A Case Study : Customer Retention For A Video Rental Retailer

I was supposed to prepare a case study for the final round of selection by a Bangalore based start-up which specialized in CRM solutions.The following data was provided to me and I came up with this case study.

NetFlicks is a DVD and video rental retailer operating in India, and other countries. NetFlicks has several databases:

1. A customer table, containing names and addresses for all their customers and when they joined, and other information.

2. A product table, containing the descriptions of all products stocked. The products stocked include videos, DVDs, computer games, and also snacks and drinks.

3. A transactions table, containing all films, DVDs and games rented by each customer and when they were rented. This also includes sales of any snacks and drinks.

Currently they treat everyone the same - could and should they do anything differently?

In order to grow further and compete they have identified that they need to take a more structured and strategic approach; putting customer data at the core of their decisions.

How would you use this data to get an understanding of NetFlicks customers?

How would this understanding help NetFlicks in marketing to the customers?

What else, besides marketing could this information help NetFlicks with?

How should they analyse the market to assess where they would be able to expand into?

“How Data Analysis Can Help NetFlicks To Have A Better Understanding Of Its Customers and Thus Give A Marketing Push To Increase Its Sales”

                                                               A Case Study by:

                                          Rohit Kachhawaha

 

Client’s Info

NetFlicks is a DVD and video rental retailer operating in India, and other countries. 

Their products stocked include videos, DVDs, computer games and also snacks and drinks. 

The target consumer base is mostly teenagers and youngsters.Also, keeping in mind the other age groups as well, kids and senior citizens etc.

The stores have data recorded at customer, product and transaction level.      

Data Available and Its Application

Data is available for customer’s info, inventory info and transaction info. This available data can be combined to form a single compounded dataset.And, we can look for the meaningful patterns in data using analytical tools.

Based on the data, products could be segmented as per their respective demand; this would help in efficiently managing the inventory. Large stocks for the products that are in high demand. And, also clearance sale for the products not in demand.

Effect of sales of one product on another could be studied through data which would help in identifying the customer’s buying patterns. For example- People who buy more computer games tend to buy more drinks as well.

Based on the data customers could be segmented into categories as: 

a) Frequent visitor with high quantity

b) Frequent visitor with low quantity

c) Occasional visitor with low quantity

d) Occasional visitor with high quantity

These categories could be depicted on a mock graph as:

Customer Quality

 I quadrant depicts the (a) category customers, these are loyal and buy in high quantity as well. These are the most favorable customers.

·     II quadrant depicts the (b) category customers, these are also loyal but don’t buy in high quantity.

·     III quadrant depicts the (c) category customers; they contribute least to the sales.

·     IV quadrant depicts the (d) category customers, these visit occasionally buy in high quantity.

Marketing Measures Based on Different Customer Segments:

  

Customers in category (a):

These are the top priority customers,they bring maximum business, loyalty programs and incentives (in the form of discounts on bulk purchases or discount coupons for referring their friends) should be given so that they don’t migrate to other retailers.They must be informed in a timely manner about the new videos,movies.

Customers in category (b):

These customers come in second priority, they are high potential customers. Promotional offers with attractive discounts should be launched for these customers to convert them into category (a) customers.

Customers in category (d):

These customers come in third in the priority list; they are also high potential customers. They should be given regular updates about new product launches via SMSs, emails etc. Social media sites and promotional mails could be used for this purpose.

Customers in category (c):

These customers visit occasionally and thus have little information about the product range available with the retailer. They could be given promotional pamphlets describing the available product range with the retailer and also giving information about loyalty programs and promotional offers.

Based on Customer’s Buying Patterns:

By giving special discounts on snacks and drinks for bulk orders for DVDs/Computer games.

By launching festive season discount offers to give further push to sales which are already high during these times.

By arranging for special gaming sessions for gamers both online as well as in house which would attract new customers as well.

By having the facility of free home delivery to the most favourable customers.

Miscellaneous

Market survey to get an idea about what kind of product is in demand, services expected and if they are satisfied with the current service provider.

Having a dedicated service portal for customers where he can raise his query or call for any help, which must be immediately looked into.

Being Proactive – asking about how a customer finds a particular service. For eg, the store can ask him about the quality of the DVDs, videos rented to him.

What a brand asks for is a happy customer. Free advertisements are always welcome.

If a customer is buying only action movies, then on release of an action movie, he can be targeted.  Same goes for movies,videos of other genres.

If someone like to try movies of different genres then he can be targeted by non-popular yet good rating movies. So, if targeted suggestion system works out, customers starts becoming loyal to Netflix, it can then really boost sales.

The address of the customer is another important data type. We can easily find out from where the major chunk of the customers are coming.Lets say the majorly are located in southern India, then we can communicate those customers about the regional movies.

If a customers rents a movie, lets say, every 6days and he hasn't rented any movie for the last 10days, we can assume he has started renting movies from any different store/site, so before he becomes a dormant or inactive customer, he can be easily pulled back by giving an aggressive offer.

I have had a lot of help in making this case study ,especially from Puneet Chouksey, who almost co-authored it, Gaurav Agrawal - without him I would not have had the opportunity of having such an amazing selection experience, Ishan Jain and Harsh Vaidya for being the mentor. Thanks to you guys for making it possible for me.