Driving Influencer Marketing ROI Through Martech Attribution Models

Influencer marketing ROI goes beyond simple metrics like the number of likes or comments on a post. To assess the true ROI of influencer marketing, businesses need a sophisticated and data-driven approach.

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  • In today’s competitive landscape, influencer marketing has emerged as a powerful strategy for businesses and is growing rapidly, with both businesses doubling down on their IM spends. However, measuring the return on investment (ROI) of influencer marketing campaigns has remained a challenge. 

    This is where martech attribution models come into play. By leveraging them, businesses can gain valuable insights into the impact of their influencer marketing efforts, helping them make data-driven decisions and maximise their ROI.

    A lot has to do with some basic first principles of the audiences of influencers.

    Is influencer marketing ROI different from regular ROI?

    Before delving into the details of Martech attribution models, it would be wise to understand the concept of influencer marketing ROI. Traditionally, calculating ROI has been relatively straightforward for conventional marketing efforts, such as advertising on television or print media. However, influencer marketing operates in a different realm, where the impact is often indirect and challenging to quantify accurately.

    It goes beyond simple metrics like the number of likes or comments on a post. It involves understanding how influencer-generated content influences consumer behaviour throughout the buyer’s journey, leading to conversions and ultimately revenue for the brand. To assess the true ROI of influencer marketing, businesses need a sophisticated and data-driven approach.

    The role of martech attribution models

    It’s all about conversion and being able to assign which touchpoint attributed to what definition of conversion, thus helping a business understand which marketing efforts contribute most significantly. These models rely on data from multiple sources, such as website analytics, social media insights, and customer relationship management (CRM) data, to provide a holistic view of the customer journey.

    There are various types of the age-old affiliate or any martech attribution model, each offering a different approach into the performance of marketing channels and some of them ignore that attribution is a journey. Some common models include:

    1. First-Touch Attribution: This model attributes the entire credit for a conversion to the first touchpoint that the customer interacted with. In the context of influencer marketing, it would credit the initial exposure to the influencer’s content as the primary driver of the conversion. However, tracking an Influencer’s content to real attribution has been a challenge unlike paid media, as a purely organic ‘influence’ on a consumer cannot be tracked per se. There are secondary methods towards that which involve ‘link in bio’, ‘swipe up stories’, coupon codes etc but that still leaves a ton of attribution out of the picture.

    2. Last-Touch Attribution: In contrast to the first-touch model, last-touch attribution assigns all credit to the final interaction before the conversion. This model would credit the influencer’s content if it was the last point of contact with the customer before the purchase.

    3. Linear Attribution: This model evenly distributes credit across all touchpoints that the customer encountered during their journey. For influencer marketing, this could mean that the influencer’s content receives equal credit along with other marketing efforts.

    4. Time Decay Attribution: This model gives more credit to touchpoints that occurred closer in time to the conversion. If the influencer’s content was more recent, it would receive a higher attribution value under this model.

    5. U-Shaped Attribution: Also known as position-based attribution, this model assigns greater credit to the first and last touchpoints while distributing the remaining credit among the intermediate touchpoints. The influencer’s content might receive significant credit under this model if it played a role in both introducing the customer to the brand and closing the sale.


    Choosing the Right Attribution Model for Influencer Marketing

    For influencer marketing campaigns, the choice of attribution model is crucial to gain accurate insights into the true impact of these efforts. Since influencer marketing operates across multiple touchpoints throughout the buyer’s journey, the last-touch and first-touch models may not provide a comprehensive picture of its effectiveness. And as all models go, it fails to recognise that awareness drives consideration, which drives adoption.

    The linear and U-shaped attribution models are often more suitable for influencer marketing, as they consider multiple interactions and give credit to various touchpoints that contributed to the final conversion. These models help identify the various stages of the customer journey where influencers played a role, enabling businesses to optimise their influencer partnerships and tailor content accordingly.

    Implementing martech attribution for influencer marketing

    To implement Martech attribution effectively for influencer marketing, businesses must follow several key steps:

    1. Data Integration: Integrate data from different sources, including influencer marketing platforms, website analytics, social media, and CRM systems. This unified data is the foundation for building a comprehensive view of the customer journey. Unfortunately, an organic post by an Influencer does have integration issues apart from the basic UTMs/Codes/ ‘Un-Clickability on Instagram’ which are only a sliver of an Influencer’s touchpoints.

    2. Define Goals and KPIs: Stating the obvious, Clearly outline the objectives of your influencer marketing campaigns and establish key performance indicators (KPIs) aligned with your overall business goals. Use Influencers for driving credibility more than Sales initially, while trying to track sales or related outcomes- Influencers are Not uniform inventory units.

    3. Select the Right Attribution Model: As discussed earlier, choose the most suitable attribution model for your influencer marketing efforts. Consider the complexities of your customer journey and the level of contribution that influencers have at various stages. 

    4. Test and Optimise: Continuously test and optimise your attribution models to ensure they provide the most accurate insights. This iterative approach will lead to better decision-making and improved ROI over time.

    5. Use Insights to Drive Strategy: Finally, use the data-driven insights from your Martech attribution models to inform your influencer marketing strategy. Identify top-performing influencers, refine content approaches, and allocate resources effectively to maximise ROI.

    Influencer marketing has become an integral part of modern marketing strategies, but measuring its ROI has remained a challenge. To overcome this, businesses must embrace martech attribution models, which offer a data-driven approach to understanding the impact of influencer marketing efforts throughout the customer journey. By leveraging advanced attribution models, businesses can optimise their influencer partnerships, create more impactful content, and ultimately drive higher ROI from their influencer marketing campaigns. As the marketing landscape continues to evolve, incorporating martech attribution will be crucial for businesses seeking to stay ahead of the curve and make informed decisions to drive success.

    Remember, there is as much art to the science of influencer marketing and it’s a multivariate problem with quite a few unknowns around each social media platform and algorithms. The supreme caveat is always ‘find your own path’ and there is no ‘one size fits all’ or even in a category, so no shortcuts, not yet. 


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