Advanced Analytics: An Introduction
The term ‘Advanced Analytics’ is an encompassing one and includes in its scope components like – Predictive Analytics, Simulation and Optimization. Advanced analytics differs from its traditional counterpart through the capacity to focus on future in terms of occurrences, audience behavior and delivering businesses with the support to run ‘what-if’ analyses for better insight at the changes made and how, in turn, such changes may affect strategy development. Some of the key techniques that advanced analytics take into account for casting the futuristic focus are:
- Decision trees and
- Data mining
The Brick-and-Mortar Retail Businesses are increasingly applying the essential attributes of advanced analytics to their respective predictive analytics with the ambition to understand in-store customer behavior better and prepare future business strategies accordingly.
Advanced Analytics: The Key Features
Advanced Analytics and its implementation is apparently complicated but in reality it is characterized with several easy-to-use features that encourage seamless implementation of this method. Some of the key features are:
- Software based
- Complete, easy-to-understand guidance
- Ability to project model situations and run multivariate analysis
- Seamless integrability in the same platform alongside traditional BI (business intelligence) and performance management
- Comes with the freedom to experiment and optimize in the simulated scenarios, compare results in real life and optimize the results
The Key Applications that Complete the Advanced Analytics System:
- Product Rationalization
Product rationalization, as one of the key features of advanced analytics, is a software-based, intuitive and auto-guided analytic process. This process comes handy with superior statistical models for segmentation purpose and characterized by weightage control. With product rationalization, assorting productivity and profit optimization becomes simpler, compared to the traditional ways. This process offers appropriate filters for data selection and the correct standard for rationalization that helps with generating to-the-point, functional rules of deletion.
- Product Affinity Analysis
Product affinity analysis delivers brick-and-mortar retailers with the power to figure out the products that are most likely to be purchased together, such as in a set. With this process, identification of items that act as anchor solution providers, becomes easy; consequently storeowners receive a better view at the capacity of those products in driving the sales of items, associated with the anchor solutions. Thanks to its fact-based analytical capacity, not only the sell-in receives the desired boost but also in-store product placement ideas near perfection. Owing to the results of the analytics, storeowners may come up with plans to optimize the promotional strategies that in turn, help both retailer and manufacturer. Segmentation between natural and artificial affinity products is also possible with the help of product affinity analysis, an essential feature of this process.
- Market Basket Analysis
Retail Businesses have a better chance to receive the necessary insight at customers’ profiles through market basket analysis. This process solves questions related to customers’ identity and figure out the reason behind their purchasing preferences. Storeowners further receive the opportunity to understand the items that need to be purchased in a set and required to be promoted. Market basket analysis delivers the opportunity in determining the items that need to go for sale, the right time to offer discount coupons and who should be receiving the offers. Furthermore, with the help of this process, brick-and-mortar businesses owners get the chance to evaluate basket features, basket clustering, popularity of the items, tracking marketing events and finally, explore product affinity in-depth.
- Customer Churn Modeling
Customer churn model is an important feature of the advanced analytics that allows businesses in classifying customers as per the parameters of value and profiling, with reference to the patterns in which they participated in a marketing campaign and customer lifecycle metrics. Business owners, based on the findings of the model, can prepare a list of probable churners, set near-to-accurate notification alarms, and promotional events that will specify the point of no return. The prediction over churn behavior is best attained by taking into account purchasing behavior, renewal behavior, effort for redemption, customer demographics in the form of regression model.
- Customer Segmentation
Customer segmentation contains the principles of data mining at the core and is used widely for the purpose of making marketing as well as merchandizing more focused, customer-centric and ROI optimizing. This process lets business owners to classify their customers in terms of behavioral and demographic similarity.
- Recency Frequency Monetary Modeling (RFM)
RFM is a highly effective advanced analytics instrument that makes promotion simpler to loyal customers, based on their purchasing behavior in terms of recency, frequency and monetary capacity. It means delivering promotional offers to customer becomes more personalized and matching with their preferences. While typical promotional efforts draw only 5% of all customers’ response, with RFM the chances are higher for the right group of customers to respond at the promotional campaigns.
- Sales Forecasting
This process is founded over the principles of ARIMA time-series model that predicts the future of product sales. The store level forecasts are done by classifying store groups of clusters in various time segments, such as – day, week, month etc. Through this process, storeowners receive valuable insight at the older data in terms of seasonal performance, success of a promotion and use the same to predict the future of promotional campaigns. Sales forecasting the key instrument when it comes to setting practical, specific and achievable sales target and plan business strategies with adequate consideration to the demand for newer products at newer stores.