A4Everyone Blog

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

How menu engineering can be the ultimate restaurant profit-boosting tool

It started in the 70`s when Bruce D. Henderson created the growth-share matrix for the Boston Consulting Group. Its aim was to help businesses to analyze a particular product performance within the entire product line. It is simple yet effective approach if correctly applied.

Restaurant owners and managers can benefit too. The growth-share matrix, also known as the Boston Matrix has its restaurants’ custom-made version, called menu engineering. It is a data-driven approach to boosting a restaurant` profit. In this blog post, we are going to share with you how to do it easy and effective as possible.

4 tips for improved sales forecast accuracy

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.

How Boston Matrix Can Boost (Almost) Any Business

Boston Matrix is a simple analytics diagram displaying business data in a convenient manner to deliver insights to managers on the performance of a product in terms of profitability and sales volumes. This data visualization approach, also known as growth-share matrix or portfolio diagram can be applied not just in terms of product performance but also in terms of ranking almost everything, including customers, revenue sources, marketing activities, etc. It is suitable even for NGOs willing to be more efficient in fundraising.

How telecommunication companies utilize data analytics

For just a decade, the telco sector is nothing like what it was like. Telecommunication companies faced transforming changes and data is the centerpiece. First, mobile telecom companies shifted their focus from voice vendors to data carriers. Fixed services became obsolete and experience downfall. Competition became more fierce than ever because a market maturity means a limited amount of end users even though IoT are percepted as a potential booster. So, such situation should be examined more as an opportunity than like a dangerous threat.