The Worst CRM Feature We Almost Shipped in Goliath (and Why We Killed It)
Discover the behind-the-scenes decision that saved Goliath from a potential misstep.
Austin Beverigde
Tennessee
, Goliath Teammate
In the fast-paced world of real estate technology, innovation is key to staying ahead. At Goliath, we pride ourselves on delivering cutting-edge solutions that empower real estate operators to prospect, nurture, and close deals efficiently. However, not every idea makes it to the final product. In this article, we delve into a CRM feature that was nearly launched but ultimately scrapped, and the reasons behind that critical decision.
Understanding the Core of Goliath's CRM
Goliath's CRM is designed to streamline real estate operations with features like one-time CRM sync, ongoing enrichment, and automatic updates. It offers real-time tracking of seller data, ensuring users are always the first to know about shifts in the market. The CRM also supports a variety of filters, allowing users to segment data by location, property attributes, price, ownership, and more.
The Feature That Almost Was
The feature in question was intended to enhance the CRM's capabilities by integrating a predictive analytics module. This module aimed to forecast market trends and seller behaviors based on historical data and AI-driven insights. While the concept seemed promising, it posed several challenges that led to its eventual cancellation.
Why We Decided Against It
The decision to kill the feature was not made lightly. Here are the key reasons:
Complexity Overload: The predictive analytics module added a layer of complexity that could overwhelm users. Goliath's value proposition is centered on simplicity and speed to value, with instant access and onboarding in approximately two minutes. Introducing a complex feature risked diluting this core strength.
Data Reliability: While Goliath provides real-time data updates and tracks shifts hourly, the predictive module relied heavily on historical data, which could lead to inaccuracies. Ensuring the reliability of predictions was a significant concern, as it could impact user trust.
User Feedback: During beta testing, feedback indicated that users preferred actionable insights over speculative forecasts. The existing features, such as seller intent signals and life signals surface, already provided valuable insights without the need for predictions.
Focus on Core Features: Goliath's strength lies in its ability to prospect, nurture, and close deals efficiently. By focusing on enhancing these core areas, such as through automated outreach and smart call lists, we ensure that users can maximize their outcomes and ROI.
The Lessons Learned
Deciding against launching the predictive analytics module taught us several valuable lessons:
Stay True to Your Value Proposition: It's crucial to align new features with the company's core value proposition. For Goliath, this means enhancing the ability to connect with motivated sellers and streamline the deal process.
Listen to Your Users: User feedback is invaluable. By prioritizing the needs and preferences of our users, we can deliver solutions that truly enhance their operations.
Embrace Simplicity: In a world where technology can often be overwhelming, simplicity is a competitive advantage. Goliath's ease of use and quick onboarding are integral to its success.
Moving Forward with Confidence
While the predictive analytics module did not make it into the final product, the decision to focus on Goliath's strengths has proven to be the right one. By enhancing features that align with our core mission, such as automated contract creation, real-time seller data updates, and personalized multichannel outreach, we continue to provide our users with the tools they need to succeed in the competitive real estate market.
In conclusion, not every idea is meant to be realized, and sometimes, saying no is the best decision a company can make. At Goliath, we remain committed to delivering innovative solutions that empower real estate operators to turn conversations into contracts, supercharged by seller intent signals.
