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Response Attribution: A New Lens for Measuring Success

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Response Attribution: A New Lens for Measuring Success
Response Attribution: A New Lens for Measuring Success

Advertising has reached an interesting juncture in recent years. It's no longer just emails or commercials. Ads appear on your smartphone while you're checking last night's MLB scores. They're there when you're listening to your favorite music on Pandora. And, they pop up on Facebook while you're scrolling through your feed to catch up with friends. Today's advertising is smart, efficient—and has a way of integrating (or perhaps, ingratiating) itself into everything you do.

But this dynamic has left many marketers scratching their collective heads, faced as they are with the real challenge of effectively measuring and attributing revenue to their various advertising and marketing efforts. In fact, the most commonly asked question I hear is this: With the myriad channels and influencers out there, how do I know which are most effective at driving revenue?

As advertising grows and evolves so does the role and importance of measurement. Response attribution (or better yet, revenue attribution) is the most important tool you can have for evaluating the complex paths your customers follow before they make a purchase.

Sorting out all the influencers when it comes to a purchase has become more difficult. There's a strong focus on understanding the customers' journeys, determining the impact of different channels on a sale, and measuring the effectiveness of marketing efforts. This in turn leads to more effective allocation of future marketing resources, efforts, budget, and an improved marketing mix.

In the past, simple models were used to give credit to specific channels. The last touch model attributed all the credit to the last channel that was used or touched before a purchase was made. Some models were based on giving a percentage of the credit to all the channels that were used along the way. Fractional allocation models were based on the amount of time a channel was used over a certain time period.

While these common, traditional models have the benefit of simplicity, they can often be inaccurate, biased, and subjective. The buying journey is highly complex and distinct. Customers follow unique paths and their historical and behavioral data must be viewed and analyzed from this perspective.

The inclusion of all data across all channels and touchpoints is critical. It's important to evaluate not just marketing channels, but all the other interactions that occurred along the way. And you can't focus solely on traditional offline channels. You also must consider the digital footprint of your customers, which could come from search, social media, email, or their online browsing history.

On top of all that, you also have to account for subjectivity. The right response attribution models, although complex, are non-biased and highly effective at estimating relationships between customers and various channels. Intelligent in their approach, these models calculate the probability that a purchase was made during the use of a specific channel. This information is then used as an influence score for each touchpoint to accurately allocate the correct percentage of a sale to a channel based on its true influence in driving that sale.

Once aggregated and analyzed, the data can be viewed over any desired time period—daily, weekly, monthly, quarterly, yearly, or over a specific window such as a holiday season or weekend. This granularity enables marketers to more accurately pinpoint the channels and even campaigns that are most influential during a specific time period. Marketers can also drill down on individual purchases to understand the dynamic of a specific sale.

Measuring and allocating marketing efforts has become more challenging, but it has also become an opportunity to create a deeper understanding of customers and buying habits. By taking response attribution away from heuristic, rules-based models and moving it to a data-driven, scientific approach, you can greatly improve your accuracy and derive deeper, more valuable insights. From there you can make more informed decisions about budgets and strategies because you'll clearly see where your focus should be and how to allocate your advertising dollars to make the most impact.




Kevin Pedde is ‎lead advisory consultant at Quaero.

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