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.
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.
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.
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.
Believe it or not, big data is the new gold. Poured into the fuel tank of the automotive industry, it is transforming itself into growth booster, creating new services and users` benefits. The core focus of big data and cars is not just autonomous vehicles, it is about humans and how they use their cars on an amazingly granular level.
It has been a long time since the work of a marketing professional doesn’t look as it was a few years ago. There were times when creativity had the best place on the main stage of marketing, now its place is stolen by data. Because, you know, data is the new oil.
If someone has told you that data analytics is something relatively new in Human Relations, do not believe them. The truth is that a big part of the HR work is to plan. You cannot have proper planning without accurate forecasting, something we have already discussed at this analytics blog. Well, it turns out that HR people and their industry as a whole are still far of maturity in terms of utilizing its data (trust us, they have a lot) into predictive analytics. Recent Deloitte survey of business and HR leaders shows that companies exploiting HR analytics are extremely small fraction of the market – 4% in 2015 vs 8% in 2016. The study highlights that this number is going to increase due to a shift of HR professionals expectations.
The world of Hollywood is full of worldwide fame, glamour, and big bucks but there is an entertainment industry which is way bigger. Please welcome video games! The revenues generated by all kinds of video games are more than twice than the financial results of the movie industry. And it is growing at a constant pace. On top of this, gaming is generating a huge amount of data. Electronic Arts have 275 million active users generating 50TB data a day. It is no surprise that gaming industry is utilizing data analytics in its full scope. Technology, financials, gameplay, marketing, strategic, just name an analytics domain and you`ll find it working on high revs in the gaming industry.
Wholesale distribution business is generating a great amount of data which is including at least thousands of products, quantities, pricing, customers, and inventory, just name it. This simple fact makes them really suitable for data analytics modeling in order to solve specific business problems or to gain efficiency within already existing processes. In this blog post, we are highlighting just 5 of the potential benefits that a wholesale business is capable of extracting from modern days data analytics.
FinTech stands for Financial Technology. It is a common term used for companies offering added value or entirely new financial services via technology. Financial business is generating a huge amount of data also known as big data. This is where the point of intersection for Data Analytics and FinTech is located. We also have to keep in mind that FinTech covers a lot of financial business domains like asset management, insurance, lending, transferring money, to name a few.