Customer Analytics at Flipkart.Com It was typical cloudy monsoon weather at Bang

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Customer Analytics at Flipkart.Com

It was typical cloudy monsoon weather at Bangalore on July 28, 2015. In the Darwin room of Flipkart’s Cessna Business Park office, Ravi Vijayaraghavan, the head of analytics and Pravin Shinde, senior manager Analytics were brainstorming various business problems that Flipkart as an e-commerce company was encountering. Flipkart had been putting in much effort and emphasis on the use of Analytics in every aspect of decision making. Forecasting demand for thousands of stock-keeping units (SKUs), predicting returns and cancellations of orders, predicting the reasons when customers contact the customer service centers, optimizing markdown pricing, identifying various types of frauds, optimizing vehicle routing, and enabling adherence to service-level agreements, were some of the typical problems that the analytics division of Flipkart was solving using state-of-the-art analytics techniques. In 2015, the team included about 100 data scientists mostly recruited from institutes such as the Indian Institute of Technology and Indian Institute of Management specifically for this purpose.

E-commerce in India had seen a compound annual growth rate (CAGR) of 34% since 2009 and was expected to exceed USD 22 billion by 2015. Under the e-commerce head, e-travel in itself comprised 71% of the total e-commerce market, e-tailing which comprised online retail, and online marketplaces have been growing exponentially and well-poised to become the fastest growing segment, expected to reach USD 56 billion by 2023. The industry believed that growth was at an inflection point with the key drivers being broadband internet, rising standards of living, wider product range, and changing lifestyles of Indian consumers. Such high growth rate also created several business challenges to e-commerce companies as well as to their market place suppliers, among them profitability still remained a major challenge. The e-commerce companies in India incurred combined losses of around INR 10 billion through heavy discounting to penetrate into the brick and mortar retail customer base.

Ravi Vijayaraghavan started the meeting by stating:

We have been analyzing our data to gain insights, but, do we know the value of our customers? I think it is important for us to differentiate our customers through metrics such as customer lifetime value, which will help us to manage them effectively. For example, we can make our promotions effective if we know the customers with high customer lifetime value.

Customer lifetime value (CLV) is the net present value (NPV) of future cash flows (or profit). CLV is usually calculated at a customer segment level. The main challenge in calculating the lifetime value of customers of e-commerce companies such as Flipkart is that the exact life of the customer is unknown owing to data truncation; that is, the actual point in time of customer churn, may not be identified in ecommerce, since there would be no prior communication from the customer about the churn. Hence, traditional models of CLV calculation may not be appropriate for e-commerce companies such as Flipkart.

ABOUT FLIPKART

Flipkart, the poster child of Indian e-commerce, was an early entrant in the nascent Indian e-commerce market and quickly established itself as the leading company in this space. It was founded in 2007 by Sachin Bansal and Binny Bansal, both alumni of the Indian Institute Technology, Delhi. They pooled in INR 2,00,000 (approximately USD 3,150) each to start Flipkart in 2007. From a startup with an investment of just INR 4,00,00 (approximately USD 6,300), Flipkart had grown into an online retail giant, valued at over USD 15.2 billion as of 2015. Flipkart was running the marathon with ample support from private equity players such as Tiger Global, which invested over USD 1 billion as of 2015. 4 Flipkart sold over 30 million products from more than 50,000 sellers in 70+ categories and consisted of 30 exclusive brand associations, with in-a-day guarantee in 50 cities and same-day guarantee in 13 cities. Flipkart was 33,000 people strong and had over 50 million registered users with over 10 million daily visits and 8 million shipments per month. A burgeoning consumer class, coupled with a rising web literate population and zealous venture capital funding propelled Flipkart to become India's answer to Alibaba and Amazon.

The use of e-commerce to buy products and services was spreading at a fairly rapid pace in the psyche of the Indian consumer. In Indian cities such as Bangalore, lack of time, ease of shopping, and attractive pricing were major drivers for online shopping. On the other hand, accessibility to a variety of products encouraged customers from smaller towns and cities to opt for the online route.

CUSTOMER ANALYTICS AT FLIPKART

E-commerce companies such as Flipkart had access to huge amount of data, available for applying predictive, and prescriptive analytics to take data-driven decisions. Flipkart had a strong analytics team headed by Ravi Vijayaraghavan, which used statistical models and machine learning algorithms to generate crucial customer insights. Flipkart had more than 50 million registered users and the transaction data of these customers could be used in a far more meaningful way using analytics to predict online consumer behavior.

In 2015, the Indian e-commerce market space was facing immense competition owing to the entry of Jabong, HomeStop18, Infibeam, Indiaplaza, Snapdeal, and a plethora of other pure-play and multichannel e-commerce companies. The continued growth of e-commerce and tough competition compelled Flipkart to seek a competitive advantage through more sophisticated analytics. Flipkart has been taking several analytics-enabled decisions, for instance, using web analytics to determine which landing pages encourage customers to make a purchase as well as which pay per click ad campaigns were most effective. In the face of tremendous competition, more than ever before, the analytics team at Flipkart aimed to predict customer demand for the products, understand its customer’s loyalty, assess the true impact of customer retention strategies (discounts, coupons, and extra services), and focus on customer segments with higher retention and spend potential.

In 2015, Flipkart wanted to understand its customers better and retain most of them through effective promotions, since customer retention is less expensive as compared to customer acquisition. Unlike the churn in the telecom sector, which was clearly defined and captured (in the instance of postpaid customers), churn for e-commerce companies was difficult to define and capture, as these events were unobserved. Across e-commerce companies, the customer churn may be very high owing to reasons such as need fulfilment, cessation of demand, competition, and so on. However, it was important to capture customer churn and identify which customers should be retained.

