Put Your Money Where Your Consumer Is: How Audience-Based Targeting Drives Higher ROI
Did you know that 25% of marketing tactics have no impact?* They don’t generate a lead, a site visit, a sale, or motivate your customers in any way. This means that you could be wasting up to a quarter of your marketing spend—draining your budget without getting your money’s worth.
It’s no secret that marketing organizations are under pressure. You need to stay ahead of the competition and meet or beat your goals on a daily, weekly, monthly and quarterly basis. But it’s a Catch-22 situation. If you don’t know the difference between what’s driving results and what’s not, you don’t know what to cut.
To improve your results, you need to know—specifically—what influences your customers to take action. Which combination of ads, in-store experience, social media, reviews, email, and other tactics motivate a buyer to visit your site, request a quote or plunk down their credit card.
When you can see the tactics that produce results, you can spend your budget more wisely. Trimming low-performing tactics and reallocating those funds to things that work. The only way to unpack this tangled path-to-purchase is by looking at person- or household-level data in the most granular way possible.
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. Data at its most granular level gives you the most accurate and actionable insights.
There are six significant benefits of granular data:
Marketers need to evaluate the performance of individual channels as well as dimensions within channels, such as publisher, campaign, creative, keyword and placement. Granular intra-channel performance data allows you to determine how channels and touchpoints work together to produce outcomes and where to shift dollars within a channel to maximize results.
When marketers use aggregate, market-level data to analyze performance, they assume that all individuals in the market behave the same way. This approach introduces noise and can result in serious inaccuracies and misunderstandings. Granular data reveals patterns without bias.
Digital marketers need rapid, granular reporting to plan and execute campaigns. Daily decision-making requires person-level data that shows variance—actionable signals. A channel manager won’t find actionable variance using aggregate data at the geographic level.
Many modeling techniques allow you to develop “what-if” scenarios to estimate the impact of changes on long-term outcomes. Channel managers and analysts who need to find the highest-performing media tactics based on their budget and performance goals will get much more accurate predictions when their analysis is based on the most granular data.
When granular data is used in an attribution model, it can reveal 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.
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.
To take advantage of the possibilities of granular data, you must use the right measurement approach. Marketers should choose the model that matches the cadence and granularity they need.
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.
Multi-touch attribution uses user-level data from across media channels to assign credit to the marketing touchpoints and dimensions according to their influence on driving a conversion (or any other key performance indicator).
Algorithmic attribution uses machine-learning to calculate and assign credit for a given success metric to marketing touchpoints and dimensions (campaign, placement, publisher, creative, offer) along the consumer journey, as well as to predict the outcome of future spending.
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.
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.
* We analyzed 109 anonymized client data sets made up of $2.8 billion in media spend and over 256 billion impressions over a 6-month period (from January 1 to June 30, 2018).
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