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[Also published on VCCircle]
There has recently been increased discussion, and mainstream press reporting, on the adoption of a ‘marketplace’ model (vs. an inventory model) by e-commerce companies (e.g. these two articles in Mint: Mint 1 and Mint 2). This discussion reflects an underlying presumption that one model is better than the other. In framing the issue as a comparison of the two approaches, I think the dialog fails to address the more important question of why this shift is taking place and whether there are other approaches that can address the underlying challenges.
The shift towards ‘marketplaces’ is taking place as companies try to find a new balance between the following priorities:
- Maximizing capital efficiency
- Maximizing customer delight (selection, post purchase experience etc), and
- Minimizing logistical complexity (which helps to maximize scalability)
The need to find a new balance is triggered by scarcity of capital. As long as capital was freely available, most ecommerce companies focused heavily on the customer experience, which was best served by an inventory model. As capital tightens, these companies must now balance the need to delight customers with the need to build a viable business.
What are marketplaces?
Let me start by defining what I believe to be true online marketplaces. These are platforms that enable a large, fragmented base of buyers and sellers to discover price and transact with one another in an environment that is efficient, transparent and trusted.
- Efficiency is a function of liquidity (enough buyers and sellers) and an effective price discovery mechanism (e.g. an auction).
- Transparency is ensured by applying the same set of rules to all participants, and because buyers and sellers know who they are dealing with.
- Trust is provided by features such as buyer and seller ratings, reviews, and integrity / guarantee of payment.
Marketplaces are difficult to execute against because they require adequate and simultaneous liquidity on the buyer and seller side. Once adequate liquidity has been established and the ‘flywheel is spinning’, these businesses exhibit strong network effects (because a market that has the most buyers will attract more sellers, and the increasing base of sellers will in turn attract more buyers). So once a marketplace becomes dominant, it scales organically and often exhibits ‘winner take all’ characteristics. Additionally, because marketplaces are essentially technology platforms that provide tools for buyers and sellers to participate and a trusted environment that facilitates price discovery and transactions (vs. actually being responsible for fulfilling transactions), they can scale very rapidly.
We’ve seen all of these dynamics play out at close range as a result of our investment in the Indian Energy Exchange (IEX; www.iexindia.com). IEX operates an electronic market for power in India and has emerged as the dominant power exchange in the country with deep liquidity.
The take-away is that when you get marketplace business models right, they are profitable, scalable, defensible and highly valued. Which is why contrasting the inventory model with a marketplace model makes for an exciting debate.
The inventory model
In India, there is no question that being in control of the product (i.e. having physical inventory) enables a superior post-purchase consumer experience. If you have the product in your control, then (assuming your systems and processes are robust) you: (i) have visibility into your stock level, (ii) know where the product is physically located, and (iii) control the pick, pack and ship process. This means that you minimize the likelihood of accepting an order only to later discover that you don’t have the product. It also means that you can optimize dispatch time. The bottom line is that being in control of the product enables you to deliver faster and with higher accuracy, and respond effectively to customer inquiries about shipping status. Given the correlation between delivery times and return rates that we’ve observed (i.e. long delivery times are clearly correlated with high return rates), this is really important.
The problem is that being in control of the product has meant that companies compromise capital efficiency – because they buy product from vendors up-front, thus tying up capital in inventory, while at the same time exposing themselves to inventory mark-down risk. This can get ugly – which is why it makes sense to explore other approaches, one of which is a marketplace model.
Marketplaces in ecommerce – how different are they really?
The reality is that most of the marketplace models we see in ecommerce are not ‘platforms’, as described earlier. For example, in ecommerce marketplaces the prices are fixed, not discovered, and the ecommerce company is responsible (from the customer’s perspective) for several aspects of the post-purchase experience, such as fulfillment and customer service. The reality is that to the customer, many of these marketplace companies look identical to inventory-led ecommerce businesses. In other words, these models are simply one possible response to the constraints and challenges of traditional inventory models. And the marketplace model is not without its downsides – for example shipping costs are higher because multi-product orders are fragmented across vendors and shipped separately. And this in turn may lead to customer dissonance because a customer won’t receive their entire order at one time.
