Data Analytics - Distribution- E-Commerce - Retail Industry - techxmedia

Role of Data Analytics in Distribution, E-Commerce & Retail Industry

Big Data Technology
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Attributed to Catalytics Datum

In the new normal of the Pandemic affected globe, Retail, E-commerce and distribution industries have been compelled to gear up entirely to counter the financial uncertainty and economic recession looming large. The approach towards sales, increasing margins and above all retaining customer loyalty with immaculate brand perception has been a key factor.

Internet and Social media have made competitive offerings by a single click opening newer and broader options for customer to choose from. Any brand loyalty puts to a great challenge since several factors like competitive pricing, availability of range and social media reviews, negative and positive product reports. Erstwhile lead magnets as Discounts, Point or rating systems, and free gifts have become clichéd strategies.

Importance of Loyalty and Brand perception - techxmedia

Importance of Loyalty and Brand perception

Researchers say the acquisition of a new customer is five times costlier than retaining an existing customer. Moreover, an existing customer is almost 70% more probable to buy rather than a new one. Besides, the profitability rate of a customer increases over the lifecycle of a retained customer as the reduction in churn rate is directly proportional to profit percentage.

Industry leaders have leveraged Big data and analytics to outline action plans for better engagement with customers such that they can surge ahead in the competition. Distribution, E-commerce and Retail domains harbor the treasure trove of volumes of customer data and by using Big Data Analytics as the key tool, those data can be mined to unravel precious information such that personalized loyalty inducing strategies can be drawn such that each customer can be catered with their tailored individual needs.

Role of Data Analytics

E-commerce stores and retailers interact with customers in every stage of  the lifecycle gathering precious data insights to describe, forecast and enhance business results.

Predictive and Descriptive Models are the two salient analytical models being put into practice. While the former mainly works on historical data and analyzes past customer performances to capture the consumer behavior to predict better effectiveness, the latter mainly acts to quantify relationships between Consumers and products.  Predictive models use algorithms like logistic regression, random forest, and neural network to forecast by combining the best of mathematics and computer science.

Any customer’s feedback to a particular product is stored in the data volume which can be analyzed to predict the increase of the value of the basket size and to provide the same customer with a better product offer. That pool of data is used to classify customers in groups using descriptive data models which can identify not only the customer behavior but also the relationship between customers and the product chain. By using Apriori algorithms, one can figure out the association between product – customer, how a customer buys different products. Identifying and recording the reactions of different demographic groups of customers towards the same product. For example, a new brand of chocolate will have diverse reactions in the age bracket of pre-teens, teens and early twenties. This customer-product relationship identification can leverage to upsell products vividly.

Data Science is also used to provide valuable insights into merchandising and operations by using different algorithms, decision trees and planograms. Using these pieces of information, Store shelf Optimization business owners deduce and decide which products are to be kept on shelf and what products to keep at the cash counter to capture impulsive buyers.

Process and challenges in collection of data:-

Data is collected across different channels, as consumers purchases and make buying decisions in both physical shops and online or social media.  By In-store analytics physical monitoring of customers can be done by video tracking movements, determining gender through recognition technology and identifying unique visitors across multiple store visits. This data is stored and used to improve in-store experience of the customer and help to pinpoint hot selling products and the area of purchase in the store. Similarly, through Online analytics similar data is collected from online store visits and various reviews and ratings. Various social media monitoring tools, mobile devices, Geographical locations, digital sensors, automation, various Point of Sale and Website searches help to track online behavior, brand sentiment and purchase behavior.

With Data analytical models business owners can achieve a competitive edge over others by adopting the following:

  • Customer segmentation and profiling. This helps to identify the most profitable customer base to target. Segmenting consumers in distinct homogeneous behavior groups based on their buying patterns.
  • Understanding Consumer behavior in forecasting demand and supply. Identifying and targeting the most valued customers to ensure to become return customers.
  • Customer Relationship Management to engage the segmented customers to influence their buying behavior by using the Key Performance Indices (KPIs) of each segment to build strategy.
  • Competitive pricing keeping a tab on profit margins.
  • Imbibing mobility in marketing analytics and improving social media outreach with customers to understand their social behavior and provide insights and important information to them.
  • Combining both in-store and online channels to analyze the buying behavior and browsing patterns.
  • Campaign propensity by giving the right information to the right customer. Targeted product offerings with proper promotions and advertisements triggering consumers to identify the need to buy the products.
  •  Identifying the proper marketing channels and media and thus assuring return on marketing investment.

Big Data analytics offers businesses to directly address cost, build a loyal customer base and turning loyal customers into effective brand advocates. It is imperative for business owners to dig deep into various customer data to utilize data analysis tools to capitalize on better connections with customers to build a strong loyal customer base and a sound brand presence in the domain.

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