Flipkart Fiasco – What Big Data Analytics Could Have Averted

Flipkart Fiasco – What Big Data Analytics Could Have Averted
Flipkart Fiasco – What Big Data Analytics Could Have Averted
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Nidhi Mahesh | The News Minute | October 11, 2014 | 6.39 pm ISTThe festive season world over is synonymous with shopping. Retailers, online or brick and mortar lure customers with offers and promotions, playing on their un-daunting streak to buy more for less. Despite the frenzy around marketing analytics and personalization, in practice it is the same old discounts that are the best bet, never failing their charm. Predictably, this year with billions of dollars injected in online mega stores such as Flipkart and Amazon in India, stakes are higher than ever. Diwali being the biggest Indian festival, with rituals such as buying of new products and precious metals and gems associated with it, the retailers are obviously aiming high.While the brick and mortar stores are following the usual trends of discounts, free gifts and novel range, it is the online retail that went a step further this year. The first to throw the hat in the ring was Flipkart, and despite unprecedented response to its “Billion Day Shopping”, the company had to end up massaging broken trust of millions of its customers with an unconditional apology from its founder duo. The promised merchandise at dirt cheap prices were flying off the shelf in nano seconds and the traffic on the website was far ahead of even the wildest expectations of the promoters. While this could have been a matter to rejoice, it became a major embarrassment, showing the under preparedness of the country’s biggest online retailer. This can be explained in two ways; either the company was over confident of its technological infrastructure and merchandise sourcing or it underestimated over 200 million deal crazy Indians’ who currently access to internet and are moving in herds towards glitz of online shopping. In both the scenarios there are unmatched lessons to learn, especially for the other online retailers planning their mega shopping festival this festive season. And, these lessons are deeply rooted in their ability to use the power of Big Data Analytics.1. Work as one, with shared vision and aligned processes: Connecting with customers with great offers and bringing them on board is a great ambition and is definitely followed enthusiastically in the organizations. However preparing the infrastructure for the groundswell and keeping plan B, C and D in quick launch mode in case of failure of original plan A is an exercise, mostly done in theory. In the recent Flipkart fiasco, what came a cropper was the preparedness of the organization for the mega event they had planned. One of the first things on the checklist has to be synchronisation of goal, ambitions and abilities. Also, a flawless coordination of different departments: Marketing, Merchandising, Supply Chain / Logistics need to be completely aligned to the IT teams. In fact the need is to ensure all work as one and have equal buy in on the desired goals. Often the targets set by one department is not realistically backed by the other. For example if the Marketing department has planned a major event the exact expected footfalls / visits expected must be evaluated and supported by the Merchandisers by ensuring that many deals from the suppliers matching the marketing promise. Similarly the logistics needs to be prepared for that many sourcing and shipments. Most critical factor is the shared vision and process alignment of the entire organization. Departments working in silos with bureaucratic attitudes only create confusion, commotion and finally critical loss of face in front of the customers. And this is clearly visible in the apology issued by the Bansal duo owning lack of co-ordination and process failures.2. Know the customer beyond cliché Knowing the customer is to be seen beyond cliché. Today organizations have enough data to understand customer preference and expected turnout in response to a planned campaign, which can be critical in planning & execution. In the case of Flipkart, as per the company they were taken by surprise by the response of the customers, there were just too many, too soon and too close on heels to handle! Now, this is clearly an opportunity lost. Let’s have a look at the possible data that the company could have looked deeper to understand and predict the response to its ad blitzkrieg alluring customers to the big billion day sale.a. Analyzing Current Customer baseFlipkart has a big customer base of both subscribed members and sporadic visitors. It also has the data to understand their buying pattern, preferred categories, response to deals and also data on a crucial indicator of access: by mobile app / website. These can explain the average footfall during the deals and during normal shopping. Also, gleaned can be the prediction on which categories or which pages get more response and when – say do people respond more to deep discounts across categories or are they wooed by new product launches. Another interesting discovery could be do customers buy cross category during campaigns and if yes, how much is the shift. For example, I am a subscribed customer and generally purchase books, but I respond positively when discounts are offered even in home furnishing or toys for kids. Now this is cross category shopping behaviour where I am not only increasing my basket share but also enlarging my category penetration. This data ca be crucial to understand traction and shift in percentage especiallyduring the deals.Also, Flipkart launched its loyalty program Flipkart First last quarter, reaching out to a section of