There are other solutions [Note that for purposes of this discussion I am not considering FDI related implications on company structure.]
Other possible ways of mitigating capital intensity while remaining in control of the product include (but may not be limited to) vendor credit, consignment sales (where products are in the possession of the ecommerce company but are not paid for upfront) or back-to-back purchasing (where the ecommerce company places the order on a vendor/supplier after receiving an order from a consumer). For example, ASOS, a UK-based online lifestyle retailer, has net working capital of less than 2% of sales while operating an inventory model. Similarly, Shoppers Stop in India has a negative working capital model – again despite being an inventory-led business.
Focus on the substance, not the glossy headlines
This is a meaty and critical subject for any company involved in online commerce. We’re encouraging our companies to experiment with strategies that resolve the trade-offs outlined in this post because we think companies that successfully do so will have more attractive scale and economic characteristics over the long-term. The purpose of the post is not to take sides on the inventory vs. marketplace model debate or address the pros and cons of each approach in detail – rather it is simply an attempt to surface the underlying issues that are driving the evolution of how ecommerce companies operate in India.
Mr Venkat R Chary, IAS (Retd), Chairman of Lightspeed-backed Indian Energy Exchange Ltd delivered a speech at the CII Southern Regional Power Conference in December 2012. Here’s the video!
(Source: Chiot’s Run)
[Published on Pluggd.in]
Founders of consumer businesses inevitably face the dilemma around when to start scaling their companies. Sometimes the decision is outside of their control, for example if their service starts to grow exponentially, but more often scaling is a deliberate decision and involves up-front investments to drive and support growth, such as filling out the management team, growing the sales and/or engineering teams, and increasing marketing spend. Because any of these activities result in increasing expenses and cash burn ahead of revenue or usage, the decision around when to scale is a critical one.
Our contention is that entrepreneurs should demonstrate product-market fit before investing in scaling up. In the Indian context, where new web services are cloned on a weekly basis, waiting to get to product-market fit can be difficult to do. Founders may feel pressured to scale prematurely, justifying this decision with reasons such as “it’s a land-grab” or “first-mover advantage”.
Scaling out prior to product-market fit can be very risky. In many cases, you have to be ready for a high-burn scenario – access to capital becomes a key constraint here, as evidenced in many of today’s ecommerce businesses. You may also cycle through lots of management (especially sales and marketing) if you haven’t got the product, value proposition and messaging right. And you may lurch around from product to product or positioning to positioning as the pressure to deliver financial results grows. All of this can distract the company from answering a critical question – do customers really value the product or service you are offering?
So, what is product-market fit? While there is no ‘silver bullet’ definition, we typically look for evidence that customers value the product or service offered by the company and engage in a manner that indicates that they cannot live without the product. For example, we look for signs of the following:
- Traction: Large and accelerating growth in monthly active uniques (MAUs) and daily active uniques (DAUs). Note the importance of the word ‘active’.
- Scalable customer acquisition: Ability to acquire customers cost effectively through scalable (and ideally organic or viral) channels
- Repeatability/Engagement: High amount of repeat visits from existing users and signs of ‘value generating’ behavior e.g. repeat purchases for an e-commerce site, songs streamed for a music service or community engagement for a social networking site.
- Virality: High k-factor
- An initial set of users who will pay money for what you have
Here are some examples from within our portfolio of companies that achieved product-market fit – along with illustrative metrics in each case of how this was measured:
- TutorVista: increasing length of stay / subscription and declining acquisition costs
- Itzcash: increasing organic transaction volumes
- Indian Energy Exchange: acceleration in trading volumes and number of participants trading on the exchange
- LivingSocial: Rapid viral adoption and repeatability of economics and customer / revenue ramp across cities
There are several different approaches or strategies to accomplish product-market fit – the blogosphere is full of wise advice from founders and investors on this subject (see below). However all these approaches hinge around a common core, namely proving that there is a reason for your company to exist before spending more money amplifying your message or building your expense base.
