Posted in Operations & IT Articles, Total Reads: 701
, Published on 23 October 2015
“Your future is your Data.”
Yes, in today’s business world, data is playing a crucial role. The total power of this data lies in the application of analytics. The strategic application of analytics in daily operations has become regular in many high businesses. Healthcare, Finance, Banking, Sports, Insurance, Retail industry and many more industries are getting benefited from analytics.
Retail sector is one of the major sectors which are playing a huge role in Indian economy, contributing around 15% to the Indian GDP. Being world’s second largest populated country India has a very fast growing retail market, which is currently worth of Rs.30,000 billion. Big retailers like Reliance, Future group, Aditya Birla, Shoppers Stop, ITC, Pantaloons, etc. holds around 4% of market share. Now along with online stores like Flipkart, E-bay, and Amazon, etc. the competition is increased with the allowance of foreign retailers.
Here comes the analytics in the picture. Out of these many competitors, if a retailer wants to survive he has to make the best use of available resources and data is the abundantly available resource. The effective use of data will relieve many hidden connections between a retailer and his customers and between him and his competitors. So, this helps in a great way to the retailer to take the lead in the retail market.
Retail analytics is imperative nowadays for retailers as there is huge competition. Retailers who are using analytics are much ahead than their competitors. Attracting a new customer is important but retaining existing customers is more crucial. One of the objectives of the retailers is to increase the number of loyal customers. And analytics is much necessary to make this successful.
Using analytics, different customer buying patterns can be analyzed to gain insights on their purchase behavior. In Analytics, two kinds of models can be best utilized for retail, predictive models and descriptive models. In predictive analytics, we can use the past data like number of different types of products and combination of products that were previously sold, quantity of products sold on a given day and during promotion season etc. to predict the customer's future buying behavior. By doing this, the basket size or the bundle value can be increased so that a normal sale can be converted into a more profit making sale.
An algorithm called Apriori can help in achieving this. It identifies the frequency of individual items in transactional or purchase data and moves to identify larger item sets from the dataset that contain the high-frequency items. From this combination of products that were sold frequently can be found out and thus, the inventory of these products can be increased. Promotions can be implemented for the identified product baskets and also, appropriate products can be added to this combination in order to generate more profit.
In descriptive models, customers are classified (like demographic segmentation) and targeted individually. Companies can identify their customer base that is more profitable and subsequently advertisements, promotions and marketing strategies can be planned to target the individual customers.
So, what all benefits are there for a retailer who uses analytics? It goes as below:
It will help to find out
• Which consumer segment should a retail company focus on? (Each customer is different, and their needs are different)?
• What promotion activities are more effective?
• What should be the promotion strategy?
• What is the impact of pricing on sales?
• How to deal with the products that are perishable?
• How to reduce the risks in operations?
• How to reduce the risks from side of supply and demand?
It will help to optimize
• Allocation of shelf space and merchandizing
• Design of the store
• Management of Inventory
• Supply chain design
• Distribution management
There are many sources of data that can provide the most valuable information for retailers, out of which social media data is gaining more importance. There are few methods used by some retailers to collect data out of which two are mentioned as below:
Here customers are monitored physically. Videos recorded in the stores are used to track the customer behavior and to find out the customers who frequently visit the store. These things will give an idea to retailer about designing his store, placing the right product at particular place (which increases the sale of that particular product), etc.
Here the information about the customer is retrieved through the online sources. For example, Text mining tools and techniques can be used to get the data from social media like Facebook, Twitter, etc. for tracing the individual customer movement in online. This is useful to find out the satisfaction level of customer, how much one likes the brand, who is the loyal customer, etc.
Consider a post in Twitter like
“I hate XYZ milk chocolate but XYZ fruit & nut chocolate is the best in the world.”
This statement passes information to the retailer that customers do not like XYZ Milk chocolate. So, retailer can reduce the quantity of this product and can save the inventory cost. Meanwhile, the twitter post also gives information that XYZ fruit & nut chocolate is gaining the attention of the customers. So, retailer can plan about the procurement quantity of this product and maintain the inventory according to it. This post also gives a hint about the brand preference of the customer. (Note: All these assumptions can be made when there are a number of similar posts).
After analyzing this data, retailers will know customers’ sentiments, brand awareness, complaints, etc. which will help in channelizing investments, planning pricing strategies to beat fellow players and generating more revenues.
A retail player who is very well-known in brick & mortar and catalog sector of the market has identified through multi-channel analytics that the behavior of customers in offline has changed according to his promotions in online. Their results were also helpful in recognizing the best-preferred communicating techniques with their customers and also helped in providing maximum satisfaction to their customers their by increasing loyalty. So, the retailer has to adopt customer-centric approaches providing information through statistical data or corrective measures during interactions with customers.
It is also important to improve the likelihood of purchase, supply chain optimization to ensure there is a timely demand fulfillment balanced against cost of carrying inventory in excess and ultimately retailers plan will be to gain more share of customers’ wallet and few things will depend on how well you know the customer. Business Analytics & Intelligence will also play an important role in achieving these improvements.
According to the survey, it is found that a sale by existing customers is around 60%, and sales by new customers is around 15%. A retail company profits can be increased up to 100%, if it can avoid 5% of its customers moving out of its business. It is evident that analytics will play a crucial role in retaining existing customers, attracting new customers, increasing loyalty of customers and thereby increasing profits and market share. Therefore, big data should be used effectively and efficiently through analytics, and firms should concentrate on customer relationships and sales in both brick and mortar stores and online portals. Retailers should also harness the mobile technology as retail analytics is getting bigger.
So as to achieve the bigger leap in the industry, a retail company should have a very good data scientist i.e. it has to employ a set of people for the purpose of analytics who can play with data like anything to bring out all the fruits in the hidden basket of data. So in today’s business world retail analytics is not only helping the retail companies to breakout the challenges, it is also generating the employment opportunities for talented statisticians in the country.
This article has been authored by Sandeep V from IIM Udaipur