India is an intresting market. There are 400 million people online of which 300 million are on smartphones. By 2020, this is expected to go up to 650 million.
The in-thing in 2016 was the native advertising. Advertisers would like to be where the consumers are. Native advertising is doppleganger — the ads blend into the editorial matter. In 2017, the in-app advertisirs replaced the native advertising. The online time is spent on 11 apps. That led to appvertising. In-app ads offer immersive experiences, and has become a mass medium of 2017. It ranges from a six-second video to an augmented reality game. It targets the users based on first party data. Predictive analysis enables to customise the audiences and deliver compelling in-feed content. There is an element of location specific messages. It increases engagement and develops an on-the-go and organic audience. In-app ads target the audience at the right moment. The users are in the frame of mind to explore, engage and discover. It can nurture conversions and leads. It promotes repeat engagement. Many ads are interactive, and appear durig relevant moments.
The challenge is a lack of rich media inventory. It is harder to discover which users have already seen the ad in the absence of cookies. There should be additional efforts for retargeting.
In-app ads are relatively more expensive to develop than those for the mobile web. However, the net return is always higher as it avoids the waste on generalised and untargeted audience.
Creative messages have opportunities in programmatic to enable the use of technology to target the right person at the righttime. Formerly, the equation was media plus creativity. It has changed now to media plus creativity plus data. The data is the vital part of the process. It is about how one engages with the customer in this world of millions of messages.
Advertisers now do not rely heavily on external agencies for programmatic buying. Brands have been increasingly using in-house capabilities of programmatic buying.
AI takes over, but while doing so it is assigned tasks with real world consequences before AI works properly. Several videos are alogorithmically produced, with names that are collections of tags. Such videos targeted to young population makes the system complicit in abuse. The fake news would not have been possible without Google’s programmatic advertising technology and Facebook’s propensity to tolerate fake accounts. The tag-filled names of the videos are designed to exploit YouTube’s search algorithms. The catchy headlines of the fake stories continue fooling Facebook’s clickbait detection algorithms. MIT scientists have developed Moral Machine to automate the ethical decisions a human driver makes on the fly — whether to hit a wall and kill the car’s passengers, including a young girl or run over an athlete and his dog crossing the street on a red light. The researchers used a website to ask people about moral choices. Then the data is aggregated. AI-based algorithm figures out a decision corresponding to the crowdsourced wisdom. The self-driving cars can make credible decisions on ethical dilemmas by implementing an alogorithm on the Moral Machine data set. But it is not the end-all solution.
Alogorithms may have improved but it will be a long time before they perform tasks that require human judgment, without some human figuring out how to game them.
Machine learning works by setting up programmes that could consider multiple variables and then taking as input huge quantity of data. The programme sifts the data. It formulates its own rules and finds patterns and co-relations. It is in effect a blackbox.
When there is a loan or credit card application, the yes/no decision is often made by a machine. The cedit limit, interest rates, tenure and other details are also likely to be automated.
Nobody except the machine knows the logic behind a decision. It is not known whether the machine is transparent and fair.
A person’s savings is allocated by intelligent agents. No fly lists of passengers are made by running algorithims. AI is used to read body language, and to figure out sexual orientation.
The Europeon Union’s proposed General Data Protection Regulation (GDPR) will be enforced from 2018, and some sections of the law give citizens a right to demand explanations about decisions made by algorithms. Article 22 implies that a human element would have to be present in the chain if algorithms are run to make key decisions that affect individuals.
Traditional marketing research originated in advertising agencies in the 1950s, as an adjunct to the account planning function.Gradually, these MR departments blossomed as full-fledged MR companies, eg. IMRB of India’s genesis was in the HTA. Manufacturers set up MR departments to interpret data later. Vikram Sarabhai set up ORG to do retail audit of pharmaceuticals.
Of late, Gigna US has shut up their MR department and it has been replaced by Marketing Analytics department. The digital era is influencing MR not only abroad, but in India too. Instead of long consumer tracking surveys, we could have microsurveys and intercepts. Data scientists could be assigned the insight management. Instead of rear-view analytics of trditional MR, there would be focus on predictive analytics.
There are multiple data streams these days. There is traffic to the brand’s web site. There are social media. The buzz here has to be monitored. The comments in the blogs must be factored in. Traditionally, data from the syndicated research flowed in. Research was commissioned. Consumer behaviour was mapped. The focus was on past data. Marketing analytics calls for new set of skills. There has to be training datasets to help project intended behaviour. Experiments are run in real life. They are run in behavioral labs. Models are built and validated. All this is done without diluting the core MR and consumer behaviour.
MR is transforming into Marketing Analytics. Traditional skills and new age skills coverage.
There is a space for high quality content on the mobile. Every major player is investing heavily in acquiring or commissioning content. The first round of growth was subscription driven.
The kind of shows that are being commissioned needs consideration. There is an increase in creating more complex storytelling. The shows for online are made differently from those on the TV. TV is driven by the consumer research and ratings. The data that powers online content decision is different. It depends on deep consumer insight and expectation. Though successful TV shows can be adapted online, there are limitations. Producers of movies upload close to 30-40 promotional videos on YouTube, as against 2-3 previously, as there is co-relation between engagement with the videos and the box office success of a movie.
Online offers more freedom creatively and commercially. Online can explore a variety of genres such as crime, mature relationships which do not fit the daily soap format on TV. In the US, the subscriptions or pay TV drive the content. In India it is ad driven and mass driven, as subscriptions or pay TV has not taken off here.
Some audience in India lapses from TV. They do catch up TV on line or view IPL like big events.
