A4Everyone Blog

H&M is suffering a $4.3B problem of unsold stock. Here is how to avoid it

The worldwide known fast fashion retailer H&M is suffering from a problem with unsold stock worth $4.3 billion. The situation became clear when the Q1 report of the Swedish giant was released. It made analysts and commentators speculating with the company capability to stay competitive. Such statements are problematic, especially for a public company with significant free float.

How AI & Data Analytics could have prevented an amazing Spotify fraud

We`re not sure if you already have heard about this and if not, keep in mind that those couple of minutes will be more than worthy.

The Spotify Fraud

Unknown scammer, allegedly from Bulgaria generated about $3m revenue out of creating and continuously playing a couple of playlists with tracks with an average length of 43 seconds. Spotify is paying artists about $0.004 per play and the fraudster has registered around 1200 premium Spotify accounts, continuously rolling the playlists.

It is believed that such ‘performance’ was achieved via bots automating the skip and play game regarding the Spotify policy to pay for a listened track only its 30 seconds or more. This way, the playlists called ‘Soulful Music’ and ‘Music From The Heart’ became played so many times they made it to number 84 globally and 22 in the US in the playlists charts.

When a computer understands more than you do

It was 1997 when the Deep Blue computer beat the world chess champion, Garry Kasparov. It was the first machine over human victory while playing the mother of all strategic games. Well, even though the Deep Blue computer was created specifically to play chess versus human, it took just a few years more for standard desktop computers to dominate our brains on the chess board. Now there are smartphone chess apps have been able to defeat exceptionally good players.

This was the first time when the machine ‘outminded’ humans. By declaring that AI software scored a better result than humans in a large-scale reading and comprehension test, it seems we are witnessing the second breakthrough.

2018: Data Analytics Trends

It is that time of the year when you take a look back and take an educated guess on what is going on to be in the very near future of data analytics. There is no doubt that in 2018 analytical industries will be not just hot but also rapidly changing. Here are the top trends that will prevail within the New Year.

3 Ways Retailers Can Use Data Analytics For More Successful Black Friday

Black Friday is marking the beginning of the X-Mass shopping hysteria since 1952 when it was created as a retailer campaign aimed to clear stock on promotional prices. More than half century later, Black Friday and the entire weekend after Thanksgiving, including Cyber Monday transformed into a shopping spree, helping retailers to generate more sales and revenues. Since data is one of the key assets for any retailer, its analysis is helping to boost the efficiency and overall campaign performance. As Black Friday revenues are estimated at billions of dollars, it is worthy to get aware how to take a bigger piece of this retail pie. In this blog post, we are going to share 3 different analytics applications helping retailers to achieve more on Black Friday.

Data Scientists are 30 years old average and Python is their most used tool

Worldwide known data science community Kaggle did something nice and sweet as industry-wide survey sharing interesting and valuable information on data scientists from around the globe. More than 16 000 data scientists, analysts, experts, and statisticians joined the Kaggle survey which is full of interesting insights. Among the most important is the fact that 3 of every 4 participants rely on Python, followed by R and SQL and the logistic regression is the most commonly used data science method, followed by decision trees and random forests.

Scary Data Analytics

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.

Coca-Cola Case Study: How spatial analytics makes perfectly targeted product sampling campaign

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?

How to apply data analytics to call centers

The call center is perceived as the nervous system of a company. It can warn of risks and potential threats, gather information about the environment in which the company operates and last but not least – it is the direct link to the most important asset that a company can have – its customers. Like any nervous system, it processes a huge amount of information. If such information is quantified, then we have big data. The application of big data analytics within call centers is the focus of this blog post.

Before we share our understanding of the application of data modeling and analytics for call & contact centers, we want to dispel a common misconception.

How weather affects retail sales?

This is a no brainer – weather definitely has its own impact on retail sales. The substantial question is to which extent. This blog post is based on our own analytical insights extracted out of a project we developed and implemented for a chain of pastry shops in Europe.

Our example is giving deep insights on how day-to-day weather change affect daily revenues for a particular retail location. Careful examination of a representative sample of pastry shops shows clearly that the revenues of such kind of retail business may change up to 40% on a day-to-day basis. No doubt, this is big!