Where statistics meet creativity
When we stop and ask ourselves what digital marketing (or web marketing) consists of, some elements unquestionably come to mind. Eye-catching banners. Search engine optimization. Blog articles. Social media platforms. Advertisements, campaigns, newsletters… And maybe, beyond all of that, algorithms. Statistics. An infinite amount of data that promises the key to success.
But how can one put that impressive data collection to use when it seems to pour in from every direction?
At Crakmedia, this is where comes into play a science that completely changes the game: statistical data analysis and machine learning.
To grow and maintain our expert title in web marketing strategy development, Crakmedia makes it its mission to analyze a surprising amount of collected data across a multitude of platforms in order to figure out what works and what doesn’t, whether we’re talking about banner creation, web pages, hooks, or sales funnels.
Demystifying data science
It sounds fascinating, but what does a data scientist actually do on a daily basis to help reach those goals?
A simple answer would be too easy. Many task categories related to data collection and analysis make up our Data and Analytics department.
The first is automation: we’re talking, in this case, about creating codes that aim to avoid having to manually import and modify hundreds of lines of data every day.
The second is data visualization through the creation of dashboards, both on a performance level for individual campaigns and on a global metric level, to allow executives to follow along.
Our data scientists are the experts that know how to decipher that famous data collected across numerous available sources.
Segmentation is also part of a data analyst’s tool belt: we’re talking here about the grouping of similar users, which allows us to get to know our customers and clients better to improve our services.
Finally, machine learning also comes into play, referring, in part, to refinement through reinforcement learning.
Machine learning system development is what allows us to build structures and processes that, in turn, will automatically optimize the selection of specific contents and allow us the chance to learn and improve continuously.
And there you go. As simple as that… or not.
But the question remains: concretely, how do those tasks apply within the digital marketing business?
Data analysis in media buy
At the heart of a rapidly-growing business, it’s crucial to provide fair, equal opportunities to all. The goal is to support newer employees in their decisions and confirm the intuition of the more experienced.
The Data and analytics team develops data analysis tools to guide and orient the decision-making process. Out of all our departments, the media buy team will benefit the most from these tools in the future and serves, in a way, as a guinea pig in this first application.
Imagine for a second that the media buy team works on an advertisement spot. In most cases, this spot will lead users to what could be over a hundred different landing pages. Media buyers then have to determine the relevance and importance that each page should be attributed according to a specific audience. They want to put the best-performing landing page forward to offer users the best option available.
How? By manually logging in over a hundred data lines in predefined fields… for each advertisement spot in their care. The goal is to determine the best-adapted landing page for their traffic. The process is necessary but undeniably long. This is where machine learning principles can come into play.
By creating automated tools that can do this work for our media buyers, we allow these resources to save a considerable amount of time (and patience!).
In other words, our data scientists simply accelerate our employees’ work by helping orient and justify their decisions. This allows our employees, for instance, to put their energy to good use on a multitude of other projects that are more relevant to both themselves and their clients.
Statistical data analysis also allows, for example, to determine trends regarding certain advertisement spots, which offers our team a stronger peace of mind or an idea of how they could adapt and readjust if need be.
Content as unique as its audience
Our communication and creation teams will also profit from analysis results from our data magicians to help develop better web marketing strategies.
Segmentation, among other things, allows us to identify our users’ behaviours, origins, and work fields, which provides us with crucial insights for copywriting and creating various content. These details give us the chance to highlight what our users have in common to adjust our message according to their real interests.
The same principle applies to our design team, which puts these analyses to good use by determining which visuals work and which fail to catch our audience’s eye. These data analysis tools provide us with the opportunity to create optimal visuals for each of our unique user groups. Fascinatingly, this is where mathematics serves artistic creation and vice-versa.
Segmentation analytics could even be helpful to our Human Resources department, which, thanks to specific data, could help us better understand what could drive an employee to leave and thus could adjust their strategy to improve employee retention. We could even aim for a more effective recruitment process by creating better-adapted messages to the current market.
The lesson is simple: relevant data is everywhere and has infinite potential, no matter the area or department.
It is still hard to quantify the possibilities that await us thanks to this data-driven investment, but one thing remains certain: we are impatient to discover them.
Do you want to join your forces to ours and help us get the absolute best out of our available data? Discover our opportunities and job offers on our career page.
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