Unit Economics in the times of Auction Marketing Models

Unit Economics is all we hear these days in the consumer technology world. Unfortunately for many start-ups seeking venture funds, this is the biggest hurdle they need to cross to build a strong case for their business.

What is the concept of Unit Economics

I will not go into the definition and relevance of Unit Economics. That’s well documented here and here. Or just Google it.

Lets refer to Microeconomics 101 for our discussion – Marginal Costs(MC) and Marginal Revenue(MR). We all know that its a healthy sign if Marginal Revenues are higher than Marginal costs. And this delta (MR-MC) is what is unit economics.

On the other hand, if we are losing money on each transaction, either we see the losses reducing or we stop growing transaction volumes.

At least rational individuals would choose to do so. Or so goes the basic Economics assumption.

Unit Economics in web/mobile start-ups

  1. Marginal costs are volatile

A big chunk of a start-ups costs is the customer acquisition cost. ( I am excluding businesses with very high repeat volumes in early days where the operating costs contribute heavily to the overall transaction costs).

Most start-ups need to market their products and services. They are in a continuous state of transaction ramp-up along with concurrent improvements in experience or efficiency.

And in a world where most advertising/marketing channels are bid/auction model driven – this translates into the marginal costs being highly volatile. How volatile?

  • In the early days when you don’t have the luxury of brand-pull or of time, almost 60-70% of transactions may be coming from Google Adwords/Facebook/Ad-networks. Meaning 60% of your business is not insulated from pricing shocks.
  • Bid-rates may vary as much as 30-40% to maintain the same positioning. Maybe more, if there is a competitor who has just raised a round. Also, if you are competing in a category where big brands play, anything can happen. E.g. At Deal4Loans, we had seen bid-rates on our key-words jump significantly every time a competitor raised venture money or a bank launched a new digital campaign.
  • Add to this that the conversion-rates of your campaigns have not yet stabilized. Remember, these are early days, you are experimenting on your landing pages, and funnel optimization is still underway. So the final cost per account gets even more volatile.

2. Customer Pricing is relatively in-elastic

Theoretically, if you could pass on the burden of increased bid-rates and hence the ups/downs in marginal costs on to the consumer, your unit economics would be safe. The neighborhood vegetable vendor who has daily-prices does exactly this and is hence able to retain his margins.

Unit Economics

But this is rarely possible. Pricing is just not that elastic.  Most mobile/web start-ups can not /do not change prices so frequently.

3. Marginal Cost CURVE is UNPREDICTABLE AND NOT SMOOTH

We know that the bid-rates can inflict wild fluctuations(as seen in pt1) in the cost of acquisition, thereby making it unpredictable. But a bigger challenge is that the Marginal Cost curve is not smooth.

Realistic MC MR Curve

One rarely finds gradual changes in marginal costs with increasing through-put. It happens in unpredictable steps. Here’s why

  • Each fluctuation in effective bid-rate leads to drastic ups/downs
  • As a start-up you are experimenting with multiple channels. Success in any one will bring down the blended MC immediately.
  • Referral/Viral coefficient and % of in-bound of the campaigns can impact the costs significantly. e.g. One PR mention may bring in huge self-select traffic.
  • SEO traffic which is typically very predictable can also swing wildly with a new Google update as we saw with Penguin and Panda.

So what do we do?

  • Keep Experimenting. Do know that customer-acquisition at optimal price is a moving target. You are never really truly there. It can always be better.
  • Invest early in content. In-bound has significant ripple effects.
  • Raise money but don’t throw it all on branding. Consumer memory is short lived. Discover and test more channels, unlock access to more segments.

I would love to hear from bootstrapped ventures as to how they are/have handled the customer acqui costs. What worked, what didn’t?

Uber and Free Market Economics

Uber has changed the way we travel within cities. On a recent trip to Jaipur, the first thing I did on reaching the city, was to top-up my PayTm wallet to get going on Uber. (yeah no card-on-file yet 🙂 )

Uber Free Market Economics

And over the next 3 days I took more than 12 rides across the Pink city. Here are some of the interesting observations I had:

