Helping global brands
win visibility on the E-Commerce shelf
hour by hour
with Hourly Bidder
Helping global brands
win visibility on the E-Commerce shelf
hour by hour
Hourly Bidder is an AI tool that automates hourly ad bids for brands across Amazon, Walmart, and Instacart. I led the product design from concept to launch to enable faster, smarter retail decisions.
My Role
Lead Product Designer
Company
CommerceIQ
Timeline
7 months
2022
Impact
+53 % ROAS
+21 % Sales
Team
Lead Product Designer (myself)
1x Product Manager
2x Senior Product Designer
5x Developers
3x QA
WHY WIN E-COMMERCE SHELF?
In E-Commerce, every search is a battlefield
Brands fight for this digital shelf because visibility equals profit.
A single brand manager handled up to 84,000 product decisions a week, spanning hundreds of products and performance variables. As decisions multiplied, teams struggled to keep up with outdated tools; that’s where the real problem surfaced.
Each click, adjustment, and placement determined who won the digital aisle.
Brand Managers
Lost visibility and profit due to delayed actions.
Brands/Advertisers
Missed key shopping hours from slow product performance insights.
Needed automation to keep brands competitive.
A small change in product rank can turn visibility into profit or loss.
This volatility made hourly optimization critical for brand managers.
PROBLEM
Brands couldn’t track product performance by the hour
They reacted late to shifting traffic and wasted budgets in the process.
Brand managers spent hours pulling retail media data, crunching spreadsheets, and emailing updates to optimize bids. Without real-time visibility, critical shopping hours passed, and sales were lost.
1
Sales data pulled from
Retail Media
2
Excel crunching for
every metrics
3
Brand Manager and CommerceIQ collab.
4
Legacy Hourly Bidder: Manual Inputs, Missed Insights
SOLUTION
Spotlights (AI-Driven Insights)
We developed an AI-driven ‘Spotlights’ panel that analyzes sales and campaign trends hourly to surface opportunities and risks. This directly addresses the user pain of manually digging through data; instead, the system proactively alerts managers to key changes (e.g., a sudden drop in ROAS or a trending keyword).
Highlights key trends
and risks to guide faster,
data-backed actions
Reveals brand gaps and leaders across categories
Uncovers performance changes and emerging opportunities
SOLUTION
Bulk Strategy Builder
We designed a flexible grid editor that lets brand managers update hourly bids across hundreds of products simultaneously. This reduced the user pain of repetitive manual edits and inconsistencies across campaigns.
The system supports multi-select actions, conflict alerts, and undo functionality, enabling faster, error-free bulk updates while maintaining full control and visibility.
Instantly switch adjustment modes to optimize spend.
Manage hourly bids in bulk with grid editing and error alerts.
Monitor hourly or 4-hour summaries for
quick insights.
Compare metrics like sales, clicks, and conversions instantly.
IMPACT
Design impact in action
“CommerceIQ helped us achieve 19% growth in market share, 21% sales growth on Amazon, and a 53% increase in return on ad spend. It's platform gave us the insights and automation needed to optimize campaigns and drive profitable growth.”
Rizwan Aktar
Head of E-Commerce, Pilgrim's
HOW DID WE GET THERE?
Process
To ground our design in evidence, we investigated the problem from multiple angles, interviewing stakeholders, analyzing user behavior data, and studying competitor solutions.
USER RESEARCH
Stakeholder interviews
We first spoke with 5 internal and client-side stakeholders (e.g., Directors of Marketing, Sales Managers) to understand current workflows and pain points. They consistently echoed the same frustrations: ‘our current process makes it impossible to keep up’ and ‘we were running in circles, reacting too late to capture opportunities’.
Ranking in the top 3 search results is critical, but our current process makes it impossible to keep up.
Brandon Titmus
Director, Marketing
We fell behind on market trends while crunching numbers.
Brittany Levine
RM and National Sales Manager
We felt like we were running in circles, reacting too late to capture peak sales opportunities.
Lars Lee
Senior Manager
These interviews confirmed that manual bidding caused delays and missed opportunities, and stakeholders expressed a desire for an automated, real-time solution.
How Brand Managers got affected?
Input Variables
Decision drivers
400
Products/Day
30
Variables per
product per day
7
Days/Week
monitoring
84000
Decisions/Week
02
Decisions/Minute
Team Resources
Available capacity
10
Team members
12
Hours/Day
working
5
Days/Week
UXR Method: Quantitative Task Analysis, Stakeholder Interviews
How Brands got affected?
PATTERNS WE CHOSE TO KEEP
Competitive Analysis
Next, we looked outward to proven solutions. I analyzed how leading platforms like Google Ads and Facebook Ads handle hourly ad scheduling, to borrow effective UI patterns and avoid their pitfalls.
