In an increasingly data-driven world, marketing analytics play a crucial role in the success of any eCommerce business. However, some companies are falling victim to common marketing analytics mistakes, which can have a significant impact on their bottom line. Here we outline these pitfalls and offer solutions to overcome them.
Garbage In, Garbage Out
According to IBM, bad data costs U.S. companies $3.1bn annually.
Perhaps the most fundamental mistake lies in the quality of data being collected and analyzed. Inconsistent or non-semantic naming of data, events, or properties, coupled with poor campaign and UTM naming standards, can make data interpretation difficult and intimidating, particularly for non-analysts.
Furthermore, customer data is often spread across various systems, including Customer Data Platforms (CDPs), Customer Relationship Management (CRM) systems, and data warehouses (DWHs), limiting transparency and consistency.
The solution is to implement a consistent standard for data naming and establishing a single source of truth or governance for customer data. This is key to greatly improving data quality and accessibility and can be achieved with robust CDP and data governance practices.
The likely business impact of high-quality, reliable data is data democratization for all stakeholders, enabling everyone in the organization to make data-driven decisions.
Incorrect Ad Platform Set-up
Getting your ad platform set-up right can reduce cost per action by 10-30%
Incorrect, duplicative, or missing ad pixels can compromise the quality of your tracking data. Failing to use server-side tracking can also lead to signal loss, hampering ad engines' ability to optimize.
The solution is to manage ad pixels in a single place and with clear naming conventions can improve tracking. For core channels, setting up Server Side APIs is recommended to mitigate signal loss.
The likely business impact of improved connectivity is a reduced cost per result. In fact, data from the Conversions API is less affected by browser loading errors, connectivity issues, and ad blockers, resulting in cost per action dropping by 10-30% using conversion APIs.
Absence of Robust Ad Measurement Systems
Running incrementality tests can identify where ad spend is being wasted as it doesn’t drive causal impact.
Poor attribution setup and a lack of incrementality measurement (i.e. understanding the causal impact of campaigns) can lead to wasted budgets.
When businesses rely solely on what is reported in Google Ads and Facebook Ads platforms, the result is often double counting of conversions (both Google and Facebook claiming credit) and therefore an overly positive view of marketing performance.
The solution is to invest in robust attribution models (that don’t rely on fragile and error-prone Google Sheets or Excel) and incrementality testing for core channels. You can use the table below to understand how many experiments you should be running per channel, per year:
Incrementality tests identify the actual impact of your marketing campaigns on your business metrics. Only with this understanding can you truly allocate marketing budget efficiently. This approach is used widely by sophisticated marketing teams - for example, Uber realized that it could cut 72% of budget on a core channel and only lose 6% of conversions.
Poorly Configured Catalog Feeds
Stop wasting ad spend by only promoting stock you have available
Bloated or incorrect catalog feeds, such as those containing old products or incorrect product IDs, can lead to wasted advertising spend. When marketers fail to filter product feeds based on stock levels or marketplace supply, they risk exacerbating this issue.
The solution is to carry out regular auditing and updating of your catalog feeds. Keeping track of stock levels and marketplace supply can also help to ensure your business is not advertising products that consumers are unable to buy.
The likely business impact is a more satisfied, loyal return customer base. You also avoid wasting funds on ads that promote unavailable products.
We see e-commerce companies time and time again make the same four marketing analytics mistakes. Fixing these issues by investing in high-quality data, improving ad platform connections, running incrementality tests, and regularly updating catalog feeds - if done properly - can be completed in 3-6 months and will have a significant impact on marketing ROI.