Category : Enterprise Business

Enterprise Business

Data analytics applications focused on the needs of enterprise business. Demand prediction, sales forecasting, and data modeling.

When AI helps you choose a gift

Good retailers sell well, and best retailers offer their customers an unforgettable experience. The story of the 1800-Flowers.com online store is interesting because it traces the path that a Manhattan flower shop bought in the 1970s for $ 10,000 to a multimillion business that generated $ 1.2 bn. in sales just last year. The achievement goes through impressive AI innovation that has cemented its leadership position in the US online marketplace.

How Predictive Analytics Made Inventory Management Better

Not matter if a business is a production, retail or wholesale focused, the inventory is a crucial piece of its smooth running. It is a challenge that has to be faced properly. If not, efficiency wouldn’t be gained, resulting in not acceptable business performance. Inventory management executed as it should mean less cash locked in a stock, on time deliveries and at the end of the day – happy clients. As we all know, they do matter.

Predictive analytics is applicable as efficiency booster in many business processes and inventory management is no exclusion. Optimizing inventory is ensuring the right SKU is available in the right quantities, at the right time and at the right location. Such optimization is leading to stock levels reduction, hence transportation costs reduction and write-down cost reduction. Relying purely on data, predictive analytics is the perfect tool for addressing issues like this. Combine demand prediction with sales forecasting and you`ll know what, when and where.

How credit scoring automation made FinTech lending possible

There was a time when the banks were the sole legitimate lending providers but the rapid increase of Internet coverage, smart devices penetration and credit scoring automation made possible to get a fast loan with just a few clicks on your phone. Welcome to the FinTech world built by much more than cryptocurrencies and ICOs. Credit scoring automation is the tool that enabled loan access to the underfinanced population. Matching credit scoring with machine learning, AI and automation, in general, made this process a viable business case.

Price Elasticity: A million-dollar retail question

A DIY retailer, for instance, has the potential to increase its sales revenue by 1 – 2% annually with price elasticity of demand application.

Meeting customers’ needs is essential for a retailer wellbeing. A retail business failing to do so will be simply pushed out of the market, soon or later. Some retailers rely on their gut feeling but as data analytics experts, we are perfectly familiar with Arthur Conan Doyle who once said, “It is a capital mistake to theorize before one has data”.

Why credit scoring as a service is the most viable option for consumer finance companies

Credit scoring is the process enabling lending businesses to determine how likely a lender will default its loan. This process is utilizing different data sources and less or more is designed around the business logic of the lending organization. In some markets like USA and UK, there are credit scoring bureaus assigning credit score derived from credit files and history of a particular person. In other markets, the credit scoring is up to the lending institutions, not matter banks or nonbank financial institutions like leasing companies, consumer finance companies, telecommunication companies, etc. Such businesses hold the credit risk and they need a solution like credit scoring. In this blog post, we explain why credit scoring as a service is the most viable option for lending businesses of different size and markets. Let us share with you the three main options in front of a company dealing with such kind of financial risk.

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.

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.

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 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.

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.