Mapfry is bringing Geomarketing closer to people and does this through its fast, simple and accessible platform
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We are not alone in this market, with a reasonable number of competitors at different evolutionary stages.
To simplify, let's talk about the Top 3
Before talking about differentiating between them, it is worth addressing the common points.
This refers to the answer that Jeff Bezos, founder of Amazon, gave when asked what was going to change in the retail of the future.
To everyone's surprise, he highlighted the importance of focusing on what doesn't change, that is, Amazon innovates technologies to meet standards that never age.
And what are those patterns when we talk about Geomarketing?
This ancient technique reached its methodological maturity between the years 1930 and 1970, with the actions of the authors:
- 1931 William J. Reilly developed the law on the attractiveness of commercial areas. This theory is one of the first to explain how consumers choose between two competing malls based on distance and power of attraction.
- 1933 Walter Christaller published the “Theory of the Central Place”, a fundamental piece for understanding the spatial organization of cities, with implications for the planning of commercial sites and the distribution of services.
- 1950 to 1960 William Applebaum enhances market research methods for retail and distribution planning.
- 1964 David L. Huff, presented the “Huff Model”, a model used to calculate the attraction probabilities that different shopping centers exert on consumers, considering variables such as distance, size, and type of offer.
- 1960 to 1970 Richard R. Nelson publishes his theories about how the economy is affected by geography.
- 1970 Rodney Davies begins to explore the application of geographical analysis in marketing strategies.
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Have you seen how Geomarketing doesn't depend on computers to happen?
Its basic elements have always been:
- dice
- Maps
- Professionals able to integrate Maps and Data
That hasn't changed, nor will it change
Since that time, the data came from official databases such as the Census, but compiled in larger areas, such as neighborhoods or divisions of a large municipality.
If the municipality were not very large, its entire area would be used in the analyses.
Geomarketing Geo has to do with spaces and the neighborhood between them, so this information was applied to paper maps, in order to help identify attractive and independent relationships between places.
Those who made all this happen were the professionals who specialized in combining Maps and Data and then analyzing them.
An immense and time-consuming job, but which always had its value recognized, since such intelligence represented enormous advantages.
Comparison criteria
The best way to analyze the competitive Geomarketing scenario, in which platforms compete for those with the most functionalities, is to compare them based on fundamental principles.
Data, Data, and More Data
The heart of any Geomarketing platform is its database.
Both in Brazil and in the world, the best data come from official sources, especially the Censuses, which are extensive and detailed surveys of the entire territory.
This basic ingredient can be supplemented in two ways:
- With more data from other sources, official or private, that enrich the contexts and/or allow a more up-to-date view of the phenomena.
- Through statistical and demographic techniques that update official databases.
In addition to information about the territory, we also work with reference points, important addresses that are central to people's lives, such as schools, hospitals, shopping and work centers.
Maps for what I want you
You open your cell phone and there you have the Google map, the Uber map, the Waze map, the iFood map and others, giving you the impression that digital maps are ordinary things.
If maps are so common, why don't Big Techs like Apple, Meta/Facebook, Microsoft, and Amazon have theirs?
And look what they try, Apple already had to Reverse the launch of Apple Maps, to Microsoft kinda tried to sell Bing Maps, but nobody wanted to buy, and Meta/Facebook didn't even get into that trap.
Let's be clear, digital maps are very complex, especially because of the dynamics, streets change direction, new neighborhoods are launched, a roundabout is replaced by a traffic light, keeping everything up to date is a lot of work.
The main providers of Maps are:
Here, former Nokia, Who sold his cell phone division to Microsoft to focus on maps.
Tomtom, always remembered because of GPS devices and, well for this reason, has access to super updated information.
Google, which today is dominant, but built its relevance based on the collaboration of its users, who update information and correct errors.
Uber, few people know, but the transportation app has its own digital map base, the result of acquisition of the startup DeCarta in 2015, precisely so as not to depend on or feed third-party maps.
OpenStreetMap, an international consortium that tries to break the monopoly of large companies, using collaboration in an open architecture.
Less is more
Combine Data and Maps and you have a visualization challenge.