TV has sheer stickiness. It reaches a mammoth 875 million in India. It has continuous engagement. This is not there for an online series that ends in five or ten episodes. Ad rates in video are a fraction of those on the TV. Thus monetisation is the biggest challenge.
Naomi Klein’s first two bestsellers are No Logo (1999) and The Shock Doctrine (2007). She wrote This Changes Evertything:Capitalism vs the Climate in 2014. Her documentary film The Take was made in 2004 in collaboration with her husband. Her latest book is No Is Not Enough. She feels the politicians are in awe of wealth, and feel that wealth can fix anything. They court the ‘Davos class’ and billionaires who are treated as Messiahs. The present US president’s sole qualification for the job was his wealth. His presidency is an extension of the Trump business brand. He brings his wealth power attitude to presidency. The merger of the Trump brand and the Trump presidency is so complete tht we fail to recognise where the one ends and the other begins. She invokes her shock docrine politics to explain this. Brutal tactics are deployed to exploit public disorientation in the wake of collective shock from a war or terror attack or a natural disaster. In the name of shock therapy, pro-corporate measures are pushed. There is a wider global phenomenon that has seen a surge of authoritarian, xenophobic, far right politics. Her tittle no is not enough presupposes active resistance to this phenomenon. However, she fails to spell out an alternative vision.
Governments fear that bitcoins may replace domestic currency and then they will lose their control over money supply. Some fear that bitcoin is a Ponzi scheme.Those who favour bitcoin claim that its mysterious originator has guaranteed that its quantity will never exceed a limit.
The creation of each Bitcoin token is an elabrate process based on blockchain technology which involves ‘miners’ who solve a mathematical puzzle each time.
We can treat bitcoin as a commodity rather than money. As a commodity, it is a store of value. Had there been no coin in the name bitcoin people would not have noticed.
Bitcoin are dressed in libertarian cloak, though it is a facade. Bitcoin is produced by humans and the operation is monopolistic limiting its supply. Though a private monopoly, it could be compared to government monopoly of the currency issue.
Since then monopoly issue gets modified, as a number of other cryplocurrencies too have emerged. They are oligopolies.
Because of its exchange value, it resembles fiat currency. It is not a means of payment and gets translated back into currency so as to spend it. It is however, a store of value. Its limited use as a means of payment and its slow mining assure that it will not substitute the national currencies. It could be compared to gold which is a store of value.
However, if these tokens command trust, they could become acceptable as means of payment. It presupposes unfettered entry and competition.
The first Industrial Revolution happened in the late 18th century and early 19th century. Its focus was on mechanization where it substituted human and animal labour by mechanical labour. It gave boost to manufacturing and urbanization. Nations used it as key to their development.
The second industrial Revolution in the late 19th centuary and early 20th century focused on mass production using the assembly lines. It gave boost to large scale production, specialization and interdependence in manufacturing. It also encouraged transportation to expand the market reach.
The third Industrial revolution happened in the later part of the 20th centuary. It relied upon automation, an alternative to mechanization. It encouraged globalisation, and manufacturing activities shifted from the developed to the developing economices.
The fourth Industrial Revolution is in the process of unfolding and depends on robotization and supporting IT infrastructure. It enables gteater freedom of choosing locations and manufacturing processes. There is flexibility of scale and customization. Here, robotization goes beyond just mechnization to undertake more complex tasks. Labour becomes ubiquitous because of robotization. Manufacturing and supply chain become closely embedded. In time to come, even distribution gets embedded to production.
India is formulating new Industrial Policy to replace the old policy that was last revised in 1991. It should meet the requirements of the 21st century’s digital world. There are four factors which would be affecting this policy.
Smart manufactring using IoT should be exploited by India. India missed the bus at the time of third industrial revolution.
Servitisation Servitisation is the trend for fourth Industrial Revolution. There should be seamlss data flow. Privacy laws must be in place. The labels of industry and services must be discarded. There are going to be changes in global supply chain. The boundaries between industry and services will blur — Google and Apple entering automobiles, Uber and Ola either as automotive value chains or the service sector, products being sold as services. All this calls for new laws for taxation. The new policy must address the challenges of moving to digitally-delivered solutions to both industrial and individual customers.
Micro-enterprises In the fourth industrial revolution, a new industrial amd work structure emerges. The way enterprises and enterpreneurs fuction would change. Digital technology enables us to overcome the barriers of scale. There is access to resources, customers, and suppliers. It is easy for an individual to be a micro-entreprenur. Servitisation facilitates this. Micro-entreprenurs are key players in the last mile eco-system.
Life-long learning system and labour laws for the 21st century must be designed.
While reaping the benefits of technology, the effects of environmental damage are minimised by using laws, social norms and international agreements.In this we are inspired by Nash equilibrium in game theory. It is named after John Nash, a mathematician and economist. In game theory, a Nash equilibrium is a set of strategies that, once discovered by a set of players, provides a stable fixed point at which no one has an incentive to depart from their current strategy.
Such an equilibrium is reached when the players understand the consequences of their own and others potential actions. For instance, in cold war, peace among nuclear powers depended on the understanding that any attack would ensure everyone’s destruction.
However, of late technology has evolved so rapidly that it is difficult to understand the consequences of any new action. Below a certain level of complexity the Nash equilibrium is useful in describing the likely outcomes. Beyond that, there is chaotic zone where players never find stable and reliable strategies. Then they cope with it by shifting their behaviours in irregular way. The outcome is random and unpredictable.
The emerging technologies in computing, software and biotech such as gene editing are unstable, evolve faster than regulatory frameworks. We may be approaching a profound moment when the guiding idea of strategic equilibrium will run up against its limits.