5 Things To Consider When Defining Competitors For Price Monitoring

5 Things To Consider When Defining Competitors For Price Monitoring

[Guest post by Daria Samokish] Who can tell the Category Manager how to correctly define competitors for price monitoring or what is in fact, competitive pricing analysis? We aim to raise the relaxing smile on your face [with this article] telling you how to build a pricing strategy towards competitors and grow the KPIs without growing white hairs. Shown:Without white hairs Competitive Pricing Analysis E-commerce retail market is highly competitive, so a Category Manager is engaged with general strategies to counteract the activities of competitors. According to the latest PWC research, price is the key factor of a purchasing decision. Well, let’s make the best use of competitive pricing intelligence. Surely, while making a competitor’s analysis you are guided by the following points: Detect the competitors actually affecting your sales. Find your price positioning towards market price/competitors’ prices. Sell some products at a higher price without a loss of profit. Define the most sensitive product categories to each of the competitors’ changes. Save the time...
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Discover the Big Data Infrastructure that Empowers our Personalization Solution

Discover the Big Data Infrastructure that Empowers our Personalization Solution

Planet Cassandra has published an article about how we use Cassandra and other Big Data solutions at BrainSINS. In the article, Andrés Velasco, one of our lead engineers, explains why we chose Cassandra as NoSQL database, and the main components of our NoSQL architecture. If you want to discover more about the "magic" behind BrainSINs eCommerce Personalization solution, we invite you to read the full article....
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Main Big Data Technologies: NoSQL

Main Big Data Technologies: NoSQL

When considering the technologies required to approach the problem of Big Data, it’s only natural to consider the database management system first. Most of the most widely used databases are already optimized to store and handle large data volumes. For some years now, systems based on the relational model have been successfully used both in the industry and in research environments. However, the threshold defined by the term “Big Data” entails, above all, a paradigm shift in the information management model. While databases based on the relational model guarantee certain properties which at first sight might seem more important or even necessary (the famous ACID trifecta), nowadays it is impossible to handle certain volumes without relaxing some of them. It is precisely out of this relaxing and out of the need to provide other properties that a new data management paradigm has arisen: NoSQL. The properties to be provided by these new systems, in particular in an Internet environment, are: High availability Failure tolerance Large...
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Big Data Opportunities #Infographic

Big Data Opportunities #Infographic

Elexio has published an infographic about how Big Data is generating bigger opportunities in several sectors. In eCommerce and retail, Big Data opens opportunities to analyze customer behavior data and personalize the user experience in order to increase conversions and sales. Big Data also opens up the opportunity to analyze offline and online user's activity in order to have a broader vision of our customers.  ...
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Infinity Magazine September/October 2013 Just Released

Infinity Magazine September/October 2013 Just Released

HuntRevenue just released the September/October Issue of Infinity Magazine. In this issue, you may find interesting articles about new technologies in eCommerce, the evolution of eCommerce in india, articles about link building, eCommerce in Romania, distant selling and the VAT issues arising, etc. We contribute to this issue with an article about Big Data applications in eCommerce, focusing on how Big Data technologies help us to personalize the user experience. Hope you enjoy this amazing issue of Infinity Magazine. ...
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The Real Usefulness of Big Data in eCommerce

The Real Usefulness of Big Data in eCommerce

Everything that falls under the umbrella of Big Data has many applications. Most of the time, Big Data is associated with social networks, where the wealth of user interaction generates vast amounts of information which can be processed for various purposes. However, eCommerce applications can also be very useful, as long as you have the suitable technology and vision. In the case of eCommerce, the main factor to take into account is that the goal is very clear: conversion adds a specificity which is harder to find in other Internet environments (e.g. in social networks there may be various goals, but they are never quite as specific). This point is fully aligned with decision-making based on well-structured data, the more the better. Organisation of information from the various agents involved in the business (stock management system, CRM, email provider, recommendation system, offline data, marketing campaigns…) is crucial, as are their correct integration and analysis. Given that these usually involve large amounts...
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Big Data: Needs and Applications

Big Data: Needs and Applications

The first main requirement of Big Data is data storage. When you reach this size, it’s hard to design a monolithic architecture that can house all the information. Distributed solutions which allow unified access to information sources are thus required.  In many Internet applications, these data must also be quickly stored and processed to offer analytics in real time. The nature and structure of the data, which in these cases are often rather heterogeneous, should also be taken into account. In most cases, solutions based on non-relational databases (NoSQL) adapt better to this scenario than traditional databases. Once a solution has been provided for storage of and access to large data volumes, many applications offer the possibility of analysing them. Distributed data analysis technologies, such as Hadoop and MapReduce, offer this functionality, providing many possibilities for application such as the following. Applications Recommendation systems:  they use the behavior information for each user to predict his or her intentions and interests, thus offering suitable contents. They are used very frequently in e-commerce. Sentiment analysis: on the basis...
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How Big Data Can Help Your E-Commerce

How Big Data Can Help Your E-Commerce

In a previous post we already described some of the most popular uses of Big Data in general. This article gives a more specific description focusing on the benefits that technologies for the storage and analysis of large amounts of data can provide to e-commerce. The data that constitute the repositories integrated in e-commerce are of different types, depending on the modelling of the relationships between them or their origin. There is usually a clear distinction between structured data (usually CRM and ERP databases) and non-structured data, such as monitoring of user behaviour, 2.0 components such as comments or likes, and even data that are external to the store (email, tweets, likes on Facebook, etc.) Even though integration and analysis of such diverse data is challenging, the potential applications of this process can bring great benefits to a business, such as the following: Web analytics: web analytics in e-commerce can mainly help in the optimisation of purchase processes and conversion rates, but it can also help to perform a monitoring that helps to acquire better knowledge of customer profiles. In this...
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Big Data, a Must for eCommerce Success

Big Data, a Must for eCommerce Success

Big Data has been and continues to be one of the hot keywords in technological business. But what sets Big Data apart from other buzzwords? Basically, the real need which already exists in the market and in particular the need which we are starting to glimpse in the near future. Big Data basically means processing large amounts of data. How large? Large enough for it to be very complex and expensive to process them using relational database technology and classic data analysis techniques. The need for new data analysis techniques has arisen over the last decade due to two main reasons. The main one is the exponential growth of the amount of information generated. In addition, there is a need to process these data in less and less time. We are already living in a “real time” age, and data must be processed within milliseconds so that the analysis results can be delivered to a decision maker as soon as possible.   The main...
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