Five Common Blind Spots in Marketing Data

1. “Walled Gardens” Due to Privacy Limitations

The challenge: In today’s privacy-focused era, tracking customer journeys across platforms is tough. “Walled gardens” limit your access to detailed personal data, preventing your ability to link ad exposure to sales. Analysts often default to click attribution, which fails to capture the true effect of marketing actions.

The solution: Marketing Mix Modeling (MMM) and Geo Tests offer robust alternatives by leveraging aggregated data, avoiding individual privacy concerns. MMM discerns patterns and relationships over time, while Geo Tests compare ad impacts across different regions, offering insights without individual data.

2. Offline Channels

The challenge: If you use offline advertising channels such as radio, billboards, or TV, you know that tracking who sees your ad and its impact is nearly impossible. Unlike digital platforms, offline channels lack direct, measurable analytics. This discrepancy complicates assessing their effectiveness with conventional methods. Consequently, analysts frequently give up on quantifying these channels’ effects, resulting in marketing strategies not based on data.

The solution: Both MMM and Geo Tests address this by analyzing aggregated data, which means individual interactions with ads aren’t necessary. These methods provide a macro-level understanding of an ad’s effectiveness across various regions or times, avoiding the direct tracking limitations of offline media.

3. Channel Cannibalization

The challenge: Channel Cannibalization occurs when advertising across multiple platforms or campaigns leads to one’s success at the expense of another, reducing the total effectiveness of your marketing. Traditional attribution models fail to detect this issue, calling for a holistic approach to assess the unique contribution of each channel.

The solution: MMM uses a holistic approach, and by looking at correlation over time it effectively isolates the impact of each platform. This enables marketers to ensure each platform’s contribution to overall marketing objectives is accurately measured and optimized.

4. Spillover Effects

The challenge: Imagine someone sees your ad, tells a friend, and that friend buys something. This sale, a ripple effect of your ad, goes unnoticed with conventional tracking. Traditional methods miss these indirect impacts. Such oversights can mislead data analysis and strategic decisions, ignoring a crucial part of your campaign’s influence.

The solution: MMM examines aggregated data over time, capturing effects regardless of whether they’re direct or indirect. Similarly, Geo Tests assess the impact on an entire geographic area, not just on those who saw the ad, naturally including these indirect effects without any extra effort.

5. Inadequate Measurement of Customer Lifetime Value (LTV)

The challenge: A significant blind spot in marketing data is the failure to accurately measure Customer Lifetime Value (LTV). Many traditional analytics approaches focus on immediate returns and fail to account for the long-term value a customer brings. This narrow focus can lead to short-sighted marketing strategies that overlook the benefits of building lasting customer relationships.

The solution: MMM and Geo Tests can bridge this gap by incorporating long-term sales data and broader market trends into their analysis. MMM, in particular, allows for the integration of LTV into its models by analyzing historical data over extended periods. This approach enables marketers to not only evaluate the immediate impact of their campaigns but also their contribution to sustaining and increasing customer value over time.