More than the tool, what matters is the decision process
Success stories filled with incredible numbers and laudatory testimonials are excellent sales material.
But what happens, when some time later, That very one Case Shipwrecked?
What is the fate of the argument that stated “our intelligence models point to the best locations”?
O Case It is archived and disaster specialists appear with explanations of why it went wrong:
- The potential estimates that did not consider the region's commercial saturation, imagining that the mere existence of a store would generate sales
- The idea of convenience was pushed to the limit, with the same customer reaching 3-4 stores in 15 minutes on foot
- Competition with new formats, such as autonomous markets, something that was on the radar when the business plan was put together
- The impersonality of the brand and the resilience of local businesses, led by families with an intense relationship with the place
All opinions are valid, but the reality is that businesses have risks, the higher the speed of growth, the greater the chances of something going wrong.
The problem was never in the plan itself, but in the decision process that delivered key factors to an algorithm.
The obsession with model accuracy creates blind spots
In a scenario of increasing adoption of prediction models generated by Artificial Intelligence, companies are discovering that the projected results do not correspond to reality.
Professionals are learning that success doesn't depend on perfect models, but rather on a broad and actionable understanding.
Inside these models, unverifiable parameters run, so it's impossible to track how the projections are made.
In the end, no one can effectively take responsibility for the numbers.
Nor should it, it is enough to recognize the limitations of the models and incorporate them into a part of the decision-making process, without making the answers generated by them their own decisions.
All models are wrong, but some are useful - George P. Box
You must build your decision process
As technologies such as Big Data, Data Science, and AI advance, so does the expectation that everything can be measured.
What can't be measured, can't be managed - William Edwards Deming
This famous phrase has a critical flaw, the excessive emphasis on what can be measured.
This focus on measurement often leads to a distortion of reality, in which, the greater the measurement accuracy, the more companies optimize for the metric, and not for the underlying objective.
Maybe now it's easier to understand why so many companies are migrating to Mapfry, They arrive upon search for intuitive insights with strategic simplicity.
Everyone knows of a network that expanded based on heat maps, but after several unsuccessful stores, it became clear that these metrics ignored a critical factor: customer satisfaction.
Or the expansion of franchises based on automatic models that ignore cultural nuances, leading to misaligned locations, even with “perfect numbers”.
In contrast, networks that adopt a simpler and more holistic approach are able to observe broad, easily explainable patterns, leading to a better decision-making process.
Going Beyond Metrics
Mapfry offers a qualitative advantage over any other platform, we are the one that values interpretation and human judgment the most.
Here's what you get when you migrate
Simplicity generates clarity
Mapfry maps eliminate unnecessary complexity, helping teams focus on what matters: insights that lead to action, and not just data points.
Discovering the Invisible
Unlike any prediction model, human intelligence has its way of perceiving patterns, and we are able to fill in empty spaces in information and incomplete images.
This gives us the unique power to distinguish the target point from its background.
Flexibility
Models impose strict analysis structures, limiting their teams from adapting insights to real-world challenges.
Decision processes may be driven by models, but never led by them.
Decided to change?
Go to Mapfry migration manual
