The Role Of Customer Segment Identification In Retail Business

Retail is no exception, entering a new era of predictive commerce. There are many advantages for online retailing when compared to offline in which the flexibility for changing price of the product is more. Online retailers are able to accurately track the customer’s interactions with the stores across multiple platforms which gives access to data that can elevate marketing efforts above anything a traditional store could manage. These data can be analyzed and used as a valuable information for predicting future demand, sales, discounts, customer’s purchase probability etc. and there by increasing the revenue. Applying these strategies may attract customers more towards our products which can lead to long term profit in business. Implementing new strategies in pricing decisions can seriously impact both revenue and profitability. Understanding the customer is an important step to make the business profitable in long-term.

Customer segment identification have many advantages like we can suggest a product based on customer interest, can give discounts and special offers based on their purchase history or we may increase or decrease the price of same product for different customers at the same time. Using these strategies we can make the customer satisfied with our product and services if we could identify what exactly the customer is interested in. Some of the segments we can treat as profitable segments if we could identify their willingness to pay. These segments will bring a long term benefits for our business. With the abundant data available about the customer, products and their purchase details the problems like optimal pricing decisions, discounts, recommendations etc. can be solved using data analysis methods. Customer segmentation can be done based on similar product purchasing, locality, behavioral or demographic characteristics. Grouping consumers based upon characteristics allows organizations to better serve the needs of their ideal customers. In terms of marketing, it means selling a product or service to the person most likely to buy it based on their unique needs and preferences. Advantages of these kind of segmentation include understanding the ideal customer, identifying new opportunities, creating unique selling points and thereby increasing sales and revenue. Segmentation can be done based on similarity in purchase, geographic, behavioral or demographic characteristics.

Demographic data about a customer include features like age, marital status, employment status, type of residence, religion etc. Resource for collecting this type of data includes demographic tools used within websites like Facebook, Twitter etc. which accounts marriage engagements, life events like graduating college, teenagers. Data can also be collected from Census bureau data, Bureau of labor statistics (include details of sex, age, race etc.), Social security fact sheets, Tax records (for residence, mortgage information) and we may collect directly from customer for some of the personal details. Mining knowledge from highly personal and behavioral details can give greater insight in to customers’ interests and priorities. Using this details suitable pricing and product recommendations to each customer can be done. Retailers will be able to make profits for these customers by selling the right product at right time to a specific customer and there by pricing can be changed accordingly. Customers may also be more satisfied with this type of interactions.

Data mining techniques are mainly used to analyze the user behavior and find the valuable patterns. The ultimate goal here is prediction. A lot of studies are carried out in this area for developing and optimizing the prediction accuracy in every domain. A minor improvement in this prediction accuracy may results higher customer satisfaction and thereby increases in the profit in our business. Machine learning uses data to detect patterns in data and adjust actions accordingly so that, when it’s exposed to new data, it develops programs that adapt to that information. ML algorithms are closely related to a number of computational methods, such as computational statistics and mathematical optimization. Automated solutions using machine learning techniques are the future of retailing, even if they are still on the margins. They handily reduce the enormous amounts of time need for manual labor regarding tracking the prices of competition. By automating the decision making retailers, both online and offline, have more time to focus on other important and time-demanding aspects of their business.