Unmeasurable Metrics in Performance: A Marketing Strategy Example

Performance marketing excels in digitizing indicators so precisely that both SEO specialists and clients can see the effectiveness of each step. This allows for the calculation of whether advertising budgets are paying off, which campaign performed better, what needs to be corrected, and what can be improved.

However, even in such precise data analytics as financial reporting, there are special scenarios where some metrics require an unconventional approach. Let’s explore how this works and whether it’s possible to measure the unmeasurable.

Let’s first give the most complete definition to dive into the details within the context of the topic. Performance marketing is not just a direction but a comprehensive promotion strategy. In performance marketing, you can see the results and measure KPIs even after a short period of time. This approach helps achieve real business goals without wasting budgets.

To make the strategy work, there are more than 30 different advertising effectiveness metrics. The essence of each is to show the ratio of the invested budget to the profit generated by a specific action.

We noted earlier that intermediate effectiveness indicators can be calculated in performance marketing. This is important because it allows for the assessment of work results at the beginning of strategy implementation, in the middle of the promotion campaigns, and at any other stage after implementing new tools and checking their effectiveness. The more accurate data available and the faster it can be obtained without waiting for the campaign’s end, the more the necessary indicators can be improved.

Despite the accuracy of analytics and the flexibility of settings, performance marketing has indicators that cannot be precisely quantified — making it impossible to create a detailed report on the advertising’s return on business goals. However, even in this case, SEO specialists clearly understand how to act: it requires considering the specifics of the interaction of ads with platforms, search engines, and user behavior factors.

It is important to determine the so-called «stop-factors», i.e. points or stages where this interaction is hindered, reducing the effectiveness of advertising.

Touchpoints with Users: Too Many. Large brands often face the challenge of tracking customer journeys across multiple sources—contextual ads, TV ads, outdoor advertising, social media, and more. This complexity makes it difficult to calculate the true advertising cost.

Decision-Making Path: Too Long. For expensive purchases, the decision-making process involves multiple steps, such as getting to know the product, building trust, and testing the product. In such cases, it is challenging to determine which channel led to the final purchase.

Purchase Format: Happens Offline. Products like cars and real estate are rarely bought online due to their high cost. While information can be found online, the final purchase decision usually happens offline, complicating comprehensive analytics.

Number of Applications: Too Few or Clearly Insufficient. For large B2B companies, where products are expensive and sales are infrequent, web analytics can be limited. Advertising investments yield results over a longer period, making immediate web analytics less relevant.

Complex products with unique specifics require a tailored approach for each business. Over the years, we have developed numerous successful cases, allowing us to create bespoke marketing strategies. Here are some challenges we address:

While we won’t disclose detailed client information, we can explain our project logic step by step.

Stage One: Website Markup

This concerns all intermediate actions that the user takes on the website:

Stage Two: Assigning Significance to Each Goal

This stage involves determining how much each action influences a purchase. The process includes:

Stage Three: Forming the «Scoring» Indicator

The metric is formed as follows: goals are multiplied by coefficients and summed up.

S = Goal1*K1 + Goal2*K2 …

This way, we checked the hypothesis of the maximum scoring value. The more the target audience visits the site and performs specific targeted actions, the more final deals occur throughout the touchpoints journey.

Stage Four: Setting Up Dashboards

Dashboards help us monitor results, collect data, and form statistics. Adjustments are made as needed to refine the scoring model. Goals that don’t contribute to the desired outcome are excluded.

Strategy Outcome

Applying the coefficients proved effective, requiring minimal adjustments. The results confirmed our hypothesis: increased target audience engagement leads to more conversions and completed deals.

The number of actual deals was small, and the touchpoint chain included both online and offline interactions. Clear analytics in such conditions were not feasible, making direct optimization of advertising campaigns for deals challenging and potentially inaccurate. However, by considering all factors, we developed a robust working method that appropriately leverages these metrics.