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In the modern retail sector the consumers are the primary drivers of change in this industry, all the decision making process has become more sophisticated and complex than ever before. The objectives of this research analysis report is to find appropriate suggestions to the most commonly faced and concerning questions faced by retailers and what is the best course of actions that can be taken to attain profitability in the retail sector and how to sustain in the retail business in this digital age, Most common questions of them include
a) Identification of the valuble consumers and the strategies to retain them
b) What sort of price should a company generally has to offer to maximize its profits?
c) How to target the potential customers in the future campaigns?
d) What are the inventory valuation and stock handling methods to be adopted so that a company can face the possibility of running out of stocks or measures to be taken so that no excess stock is maintained
There is a quite a bit of uncertainity in this retail business especially in the unorganised retail business models. The key to solve such complex business challenges lies in the efficient and effective way of using modern technological methods with the help of data analytics, a key discipline that involves extensive usage of data and statistics with the help of machine learning to reveal the valuble insights that help the business to prosper and these can be a valuble business aiding tool in the decision making process
The practise of using analytics in retail sector began from simple mining techniques like in the IT programming language of SQL queries that would just contain the subset of interests which are filtered from the main database which by example we can understand like in a small scale retail store which uses computer entries of every customer with the help of this software it could show the details of revenue the unit was generation from its top 10 percent of buyers every month.
But since the cloud based POS system came into usage along with advancements in mobile technology and the excessive usage of social media by the millineals has lead to the exponential growth in the volume of data that is being generated and stored with the retail organizations. The scope of analytics for the retails using statistical tools and machine learning algorithms to findout the patterns in the data to predict the future outcomes has become a possibility.
For instance a simple regression analysis can be used on the demographic and connection with the pattern of their buying behavior in the past can be used a vital tool as respective propensities to respond to a future campaign. The fact that such insights are based on hard numbers makes analytics a very powerful tool for enhancing the objectivity in decision-making.
Given the wide range of analytical practices followed in the industry, it is not viable for any retailer to pursue all of the available options with equal thoroughness. This makes the selection of practices a vital decision from the wide range of option. Such a selection must be in sync with the model of the business that suits the goals and matches the aim of the firm in the long run
To further explain this concept let’s look at the possible points of focus
1) For a retailer aiming to be the best price provider in its segment, developing analytical solutions related to pricing and supply chain would probably be the best starting point.
2) An organization looking to expand into new markets should venture into analytics with a focus on solutions related to the selection of store location and store size that suits its business needs..
The next section of the report explores the breadth of analytical practices in retail, demonstrating their major applications through nine frequently used solutions across three different categories marketing, supply chain management and customer experience.
The Business Analytical tools that can help to enhance and improve the customer experience
With the increase in competition and advent of new channels like web and mobile technology, customers can now choose from multiple options when it comes to making final purchase decisions. So, providing a superior customer experience across channels has become critical for both acquiring new customers and retaining existing ones. Retailers can improve customer experience through prompt response and appropriate messaging based on customers’ preferences. Some examples are given below.
Step-1 Recommendation of Products
Retailers can use customer purchase history and past transaction baskets to recommend products that they are most likely to buy.
For instance in a retail store it is presumed that there is one customer who visits the store frequently, the system analyst has all the data records of purchases being made by the customer every month so simple with the use of the modern data analytics a company can identify the frequent buying patterns and next time the customer visits use this data to specifically suggest the customer’s favorite brand or product or recommend the upgraded version or different models of same product so this gives a better personal experience from the shopper’s point of view and easy for the company to analyze its best moving stock or consumer preferred products so that they can maintain required stock in case of contingency.
Step 2: Bundled products or Combo Selling technique
This analytical solution can help retailers in developing targeted product recommendations for customers. Doing so can lead to an increase in the wallet share of customers and improve customer experience and loyalty.
A typical example of product recommendation is the instant suggestions given by various e-commerce websites as soon as a person selects a product. They present a list of similar products under the heading ‘You might also be interested in.’ This helps customers to find meaningful products and helps the organization increase its cart size.
Customer conversations on social media platforms can be leveraged by retailers to engage them online and manage their experience in real time. This can also help retailers obtain meaningful insights, which can be shared with relevant functions within the organization for further action.
a) Social media analytics tools consist of web crawlers that can capture unstructured data across multiple social media platforms.
b) A text mining model then parses conversations into positive, neutral and negative buckets based on their content.
c) Sentiment analysis can help track consumer behavior in real time across channels and monitor brand health online.
Retailers can create customized promotions for their customers based on their product preferences as well as their past responses to similar promotions. The analytical solution involving regression techniques first identifies customers who are to be targeted and then helps design customized promotions for them.
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