Granular things such as sand and sugar are made up of tiny bits. Granular data is detailed data, divided into its lowest level. As your data becomes more subdivided and specific, it is also considered more granular.
Granularity matters to marketers because it gives you the ability to distill huge chunks of marketing activity so that you can understand the smaller components.
For example, with granular data:
- If your agency uses multiple ad networks, you can see how each performs.
- If each ad network contains different publishers, you can see which ones drive traffic.
- If each ad campaign uses different creative or offers, you can see how each converts.
- If your campaign appears across different channels, you can see which ones engage.
- When a prospect or customer engages with your brand, you can see which touchpoints work.
Data at its most granular level gives you the most accurate and actionable insights. But to take advantage of the possibilities of granular data, you must use the right measurement approach. Not all attribution models use granular data. Some, such as marketing mix modeling, use summary-level data combined with exogenous factors to produce high-level insights on a monthly, quarterly or annual basis.
Other approaches such as multi-touch attribution can parse performance at the finest levels on a daily basis. Marketers should choose the model that matches the cadence and granularity they need.
Selecting the Right Attribution Model
Multi-touch attribution uses user-level data from across media channels to assign fractional credit to the marketing touchpoints and dimensions according to their influence on driving a conversion (or any other key performance indicator).
Broadly speaking, there are two types of multi-touch attribution models:
- Rules-Based: A subjective methodology that distributes credit for a given success metric across one or more marketing touchpoints using a defined or assigned weight.
- Algorithmic: An objective methodology that uses machine-learning to calculate and fractionally assign credit for a given success metric to influential marketing touchpoints and dimensions (campaign, placement, publisher, creative, offer) along the consumer journey, as well as to predict the outcome of future spending.
Hitting the Limits of Rule-Based Models
Most marketers start with a simple rules-based model in a site-side tool, but soon recognize its limitations. Though rules-based models are quick and easy to implement, they lack the accuracy marketers need to truly understand the effectiveness of their media at driving conversions and other desired outcomes. Each of the rules-based models — first-touch, last-touch, time-based or even-weighting — has biases that skew results.
Some marketers then adopt a more sophisticated algorithmic approach, which helps them understand media performance more accurately, even at granular levels. An algorithmic approach calculates cross-media, cross-device, and converter propensity impact on media performance.
Algorithmic approaches achieve more accurate results because they model user-level data at every level of your media hierarchy. They enable marketers to discover their best-performing marketing all the way up the funnel, from the impact of specific creative elements to the channel overall.
Even when data gaps arise due to unavailability of user-level data, an algorithmic model takes multiple factors into account for deriving fractional credits, and will still produce accurate and actionable insights.
Why You Need Granular Measurement For Attribution
Understanding why certain consumer interactions result in conversions and others don’t is the secret sauce to media optimization. Marketers need a model that takes both converter and non-converter consumer journeys into account and understands what aspects of the media are driving the conversion.
If you don’t know whether the publisher network or the creative message drove a conversion, you might be spending money in the wrong places.
There are six significant benefits of granular measurement.
1. Granular measurement shows you how to optimize within channels.
Multi-touch attribution allows you to evaluate individual channel performance as well as the performance of dimensions within channels such as publisher, campaign, creative, keyword and placement. Granular intra-channel performance data allows you to determine how each channel is performing relative to other channels and where to shift dollars within that channel to maximize results.
2. Granular measurement helps reduce errors.
Simple regression modeling techniques used in marketing mix approaches fail to recognize hierarchical dependencies in a media mix. Regression models are largely linear and used to predict the future behavior of variables based on their past. This can result in an overstatement of statistical significance. Multi-touch attribution reflects dependencies and gives an accurate reflection of significance.
3. Granular measurement expands simulation capabilities.
Multi-level modeling allows you to develop accurate media optimization “what-if” scenarios. Channel managers and analysts who need to find the highest-performing media tactics based on their budget and performance goals can analyze the impact of each scenario and see where to make changes to produce the best results.
4. Granular measurement reveals impact on brand engagement and conversion events.
Multi-touch attribution reveals the effect of a particular media mix — and each element within the mix — on consumers, allowing you to optimize correctly and to identify opportunities for growth.
5. Granular measurement lets you test creative.
Smart marketers want to know their conversions from different offers and creative types: banner, text, interstitial, video. Multi-touch attribution lets you create a control group to see the incremental lift of exposed versus unexposed consumers.
6. Granular measurement helps you avoid ad fraud.
Measuring at the impression level allows you to verify that traffic sources are valid. By comparing results across publishers and ad services, you’ll see conversions that differ from the norm. Since you’ll naturally want to reallocate budget to those channels and tactics that are most effective at driving conversions, you can filter out fraudulent placements while improving marketing performance overall.
How Granular Measurement Works
How does multi-touch attribution actually leverage granular data to generate granular performance metrics? This type of algorithmic attribution processes user-level data to create chronological consumer journeys for every individual that has interacted with your marketing efforts.
It then determines the lift of each aspect of each touchpoint (placement, campaign, creative, size) — both alone and in combination — using an algorithm which compares converters and non-converters across all journeys and performs tens of millions of A/B tests to determine each attribute’s value.
Finally, it assigns the value of each touchpoint (based on weight and impact) toward your desired outcome (which can be any type of conversion event or brand engagement). The result is a very precise calculation of the impact of each dimension of each touchpoint.
This allows the model to accurately quantify how much each touchpoint contributed to your desired outcomes. This can be rolled up to determine the credit earned by every channel, campaign and tactic in your marketing portfolio.
When you use granular measurement and modeling you can:
- See how KPI credit for conversions shifts when multi-touch attribution replaces traditional rules-based measurement. This helps you gain insight into which channels, subchannels or media tactics are working together.
- Learn how conversions break down by KPI attributes at the dimension level so you can learn more about the nature of your conversions by any media dimension.
- Understand how the time lag between key events influences conversions so you can identify the impact of each publisher on conversions and build an effective mix.
New Measurement for a New Era
Multi-touch attribution lets you take advantage of the unique set of data produced by addressable channels to understand effectiveness at granular levels, by audience, and at a much faster cadence.
Measuring media at granular levels is a strategic benefit for any marketer. The key is ensuring that hard-earned granular data is used for modeling and deriving insights for media optimization.
Using granular data to understand consumer behavior and the impact of media tactics on conversions helps you make educated decisions and get the highest ROI from your marketing investments.
Check out our case studies to see how we helped leading brands improve their marketing effectiveness.
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