Last week we asked the data scientists in our Turin office to explain, in their own words, what Evo Pricing does and the ‘secret sauce’ we use to get great results for our clients.
We often liken ourselves to satellite navigators. Why? First of all, because of our working method.
A sat-nav has some complicated logic inside but then the interface with the user is very simple:
it tells you if you have to go straight, left or right; it's a bit like what we are doing.
We use a lot of data and algorithms that are quite complex to then give fairly simple indications
to the management.
Giuseppe (Senior Data Scientist):
Companies have a lot of data but sometimes they do not find the right way to look at them.
We try to help them to interpret what's going on. Our recommendation comes from a deep
understanding of the phenomenon.
The amount of data available to people and companies is exploding and it’s exploding specifically
because the cost is decreasing exponentially. But all of these data are like a noise and so in reality
the difficulty of our job is increasing, not diminishing.
Since reality is complex and data is complex, fragmented, we need to use tools to capture data
fragmentation and their complexity, tools that go beyond classical enterprise productivity analysis
done by using Excel.
A hands-on response to market conditions
Blanca (Data Scientist):
We're studying what the results are, what's going on... you can see if things are getting better or
getting worse. When they're going well you try to make them go even better while, when they're
getting worse, you say, "OK, maybe I would change some things here, maybe this item is too
expensive and I would do it cheaper, or maybe it's too cheap".
And then there's also the direct intervention of the managers in the stores, so every week the
shopkeeper can give us their opinion on what they think will be selling or not selling in the next
Strategies that respond to the clients’ needs
Amedeo (Data Scientist):
The strategy must always adapt to the needs of the individual case, of the client.
We have to understand well what our customer expectations are.
We start with an idea that can be a good approximation of the reality but, moving forward,
we can find what might be the problems, things to improve or to change.
A system that keeps learning and improving every minute of the day
Just like a car driver that changes the route and then the navigator updates the entire route,
so we learn from decisions that management takes and we try to adapt all this automatically,
improving the quality of our recommendations.
We work on all these things together, to merge the machine prediction with the human factor.
Machines and humans working together
Our first fashion client in Italy, Miroglio Group, has publicly talked about one of our most
successful and scientifically interesting experiences.
We did a research project with them on distribution of items in retail fashion stores. We
demonstrated that the involvement of people working in the store helps artificial intelligence to
improve the quality of solutions. Algorithms improve predictions but do not win alone.
We believe in what we call a new alliance between man and machine.
It's a collaboration between the two things.
The quality of human intuition doubles the effectiveness of solutions, so it is a very significant
We've seen that this alliance between man and machine brings good results.
About the author
Martin Luxton is a writer and content strategist who specializes in explaining how technology affects business and everyday life.
Big Data and Predictive Analytics are here to stay and we have only just begun tapping into their enormous potential.