Imagine someone displaying all the information from the database at once on the map?
Aside from overwhelming computer memory, no one will be able to understand anything other than a blur.
Then the less relevant information will be removed and we can begin to interpret something.
The true ability to visualize is to seek the essentials of analysis and nothing more.
Even today, we are faced with maps saturated with information, which seem to say many things, but only confuse, even causing nausea.
The basics of displaying information include:
- View data in areas
- Display data in dots
Points is easier, as simple as a pin on the map, just let us know that in such a place there is a mall and stop!
When we talk about areas, the question comes up, which area to use?
We can make maps of municipalities, but those are too large for objective intelligence.
Perhaps we can go down to the neighborhood level, but neighborhoods are not official areas and, when they exist, they can vary greatly in size and are not very comparable.
The most prestigious area for Geomarketing analysis has always been the Census Sector, an IBGE design comprising approximately 400 households, comparable in nature but visually stressful.
Until, in 2018, Uber made its base of hexagonal areas, called H3, available for general use.
This feature has been growing in adoption by the most modern platforms because it is absolutely comparable, internationally compatible, and optimized for processing.
And Uber didn't stop there, it even made its powerful data visualization platform available to Kepler.gl, completely changing the solution landscape.
Created by technicians for technicians, it is complex and full of options, which limited its adoption by the general public.
Who makes it happen
There are so many details linked to data and maps that people can end up in the background.
Companies are thinking about how Big Data and Data Lakes can transform data into actionable information, executives plan to hire heavy solutions, and professionals are envious of having access to these resources.
It turns out that, at the end of the day, it's the people who make it happen, as they did between the 1930s and the 1970s.
When, starting in the 1970s, the use of computers intensified in companies, all evolutionary expectations began to come from them.
From a poor display on a black and white screen, each evolution brought more storage for data, more memory to request them, video cards for display on maps, processors performing queries.
Without a doubt, between 1970 and 2010, we moved away from paper maps and fully embarked on the digital age.
From then on, the story stopped, to the point where proprietary technologies such as H3 and Kepler were available for free to anyone who wanted to use it, but there was no one who knew how to articulate these and other resources.
As the computer advanced, people's dominance stagnated or even regressed, as they were relegated to mere observers and operators of the technology.
For decades, Geomarketing advanced leaving people aside, it was clear that the path of evolution would have to go through the rescue of this powerful resource.
Let's compare the Top 3 Geomarketing platforms on 10 criteria
Comments
- Data: aspects in which the platforms are more comparable, with conceptual and stylistic differences affecting the accuracy of the projections
- Projections: an analysis is only as good as the database on which it is based, small errors here can lead to large decision errors
- Integrations: varying in size of the partners, both Mapfry and Geofusion access valuable information from other companies
- Maps: Economapas and Geofusion license high-precision maps, Mapfry is supported by an open consortium
- Visualization: A highlight is Mapfry, which has been able to integrate the most modern technologies for the benefit of interpreting data in maps
- Manipulation: quite equivalent, even though there are different resources here and there
- Consultation: only the Geofusion advanced query allows multi-criteria conditions
- Reports: allowing the export of data helps the customer to create their own versions, models facilitate quick comparisons
- Support: Geofusion and Economapas in standard formats, while Mapfry preferred to offer its highest level of service as a separate consultancy
- Learning: The aspect in which Mapry stands out the most, due to its focus on people, offers a wide range of training, associated with a rich repository of knowledge
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It is remarkable the amount of advanced features that Mapfry offers in a short time, many of them available in the initial plan, which is free.
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It is worth noting the large market presence that the Economapas platform has achieved.
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Even losing space, Geofusion is still the benchmark, with well-implemented complex features and highly qualified analysts in its user base.
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Authors' note
This is a comparative material developed by the Mapfry team, based on our perspectives and with probable inference errors regarding the competitors.
The market is dynamic and may have changed or we may even lack knowledge.
Even so, we chose to publish this article to facilitate your decision-making process and to present our differentials in a transparent way, as we already do with our pricing policy and our knowledge base.
We hope to have helped, if there are any serious errors, we ask that you inform us so that we can correct it:
hello@mapfry.com