Marketers are no strangers to complexity, often managing multiple campaigns across a myriad of channels and ad networks, all at the same time, making measurement challenging.
The growing magnitude of data these campaigns produce require targeted measurement methods to help make sense of it all. Methods that can distill and process data at its most granular levels give marketers the most accurate and actionable insights that they can use to not only meet their department’s goals, but also help drive broader business objectives.
Benefits of granular measurement
Consumers’ attention spans are shorter than ever, giving marketers a fraction of a second to capture their attention. If something isn’t working, marketers need to know right away so they can make adjustments quickly, before losing potential customers to competitors. Using budget effectively is critical not only to driving results, but also to proving the value of marketing to the business.
But if marketers don’t know if a particular placement, keyword, creative message or other tactic drove a conversion, then they might be spending money in the wrong places. Granular measurement enables marketers to evaluate individual channel performance, and drill down to see the performance of each dimension within a channel. They can also test different offers and creative types to see what’s resonating with different audience segments and influencing them to act. Marketers can then use these insights to make more precise decisions and continuously rebalance spend to maximize results.
Granular measurement also provides the ancillary benefit of reducing the impact of online ad fraud. For instance, analyzing performance at the impression level enables marketers to verify traffic sources are valid. Bots and other fraudulent traffic may be able to simulate a page view or an ad click, but they can’t generate real conversions, such as completing a purchase with a payment. By comparing results across publishers, marketers can reallocate budget to the channels and tactics that are most effective at driving actual sales and revenue, while filtering out fraudulent placements at the same time.
Data at its most granular level gives marketers accurate and actionable insights. But taking advantage of its possibilities requires the right measurement approach. Not all measurement models use granular data. Marketing mix modeling, for example, uses summary level data combined with exogenous factors such as weather and holidays to produce high-level performance insights and budget allocation recommendations on a monthly, quarterly or annual basis.
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Other approaches, such as multi-touch attribution, can parse performance at the finest levels and deliver accurate, up-to-date insights as often as every day. Multi-touch attribution collects and processes person-level data from addressable channels and assigns credit to tactical marketing activities according to their influence on driving a conversion or other desired success metrics.
Multi-touch attribution models that use an algorithmic methodology are the most accurate because they model person-level data at every level of a media hierarchy, from the impact of specific creative elements to the channel overall. Even when coverage gaps arise that limit access to critical interaction data, an algorithmic approach will take multiple factors into account for deriving credit and still produce accurate and actionable insights. Moreover, the output of an algorithmic model can be used to predict outcomes and help marketers plan and optimize future marketing efforts, which is essential to continuous improvement.
Measuring marketing and media performance at granular levels is a strategic benefit for any marketing organization. Using granular data to understand the impact of marketing and media tactics on conversions and other desired business outcomes helps marketers make more informed decisions. The key is ensuring that hard-earned granular insights are acted on and optimizations are put into market.
Wayne St. Amand is head of marketing at at Nielsen Marketing Effectiveness.
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