  • Jaipur is really a small city – Only one ride was over Rs 100/-. All others barely crossed the Rs 75/- mark. Given the distances are not too much, the per ride fare is expected to be low. This is a critical point because the supply-demand balance can be easily titlted in a small-population. Also the per ride metrics are sensitive to even the slightest changes.
  • Free market economies tend to be cyclical – Almost all the drivers I spoke to talked about the good old times they have had, driving around as Uber cabs upto almost 6 months back. It seems back then Uber was super aggressive in signing up cabbies and were paying as high as Rs 1800/- per day. Guaranteed. This came down to 1600, 1400 and now is at 1200/-. And its all because of the immensely huge supply. Most cabbies now complained of getting too few rides on a daily basis. Add to that the low average per ride fare and it is clear that this city needs volume of rides to be high. Or to quickly reach an optimal sweet-spot of supply and demand match. As the word of tough times (for the cabbies) is spreading,  fewer are joining and many who had joined Uber are reportedly quitting it. Some can’t even pay their loan EMIs.
  • There is no consistency of vehicle experience – I got from a Nano to an Innova under UberGo. Firstly, UberGo is where most customers go, hence even cabbies are registering themselves as UberGo. So you are better off choosing an UberGo. The Innova guy said that he wasnt getting any rides so he switched from UberX to Uber Go. Also it seems you make the same per ride across both categories. Hence UberGo seemed a logical preference. The Nano guy was proud of his decision, he claimed that he would recover his investment much faster. And thats true. I think this is a classic example of how the market evolves when its close to a free market.
  • Drivers understand and give importance to rider feedback – I have never seen so much sensitivity from an Uber Driver towards the feedback/rating. To have been able to crack this is really commendable on Uber’s part. The drivers have strong appreciation for this feedback being utilized for giving them ride bookings. Again, there might not be a completely transparent system but the fact that information and feedback is flowing across the supply and demand side, is strong enough motivator to influence decisions.
  • Locals are avoiding taking own vehicles – Lot of areas constantly face bad traffic due to construction activities. Parking is a challenge. Most of my local friends have either started using an Ola or Uber over self-drive or are seriously considering to do so. Atleast till the fares are this low !

Update:

And back in Delhi.

  • There was a surge charge of 1.9X due to high demand and unmatched supply I guess. This allowed UberX  guys to also pick up UberGo customers without formally registering into the UberGo. Complete reverse of what’s happening in Jaipur. I guess Delhi customers prefer the more spacious UberX and there is sufficient demand therein.
  • The first cabbie who picked my request, called me and asked me where I need to go (instead of asking me where to pick me up from), and hearing my destination – declined. Just put the phone down and on my Uber screen I was back at fresh request. No way to even go and give feedback on this bloke ! So I guess Delhi cabbies have a hack to the feedback-driving-behaviour loop also. Land of Jugaad !!

Impact of un-utilized assets : A Mathematical Model

A few weeks back I was wondering what happens when we buy a car but don’t drive it. While the automobile industry witnesses a growth but is it something that increases the drag on the economy.

I spent a few hours to work on a very simple model to find what happens in various consumption scenarios.

Approach:

I had to create a simple-one -product economy. Hence  I assumed that

  • Our economy produces cars each worth Rs 12 lakhs.
  • Average utility lifetime of the car as 1lakh miles
  • Additional spends required based on consumption as 5 Rs per mile

I also simulated 6 different scenarios from a household’s perspective. namely:

  • Scenario 1: Normal usage. Uses for full life of the product
  • Scenario 2: Stops using after a time. Does not sell it or buy another
  • Scenario 3: Stops using and sells it off but doesn’t buy another
  • Scenario 4: Stops using, doesn’t sell and buys another which is used
  • Scenario 5: Sells this and buys another which is used
  • Scenario 6: Doesn’t sell, buys another, stops using that also and buys a 3rd

Once these assumptions were plugged in, I calculated a few ratios and interesting things emerged.

 Unutilized Assets

  1. The value derived from each product drastically changes between two similar scenarios where the old stuff is traded vs where it is not sold. Does this mean that in a resource constrained economy, a market place optimizes the return of invested resources through higher utilization?
  2. In the scenario 6, income generation is highest for every 1 Re spent by the households. So maybe hoarding is a good strategy when domestic income generation is important. On the contrary imagine if we do this in categories where we import the products – we might be supporting Chinese economy more than we ever wanted to.

Not knowing fully well what these ratios meant, I spoke to my Professor friend Dr Dash who gave some very interesting path of analysis. Essentially what he mentioned was that I should look at this top down rather than at a micro level. He also mentioned about ICOR – Incremental Capital Output Ratio – how much additional capital do we require for each unit of GDP increase. E.g. an ICOR of 3 means we need 3 Rs for every 1 Re contribution in GDP.

  • Assume a market size of 75 Bn USD and lets say one third of this is from private cars. Hence a market of 25 Bn USD
  • Now assume that 5% of all cars produced in a year lie idle, which means about 1.25 Bn USD worth of cars remain idle. (and this is incremental value of locked capital every year)
  • So with an ICOR of 5 , these un-utilized cars translate into 6.25 Bn USD worth of capital that is “wasted” every year.
  • But where it became tricky for me was that whether the asset is utilized or not, the GDP is impacted depending on ICOR. So is this a case of smarter capital allocation? Would this “wasted capital” helped us in producing some other more needed product or service?
  • A very interesting and probably extreme case of this top-down analysis would be the scenario where the asset is imported. In such a case, a reduction in “wasted capital” would result in better trade-balance
  • With a marketplace, if 40% of these cars are sold in the second-hand market, then we free up that much capital.
  • Moreover with cars available at a lower price, many category shifts might happen. E.g. say a Maruti Alto being sold in the second hand market might attract a person who was in the market for a 2 wheeler earlier.