FirstCry.com: Leveraging the power of offline

In my blog post last week, I wrote about how a hybrid online and offline strategy is useful for collecting small data. As a couple of my readers pointed out, what marketers and strategists call small data, ethnographers and sociologists call as thick data. Honestly, I had not heard of thick data. @fernandogaldino introduced thick data to me. I dug through the online references on thick data, and realized we are talking the same thing. Exactly the same thing. Thank you Fernando! So, I am going to continue using the term small data (that is what I have read academic articles about) with the caveat that small data is also thick data. Last week, I promised to delve deep into FirstCry.com and its online-offline strategy. Here it goes.

FirstCry.com

The firm was founded by Supam Maheshwari and Amitava Saha in 2010 as a pure online venture. By 2011, FirstCry.com opened its first offline stores. In an interview to TechCircle, co-founder Supam Maheshwari elucidated how a vertically focused ecommerce firm could survive and make money in a market dominated by horizontally spread competitors (you can read the interview here). He talked about replicating Quidsi’s business model in the Indian market, by owning a set of vertical markets like diapers.com and soap.com. In replication, firstcry.com has a sister website goodlife.com. The key difference, he said, between the Indian and the US market for baby care products was that, more than 95% of the products were imported. In fact, that was the seed for the enterprise – his own difficulty in finding good quality products for his child in India, whereas he could buy a lot of them during his international travels. That effectively makes this business inherently inventory-heavy. One needs to leverage economies of scale and scope in sourcing, hold inventory and invest in logistics to be able to service customers across the length and breadth of the country (read about firstcry.com inventory model here).

Omni-channel strategy

Here is where the omni-channel strategy helps. Instead of keeping inventory in dark warehouses, ready to be shipped, it was possible for firstcry.com to open retail outlets in tier II and tier III cities (where real estate was also likely to be cheaper), where ecommerce penetration was not as much as the tier I cities and the metros like Mumbai, Delhi, or Bangalore. The inventory holding was thus distributed across the various franchised retail outlets. The outlets also provided customers with the look and feel of the products before they bought them – you need to appreciate that baby apparel and shoes dominate the market, only to be followed by toys and diapers. Clothes and shoes … when was the last time you bought your own shoe purely online? Inventory provided increased footfalls to the store, created brand awareness, and inventory off-take. The decision to have the same prices between online and offline stores, coupled with large touch screen interfaces to shop online from within the offline store could have provided exponential growth in traffic and sales.

Promotion: The FirstCry Box

Firstcry.com began promoting using traditional mass media – television and online ads. They invested in Bollywood’s longest serving (possibly) celebrity, Mr. Amitabh Bachchan as their brand ambassador and launched a few television advertisements (see some of their ads on YouTube here). However, they soon realized that mass media advertising was highly expensive and yielded low returns for a niche range like baby care products. That is when the idea of the FirstCry Box was born. The FirstCry Box is a bundle of some essential products that the mother would need during the first few days of the baby and mother reaching home from the hospital. Firstcry.com has agreements with over 6000 hospitals, through which these FirstCry Boxes are gifted to the new mothers, congratulating them on the birth. These boxes also contained gift and discount coupons from major brands of baby products, that the parents could redeem at either online at firstcry.com or any of their retail outlets. This ‘welcome kit’ to parenting provided firstcry.com a significant opportunity to build brand equity and recall amongst the over 70000 mothers receiving these kits every month. Some marketers call this permission marketing (read about it here), or direct-to-parents strategy. For me, it is a wonderful platform, a two-sided platform mediated by firstcry.com. Parents, especially first-time mothers, are initiated into parenting with the help of these grooming products (basic diapers and lotions) and the gift coupons for free. The new mothers as a subsidy side is being financed by the brands that provide the products and coupons to be included in the box and act as the money side in the platform. For the brands, this is highly targeted sampling of their products, and most mothers would stay loyal to quality brands/ retail stores in baby products. In the entire transaction between the mothers and the brands, firstcry.com benefits significantly in three ways: (a) store loyalty resulting in increased sales, (b) small data about how these mothers use these products, the basket, frequency of purchase, and willingness-to-pay for quality; and (c) good quality prediction of demand in specific geographies, leading to efficiencies in inventory and supply chain management practices.

By the way, such welcome kits are not entirely new – a lot of employers have been on-boarding their employees with such welcome kits. I first heard/ saw such welcome kits when I was part of a team that delivered a customized training programme for the ITES service provider ADP India, a few years back. It was fascinating to see how the entire family was on-boarded into the firm! Not just ADP, a variety of other new age firms, I see have adopted this practice (read this article on how some Indian organizations welcome their employees). I wish I was welcomed like this by my employers!

Are hybrid models here to stay?

