Results
The Challenge

The Process
To meet our goals for Bradshaw Home, we implemented a comprehensive strategy focusing on ruthless prioritization and data-driven day-parting. By strategically aligning budget deployment with peak performance periods and focusing on specific products, Bradshaw Home experienced a significant boost in profitability.
We reduced the advertised product count from over 500 to the top-performing 80, focusing on items responsible for 80% of expected revenue and allocated resources to hero products to maximize impressions, traffic, and sales.
Our team examined hourly sales trends from the last four major promotional events using Seller Central data and incorporated Amazon Marketing Stream insights to identify peak traffic and conversion times.
We used data from our own account and our 3P partners to determine the optimal budget timing throughout the day. Ad spending was adjusted to prioritize high-conversion periods, avoiding waste during low-activity times. We reserved budgets for evening hours when competition decreases, leading to lower CPCs and higher conversion rates.
Bradshaw Home was highly receptive to this data-backed strategy. With their support, we streamlined the implementation process, enabling quick adaptation to the new budget allocation model. Our approach to Fall Prime Day allowed them to thrive on a reduced budget. By focusing on key revenue-driving products and utilizing data to optimize ad timing, we achieved significant cost savings and revenue growth.
The Solutions
Our main goals were to achieve a higher return on a reduced budget, maintain a higher efficiency to meet adjusted revenue goals, and optimize budget allocation around the brand’s critical products. Blue Wheel’s new approach to Fall Prime Day allowed Bradshaw Home to thrive on a reduced budget.

Advertising on Amazon is like breaching a castle wall. A scattered budget is akin to shooting arrows, whereas a consolidated budget launches cannonballs, breaking through more effectively. Our ruthless prioritization meant we were highly objective about the client’s situation and made tough decisions based on data.


