As any sales team knows, building proposals can be a tedious, painful chore. So in 2013, Nova Scotia startup, Proposify, set out to revolutionize the entire proposal process, from creation to close and every deal-making moment in between.
In 2017, Proposify began to see a significant uptick in growth. While this was a net positive for the business, rapid expansion brought a whole host of problems for their sales team.
Specifically, Proposify struggled with turning inbound leads into paying customers. Inbound interest was outpacing their small team’s ability to execute. Max Werner, Proposify’s marketing operations and analytics specialist, decided it was time to figure out how to improve the conversion rate without increasing headcount.
He identified three major opportunities:
Scoring leads to focus on high-value conversations
Firstly, Max identified that Proposify’s lean and nimble sales team had no way to identify which prospects were the best fit to focus their energy. Some visitors were genuinely interested in buying, but others were just kicking the tires. They didn’t have the resources to give all visitors the same level of service, so they needed some way to prioritize their leads.
Enabling all teams to use their preferred tools with little engineering work
Second, with a major new product release on the horizon, Max worried he did not have the proper tooling in place to quickly and reliably add tracking to the app. Various departments were using different systems for analytics, all of which needed to have customer information from the launch.
This meant that the development team needed to handle multiple APIs and maintain numerous integrations. Because integrations were done at different times to different tools, Proposify lacked data parity across its various systems.
Empowering support and success teams to get deeper insights
Third, Proposify’s customer success and support teams were crying out for a solution to gather deeper engagement and utilization analytics. In order to provide personal, helpful experiences, they needed a better understanding of their customers, all but impossible without clean and reliable data. The support and success teams could have enlisted the support of their engineers to get the data they wanted, Max didn’t want to burden the development team who was already strapped for time building product features.
We've always strived for datadriven decision making, but without proper data, it was hard to do. Our sales team was prospecting every trial user we had coming in. Marketing had a hard time keeping track of churn. Support had a hard time reporting on SLAs.
To streamline this process and bring scale to teams across the organization, Proposify chose Segment as a backbone for its customer data.
Proposify’s development team just needed to add Segment to identify, group, and track events during product development. From there, Max and the marketing team could easily connect various destinations like Marketo, Salesforce, Intercom, and more.
Here’s just a taste of the integrations set up by each team
Support and Success: Intercom, Gainsight
Marketing: Marketo, Visual Website Optimizer, Google Tag Manager, Clearbit
Operations: ChartMogul, Amazon Redshift, Recurly
Now that Max had a better handle on his data, he could start tackling the challenges impeding Proposify’s growth.
A new lead scoring model using Segment and Clearbit
Onboarding questions can help you triangulate the value of a prospect, but they don’t give you all the information you need to complete your qualification. Plus, the more questions you ask, the lower your conversion rate will dive. Max wanted to create a more sophisticated model.
First, he added in customer behavioral data (i.e. how many times a user performs a certain interaction; the last time a user performed a behavior).
Second, he also enriched each lead with firmographic data from Clearbit, such as information about a company’s funding, tool stack, and industry.
Using these inputs, Proposify generated a new lead scoring model and piped it back into Marketo through Segment. As a result, the sales team could more quickly disqualify leads with incomplete profiles and low scores.
Real-time data, without the engineering work
Proposify’s development team uses the Segment SDKs to add user/company traits or track events as the team develops and refines features. Now that Segment is implemented, Proposify is confident that any tools connected through Segment are piped the same data in the same format in real-time.
The time and effort saved through standardizing tracking against one API also helps Proposify iterate, extend, and improve upon its tracking significantly faster than was possible before; and the team doesn't have to worry about random APIs deprecating.
Thanks to this:
The product team can use customer traits inside Heap to segment its user base more effectively to evaluate how features of the app impact conversion.
The product team connects customer behavior data via Segment to find out the optimal number of proposal pages and which pages get the most visibility.
Self-serve analytics for success and support
Additionally, Proposify’s sales and support teams benefit from better customer data and a streamlined process for their downstream tools. With the Intercom and Gainsight integration via Segment, Proposify’s Customer Success team can self-serve customer health information.
With all customer info in one location (Intercom), the success team can provide quick support without having to dig around for details about each customer. Due to their fast and accurate service, the Success team has been able to maintain a negative net MRR churn almost every month.
A stable data infrastructure to help future growth
Since implementing Segment, here are just a few of the results Proposify have seen so far:
With Proposify’s new data infrastructure, the sales team has increased the size of its sales pipeline and velocity by 152% and 312% respectively.
This directly leads to the ability of the company to scale, ensuring data parity across its various systems to effectively turn interested prospects into happy, paying customers.
Knowing more about the app-usage of high-value customers, customer success has managed to maintain a negative net MRR churn almost every month.
In addition, the average data preparation for a Gainsight implementation is three months. Segment enabled Proposify to do it in just one month.
Thanks to their work, the Proposify sales team is now able to spend time talking to their most valuable prospects, and ensure they turn into long term, successful customers once they do convert.