Overhauling the Dexible Order Form

Problem Identification

Following the initiation of our comprehensive product strategy for Dexible, we launched successive rounds of frontend testing and iterations.

We recognized a persistent and costly issue: traders were initiating trades but failing to follow through, leading to a high rate of order abandonment.

This behavior was not merely a byproduct of individual user decisions but a symptom of deeper inefficiencies within the UX—specifically, friction points within the trade setup process that were causing users to disengage.

UX has consistently been the shortfall of creating successful Web 3.0 products. Users need to connect wallets, sign messages, sign and grant approvals, sign transactions, then monitor transactions.

The platform's inability to convert trade initiation into trade completion represented a significant loss of economic value, and it threatened to undermine Dexible's competitive positioning in a rapidly evolving market.

To address this, I initiated a comprehensive analysis of user behavior, leveraging **Google Analytics** and **PostHog** to track and quantify abandonment rates across the platform. We needed to gather new metrics and dive into user interactions.

GA enabled us to capture key behavioral indicators—such as where in the order form, users were encountering friction. It wasn't always clear and the frontend team and I encountered a number of additional pain points with how our recordings were being processed. We lacked the ability to standardize the viewport leading to inconsistent recordings where our UI would be entirely messed up on the screen.

To resolve this, we reworked our Typescript to ensure that the frontend would render appropriately. By integrating these tools into our **frontend event tracking system**, we were able to gather precise data at each stage of the trade setup, from the initial input of trade parameters to the final confirmation screen.

As we fixed the recordings were getting from PostHog, these **session recordings** allowed us to visually inspect individual user flows, pinpointing moments of confusion, hesitation, and eventual abandonment. What we saw was that there was significant confusion around communicating the details of the swap. We weren't able to signify the implications of the swap order panel solely in the swap form, and needed to render Order Estimates revealing more implications of each of their choices.

To synthesize and evaluate our frontend data effectively, we used GA dashboards and integrated GA data into a **Grafana dashboard**. This dashboard became our primary tool for visualizing the collected metrics, offering detailed insights into user behavior patterns, drop-off rates, and the friction points within the UX. The dashboard's real-time data streams allowed us to track the evolution of these patterns across multiple user cohorts, providing a clear picture of how abandonment behavior manifested across different user types.

Mission Objective

The high rate of trader order abandonment on Dexible presented not just a UX issue but a major threat to our competitiveness. Every abandoned trade was a failure to actualize value—a clear instance of deadweight loss significantly affecting our revenue margins.

Traders were engaging with the platform but hesitating from the multiple decisions in the trade setup process, resulting in incomplete transactions. Worsening our ability to understand the nature of the problem, our users were totally anonymous, and some hadn't even connected their wallet before initiating their swaps. Thus, we weren't sure if we were causing hedge fund institutional traders to bounce or less significant individual retail traders.

The UX presented unnecessary friction in key areas, creating conditions under which users would opt for suboptimal decisions—specifically, abandoning their trades to avoid the cognitive burden of considering multiple complex configurations up front.

The challenge, therefore, was to reengineer the interface to reduce cognitive load and to indicate an alignment of platform incentives with the trader's.

Strategic Planning

In reimagining the frontend experience, I anchored the project in concepts from behavioral economics and decision theory. At its core, the problem stemmed from **bounded rationality**, where users operate under constraints of limited cognitive capacity and incomplete information. In the context of Dexible, traders faced high cognitive costs in understanding how the platform operated. We had to position Dexible as an institutional grade product available to everyone. It was an automated, algorithmic DEX aggregator. However, the implementation of segmented orders and the implementation of non-custodial order execution presented significant confusion to our customers.

I needed to get inside their head. Work bottom up from their perspective, understand what it would take to process all the variables—the trade parameters, the definitions of the order types, the market volatility, execution, and transaction costs—which led to decision paralysis. Cognitive load, in effect, was acting as a form of transaction cost, reducing the probability of trade completion.

We needed to convey that Dexible had to be positioned differently than what the traders were familiar with. When they were comparing Dexible spot swaps to a DEX's swaps on the spot market, they would see a significantly higher transaction cost due to the complexity of our backend contracts.

My solution was to minimize these transaction costs by simplifying the interface and assuming our customer knew less. Drawing on principles from **prospect theory**, I sought to shift traders' reference points by framing actions in terms of potential gains rather than losses. This required us to highlight new metrics from our Order Eval component and highlight the comparative outcomes relative to a standard single swap on Uniswap.

A critical component of this strategy was real-time feedback. By leveraging **feedback loops** derived from game theory, I introduced dynamic interaction points that provided traders with immediate information about the impact of their choices. This effectively reduced the **asymmetry of information** and allowed traders to make more informed decisions.

When users were given immediate visual confirmation of their order configurations and potential outcomes, they were more likely to proceed to execution, as the uncertainty that previously caused abandonment had been significantly mitigated.

Tactical Execution

In executing this vision, I employed an iterative approach grounded in **Bayesian decision theory**. The frontend overhaul was not a one-time event but a series of controlled experiments—each UI adjustment acted as a hypothesis, tested rigorously through A/B testing and continuous data analysis.

Step by step and feature release by feature release, our methodology was rooted in feedback. I would also arrange interviews, message members of our community to hear their perspective on all the new designs. It wasn't easy. Talking with anonymous traders who owe you nothing and some who are highly transactional speculating that their order volume would merely justify their participation for a future airdrop reward felt like pulling teeth.

Each iteration of the frontend design incorporated insights from the previous round of testing. It allowed us in retrospective to dissect these outcomes in **posterior probability distribution** analysis.

There was a difficult balance to strike. It existed between not merely reducing functionality, but rather, more intuitively arranging those elements.

Our sprint cycles followed the principles of **Agile methodology**, allowing for rapid deployment of new features and immediate collection of performance metrics. This allowed for a fine-tuned approach where any deviation from expected user behavior was corrected in subsequent iterations.

Outcomes

The results of the Dexible v2.0 overhaul were extraordinary. Post-implementation data showed a **70% reduction in order abandonment**, which directly corresponded to a significant increase in platform engagement and trader satisfaction.

This reduction in abandonment can be understood as a recapture of lost economic value, akin to eliminating inefficiencies in a closed-loop system, where friction previously drained potential returns.

Additionally, the **18% increase in average trade order value** indicates that traders, now equipped with a more intuitive interface and real-time feedback mechanisms, felt more confident executing larger and more complex trades. This outcome aligns with the **rational expectations theory**, where users, provided with better tools and clearer information, make decisions that reflect their true economic preferences. The redesigned interface not only reduced friction but also enhanced the **expected utility** of each trade, leading to higher engagement and larger transactions.

These improvements, while driven by UX, had a cascading effect on the platform's market positioning, allowing Dexible to capture a larger share of its target market and establish itself as a leader in advanced DeFi trading.

Reflections

Going forward, this project deeply informed how I will continue to lead product development and consider the importance of continuous integration for frontend testing.

Future iterations will focus on increasing the personalization of the user experience, driven by predictive analytics and advanced **behavioral modeling**. The platform's ability to continually adapt to user preferences will create an even more frictionless environment, where the boundaries between intention and execution are seamlessly bridged.

In conclusion, the Dexible v2.0 overhaul was not simply a technical improvement but a philosophical rethinking of how users interact with complex systems. As a leader, I see my role as not only solving immediate product challenges but as pioneering new ways to harmonize technology with human behavior. The principles of **decision theory**, **incentive alignment**, and **real-time feedback** will continue to guide my approach to product management, ensuring that every platform I oversee is engineered for maximum utility and market impact.