Advanced Attribution

The what and how of Attribution in Digital marketing

This article is a contribution from Pranav Thapak, Management Trainee at HDFC Life 

Today’s marketers rely on multi-channel strategies to carry out marketing campaigns, both online and offline. While this practice enables marketers to customize the customer journey along every step, it also presents unique challenges once it’s time to analyze the overall impact of a particular campaign on marketing ROI.

What is Marketing Attribution?

The Short Definition: Put simply, marketing attribution is the analytical science of determining which marketing tactics are contributing to sales or conversions.

The Long Definition: Marketing attribution is the practice of evaluating the marketing touchpoints a consumer encounters on their path to purchase. The goal of attribution is to determine which channels and messages had the greatest impact on the decision to convert or take the desired next step. There are several popular attribution models used by marketers today, such as multi-touch attribution, lift studies, time decay, and more. The insights provided by these models into how, where, and when a consumer interacts with brand messages allow marketing teams to alter and customize campaigns to meet the specific desires of individual consumers, thus improving marketing ROI.

Why is Marketing Attribution Important?

Advanced marketing attribution programs require marketing teams to aggregate and normalize consumer data from across channels to ensure each interaction is properly weighted. For example, if a consumer is exposed to a display ad and an email campaign, but only converts after seeing a special promotion in the email, marketers can note that this piece of collateral played a bigger role in driving the sale than the display ad. They can then devote more resources to creating targeted email campaigns.

To achieve the level of data granularity required for effective attribution, marketing teams need advanced analytics platforms that can accurately and efficiently distill big data into person-level insights that can be used for in-campaign optimizations.

Benefits of Marketing Attribution

Advanced attribution models can be time and resource-intensive to get right, especially complex models that evaluate a variety of datasets for online and offline campaigns. However, when done effectively, attribution brings a myriad of benefits including:

Optimized Marketing Spend

Attribution models give marketers insights into how marketing dollars are best spent by showing touchpoints that earn the most engagements. This allows marketing teams to adjust the budget and media spend accordingly.

Increased ROI

Effective attribution enables marketers to reach the right consumer, at the right time, with the right message – leading to increased conversions and higher marketing ROI.

Improved Personalization

Marketers can use attribution data to understand the messaging and channels preferred by individual customers for more effective targeting throughout the customer journey.

Improve Product Development

Person-level attribution allows marketers to better understand the needs of their consumers. These insights can then be referenced when making updates to the product to target the functionality consumers want.

Optimized Creative

Attribution models that can evaluate the creative elements of a campaign allow marketers to hone messaging and visual elements in addition to better understanding how and when to communicate with users.

Common Marketing Attribution Challenges and Mistakes

While marketing attribution can offer many benefits, there are a host of common mistakes that can result in misattribution, obscuring the success of campaigns for marketers.

To ensure they are getting the most accurate data that reflects their users’ customer journey, marketers should avoid:

Correlation-Based Bias

Attribution models can be subject to correlation-based biases when analyzing the customer journey, causing it to look like one event causes another, when it may not have.

In-Market Bias

This refers to consumers who may have been in the market to buy the product and would have purchased it whether they had seen the ad or not. However, the ad gets the attribution for converting this user.

Cheap Inventory Bias

This gives an inaccurate view of how media is performing, making lower-cost media appear to perform better due to the natural conversion rate for the targeted consumers, when the ads may not have played a role.

Each of the biases threatens to have marketers make optimizations in favor the less effective messaging, causing immense damage to ROI.

Digital Signal Bias

This occurs when attribution models do not factor in the relationship between online activity and offline sales. For marketers who make sales both online and offline, they must make optimization decisions based on both online and offline data, not only what they can trace digitally.

Brand & Behavior

Attribution models can often overlook the relationship between brand perception and consumer behavior, or will only look at them at a trend regression level.

Marketers must ensure their attribution models are able to detect relationships between brand-building initiatives and conversions. Not understanding how their attribution model measures branding impact is a common and detrimental mistake, leading marketers to make decisions based on incomplete recommendations that devalue brand building.

Missing Message Signal

Creative and messaging are just as important to consumers as the medium on which they see your ad. One common attribution mistake is evaluating creativity in aggregate and determining that one message is ineffective when in reality it would be effective for a smaller, more targeted audience. This emphasizes the importance of person-level analytics.

Pranav Thapak – You can read the full article also here.

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