blog21 | 07 | 2025

Analytical strategies in the face of uncertainty: how to plan when everything changes

Author

Federico Kalos

Index

In marketing, we work with two types of variables: those we can control and those we can't. However, the growing number of uncontrollable variables forces us to review how we design our analytics strategies. In this article, we explore a methodological framework that allows marketers to determine which tools are available to tailor their strategies to their analytics needs.

IN THIS ARTICLE

1. Planning is in crisis (and it's not your fault)

In recent years, marketing planning has become a leap of faith. We project based on "normal" scenarios, and a few months later, reality changes. Inflation. New regulations. Changes in algorithms. Wars. Pandemics. Artificial intelligence. What seemed certain in January becomes uncertain in March.

This isn't an exaggeration. It's a sign of the times. And if you're dedicated to marketing analysis or planning, you know this better than anyone. The question is no longer whether there will be disruption. The question is how prepared we are to react or anticipate when it happens.

2. Uncertainty: What we can control and what we can't

In marketing, we always work with two types of variables:

Those we can control: budget, calendar, segmentation, channels, creatives.
Those we can't control: inflation, social trends, politics, weather, algorithmic decisions by platforms.

The difficulty lies in that the latter strongly affect the former. A well-targeted campaign can fail if purchasing behavior changes. Increased costs can arise from factors we didn't even know how to measure.

This is the heart of uncertainty: the growing weight of uncontrollable variables over which we do have control. And this forces us to review how we design our analysis strategies.

3. Strategy #1: React Quickly

A possible first response is to optimize our reaction capacity. When the environment changes, we must move quickly. This strategy prioritizes three things:

Speed of analysis
Operational agility
Minimizing costs or losses

This is the option many teams choose in contexts of crisis or extreme uncertainty: cut expenses, adjust investments, save the quarter. Key capabilities for designing a rapid reaction strategy:

Real-time operational dashboards
Report automation
Campaign performance alerts
Artificial intelligence for quick insights
Agile teams with direct access to information

This strategy is especially useful when there is little room for error and short-term operational decisions need to be made.

4. Strategy #2: Find certainty

The second strategy is to build more robust and predictable scenarios, even if it takes longer. Here, the focus is on:

Reducing risk
Increasing the quality of insights
Making better strategic decisions

This is the strategy for those who can't afford superficial answers. CEOs, CFOs, growth and planning teams who need to justify every investment and anticipate the impact of their decisions. To find certainty, the key capabilities and tools are:

Advanced attribution models
Marketing Mix Modeling (MMM)
Predictive behavioral or sales models
Simulations and "what if" scenarios
Prescriptive analytics with AI

This isn't a strategy for moving quickly. But it is a strategy for regaining control when there are too many variables at play.

5. One or the other? Better both.

These strategies aren't mutually exclusive. They're two lenses for looking at the same problem. The important thing is to know when to use each one, and to have the skills to do so.

A good way to evaluate this is to cross-reference two axes:

Time available to decide
Level of certainty required

Do this with your team and ask: Which of these strategies did we prioritize last year? Did we respond more from urgency or from anticipation? Where are our strengths and weaknesses today?

6. A 3-Step Framework for Structuring Your Analytics Strategy

Beyond choosing a strategy, it's key to have a framework that organizes the conversation.

Step 1: Input Diagnosis

Identify the 3Vs of your data: What volume do you handle? How quickly do you need to respond? What variety of sources are you using?

Step 2: Output Diagnosis

Review the maturity of the insights you're generating: Are you only reporting KPIs? Are you generating recommendations? Are you predicting scenarios?

Step 3: Bridge Diagnosis

Evaluate your analytics stack: Do your tools allow you to answer key questions? Can your team access insights without friction? Do you have the necessary capabilities, or are there gaps?

For example, at Bunker, we apply this methodology to analyze which products we should develop based on our customers' needs. To do this, we create a simple two-way table. In the rows, we list our products. In the columns, we list the features our customers highlight for each product. These results can be analyzed with a simple radar chart:

This framework is simple but powerful. It allows you to talk with your team beyond tools and talk about capabilities.

7. Conclusion: Measuring doesn't eliminate uncertainty, but it helps navigate it.

Uncertainty isn't going to disappear. No dashboard can eliminate a crisis. But having a strategic approach to analysis can make the difference between improvising and leading.

It's not about predicting the future. It's about preparing for different possible futures. And for that, you need an analytical strategy that combines reaction and anticipation, speed and depth, efficiency and vision.

That's not a luxury. It's a condition for remaining relevant.