CHURN ANALYSIS AND LIFETIME VALUE

E-commerce companies faced a scenario of inconsistent customer purchase pattern wherein the gap between purchases could stretch far more than 6 months. Even though most of these buyers could come back after a gap of 5–6 months, Flipkart aimed to identify high value customers, and subsequently increase purchase traction among them.

The analytics team at Flipkart wanted to model customer purchase patterns, repeat buyer trends and calculate churn probabilities to help them identify the repeat customer segment to focus more on these customers for their marketing and promotional strategies. The final objectives of this exercise were to forecast the revenue generated from existing customers and calculate their lifetime value.

DATA DESCRIPTION

In order to carry out a detailed customer value assessment addressing customer churn issues, Flipkart collected sample transactional data spanning across 2 years: January 2013 to December 2014 such that all the 30,000 customers in the sample had made at least one purchase in January 2013. This was done to ensure new customers in the above said period were excluded from the study.

DATA ANALYSIS

To understand customer churn and lifetime value, Pravin’s team decided to use Discrete Time Markov Chains (DTMC). To build the churn model, the team first had to identify the period of inactivity (gap between transactions) to define churn. Gap was thus defined as the difference in months between two successive purchases or the difference between the current month (despite no purchase) and the last purchase month.

Pravin’s team also wanted to forecast revenue from existing customers and therefore built a model using Recency (defined as when was the last purchase was made by the customer) and Monetary (defined as how much money was spent in the latest month which had a purchase). The team retained the recency state definitions and augmented the state space by adding monetary slabs for each recency level.Whenever a customer is in an inactive state, the monetary values are retained from the month of the last purchase. The TPM for the Recency–Monetary DTMC is provided as an accompanying file to the case.

The team was also keen to identify customer segments based on their current transactional attributes such as recency, monetary, and frequency. The objective was to provide a fairly accurate mechanism to study the purchase patterns of various customer segments and thereby enable effective promotions to increase customer spend and arrest customer churn. The team chose a quarterly transition time period to account for macro-level stochastic changes in the model. Whenever a customer is in an inactive state, the frequency and monetary values are retained from the quarter of the last purchase.

The transition probability matrices from each of the models were used to calculate the customer lifetime value for customers in each segment and subsequently build an effective campaign strategy to reduce churn and increase customer spend.

Outside sources may be needed (https://www.flipkart.com/). Determine the price characteristics of the marketing mix.

Price: What are your objectives and strategies?

Nature of demand (price sensitivity, price of substitutes):

Demand and cost analysis:

Markup in chain:

Price flexibility:

Price level and impact on customer value:

Adjustments to list price (geography, discounts, allowances):

Explanation / Answer

Determine the price characteristics of the marketing mix.

Price: What are your objectives and strategies?

Ecommerce is a money making business only after the economies of scale being achieved. At the initial stage of the business it will guzzle lot of financial resources before even the first order is fulfilled.

Also once the firm is big enough like Amazon, Alibaba etc it would command more channel power and thus would gradually increase it operational and net margins.

Hence in the beginning stages of growth the pricing has to be kept low (in fact in Indian ecommerce market, they have been undercutting to drool the masses from the traditional retail setup to etail).

In fact Flipkart at one point of time, even had a vision that we need to scale to such a level that we would just maintain a net profit of INR 1 ($ 0.015 ) on each pc but with the entry of Amazon and its’ big push in terms of investment Flipkart got derailed. With the latest round of funding coming from Softbank, it has SURVIVED the onslaught of Amazon.

Hence we can conclude that pricing HAS to be kept at its’ minimum in order to get a stronghold in the Indian ecommerce market until the population graduates from price as the key driving force to other factors such as customer experience which would definitely happen but may be 10-15 years down the line.

*I have worked in Indian ecommerce industry for last 5 years

Nature of demand (price sensitivity, price of substitutes):

The ecommerce market is highly elastic in terms of price sensitivity. Due to the ease of purchasing, if the prices are discounted (volume based, free delivery etc), the demand is readily pushed up. Also gradually the majority of the purchases are happening with plastic money, customers impulsively buy more if prices are dipped slightly.

But the same sale can be lost to another ecommerce player, in case he offers a tad lower price on the same product. Customers in fact always compare the product on different websites and are ready to place order on the site which costs them least (other factors like speedy delivery, better customer care etc. are minimal and work only for the customers in the higher income brackets, the general masses is primarily driven by cost)

Markup in chain:

ALL the supply chains for the ecommerce industry in India are bleeding to say the least. It is a tough decision whether to outsource the supply chain to a 3PL, 4PL and take a hit on customer service and experience or to build your own supply chain at the cost of humungous Capex. Amazon, Pepperfry has taken the latter route in India. Flipkart is dabbling a mix of both in-house and outsourced supply chain partners. Smaller players like healthkart, firstcry etc has outsourced it largely to focus on their core competency rather than building a supply chain competency.

Price flexibility:

Adjustments to list price (geography, discounts, allowances):

Indian ecommerce players have heavily discounted products to achieve the initial traction needed to woo customers either from traditional retailers or other retailers (so much so that there were multiple protests, lobbying by the brick and mortar players some of them even asking the govt. to allow them to keep shops open 24/7).

Allowances in the form of unlimited free deliveries, unlimited returns for an INR 500 ($ 13) fees per year offered by Amazon, Flipkart first membership for a similar amount and offered facilities. Some players have even offered online videos, movies in the same membership. Another big allowance offered is the ZERO cost EMIs or EMIs starting after 3 months which pushes the customers to buy stuff immediately.

Thus the regular strategy is to keep the list price high and give high discounts to push up sales. In case of ecommerce business, the list prices are the same across all regions as the channel is online.

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