its regular customers with free membership for the first three months. These three months certainly would have provided the company with enough reference data to understand buying patterns, density and frequency of purchase. Also, with its free in a day delivery to members, the loyalty program would have given enough pointers on the strength of the company’s logistics and customers’ response to it. This data could have been critical in objectively assessing the impact of the ambitious #BigBillionDay campaign, giving the planers enough indication on the possible response.b. Mining time and access data to uncover possible tractionThe data on average time spent while purchase both during an induced buying scenario and voluntary shopping expeditions are important indicators to build resilience of IT infrastructure. For example if an average shopper spends x amount of time while surfing, choosing, deciding and finally purchasing generally, while he spends X+1 time if some campaign is on – the difference in time spent is an important pointer on how long will the visitor be o the site and how deeply will he navigate before reaching the pay option. The infrastructure needs to be updated to take the required load. Another important indicator would be the time of the day when the footfall is at the highest. How quick or how laid back are customers in responding to deals? Also, how customers react to different types of deals – time bound deals like deals of the day, category oriented deals like discounts or offers on say electronics, brand based deals such as promotion for a particular brand etc. Flipkart had been experimenting with all such deals and would have a comparative data allowing it to be objective in its predictive analytics. c. Learning from the marketThis is rather simple, but often overlooked. Customers have a pattern in their response and this could be unearthed by keeping the ear to the ground and eye on the shop next door! This discount crazy country has seen stampede like situations whenever big deals and deep discounts are announced. Take the republic day sale at Big Bazar when the stores run for more than 18 hours a day and yet are unable to manage public expectations! This may be a brick and mortar example but it ha relevance in understanding the customer psyche and behaviour given a particular stimulus. Similarly there are lessons to be learnt from other sites and sales the world over. Lessons that are seen, observed but maybe not internalized.3. Be alert, converse real-timeThis is an era of constant real time communication. One cannot afford to wait for the storm to rage and die before coming up with a response. While the fist few minutes of the Big Billion Day gave the indication of things to come as the campaign progressed, the response was forthcoming only the next day and that too an apology. The customers felt cheated and left disgruntled. One approach the issue could have been to take the customers in confidence and make them a partner. For example if there was constant communication about the response the organization was getting on the campaign and how it was meeting it would have built not only confidence but a sense of enthusiasm among customers. Say, a message “We have 2 million customers on our site right now, we are excited and doing our best to serve you all, please be patient and support us as we work to solve any issues you may face!” or “Update – tablets for Rs. 1234 just go over – 3,000 pieces flew off inless than 30 minutes. If you wanted to buy one of those, please let us come back to you. But hurry to other departments such as XYZ where you can still grab your deals….” – these messages delivered to the subscribers or flashing on the app / website as ticker updates would have kept the customers informed and updated. Not to mention the almost complete ignorance of the rants on social media which started almost as soon as the day began! What was needed was proactive conversations, but what happened instead was that guests were called for the feast and gates were closed on their face! Now that’s not fair and certainly not a good communication strategy either.4. What’s the Plan B?Well short of a bride leaving you stunned at altar by eloping with a stranger, there can be a plan B for almost any exigency. And when you have a campaign or an event of a mega scale the alternatives also must be multiple. Apparently there was no plan B, C or D in action during Flipkart fiasco, except for an emotional apology. And, if there was one obviously it did not work. It is imperative to wish for the best and plan for the worst, as the proverb goes. In the case of Flipkart, the best turned out to be worst – now what could be more disillusioning than that. Finally it is not enough to have alternatives planned, but ready for execution – drills done, emergency response teams ready with their next steps and a chain of command pre-approved and practiced. We often make plans but do not check out the operational details, it’s like reviewing your answer sheets before submission during exams and more often than not the studious students want to cram one more line in before the final bell than reviewing what has been already written. It’s a matter of making the right choices at the right time, and hopefully we learn from others’ mistakes.Disclaimer: The opinions expressed in this articles are the personal opinions of the author. The News Minute is not responsible for the accuracy, completeness, suitability or validity of any information in this article. The information, facts or opinions appearing in this article do not reflect the views of The News Minute and The News Minute does not assume any liability on the same.

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