In today’s saturated media environment, leveraging granular data in media buying is essential for maximizing return on investment (ROI). That’s because, unlike more general data sets, data with a high degree of granularity unlocks more precise customer insights so you can reach the right consumers at the right time with the right message, which results in more effective and efficient marketing outcomes.
Leveraging granular data also improves the customer journey by enabling you to more effectively tailor your marketing messages to specific demographics, behaviors, and preferences at each stage of the buying process. You can avoid wasting ad dollars on uninterested customers with messages that feel irrelevant – and focus your efforts cultivating relationships with customers who are ready to be engaged.
For example, people who are 25 to 40 with an annual income of $100,000 who frequently shop online, have pets, and live in a major metropolitan area might be interested in a subscription-based pet food delivery service. But without access to granular data points on a range of characteristics, it’s not possible to unlock the precision required to effectively reach this group of potential buyers.
What is granular data?
Granular data refers to the most detailed or precise data in a data set. All data exists on a scale of granularity. For example, in terms of demographic data, a customer’s home address is a less granular data point than the geolocation data of where they completed a purchase. The more detailed information you can access about your customers, the more options you have for identifying and reaching specific subsets or segments of your audience with precision.
Why is data granularity important?
Data granularity enables you to reach and activate audience segments with highly personalized marketing content. Speaking directly to specific needs and preferences provides a better customer experience, which leads to higher engagement rates and, ultimately, better ROI.
Historically, leveraging highly granular data was the purview of data scientists and analysts because combining datasets and extracting insights required specialized data engineering and analytical skills. But new platforms and tools make it possible for marketers to access and analyze granular data for their campaigns without extensive technical expertise.
What’s the difference between aggregate and granular data?
The primary difference between aggregate and granular data is the level of detail insights they provide:
Aggregate data
On the scale of data granularity, aggregate data is less precise. Aggregate data is summarized information that combines multiple data points into broad categories, which makes it useful for identifying general trends and patterns. For example, aggregate data might show the average sales figures for a product across different regions or the total number of website visits per month.
Granular data
Granular data provides a much closer level of detail, which allows for more in-depth analysis and precise audience targeting. Aggregate data might tell you how many customers made purchases through your website last quarter; but access to granular data enables you to segment those customers based on the number of purchases made, their income brackets, or lifestyle choices (e.g., eco-friendly, luxury seeker).
What is an example of granular data?
You can segment granular data points based on a wide range of characteristics. Some examples include:
Customer purchase history
Granular purchase history data enables you to segment customers by transaction details, including specific products purchased, purchase dates and time, cart total, purchase frequency, and seasonal buying habits.
Site visitor behavior
You may choose to analyze granular site visitor behavior data to gain insight into how users interact with your website. These metrics can include pages visited, time spent per page, click-through rates, navigation path, exit page, bounce rate, and interaction with specific features or tools.
Geographic location
Breaking down granular geographic location allows you to understand where your most loyal customers are located, as well as reach customers near your physical locations. You can break geographic data down by factors like zip code, neighborhood, urban/suburban/rural classifications, and geo-fencing data for localized marketing efforts (e.g., proximity to physical store locations).
Product preferences
Granular data about customers’ product preferences can help you tailor your marketing messages so they’re more likely to resonate with individual customers. Potential segments include: preferred product categories (e.g., electronics, clothing), specific product features (e.g., size, color), purchase history by product type, past reviews and ratings, and wish list or cart additions.
Responses to survey questions
Extracting granular data from survey responses provides direct insights into customer satisfaction and loyalty, as well as frustrations. Consider segmenting customers by product satisfaction rating, customer experience feedback, or net promoter score.
Social media interactions
You can use granular data extracted from social media interactions to understand how customers perceive your brand and who your most loyal customers are. Granular social media data can be segmented by number of likes, shares, or comments on posts, engagement rates by content type, influencer interactions, and sentiment of comments or mentions.
Benefits of granular data in marketing
With granular data core to your marketing and consumer data strategy, you can make data-driven decisions that improve campaign success and customer reach. These may include:
Enhanced targeting precision
By understanding specific customer attributes, behaviors, and preferences, you can more effectively reach individual customer segments to optimize ad spend.
Improved campaign personalization
Customers are more likely to respond positively to content that feels relevant and tailored to them. Personalized campaigns that cater to individual customer needs and preferences fosters stronger customer relationships and increases engagement.
Increased advertising efficiency
Granular data helps in identifying and focusing on the most promising customer segments, maximizing return on ad spend (ROAS) . By targeting high-impact audiences with the right message at the right time, you can achieve higher ROI and maximize your marketing budget.
Deeper audience insights
When you gain a more detailed and comprehensive understanding of audience behaviors, preferences, and purchasing patterns, you can make data-driven decisions and optimize your marketing strategies for better results.
Optimized budget allocation
Identifying high-value segments and channels enables you to invest marketing resources where they’re most likely to generate the highest returns and have budget left over for testing and innovation.
Higher conversion rates
You can use granular data to deliver highly relevant and personalized content that improves your conversion rates. When customers receive messages that align with their interests and needs, they are more likely to take the desired action, whether it’s making a purchase, signing up for a service, or engaging with your brand.
Real-time performance analysis
Granular data allows for real-time analysis of marketing performance so you can quickly identify what’s working and what’s not. This immediate feedback loop allows for rapid adjustments and optimizations, ensuring that campaigns remain effective and responsive to changing market conditions and customer preferences.
Challenges of granular data in marketing
The primary challenge to using granular data is gaining access to it in the first place. Many organizations struggle to consolidate data sources in a fragmented, complex digital ecosystem.
Walled gardens
Walled gardens refer to the closed ecosystems managed by large digital platforms where data access is restricted. These limitations can prevent marketers from seeing a holistic view of audiences across platforms.
Siloed analytics and lack of interoperability
Granular data often exists in isolated silos within an organization, which makes it difficult for marketers to integrate and analyze data across different sources. This lack of interoperability can lead to fragmented insights, inconsistent messaging and wasted budget.
Complex and inhibitive user experience
The detailed nature of granular data can make it complex to manage and analyze. For marketers without a strong technical background, navigating these complexities can be difficult. This often prevents marketers from effectively incorporating granular data into their campaign strategies and decision-making processes.
Maximize ROAS with LiveRamp’s data clean rooms
LiveRamp’s data clean rooms help you to overcome the barriers that traditionally prevent marketers from leveraging granular data. By providing a secure environment for privacy-centric data collaboration and integration, our clean rooms safely connect data across walled gardens, media networks, social platforms, and CTV so your campaigns can deliver superior reach and personalization. Your team can easily navigate the LiveRamp Clean Room, powered by Habu to access the insights they need through pre-built templates and gen AI support.
It’s time to put your granular data to work for you, optimizing campaigns and making budget go further for greater marketing impact. Start a self-guided demo of the LiveRamp Clean Room, powered by Habu and see for yourself how easy it is to gather granular data across channels and optimize your media investments.