Google Ads
We noted Google Ads’ familiar time-grid as a good way to visualize hourly intent, but saw it lacked campaign-level context and real-time adaptability, a gap we aimed to fill.
Familiar time-grid UX pattern helps users visualize hourly intent
Preset bid adjustment toggles for fast setup
Grid lacks campaign-level context (only shows time)
Static toggles don’t adapt to real-time data or strategy changes
Facebook Ads
Facebook Ads offered timezone alignment and a minimalist toggle UI, which we liked for clarity, but it didn’t show performance metrics, which we knew our users needed.
Minimalist UI with clear active/inactive hour visibility
Time zone alignment for user targeting
No performance metrics layered on top (like ROAS or CPC)
Single-retailer focus limits broader retail media needs
Quartile
Quartile’s dense heatmap catered to advanced users, affirming that power-users want granular data, though its raw tables felt too opaque for our broader user base.
Dense hourly heatmap for advanced users
Compact, table-based bid data for precision
Raw data tables require expert interpretation
No transparency in logic or AI-backed bid suggestions
USER SIGNALS THAT DROVE DESIGN
Product Analytics & Support Data
Using FullStory and support data, we discovered pain points in the existing tool: users struggled to create strategies (high drop-off on the Strategy page, which also had one of the highest bounce rates) and frequently complained about the lack of bulk editing and unclear defaults.
Session replays showed high frustration during strategy creation and scheduling.
The Strategy Page ranked among the slowest pages in terms of load time and bounce rate.
Tickets clustered around missing bulk editing, confusing defaults, and lack of visibility into strategy performance.
These insights helped us prioritize improvements; performance optimizations and bulk-edit capabilities became top requirements.
Strategy & Planning
After collecting evidence by investigating problems from multiple angles,
I translated research insights into a game plan for the design.
SURFACING OPPORTUNITIES THROUGH SYSTEMIC GAPS
Manual Budgeting was slowing teams
when it mattered most
Through journey mapping and support data, we uncovered where teams lost time: syncing scattered spreadsheets, waiting for approvals, and juggling Slack threads just to shift bids. The process broke down right when speed mattered most.
How might we help brand managers optimize budgets in real time without manual coordination?
This visual captures the shift from fragmented, manual workflows to a streamlined system that delivers prioritized insights and fast, scalable execution.
This journey exercise revealed that the slowest point was data aggregation and approval; managers wasted hours just gathering data from disparate sources and getting buy-in via email.
DEFINING FEATURES
Prioritization Matrix
We plotted potential solutions on an impact/effort matrix to select our MVP features strategically. High-impact, like bulk edit tools and automated performance alerts, rose to the top. I led this prioritization workshop with the PM and tech lead, ensuring we aligned on what would deliver value fastest.
A/B Testing Framework
We postponed A/B testing to Phase 2, focusing the MVP on automation while leaving room for future experiments.
Trade-off
Some ideas (e.g., AI Insight drilldowns) were deemed high-impact but very high effort, so we deprioritized them for the first release to focus on quick wins that fit our 7-month timeline.
With a clear understanding of priorities, we defined a solution approach that would introduce automation and real-time insights into the workflow, directly addressing our ‘How Might We’ question.
Ideation
I led a fast-paced ideation sprint to reimagine Hourly Bidder from a manual bidding grid into a system that surfaces strategic opportunities with clarity and speed.
USER INSIGHTS THAT SHAPED IDEATION
Redesigning workflows to turn insights
into actions
Through early concept testing and internal shadowing,
I found that brand managers were overwhelmed by static reports and tabular grids.
They needed smarter, guided views, not just raw data.
DESIGN PRINCIPLES AND COLLABORATION
Turning complexity into clarity, together
To ensure the redesign was both strategic and practical, I grounded it in two principles: reduce cognitive load and surface timely actions.
I worked cross-functionally to align these principles with real constraints.
During ideation, I reimagined Excel-based workflows, prototyping tabular and calendar layouts with integrated sales metrics.
I collaborated with PMs, Sales Teams, and Customer Success Teams to understand how they communicated with conglomerate brands.
Reflection
Lessons Learned
This project taught me the importance of involving stakeholders early when introducing AI into workflows – their input helped shape features like Spotlights in a way that users trust.
If I were to continue this project, I’d focus on refining the AI’s predictive accuracy and perhaps automating more decisions as user trust grows. I also learned how critical it is to visualize data in a digestible way for quick decisions, a lesson I’ve carried into subsequent projects.
Given the 7-month timeline, we had to launch without the AI Insights drilldown, but it turned out to be fine. In retrospect, focusing on the core features that solved the main pain points was the right call.
Ultimately, Hourly Bidder didn’t just boost metrics; it changed how our customers managed their campaigns, proving the power of user-centered design combined with AI.