Just hearing the words “Corporate Structure” probably puts a lot of us to sleep. It sounds like one of those boring (sorry) topics that is the bread and butter of Lawyers (sorry, again!) but like raw eggs and kale for the rest of us. But what if we told you that we could give you a different, more interesting perspective? One that could potentially open your eyes into the several ways corporate structures (and the people behind them) reveal the reasons why they are built the way they are.
Interested? Great! We’ll try and give you such a perspective using Network Science, a less well known (but powerful) part of the Data Science world.
The Analyst’s dilemma is this: On the one hand, our brains are wired to rationalise the reasons for the current state of affairs all the time. We think “You can’t manage what you can’t measure” is a not a quote by a fellow analyst but rather gospel. On the other, 43% of Analysts [in the workforce] say they would be better than their boss at their boss’s job, which is classic Dunning–Kruger Effect at play. The boss is the boss, and we’re not them, for a reason, right?
PeriScope is an AI powered review analytics platform that allows you to search through user reviews to find gaps in the market that your business can fill, measure sentiment to identify key factors that drive customer purchasing decisions and get valuable insights into the strengths and weaknesses of your competitors.
Personalized Customer Experience is one of the many ways major retailers continuously improve customer service and increase sales. The analytic techniques we mention in this blog have been used by major retailers around the world for decades. The worst part about all of this – small businesses don’t even know these technologies exist.
Human behavior can completely derail your data modeling efforts, no matter how advanced your model or skilled your team. Understanding and accepting this unpredictability is the key to making better decisions. Learn why human behavior is the X-factor in data modeling, and how it can impact even the most sophisticated models.
Welcome to the final part of our three-part blog series, “A Retail Success Story.” In the previous two posts, we delved into how we helped our partners uncover previously hidden insights in their data and the obstacles we faced along the way. Now, we’ll be discussing what the analytic process led to: the development, operationalisation and growth of a new loyalty program.
Welcome to Part 2 of our 3-part blogpost series where we delve into the topic of “how deep data analytics will dramatically change your business.” In this part, we will explore the importance of “data” for businesses, ecommerce and traditional brick and mortar retailers of all sizes alike and how “data” significantly improved the financial results in the Himalayan java eco-system.
In this three-part blog series, we will take a deep dive into the journey of the largest coffee chain in Nepal, Himalayan Java, and show how advanced data analytics unlocked hidden insights, leading to positive business outcomes and increased financial success. Join us as we delve into this exciting case study and discover how you too can revolutionize your business operations.
In our quest to make companies like Himalayan Java, the largest coffee chain in Nepal, more data-driven, we’ve learnt a thing or two about what retailers need to know when it comes to data and data analytics. For the benefit of other retailers considering a move into more data-driven operations, we thought we’d list out the lessons we’ve learnt so far.