Let's separate zero rating from the net neutrality debate

The current debate on net neutrality has 2 key parts

1. Additional charge for using certain apps like Whatsapp, Skype etc. (primarily apps that cut into voice and messaging revenue of telcos).
2. Zero rating - The app owner (Flipkart, Facebook etc.) pays for data instead of consumer

Unfortunately the 2 parts have been hopelessly mixed together.

Opposition to additional charge for certain apps is understandable. The consumer has already paid for the bandwidth and telcos or anyone else has no business telling how to use or not use that bandwidth.

Opposition to Zero rating is puzzling. Amongst all the rhetoric, the strongest argument for the opposition is that it will maintain a level playing field for all, primarily start ups.

Really?

It is absurd that level playing field is being defined in the narrow terms of data access cost. There are far more important things than data costs when it comes to level playing field.

1. Pricing power – Forget start ups, can established offline players match pricing of well funded online companies?
2. Access to talent – Huge salaries not only for top engineering talent but even for logistics personnel
3. Service terms – 30 day no questions asked returns, 1 day delivery, COD etc.

Should pricing, employee salaries and service terms be also regulated in interest of ‘level playing field’?

Zero rating will help in on-boarding a much broader section of society and enable them access to the same content, services, market places from which limited section of the society has benefited so far. The scale of impact will be so massive that the advantages of zero rating will far outweigh any concerns around stifling innovation or maintaining level playing field.

It’s time that the issue is looked at from the perspectives of those hundreds of millions of people who are going to benefit from zero rating immediately. Sure, large companies would be the first beneficiaries but it’s just a matter of time before the entire ecosystem benefits due to the significantly bigger market that would be created.

Robin Hood Marketers

Analyze this:

H&S: 7.5 ml – Rs. 3; 100 ml – Rs. 69
Surf Excel: 20 g - Rs 2; 1000g - Rs. 150
Aquafina: 1000ml - Rs. 13; 750 ml - Rs 20

This pricing scheme beats all convention; after all it does not require a MBA degree to know that as the SKU size increases, the cost per unit decreases; essentially because the fixed costs are spread over a larger quantity. Mind you, these are exactly the same products in each case and apart from packaging, everything is same. The question then that comes up is do these bigger packages cost more than the product itself?

The only explanation is that the bigger package encapsulates the Robin Hood strategy that is increasingly becoming a norm in FMCG, minus the Samaritan motive. In a desperate attempt to increase the reach and to cater to CKP's bottom of the pyramid, marketers are deliberately pricing products with such an unjustifiable difference; ignoring the ethical need for being consistent towards the consumer. After all, a Rupee from a SEC A consumer is as good in the balance sheet as a Rupee from a class C consumer.

Some marketers might try to argue that huge advertising costs are being apportioned to only the bigger SKUs, which in turn increase their cost. This is a faulty premise from the start. It is only proper that advertising costs are divided brand-wise and not SKU-wise.
Does advertising not impact the sales of smaller SKUs?

Of course, marketers are free to price their products as deemed. Many indeed choose to exercise this option and get as much from the consumer as possible through rampant pricing. This is the marketer’s choice in a free market economy. However, a biased pricing scheme as above, with glaring and unjustifiable inconsistencies is akin to deliberate cheating. After all, one doesn’t pay only half for a movie that is only 1.5 hours long.

To productize or not

‘To productize or not’ is the question that many boutique analytics firms are facing. Faced with an urge to grow beyond the small company / start up tag, and being unable to match these aspirations with the current business model of providing high end, customized solutions analytics firms are now trying to evolve products based on the experience that they had with their experiences.

At a certain level this indeed seems the way ahead and a very logical extension of the domain and technical skill sets that these companies have build in the past year.

However, the potential of ‘productization’ of analytics solutions need to be viewed from the prism of two critical things:

1. Re-applicability
2. Statistical robustness

1. Re-applicability:
Some of the questions that concern with re-applicability of analytics solutions are:
- Is the product, with minor customizations, ready to be installed?
- Are there any category / industry / domain specific nuances critical and can be they be programmed in the model?
- Is the list of variables / scenarios considered for building the model exhaustive?
- Do the variables change so much over a short period of time that using an outdated model would actually back-fire? E.g. A model that is used to predict likeability of clothes (Since fashion changes so much over time)

Is the effort to address the above issues significantly less than creating a new model?
If answer to all above is yes, then the product can be said to have high re-applicability.

2. Statistical robustness:

Any product needs to stand the test of accuracy. In analytics, it means it has to be statistically robust. A weak model would result in unpredictable results. A weak model is unable to stand the test of integrity, of the model as well as the modeler.

If Re-applicability as well Statistical robustness is high, then go forth comrade and productize - and may your tribe muliply!

List of Analytics companies in India

Niche Analytics providers:
Absolutdata
CrossTab DecisionCraft
Fractal Analytics
Inductis
Marketics
Meritus
Modelytics
Mu-Sigma

Large analytics providers:
Genpact
Symphony Services

IT companies with a strong analytics practice:
Infosys
IGate

MNC’s Captive Units:
Amazon
Amex
Citibank
Dell Analytics
Fidelity
HP Analytics
HSBC
RCI
Standard Chartered Bank

Global Analytics firms having Indian Operations:
Fair Isaac India
Dun & Bradstreet
Global Analytics Inc.