Big Data change the game in retail
Apply the cutting edge applications of analytics in the world of shopping
Nowadays retailers are facing digital and competitive environment and with increase of globalization and competitiveness, they are seeking better market campaign.
Retailers are collecting large amount of details regarding customer daily transactions. This collected data requires right mechanisms to convert it into knowledge, and, using this knowledge, retailer can make better business decisions.
Retailers are looking strategy with which they can target proper customers who may be profitable to them. Data mining is the extraction of hidden predictive information from very large data sets. It is a powerful technology with huge potential to facilitate organizations with focus on the most important information in their data warehouses. Data mining tools forecast future trends and behaviors to facilitate organizations to make proactive knowledge-driven decisions. Data mining tools answers the questions that traditionally required too much time to resolve. They prepare data for finding hidden patterns and predictive information that experts may omit because it lies outside their expectations.
From the last decade data mining have got a rich attention due to its significance in decision making and it has become an essential component in various industries. Hence, this paper reviews the various trends of data mining and its relative applications from past to present and shows how effectively can be used for targeting profitable customers in campaigns.
…Data mining tools forecast future trends and behaviors to facilitate organizations to make proactive knowledge-driven decisions
Our data mining project lifecycle consists of 6 stages. It is nearly always require to move back and forth between different stages. And these movements depends on the outcome of each stage. The main stages are as follows:
- Business Understanding: This stage focuses on requirements from a business perspective and understanding the project objectives. Then it converts the gain information into a data mining problem definition and a preliminary plan designed to achieve the objectives.
- Data Understanding: It starts with an initial data collection and desription, to get familiar with the data, to identify data quality issues, to find first insights into the data or to descover interesting subsets to form hypotheses for hidden information.
- Data Preparation: It covers all activities like data cleansing, transformation and aggregation to construct the final dataset from the initial raw data.
- Modeling: In this stage we use diverse modeling techniques are applied and their parameters are calibrated to optimal values.
- Evaluation: In this stage the model is reviewed and thoroughly evaluated. We execute the steps to construct the model to be confident it best achieves the business objectives. At the end of the stage a decision on the use of the data mining results will be reached.
- Deployment: The aim of the model is to increase knowledge of the data. And the gained knowledge will need to be organized and presented in a way customer can use it. Based on it the deployment stage can be as simple as generating a report or as complex as implementing a repeatable data mining process across the whole enterprise.
Application in retail sector
1 Churn Prediction and Customer Retention
It is more expensive to reach new customers than to get existing one. Therefore by knowing existing customers’ purchase behavior, direct marketer can predict customers need and interest in buying particular product. Using this type of prediction retailer can retain existing customers by providing discounts or offer, attract and acquire customers.
2 Market Basket Analysis
Market basket analysis is a technique to understanding what items are likely to be purchased together according to association rule. It provides valuable designations about customers and their shopping patterns by showing associations among variety of items. This type of item combination is useful for shelf design to determine the location and promotion of items by means of union. And customers can easily reach items and this way helps in product cross-selling.
3 Customer Segmentation and Target Marketing
Segmentation is to divide the market into several parts sections by some characters. Data mining can be used in grouping or clustering customers based on their behavior. This type of information is useful to identify similar customers in a cluster and recognize likely responders for target marketing. Customer segmentation technique identifies customer behaviour using a recency, frequency and monetary (RFM) model and then uses a customer life time value (LTV) model to evaluate proposed segmented customers.
4 Demand Prediction
The demand on a given product or group of products depends on many factors including both a product’s own properties such as price or brand, prices of competing products in the category, weekly seasonality, year seasonality, sales events, and even the weather. The goal of demand prediction is to build a demand model that incorporates these factors and allows retailer to compute optimal stock levels by predicting seasonal effects and minimize losses due to limited shelf life, optimize category management and improve assortment planning.
5 Price Discrimination
Retailers offer a category of products to customers. The goal is to assign an individual price for each customer in order to increase as much as possible the overall revenue. Alternatively, the issue can be stated differently as targeting discounts that change prices compared to the common baseline. Although we have stated the issue in a way that propose individual prices, there is a case and the more typical approach to set prices for larger customer segments.
…Data mining can be used in grouping or clustering customers based on their behavior. This type of information is useful to identify similar customers in a cluster and recognize likely responders for target marketing
Many retailers such as Wal-mart and American Greetings have already applied data mining technology. They have been using data mining for category management for several years.
J. Crew Group, Inc. wanted to determine what clothes, shoes and accessories customers most purchase together. So they used market basked analysis combining click stream analysis from its website, point of sale (POS) data from retail locations to perform product affinity analysis. The data was then used to make complementary product suggestions for online shoppers.
Macy’s is combining customer and transaction data to define best customers and offer them exclusive extras. For example, Macys by identifying customer buying and habits can predict life changing events such as marriage and baby born within one year.
Costco use big data and advanced analytics to find and if needed recall tainted products. Within 24 hours Costco know who was potentially affected by the recall. Targeted phone calls and emails began to be issued to customers, and what normally is a retail nightmare turned into an opportunity to strengthen customer loyalty. Customers complimented Costco’s reaction to the issue and expressed appreciation for the effort. Maybe big data is a wonder, turning a product recall into a point of praise. Whether or not this is considered unpleasant feeling, you can sleep well at night knowing Costco watch your back with the help of big data.
Tesco with help of data mining significantly succeeded in better target mailings of vouchers and coupons to customers, resulting in a huge increase from 3% to 70% in rate of coupon redemption. One of the company’s most profitable case was examine past sales and weather condition data and using predictive analytics to optimize their stock system. By being able to predict sales by product for each store, Tesco managed to save up to 100 million pounds ($151,718,000 US dollars) in stock that would have otherwise expired and thus wasted.
In this paper is made an attempt to define data mining as a tool used to extract important knowledge from large databases so that retailers can make better business decisions. A wide range of industries have deployed successful applications of data mining. Data mining in retail industry can be deployed for better understanding and serving the customers, customers retention and targeted marketing campaigns. The retail industry will gain, sustain and will be more successful in this competitive market if adopt data mining technology for its data strategy.