- iterating constantly, starting with a minimum-viable product (a la Eric Ries and Lean Startup)
- focusing maniacally on actionable metrics (a la Dave McClure and AARRR)
- keeping a low-burn with a small, nimble and technically-oriented team
- getting detailed feedback by directly observing users interacting with your product (a la Scott Cook of Intuit)
- clearly detailing a hypothesis on your value proposition and disproving and proving that through actual data
- making things people want (a la Paul Graham of Y-Combinator)
- optimizing customer sataisfaction, perhaps through tracking Net Promoter Score (NPS)
Once you have product-market fit, you have evidence of a strong value proposition for consumers, advertisers and other customers. This provides a foundation for a viable business model. Now you can – and should – scale.
Yesterday, we formally announced that Lightspeed and Sequoia invested in OneAssist, a company that creates and markets assistance and protection-oriented membership plans for consumers. OneAssist was incubated and founded in the Lightspeed offices during 2011 by Gagan Maini and Subrat Pani. We’ve known Gagan for almost five years and frequently traded views on business ideas and opportunities in the payments, loyalty and concierge space. Gagan and Subrat have known each other since the late-90s, when they worked together at the SBI-GE cards joint venture. They have each built new businesses from scratch inside larger companies – Gagan most recently started the Indian operations for CPP and Subrat built the credit cards business at Kotak – and we’re excited to back them in building a new company in this white space.
The thesis behind OneAssist is that consumers increasingly value peace of mind, convenience and assistance with respect to certain events that can disrupt or interrupt our everyday lives (such as the loss of a phone or wallet, health emergencies etc.). This is driven by: (i) increasing time scarcity – especially in double income households, (ii) a cultural preference for ‘assistance’ (witness services such as Naukri’s ‘assisted’ online job postings, where sales reps hand-hold employers through the job posting process, JustDial’s assisted directory service, IRCTC’s agent channel for booking online tickets etc), and (iii) an emerging orientation towards protecting oneself from unforeseen future events (health insurance didn’t meaningfully exist 10 years ago and is a 13,000 Cr industry now). And perhaps more anecdotally, a growing sense of taking responsibility for oneself and one’s family.
We believe there is a large opportunity to create a branded, consumer-oriented assistance and protection platform across multiple segments. The initial use cases OneAssist will support are the loss (or theft) of a wallet (including cards, driving license, PAN card etc) and the loss (or theft) of a mobile phone. In each case, the company takes on the chore of cancelling credit/debit cards (or remotely wiping and locking a phone), protecting against misuse of cards and/or data, providing emergency assistance (such as providing a replacement handset with data fully backed-up), and replacing essential identity documents such as a driving license or PAN card. Each product also offers certain group insurance benefits to protect against financial loss. Plans are priced at Rs 1,000 to 2000 per annum depending on the chosen package plan, which equates to just about Rupees 3-5 a day.
In addition to marketing directly to consumers, the company would also market their products through affinity partners (such as banks, telcos, retail, etc) and corporates who have large customer or employee bases and can offer such products as very relevant value added service or benefits for a fee.
This business model is notoriously difficult to execute against given the importance of delivering against the promise in ‘moments of truth’, the myriad of supply-chain partnerships and capabilities that must be developed and coordinated, the direct and partner-driven sales and marketing capabilities that must be built to achieve scale and the customer engagement strategies that must be deployed to ensure high customer satisfaction and loyalty. We believe that Gagan and Subrat are the best entrepreneurs to take on this challenge and wish them and their team the very best as they embark upon this adventure.
[Published in Pluggd.in]
Several of our portfolio company founders and I have recently been debating whether to launch a new product/company quickly (and sometimes prematurely) or instead take more time to launch with a ‘fully baked’ product. The most compelling reason FOR launching early is to expose the product to real customers and begin the cycle of learning (and sometimes also to establish a first-mover advantage if relevant and important). The principal reasons AGAINST launching early are that you deliver a half-baked or imperfect user experience and worse, you risk failing to meet basic user expectations.
Through discussions with other CEOs it became clear to me that many entrepreneurs struggle with different variations of the same question. For example, while we’ve been debating this trade-off in the context of when we should launch a new company and how ‘perfect’ the product should be, other entrepreneurs whose companies are already in market wonder whether they should begin to scale-up aggressively or first invest in their infrastructure (provided they have validated product-market fit).
