Retail stock brokerage firms in the US build their software in-house for their customers to research and trade stocks on US-based exchanges. These brokerages used to frequently incorporate outside research tools to round out their product offerings. But recently, driven by trading commissions dropping sharply (and now many firms offering free trading), these brokerage firms' appetite for paying for external research tools has largely dried up.
Trade Ideas anticipated these moves several years ago and has rapidly grown the direct subscriber base to their Artificial Intelligence-driven stock screener tools. This change has more than covered the lost income from institutional brokerage clients. To manage these subscriptions, Trade Ideas used Infusion Soft's cart and processed transactions through Authorize.net. We spoke with Skip Shean, TI Advisor & Marketing Consultant, who pointed out that Trade Ideas fundamentally suffered from three key issues in running their business:
Revenue loss from ineffective payment collections: With their previous vendor, Infusionsoft, Trade Ideas realized they had an ‘alarming amount of missed opportunities on dunning’ due to either expired cards, or not acting on failed payments. This caused significant issues that several Customer Service staff members were spending nearly all of their time managing credit card data and dunning. Additionally, the data lock-in by InfusionSoft led to their inability to test subscription scenarios in alternative solutions, risking the future growth of their complete business.
No Single Source of Truth = Partial picture with data discrepancies: One of the biggest issues for Trade Ideas was not getting a clear picture of the health of their business due to multiple sources of data — from an arbitrary Excel report to incomplete data from InfusionSoft. The problem was exacerbated by the fact that upgrades and downgrades in subscriptions would be calculated as cancellations and re-subscribes, which skewed their actual sales and ability to calculate valid recurring revenue metrics for the business.
Productivity loss: Skip summarizes this issue as overheads from marketing and sales teams on identifying which data was accurate, when, in fact, neither of them was accurate. This doesn't even count the risks of “making a bad choice on what to do because of the bad data.” They found themselves worried about walking into projects with the wrong data 50% of the time.
According to Skip, the concerns about data accuracy cost them about 15-20 hours a month in productivity.