Ruben Rodrigues, Manager at Enlightenment.AI
Miguel Cabrita, co-founder and Data Scientist at Enlightenment.AI
Fabrice Blanc, Head of Group Strategic Alliances at OAKland Group
Charles Darwin mentioned that “it is not the strongest of the species that survive, nor the most intelligent. It is the one that is most adaptable to change”. This quote could not be more appropriate to the current business panorama. In order to stay alive, you must do better than your competitor…
In these changing times, the adaptive capacity is the main strength to strive through your competition. But to be unique, it’s not a question of copying your competitors but rather of distinguishing yourself. This is why it is important to know their situation.
Assessing competition is historically difficult due to the scarcity and secrecy of competitors’ information. But nowadays can be even more challenging to know, evaluate and measure how your competitors are changing their companies, products, strategies and initiatives due to the pandemic changes.
Competitor analysis is crucial to validate and adapt your unique value proposition or also identify analogue markets to go forward on creating a Blue Ocean strategy.
It is about understanding how your competitors or other types of businesses develop solutions to address the same kind of problems. And also, know how they are approaching their market and having new customers.
How can data science improve the analysis of global competitors’ landscape?
The aim of Data Science is to extract from available information the valuable insights that can guide better business decisions. In this case, it is essential to know what kind of information you need to position your business.
Of course, at first, Data Science will help you better identify your competitors and similar companies, and which are the most relevant to consider. After that, you won’t need to have this in your radar as the data reports and visualizations will tell you what to do.
Data science defines for you the best possible model by analyzing material & tools; resources & organization; image & reputation; business models & market penetration; …, then creating comparisons between different companies and their ability to change.
Data scientists have the background to know the different data sources available and choose the relevant ones. She/he will also be able to use the knowledge gained to improve your business.
Also, as the data scientist bases his analysis notably in real-time, she/he will observe the new market landscape resulting from mergers and acquisitions in the post-crisis world.
How to target the relevant data?
Data Scientists combine various data sources to properly train models and analyse the competition. Of course, asking your competitors for their secrecy data is not an option.
The most common data sources are either internal data, data providers, open data, data from social networks or media, data brokers and mutualised data, … and need to be considered as a whole for having the larger scope and being as meaningful as possible. Consequently, you will be equipped to take the best decision to beat your competitors, or to create your own new market.
Finally, in the new world, what if companies would accept to share some of their data, even anonymised and secured, in a central and mutual data repository which can benefit all in their competitive analysis?
