Data blending helps combine data from different sources for a complete analysis thus showing insights without changing the original data.

Data blending uses external code and formulas in data analytics platforms. It queries data sources separately and merges the results into clear visualizations. This method is faster and more flexible than traditional data combination techniques.

Why Data Blending is Important

Data blending is essential for getting useful insights from various data sources. By combining different datasets, businesses get a full view that helps in better decision-making and operational efficiency. For example, an e-commerce company can blend website traffic data with email marketing metrics to understand how different campaigns drive sales. This helps create targeted email strategies that boost customer engagement and increase conversions.

Data blending also supports real-time analytics, which is vital for quick responses to market changes. Imagine a company running a major advertising campaign during a sporting event. By blending live TV viewership data with social media interactions, the marketing team can adjust their advertisements in real-time to maximize engagement and brand visibility.

Additionally, data blending simplifies data processes, reducing the need for extensive IT support and expensive data warehousing. The savings can be redirected to important initiatives like product development or market expansion.

How to Use Data Blending in Marketing

1. Checking Campaign Performance Across Channels:

Combining website data with paid media metrics helps identify the best campaigns. Different channels have strengths; some drive traffic, others boost engagement. By blending these data sources, marketers can understand:

• Overall performance across all paid channels.
• Which campaigns and channels generate the most traffic, conversions, or engagements.
• Regions with the highest conversion rates.

2. Monitoring Organic Social Media Performance:

Marketers often run social media campaigns on platforms like TikTok, Instagram, and LinkedIn. Blending data from these channels helps spot which ones perform best. For instance, if Facebook isn’t performing well compared to Instagram and LinkedIn, you can shift resources to the more effective platforms.

3. Tracking Budget and Ad Spend:

Data blending is crucial for budget tracking. By merging spend data from different channels, marketers can create detailed budget reports, calculate projected spends, and stay within budget.

4. Improving Search Performance:

Blending data from Google Ads and Google Search Console can reveal keyword opportunities for both paid and organic search. For example, if certain keywords drive significant clicks and conversions in paid ads but lack organic rankings, it indicates a need for organic optimization. Similarly, high-performing organic keywords can be targeted in paid campaigns. Including quality score data from Google Ads helps optimize performance and budget efficiency.

Conclusion

Marketers often work across multiple channels. Without combining data from these sources, they miss out on the full picture. Data blending helps marketers use their data effectively, leading to better strategies and results.

By adopting data blending practices, businesses can improve their marketing insights, streamline operations, and achieve greater success in their campaigns. For more detailed strategies on using data blending, check out our article on Top 5 Use Cases for Blended Connectors.

If you want to integrate data blending into your workflow and start using all the benefits of it, schedule a meeting with us to explore how DataSlayer can assist you.