Clearly there is no generic answer to this question as many factors play a role – the nature of the business, competitive dynamics, user expectations, regulatory requirements etc. For example, Indian Energy Exchange (IEX), a Lightspeed portfolio company, operates a national, electronic market for power. The company’s platform is used by state utilities and industrial buyers to buy and sell power as well as ensure payment integrity and schedule power delivery. Given the company’s mission critical role in the power market, it is essential to launch with a pressure-tested product that works flawlessly at scale. However for many consumer internet or mobile companies that do not serve such mission critical use-cases, there is a strong argument to launch early with a minimum viable product and begin the learning cycle as soon as possible. The key question we’ve been debating is how early?
My current thinking is that entrepreneurs must focus on getting to market quickly with a lean or light-weight version of the product (note that this is different from an incomplete or half-baked product), PROVIDED that:
- The product supports the core customer use case,
- The company has a level of technical and organizational readiness that will enable them to iterate, innovate and improve the product post-launch rather than engage in months of fire-fighting because the product or service was launched prematurely and fails to meet basic customer expectations, and
- The company is ready to track user behavior, engagement, funnel metrics, cohorts etc so that iteration and improvement can be done in a data-driven manner – and the insight gained from an early launch can be actioned
Note that this does not require the product to be perfect or ‘complete’ but it does require that the limited set of features and functionality work well and support the core use case. For example, an online retail company need not have full product selection, support every payment method or offer a full feature-set on day 1, but it should offer a frictionless user experience and be able to accurately ship an order to a customer within a reasonable time frame. If the company has made appropriate investments in its organizational and technical infrastructure, it will be able to layer on additional products, functionality etc on a continuous basis post-launch and will be in a position to accelerate growth going forward. Flipkart is a great example of a company that did this the right way – by focusing on getting the user experience right and establishing product-market fit before aggressively investing in marketing to scale rapidly.
Conversely, a company that launches prematurely, simply to get to market quickly, runs the risk of demonstrating early success, only to have to pull back later in order to fix a long list of ‘bugs’. This can lead to unsatisfied customers, demotivated employees and a compromised competitive position. Worse, this type of company wont be in a position to run experiments, learn from its customers and make rapid product improvements.
Companies with purely ‘virtual’ business models such as social gaming or music streaming can launch even more quickly with very lightweight minimum viable products to help establish product-market fit. Once they see positive signs around traction, usage and engagement, they must prepare to handle scale prior to ramping usage. After all, what would have happened to a company like Instagram if the system buckled under the tremendous growth?
[Published on Yourstory.in]
So, how long will it take to get a term sheet?
This is a question that most entrepreneurs appropriately want to know. While there is no one size fits all answer to this question, the focus of this post is to ask what I think is an equally important question for all entrepreneurs – what does a term sheet really mean?
The reason this is important is because all term sheets are not equal. Some firms issue term sheets early in their investment and diligence process (Firm A), while others issue them at the end of their process (Firm B). While Firm A will be able to issue a term sheet more quickly than Firm B, there is likely to be a higher risk that the deal does not close as most of the detailed diligence is yet to be done. Conversely, while Firm B might take longer to issue the term sheet, if/when when they do so, they will likely have a very high likelihood of completing the investment, thus providing the entrepreneur with a higher certainty of close.
Since most term sheets contain exclusivity clauses that restrict the entrepreneur’s ability to speak to other firms and evaluate other financing options, wouldn’t you rather accept a term sheet that has a higher probability of close, even if this takes a little longer? So next time you ask an investor how long it takes to get a term sheet, be sure to also ask what level of commitment their term sheet represents.
Here’s the presentation I gave at the IAMAI Digital Commerce event this morning:
[Published in Pluggd.in]
I am speaking at IAMAI’s conference on Digital Commerce later this week in Mumbai. I thought I would put down a few thoughts here that I believe affect ecommerce in India as an industry.
There has been an increasing amount of debate recently around the sustainability of ecommerce companies in India. I believe that a key driver of sustainability is a sharp focus on long-term customer value and a deep understanding of customer metrics. Delivering strong value to customers results in high repeat purchase rates and low customer acquisition costs, while an analytical orientation enables companies to measure key metrics and take important business decisions based on real data.
