A4Everyone Blog

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!

Why AI can`t beat the fake news?

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.

Leo Messi should be the best-paid football player but he is overpaid. Data analytics explains why

Named as one of the best currently active football players in the world, Lionel Messi is also one of the best paid among them all. His Barcelona contract alone is securing him a payment of €40 M per year or €770 000 per week.

Well, recently performed data analytics model confirmed what we all suspected. Even though Messi should be the best paid, he is extensively overpaid. The model and its results are described in a study conducted by a team of Lawrence Technological University in Michigan, which used machine learning and data science to analyze the salaries of 6082 professional football players in Europe. The salary of each was compared to a set of 55 attributes, reflecting each player`s skill set. The model is evaluating scoring and passing accuracy, aggression and vision on the field, speed, acceleration, ball control, physical condition, etc.

How Banks Utilize Data Analytics. 8 Areas of Application

The banking industry is a specific one with the fact it generates and collects an impressive amount of data. Combined with predictive analytics and connectivity, data opens the door for endless opportunities of boosting business efficiency. In this blog post, we are pointing at just 8 of them. Feel free to add some more in the comments below.

Product portfolio analysis: What to do when it`s made

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?

Puzzles