I would say, yes. We saw how Amazon was opening stores in our blog last week. We also discussed how Amazon.in was using firms like StoreKing to reach the Indian retail hinterland. I read last week that their Indian competitor, Flipkart.com was also opening offline store to reach users in small cities (Flipkart to open offline stores as well). And in vertical markets like baby products, it has become all the more important to target your promotion very narrowly, and focus on the backend (inventory, supply chain, and logistics) efficiencies, while at the same time achieve scale.

Is vertical ecommerce a winner-takes-all market?

Three industry conditions define a platform market as a winner-takes-all market: presence of strong cross-side network effects, high multi-homing costs for the users, and the absence of special requirements. The baby products retail market is dominated by imported brands, is a highly fragmented industry, and the brand owners are dependent on their retail partners to promote their brands. The demand for these products are relative price inelastic, and consumers would be willing to pay premiums for sustained quality and reliability. An aggregator platform like firstcry.com would significantly aid in establishing and reinforcing the cross-side network effects between the brands and consumers. Second condition – the quality and reliability concerns of the parents would ensure significant store loyalty and brand loyalty. As long as there are no serious concerns, consumers would be loath to switch; and when the fill rates are high (there are no stock-outs of items that they want to buy) in their preferred stores, would not multi-home. In other words, consumer switching costs for brands are high, and as long as these brands are available with their favorite retailer, they would not shop from multiple outlets. And most infants have the same needs – diapers, creams, lotions, oils, and basic toys. Special preferences begin showing up only when they ‘grow up’. Some of them don’t ever grow up, but that is a different matter!

Firstcry.com and BabyOye merger and further consolidation

Given the industry conditions of geographically distributed year-round demand, operational efficiency and leveraging economies of scale and scope become key success factors. Consolidation is inevitable to achieve both backend (sourcing, inventory, and supply chain/ logistics) efficiencies as well as frontend scale (online and offline stores distributed across the country). That is why we would see waves of consolidation in such strong vertical markets. Like how firstcry.com and BabyOye merged their operations, I agree with Supam that this market will see more and more such mergers (read his interview here).

Lessons for enterprises focused on vertical markets

Based on what we have discussed over the past few weeks, I would urge enterprises focused on vertical markets (like firstcry.com) to (a) seriously consider your business model to include online and offline consumer touchpoints … for instance, online furniture store, Urban ladder is ‘pivoting’ to offline stores (read the news here) and are positioning their offline stores as customer experience centers; (b) invest in collecting and analyzing small (or thick) data through these omni-channel (or hybrid) business models; and (c) critically evaluate if the market conditions favour winner-takes-all dynamics.

Hope my readers from India and the diaspora had a great deepavali festival! Greetings from Bangalore.

Disclaimer: I am in no way related to FirstCry.com, Goodlife.com, its investors, or its founders.

(C) 2016, R. Srinivasan

Reference class forecasting using pluralism: Fighting single parameter obsessions

Traveling around prestigious Universities and Business Schools in the US this week on an institutional assignment (this post comes from Chapel Hill, NC), one thing struck me in this society, pluralism. I read with interest my friend Suresh Satyamurthy’s piece in yourstory.com (link here) that uses a hangman metaphor for an investor review in the start-up world. In Suresh’s start-up world, the investor is hung-up on a single parameter – scale (pun intended). It set me thinking – any evaluation of performance (more importantly, assessment of future performance) needs to be grounded in as many parameters as possible. In this post, I will introduce Reference Class Forecasting (RCF) as a technique for fighting such biases like single parameter obsession. Drawing on research on behavioural economics, I attempt to provide guidelines for entrepreneurs and investors to make better forecasts of future performance.

Intent-outcome relationship

This is possibly the first and the most obvious starting point of any assessment. Start with what was the intent in the first place. If the stated intent of the platform was to transform the industry, please define what is industry transformation and measure those, and not start harping on profitability. Not every business needs to show the same kind of performance on the same parameters. Take the example of baby products company, firstcry.com. The founders’ motivation to start-up arose from the difficulty in finding products for their own children – availability, variety, poor quality, and certain international products/ brands not available in India (read their interview here). So, the best performance metric for assessing the performance of firstcry.com would be to see if they have been able to “make a wide variety of good quality international products and brands available to parents”. The performance metrics would therefore be (a) number of outlets – online and offline, (b) inventory size and variety, (c) number of brands, (d) number of products uniquely available at firstcry.com, at least in a specific geography, and (e) number of parents reached. Scale here would mean growth in number of customers, brands, products, and channels. Not GMV, not anything else. Yes, profitability is important, but not the first parameter of success.