Everyone understands intuitively that repeat customers are good for business. Yet very few e-commerce companies develop a deep understanding of customer behavior, measure and analyze key metrics and tailor their business strategy and internal focus accordingly.
Understanding the value of a customer – and how to measure it – is perhaps one of the most important questions for anyone running an ecommerce business. If you don’t know what a customer is worth, you run the risk of not knowing: (i) how much you should spend on marketing/customer acquisition, (ii) how much you should spend on customer support (customer service, fulfillment etc), and (iii) the levers that drive an increase in the value of your customers (and therefore the value of your business). Companies that don’t understand customer value may be able to grow rapidly, but this will be unsustainable over time, because the costs they incur to acquire, reacquire and support their customers may greatly outweigh what those customers are worth.
All e-commerce entrepreneurs I meet share a repeat customer metric (indicating an appreciation that repeat customers are valuable), but more often than not these metrics don’t reveal insight into customer behavior. Some examples include:
- “33% of the orders last month were from repeat customers”, OR
- “50% of the customers that bought last year bought again this year”.
OK, this sounds great, but what does it actually mean and how is this data actionable for the business?
Let’s take the first statement: “33% of the orders last month were from repeat customers”. Take a company that has been in business for a couple of years and during that time served over 200,000 customers. Assume that they currently serve 21,000 customers per month. So this statement means that 7,000 customers (or <4% of their cumulative customer base) in the month had bought at some point before. Is that good? Bad? What does it mean? What actions should be taken? It’s tough to tell because this statement is meaningless without more context.
Let’s take the second statement and apply it to the same company above: “50% of the customers that bought last year bought again this year”. This implies that 100,000 existing customers bought again this year. I think we would all agree that sounds pretty good. But is it actionable? What did they buy? How much did they spend? How often did they buy again?
To help answer some of these questions and drive specific, actionable insight for an online business, it helps to think of customers a little differently. On the internet, a customer is like a store in the physical world – you need to make upfront investments (acquisition cost on the internet; capex for a physical store) which will yield a margin stream in the future. Understanding the ratio of the upfront investment to the expected margin stream, as well as how you can reduce the investment and increase the margin stream, is critical. Just as the operator of a physical store thinks about time to break-even and pay-back, operators of online stores can do the same with their customers.
Once you accept this premise, you can use cohort analysis to estimate customer lifetime value. Cohort analysis tracks the behavior of a specific ‘batch’ of customers (for example the January 2010 cohort means customers acquired in January 2010) over time. You can do this for multiple cohorts as follows:
The table below illustrates the behavior of the January cohort, which is the customers that were originally acquired in January.
The next table indicates the order value generated by the January cohort.
What we see is that the January cohort of 1,000 buyers spent a total of Rs. 29L during the year. If this business has a contribution margin (i.e. shipped revenue less COGS and variable costs such as discounts, payment gateway, shipping & handling) of 15%, then the cohort of 1,000 customers generated Rs. 4.35L of contribution margin in the year or Rs. 435 per customer. Because the number of transacting customers as a % of the starting base has begun to asymptote in 12 months, you can project this forward and calculate lifetime value over 2 or 3 years (note, this is NOT year 1 value multiplied by 2 or 3). Lets assume the 3-year lifetime value of a customer based on this analysis is approx. Rs 800. This should become the maximum allowable acquisition cost you pay to acquire a customer in steady state.
Many of the most successful e-commerce companies in our portfolio achieve lifetime value to customer acquisition cost ratios (LTV/CAC) in excess of 3:1, which enables them to grow rapidly and profitably through aggressive marketing. Since acquisition costs can only be controlled to a certain extent given media costs, competitive dynamics etc, they do this primarily by focusing on customer economics, and specifically increasing: (i) frequency of purchase, (ii) margins, and (iii) order values (through effective retention marketing and initiatives).
Most companies that measure this data carefully allocate more of their resources to retaining customers and improving customer economics than companies that don’t. After all, it’s much easier to ramp up customer acquisition if you already have systems in place to maximize the value of those customers than it is to change the DNA of an organization that focuses only on customer acquisition.