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Advanced analytics are critical for modern and effective retail assortment planning

By Robert Hetu | December 01, 2017 | 0 Comments

Merchandising ProcessAI

Having just completed publication of the 2017 Critical Capabilities For Retail Assortment Management Applications it’s a great time to reinforce the importance of using new advanced analytic capabilities. Advanced analytics and big data from sources like the Internet of Things (IoT) are enablers of customer-centric assortments as well as effective category planning and management. This year’s research placed special emphasis on provision of advanced analytics as key differentiation for assortment planning and category management solutions. Gartner describes this as “Algorithmic Retailing”.

Algorithmic Retailing

Retailers must transition from Excel-based planning to these more advanced solutions to improve speed and accuracy of planning.  Any advanced solution under consideration should utilize the majority of these capabilities or should be dropped from consideration.

  • Complex-event processing (CEP)-A kind of computing in which incoming data about events is distilled into more useful, higher-level “complex” event data that provides insight into what is happening. CEP is event-driven because the computation is triggered by the receipt of event data. CEP is used for highly demanding, continuous-intelligence applications that enhance situation awareness and support real-time decisions.
  • Data/text mining-The process of extracting information from collections of textual data and utilizing it for business objectives.
  • Forecasting-Demand forecasting applications incorporating historical and predictive customer demand information into production lines and sales quotas.
  • Graph analysis-In computational biology, power graph analysis, which is a method for the analysis and representation of complex networks. Power graph analysis is the computation, analysis and visual representation of a power graph from a graph.
  • Machine learning-Advanced machine learning algorithms that are composed of many technologies (such as deep learning, neural networks and natural-language processing), used in unsupervised and supervised learning, that operate guided by lessons from existing information.
  • Multivariate statistics-A technique for performing advanced and predictive analytics.
  • Network and cluster analysis-The task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). It is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, bioinformatics, data compression and computer graphics.
  • Neural networks-A neural net or neural network, which is an artificial-intelligence processing method within a computer that allows self-learning from experience. Neural nets can develop conclusions from a complex and seemingly unrelated set of information.
  • Pattern matching-The act of checking a given sequence of tokens for the presence of the constituents of some pattern. In contrast to pattern recognition, the match usually has to be exact. The patterns generally have the form of either sequences or tree structures.
  • Semantic analysis-The process of relating syntactic structures, from the levels of phrases, clauses, sentences and paragraphs to the level of the writing as a whole, to their language-independent meanings.
  • Sentiment analysis-The process of computationally identifying and categorizing opinions expressed in a piece of text, especially in order to determine whether the writer’s attitude toward a particular topic or product, for example, is positive, negative or neutral.
  • Simulation-The process of creating and analyzing a digital prototype of a physical environment to determine how the part will be restrained during use. Perform finite element analysis, review results, and make engineering judgments based on results.
  • Visualization-The illustration of information objects and their relationships on a display. Strategic visualization graphically illustrates the strength of relationships by the proximity of objects on the display. Advanced technology can make a significant difference in users’ ability to interface to large knowledge repositories.


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