvision is an intriguing company that started in educational media, evolved into a popular game software developer, and now offers the unique, interactive employee communication solution ALEX. Today, more than 1,200 companies use ALEX to navigate the complexities of employee benefits and personal finance.
Like other companies, Jellyvision’s revenue team regularly captures new leads from multiple channels, including website visits, digital marketing campaigns, and on-the-ground events. But it faced challenges and also saw opportunities for improvement in its lead management processes. Strategically, the team wanted to improve lead flow and speed up lead response time to improve conversions and enable sales to win more deals. Technically, the team struggled with passing vast amounts of lead data through its tech stack without running into limits that throttled the flow of data after too many requests - even when making minor, granular updates.
Challenge: Rapidly matching leads to accounts and expanding revenue ops
Ideally, every lead a company captures would be sales-ready: Fitting into the total addressable market (TAM), familiar with the company’s product, and ready to buy. But this isn’t always the case. To prevent sales teams wasting hours chasing irrelevant leads, one of the most important aspects of lead management is ensuring leads are sales-ready. Ensuring sales-readiness requires two subtle, but critical processes: lead-to-account matching and lead routing.
“We have a large lead database containing thousands of companies with anywhere from 100 to 10,000 employees,” explains Senior Manager of Platforms and Automation Zack Blois of Jellyvision’s revenue team. “We also need to segment that by territory and applicability as a potential customer. So we needed a system to instantly pull in lead details, match them against our existing database, confirm they were associated with the right accounts, and route them to sales.”
Previously, the team had used the out-of-the-box features of its marketing automation platform Marketo and its CRM, Salesforce. While many companies use Marketo to execute email marketing campaigns, and many companies use Salesforce as a system of record, neither is a lead matching or routing solution.
Initially, the Jellyvision team used a point solution built primarily for lead-to-account matching and lead routing. However, the point solution didn’t support Jellyvision’s custom data needs. And given the nature of point solutions, it couldn’t provide new paths for the company’s future growth.
“We had historically used our point solution for lead matching and routing, but what we really needed was something more extensible,” says Blois. “We needed something for broader use cases across our entire organization.”
Jellyvision’s revenue team frequently fielded a variety of requests, not just for lead routing. There was a real need to close operational gaps that slowed everyone down with tedious manual tasks. And as mentioned, the team was also mindful that lead follow-up delays could affect conversion rates and the sales team’s ability to win. So the team partnered with Tray.io to build a better, faster solution to its lead management challenges.
Using the Tray Platform’s API integrations, the Jellyvision team created a custom integration between Marketo and Salesforce, using additional tools such as Bizible and RingLead to de-dupe leads, segment leads by product line, apply filtering processes to personal email domains from the account-matching process to ensure they’re recorded properly, and divvy up leads for new accounts to the appropriate sales team member. The team built an automated workflow that not only connected Jellyvision’s tech stack but also nimbly managed a lead database that was too big for Marketo to handle on its own.
“Here’s one way to think about it: Marketo has a hierarchy of objects. You can make changes at the campaign level, but these changes also carry over to other records and data in your Marketo instance, which just makes things slower overall,” Blois explains. “With the Tray Platform, we’re able to get granular enough to update individual records on a one-to-one basis, rather than having to sift through thousands of API data changes.”
Jellyvision’s automated workflow elegantly managed its substantial lead data payloads and significantly sped up lead response time.
“Previously, pushing so much data through Marketo meant delays in our lead qualification process as we verified the integrity and accuracy of leads before routing them to sales. We had even resorted to pushing this huge amount of data out during the evenings when no one was at work,” says Blois. “With the Tray Platform, we got our lead response time down from about 30 minutes to 10.”
“We’re finding ourselves working with the Tray Platform more holistically these days,” says Blois. His sales and marketing colleagues now frequently come to him and his operations team with additional requests to integrate different apps and automate time-consuming processes.
“In the future, we’re looking to use the Tray Platform build a Salesforce-to-Slack integration since our sales team uses Slack extensively. We’d like to build an automated workflow that compiles sales cycle details into a single Slack alert. This virtual sales assistant will help our sales team respond to leads more quickly and more meaningfully,” explains Blois.
“Thinking longer-term, we’d also like to use the Tray Platform to integrate our data warehouse Snowflake with our sales records database to expedite our sales and operations processes. Thankfully, we don’t have to worry about the data limitations we used to struggle with. The Tray Platform is powerful and flexible enough to rapidly manage huge amounts of data at enterprise scale.”
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