DoorDash, a popular food delivery service, offers an advertising platform that’s particularly beneficial for Consumer Packaged Goods (CPG) brands. These brands can leverage DoorDash’s large and loyal customer base to effectively reach their target audience.
CPG brands can learn more and get started with DoorDash’s advertising opportunities through these platforms.
DoorDash also provides an API for brands like PepsiCo to integrate into their proprietary Ads Management Platforms, allowing for more direct control and customization.
DoorDash uses ML algorithms to create targeted ads and promotions based on user behavior, transaction history, and demographic data. This increases the effectiveness of marketing efforts and boosts sales.
With in-app advertising, businesses only pay when an order is placed through the ad, not for clicks or impressions. This model has been shown to significantly increase sales when combined with promotions.
An analysis comparing the CPA on DoorDash and Google’s AdWords can provide insights into the efficiency and effectiveness of advertising on these platforms.
- Target Audience: Options include all users, new customers, existing customers, or lapsed customers.
- Scheduling: Ads can run all day or within a specific timeframe.
- Average Weekly Budget: Determined based on the estimated weekly orders from ads.
- Bid Strategy: Includes smart bidding and custom bidding options.
This approach involves offering promotions to customers directly through the DoorDash platform.
DoorDash has been optimizing its marketing spend on Google AdWords through A/B testing with a new bidding algorithm. This approach includes identifying key performance metrics and determining the appropriate sample size for statistical significance.
The primary metric for DoorDash’s marketing efficiency is the cost-per-acquisition (CPA). The challenge lies in balancing increasing ad spend with the diminishing returns in customer acquisition.
To manage campaigns more effectively, DoorDash has developed a Marketing Automation platform powered by machine learning. This platform allocates budgets and manages bids across various channels.
DoorDash employs ML models to generate synthetic data, aiding in the construction of more accurate cost curves. These curves help understand the relationship between ad spend and user acquisition.
However, it becomes more and more expensive to acquire customers who have lower intent, meaning they need convincing rather than just being pointed in the right direction.
As ad spending increases, it hits diminishing returns at some point with a very high CPA. Therefore, we need to ensure that whatever CPA target it set still performs well at the spend level need.
Some weeks these campaigns don’t spend at all, which makes the data sparse. Using this data as-is will result in unreliable cost curves and in turn suboptimal (potentially wildly so) allocation. For other types of campaigns, the data can be clustered in a narrow band of spend.
Each channel (like search engine marketing) has its own ML model, tailored to its specific needs and characteristics. This approach ensures that each channel’s model is optimized for its unique data and performance trends.
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