Influencing Influencers

30 Influencers were generating value. No one knew which ones were the most valuable.

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Customer Challenge

As part of an influencer marketing team, a marketing firm was managing nearly 40 influencers across several platforms. Each relationship, performer, and audience was unique. At the highest level, the marketing firm could identify their top and bottom performers, but the middle tier was chaotic. Some influencers had small contracts with a low cost per impression, while others had large contracts with high numbers but only average cost per impression. Additionally, other qualitative factors needed to be captured, such as the ease of working with the influencer, their audience's association with the brand, and the end-customer's perception of the influencer.

The marketing team introduced a framework for scoring the influencers, but it required refinement. There were numerous smaller concepts with inflated value and some significant issues that were underrepresented. The key areas of Partnership, Program, Performance, and Perception needed better alignment with their respective concepts.

The customer needed a way to assess their influencer team without relying solely on a mathematical performance framework.

Solution

In this case, the business brought a solution that needed refinement to be operationalized. Starting with the business solution, I molded it into a more traditional benchmarking solution.

1. Analyze the Business Solution: The initial solution had many good ideas and perspectives. The goal was to highlight the most powerful parts and minimize weaknesses.
2. Determine the Right Mix of Quantitative and Qualitative Inputs: The original solution weighted Performance-related KPIs at 40%. Other quantitative elements were scattered throughout the framework. Consolidating all data work into a new Performance bucket clarified the concept and delineated the responsibilities between data and business management. Two other categories from the original solution were refined as Partnership and Perception.
3. Identify the KPIs and the Weights: Data-driven performance accounted for 60% of the total score. Four metrics were developed: Impressions to Target, Engagement Rate to Target, Cost per Impression, and the number of non-sponsored posts including the product suite. Cost per Impression was allotted 20 points, the “to-Target” metrics 15 points each, and Non-sponsored posts 10 points.
4. Collect the Data: Like every data project, identifying the right sources and refining raw data into useful KPIs was crucial, particularly for cost per impression, which required contractual data.
5. Assemble the Piece Parts: The solution was created in Excel, prioritizing functionality over aesthetics to deliver the product to the customer.
6. Automate: Automation was scheduled for completion within a couple of months.

Major Insight

Typically, solutions are assembled using a set of predefined rules. In this case, each concept was assigned a specific number of points to total 100. However, when using a business-weighted form of benchmarking, some forms of scoring cannot be reasonably captured within a 100-point scale.

During the assessment, the Perception category had three points allocated for “Authentic Product Usage Outside of Deliverables.” Influencers, bound by exclusivity clauses, could not use competitors' products. A three-point deduction did not adequately address the Perception quality. Instead, I recommended a 30-point deduction for breach of contract. This penalty wouldn't be fatal but required the influencer to excel in all other areas to remain valuable.

Results & Value Generated

The business-weighted benchmark provided the marketing team with a tool to analyze each influencer's effectiveness. It helped influencers understand their performance and how to improve, and allowed the client to make data-driven decisions about contract renewals. The outcome included a four-tiered status analysis: Renew, Renew at a Lower Rate, Observe, and End the Relationship.

The tangible value was evident, with the cost per impression dropping by more than half between Q1 and Q2 across the team. The deeper analysis motivated influencers, knowing their overall performance was closely monitored.

Reflection

Having managed five analytics benchmarking projects, I recognize the importance of senior stakeholder engagement. One project failed due to a lack of interaction with the senior stakeholder, resulting in a lack of trust. Benchmarking projects hinge on trust: trust in the data, the KPIs, and the data team. This influencer benchmarking project succeeded because it had all three elements. The data made sense, the KPIs aligned with client goals, and the data team understood the business perspective. In this case, the business was paramount in making sure we had all the contextual information necessary.  It made the project so much better.

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