Constructs, variables, and measures

Hmm, I may sound like a research methods teacher, but I think this is important to understand. Everyone (at least those reading this blog post) understands that everything could be measured in a variety of ways. A construct is an attribute of a person/ entity that cannot be observed or measured directly, but can be inferred using a number of indicators, known as manifest variables. For instance, entrepreneurial success is a construct that is measured by a variety of variables ranging from firm performance, firm growth, market power, firm’s influence in industry standard setting, pioneering innovation, to even investor wealth creation (or exit valuation) at sell-out to a large corporation. Each of these variables could be measured using different measures; see for instance, the number of measures we identified for firm growth in the context of firstcry.com in the last section. Can you see a decision–tree like structure here?

Indices

So, when I think of multiple parameters, I am reminded of indices. Indices like Human Development Index (HDI) as a measure of economic development, or a Consumer Price Index (CPI) as a measure of inflation. Each and every of these indices are prone to discussions and debates about what constitutes these indices and why; and in what proportion/ weights. Take for instance HDI that is a composite of life expectancy (personal well being), education (social well being), and income per capita (economic well being). Why only these? What about social and racial discrimination? What about ecological sustainability? Similar is the case with consumer price index (CPI), which is calculated using prices of a select basket of items, with price data collected weekly, monthly, or half-yearly for specific items. Again, why should tobacco products prices be included in CPI calculations? Or we could debate of how the housing price index is calculated for inclusion in the CPI. Does age composition of the household matter in calculating the CPI basket? For a relatively young family, would the basket of goods not be different than those families with more elders than children?

So, to cut my long argument short, please refrain from creating indices that just simply represent a mish-mash of parameters to evaluate a start-up.

My recommendation: Use reference class forecasting

Reference class forecasting (RCF), sometimes also referred to as comparison class forecasting is a method recommended to overcome cognitive biases and misplaced incentives. My favourite article on this appeared in The McKinsey Quarterly (see here). Let me elaborate the theory first.

Nobel laureate Daniel Kahneman and Amos Tversky’s work on theories of decision making under uncertainty is the starting point for understanding RCF. They described how people make decisions that are seemingly irrational while dealing with probabilities and forecasts using Prospect Theory (see an insightful class by Prof. Schiller, another Nobel Laureate, on YouTube here). Summary relevant to us: people are more concerned by smaller losses than equivalent gains; and people round off probabilities of occurrence to either zero or one, when it is close to either, and in between, exaggerate.

Let us understand how an entrepreneur could use this theory to manipulate his capital provider. She shows some initial success, and likens her business model to an already successful model somewhere else, in some other context; and gets the investor to exaggerate the probability of her success. For example, I know a friend wanted to build the Uber of toys in India. Why buy toys, just rent them, let the child play for a week, and return it back to the library next week to issue a new set of toys. Sounds exciting? Just that the economics did not work out the cost of damages to the toys small children could do, that would render it useless for the next borrower (like breaking one car wheel). The entrepreneur kept the rentals high enough to account for such losses, and soon her customers realised that the rentals were working out far more expensive than buying new toys, notwithstanding the child refusing to part with his toys at the end of the week. The entrepreneur continued to convince his investors to keep investing in her, luring them to wait for the economies of scale to kick-in and she could have enough bargaining power with toy manufacturers to directly import from the North of Himalayas, but that never happened and the investor exited the firm at its lowest valuation.

These biases manifest themselves in the form of delusional optimism, rather than a clear understanding and detailed evaluation of costs and benefits, even when hard data is available.

Steps in using RCF: A field guide

RCF helps forecasters and planners overcome these biases by situating the reference point outside of the subject being assessed. In order to forecast (or assess future performance) a business, investors need to identify a reference class of analogous businesses, estimate the distribution of the outcomes of those firms, and benchmark the enterprise at an appropriate point of the distribution. Firstly, the investors should identify appropriate reference class for the enterprise. These reference classes need to be identified using a variety of parameters that match the enterprise. The next step is to analyse the performance of the firms in the reference class and map them into a probability distribution. There may be clusters of firms that may emerge during this distribution-mapping exercise; there may be instances of only extremes of firm performance observed (say in winner-takes-all markets); or there could be continuous distributions.

The next task is to use pluralism in the parameters to position the enterprise in the distribution. Here is where multiple parameters would help in an reliable estimate of the position. For instance, an Uber for toys in India would only work when the marginal costs of renting out a car (wear and tear) is negligible compared to the fixed (sunk) costs of buying the car. Whereas in the toys market, the marginal costs of a child playing with the toy is a significant proportion of the market price of the toy, and therefore this enterprise would not be subject to the same evolutionary direction as Uber. However, if the enterprise was repositioned as a toy library (as my friend ultimately did), it would work – look at how the cost structures of library and toys work. It provided her a benchmark on only buying those toys that would be durable, held the customer’s attention for only short periods of time, and were very expensive to buy. Typical examples were multi-player games, which no child wanted to own independently (given the small size of families today), but would rent out during the weekends/ birthday parties for a small proportion of the cost of the game.

So, hers is calling entrepreneurs and investors to overcome such cognitive biases and forecast better.

Comments and feedback welcome.