Inspiring companies to explore new challenges through the power of data. This is the motto of Intelligent Algorithms and it guides its activity in Data Science and Artificial Intelligence.
Until very recently, innovative solutions came from bright minds and teamwork. These were usually the ingredients of success for facing challenges and finding solutions. But the reality is increasingly digital and one of companies’ main innovation performance metrics is the ability to work with data and extract value from them. It is based on this capacity of companies to explore data and seize new opportunities that Intelligent Algorithms works daily. In fact, exploring data is a mindset that no company or organization should ignore.
But how can new opportunities be explored? How can we innovate based on Data Science or Artificial Intelligence? Here are three examples:
1) Data Science and Artificial Intelligence help in the direct development of new products or improved versions of already existing products.
Through Artificial Intelligence it is possible to simulate millions of product configurations in order to obtain the most optimized version possible. Siemens uses this procedure to create safer warning systems at railroad crossings in order to avoid train collisions.
Take the case of electric vehicles. One of the biggest difficulties in establishing themselves in the market was battery life and longevity. If things are currently better, it is because Artificial Intelligence and Data Science have played an important role in optimizing the use of batteries, extending their lifespan, reducing energy consumption or even providing real-time advice on how to drive more economically. In the same line of thought, it is possible, through Artificial Intelligence, to track the movements of electric vehicles so that electricity supply stations are surgically placed in places where they can be used the most.
Through the data, it is also possible to establish the best product line extensions or, if applicable, to create new lines from scratch, making life easier for managers.
Through Data Science and Artificial Intelligence, we can analyze complex management systems (which may include suppliers, customers, seasonality, logistic factors, etc…) in order to reduce the time to launch new products on the market.
Back to the automotive market, some brands are developing Artificial Intelligence systems that will allow, in the near future, to create the design of new vehicles, from scratch. This is something that should not come as a surprise since Artificial Intelligence is already able to create works of art, music or literature.
2) Data Science and Artificial Intelligence help detect new trends and demands.
Fashion is where the detection of new trends is at its height. There are companies that monitor billions of Social Media posts every year through Computer Vision to determine which shapes, materials, colors or textures are most popular in terms of clothing. Based on this data, they can detect trends about what will be fashionable a year from now, with considerable accuracy. Did you know that, for example, there are companies with video recordings of catwalk presentations over the last 20 years? The goal is the same: to analyze in order to be able to make predictions over the years.

At the retail level, Data Science and Artificial Intelligence also help explore new challenges with greater confidence. How many times have you asked yourself the following question: “How can I forecast the demand for product X for the next 6 months with reasonable accuracy? With the right conditions, it is POSSIBLE to answer this question. Current forecasting methods use quality data to reach very accurate conclusions. They can use company sales data, inventories, loyalty card data, CRM, Social Media or others, and unveil patterns that indicate demand.
3) Data Science and Artificial Intelligence help identify new sales opportunities in customer databases.
Through clustering and segmentation techniques, it is possible, for example, to group the customers of a specific company based on the items they buy, when they do so, which channels they use (store, online, catalogue, subscriptions…), how they buy or how much they spend. In this way, it is possible to identify broad groups willing to spend money on new combinations of products that are better adapted to their consumption behavior.
Clustering techniques also make it possible to adapt the offer variety taking into account the sales channels to improve the customer experience and boost sales. It is possible to detect a misalignment between what is offered in a sales channel and the consumption habits of those who use it. The smaller the misalignment, the greater the gains.
Inspiring companies to explore new challenges through the power of data