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Submitted By:
Anant Katyayni (G039)Krishna Kashid (H025)Rishabh Rastogi (H047)Kanwar Pal Singh (H059)
The deep-discount model followed by e-tailers is UNSUSTAINABLE
Are Deep-Discount models Sustainable?
http://articles.economictimes.indiatimes.com/2015-07-13/news/64370623_1_kumar-mangalam-birla-coal-payment-bank
E-commerce business model only works if you have growth. Month-on-month growth has now stopped. The day you reduce discounts, your sales drop by 40-50 per cent. This model has to survive on its own in some form. It will be reinvented. It will emerge and find its own space.
Predatory Pricing
The Dangers of Discounting
Effect of Discounts on Sales Target How much more you need to sell?
Discount
Source: Business Victoria, State Government of Victoria, Australia
Addressing real problem: Why don’t people buy?
Source: http://www.slantmarketing.com
1. Free Shipping: 61% consumers want free shipping
2. Special offers : e.g. Free Gift wrapping3. Bundled Pricing: Show customer the benefit of
buying complimentary products/services (Upselling) e.g.
[Shampoo + Conditioner] [Manicure + Pedicure]
4. Add VALUE to the purchases: Computer hardware supplier - free an
installation support. Hairdresser – a free facial massage or
blow wave with haircut.5. Quantity discounts / Bulk Discounts:
Offer a % discount when customers buy in bulk quantity Increases the SIZE and VALUE of customer orders . e.g. Buy 5, get-one-free
Alternatives to Discounting
71%Women search apparel online, but buy offline
Major Reason“Fitting Issues”
Current Scenario Business Insights20%
Average Return Rate in Apparels
Choose Body Type Hair wear Type Choose Model
How to ADD VALUE?
18Different
angles
Recommendation 1: Add VALUE by Differentiation
Source: IndianExpress.comeconsultancy.com
3D Trial
Room
Recommendation 2: Dynamic Pricing (Analytics)
Big Data competitor pricingmarket trendssales volume etc
Improves profit margins by 25% on average
Analytics and Insights
Analytics
Neural Network
Decision Tree
NearestNeighbour
Cluster Analysis
Decision Tree
• Strategic Data based decisions - Gerber Products Inc.
• Used to predict CLV• Value based
segmentation• Customer
Classification
Nearest Neighbour
• Identify violations in business transaction
• Unconventional or sporadic behaviour of consumer
• To study the impact of campaigns on top line
Cluster Analysis
• Grouping of certain type of customers for consumer insights
• Behaviour of teenager male and female can be studied
Neural Network
• Human brain neurons• Learning algorithm
used to increase accuracy over the time
• Robust system be built over a time
Exposure funnel: Suspects -> ProspectsAdoption funnel: Prospects -> CustomersRetention funnel: Customers -> Advocates
Fixed Segmentation vs Dynamic Segmentation
• Broad division based on customers needs/wants
• Limited number of concurrently active offers/messages
• Over reliance on discount marketing• Largely single channel• Low investment on technology
Fixed Segmentati
on
• Supporting next best offer for each customer
• Unconstrained set of offers/inspirational content
• Intelligent, highly targeted multi-channel communications
• Marketer friendly point and click interface
Dynamic Segmentati
on
Average order size Revenue per customer Customer profitability Cross brans customer
synergies Customer Tenure
Time to market new programs or offers
Sample Twitter Sentiment AnalysisVisualisation1
Visualisation2
Visualisation3
Visualisation4
For sentiment analysis, we have used text mining and statistical models to gauge twitter sentiment using R-programming for Sunslik. The R file of code is attached below.
Sample Twitter sentiment analysis:
Visualisation1 – Quantitative graph to measure polarity of the sentiment. For e.g.. +3 denotes strong positive sentiment, while -3 denotes strong negative sentiment and 0 signifies neutral commentVisualisation2 – This visualisation categorizes tweets on the basis of emotions such as joy, anger, sadness etc.Visualisation3 – Qualitative depiction of all the tweets in the form of positive, negative and neutral commentsVisualisation4 – WordCloud plot shows recent words used by people mentioning #Sunsilk
Twitter.R
Twitter Sentiment Analysis
Big Idea: Agent on hire (Portal & Mobile Application)
Hiring: Agents will be hired in collaboration with NGOs and Students, petrol pumps and kirana stores to act as delivery boys
Registration: A portal/app to register local and-hoc agents who can act as ideal manpower to furnish orders from e- commerce companies
Verification: Due background check to be done before getting the agents on board. If case of any misconduct, the source network of the agent will be blacklisted
Delivery Broadcast: Orders for delivery will be published on App which will be broadcasted to all agents; Agents confirming first would be responsible for delivering
Package Collection: Agent would collect the package from sub warehouse/locker located at a maximum distance of 50kms from city and deliver it
Cash Deposit: In case of CoD orders, the cash has to be deposited in the nationalised bank in the city within 1 day
Payment: Agent will be paid per delivery (6-8 packages will constitute one delivery) and commission on high value items
How It Works
Our Offering
Creating crowd sourced aggregator model for last mile deliveryOn demand pickup and delivery of packages by ad hoc agents
Value proposition
Facilitating CODIncrease in area under coverageEfficient DeliveryRecognition for partners
Target market
Unpenetrated areas of tier II and new tier III cities where maintaining a last mile delivery person is not feasible High mobile penetration
Marketing channelTraditional print media to execute a pull strategyArea Manager responsible for bringing business and people on board
Go To Market Strategy GAP Addressed
Optimizing the cost of last mile delivery by saving up on the cost of maintenance of delivery personnel in locations with lesser order traffic, by outsourcing the job to ad-hoc vendors
Game-Plan We will start with 20 tier 3 cities all over India
for Pilot run with following prerequisites: Sub Warehouse/ Locker in 50km radius
with good public transport connectivity Decent mobile/internet penetration Nationalized Bank availability
Using the local print media to publicize the launch and inviting applications
Creating localized campaigns focussing on engaging vendors and their people.
Launching the app in regional languages for better reach
Promoting the positive goodwill generated for both the parties after this association
In future Tie ups with local transport provider and cab aggregators
- Agent equipped with smart phone- Can accept / reject the order
based on the convenience- Advance Analytics to ensure the
agents are belonging in vicinity of destination addresses
- Real time GPS enabled tracking of the shipment
- Geography wise scheduling of deliveries
- Efficient planning of deliveries- Security of the agent will be
ensured by giving only one high value item at a tie
Predictive Shipping: They know it even before you think itofficially known as- method and system for anticipatory package shippingPatented by Amazon in Dec 2013
a method for shipping a package of one or more items “to the destination geographical area without completely specifying the delivery address at time of shipment,”
Predicting customers’ orders could Increase Sales and potentially Reduce Costs in: Shipping Inventory and Supply Chain
Forecasting model uses data: time on site duration of views links clicked/ hovered over shopping cart activity wish lists
Additionally the algorithm also sprinkles in real-world information: customer telephone inquiries responses to marketing
materials