Most commonly applied in boosting the efficiency of a measurable processes, data analytics is hardly scary by any means. Well, just like the machines or any product of the human knowledge at all, data analytics can be a scary tool. The frightening element within data analytics is not the power of data but its usage. Prior to Halloween, we would like to share a few scary data analytics applications out of real life.
We all love nice product samplings. This is the best way to meet a product, to experience it from first hand. There are no words or even a picture capable of depicting particular taste. That’s why product sampling campaigns are deeply loved by marketers of foods & beverages. Because they work as nothing else.
Since the A4E blog is dedicated to all thing data analytics, we are willing to share our own experience on how to enhance the performance of such sampling campaign. The Coca-Cola Company representatives approached us with a pretty interesting case regarding their new 0.75l product pack. It is aimed to households consisted of 2-3 members which had to receive the sample during dinner time, providing a meal pairing experience in the comfort of their own home. Nice and sweet, isn’t it?
As data analytics company, at A4E we are more than familiar with the capabilities and potential of Artificial Intelligence widely known as AI, especially combined with some kind of automation. As society members, just like you, we also know that the fake news is not just pieces of information heavily disliked by politicians with disputable reputations. This is why we are curious whether AI is capable to help users and media businesses to distinguish the real news out of fake ones.
Product portfolio analysis is a crucial tool for any business with a stack of products or services on the market. The approach can differ but at the end of the day, the desired result is a valuable assessment of business units. Boston Matrix, also known as growth-share matrix is one of the proven methods and at A4E we`ve built a tool to simplify the task by evaluating a product contribution to the overall profit and its popularity among the customer base.
Here comes the question – what to do when our report is done? Are there recipes for successful business decision when the data is sorted and properly displayed? What are the insights we can extract out of the products categorized as Stars, Dogs, Cows, and Puzzles? How to turn a Puzzle into Star or a Dog into a Cash Cow?
We already discussed how important sales forecasting is to any business, not matter B2C or B2B. Proper forecasting is an efficiency booster since it is enabling proper recourses planning like staff, stock, financials, logistics, production, etc. This is especially important for businesses producing limited shelf life products.
One single KPI makes the sales forecasting good or bad. It is the accuracy. The accuracy is the crucial indicator paving any business plan to the road of success or failure. In this blog post, our data analytics team at A4E is sharing tips and hints on how to boost your sales forecasting accuracy.
Like it or not, we all are living in a world where many people are starving while in the same time a tremendous amount of food is wasted by consumers, retailers, manufacturers, etc. Food wastage is a problem not just because is a missed opportunity for people in need. It is an issue because wasted food is incinerated in combustion facilities or is stored in landfills. Once in landfills, the food breaks down to methane, which is a strong contributor to the greenhouse effect boosting hazardous climate change. Speaking of food waste we should know that throwing an apple means we are spilling about 100 liters of water, needed for its growth. The water footprint of a beef is more than 7 tons of water.
Elon Musk and his Tesla Model S started it all. The premium electric sedan was equipped with advanced and self-learning Autopilot system, which can take the chauffeuring work out of the driver. It was a complete breakthrough because if it works as good as it should; it is going to change driving forever. It is all because of Artificial Intellect (AI) and the way it works fueled by data sensors and powered by video analytics. And since we have, to be honest, it wasn’t started by Elon Musk but the automotive industry itself. It doesn’t matter if we talk about rain sensing wipers (Cadillac) or adaptive cruise control (Mitsubishi) or lane departure systems (Mercedes-Benz). Such systems work with predefined events with some variables in the input data and one particular, predefined outcome.
Data is all around nowadays so it will be wise to explore how beneficial it might be. Unfortunately, tourism is among the sectors where big data analytics exploitation is not at its best. That’s why we would like to share some insights coming right out of our data analytics startup enthusiasm.
We don’t know if you know but a few years ago, the major music labels had pretty sweet business. They profited from any single or album, not matter it was vinyl, CD or even cassette. It is not like this anymore. MP3, torrents, piracy, streaming, and video sharing networks killed this particular business model. What music labels did to stay afloat? They asked data analytics for some help. And they were not alone – a lot of businesses emerged and shaped their models by applying data analytics to music services. We are going to explain this.
The sinking of the RMS Titanic is probably the most tragic event in the modern maritime history. It was not the deadliest though but it became the symbol of the human bigotry in terms of technical advancement. Back in 1912, Titanic was the largest, and most luxurious ocean liner ever made. She was claimed to be unsinkable. And she sank on her maiden voyage to New York.