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With the recent announcement of ApplePay, platform wars in the mobile payment industry have picked up steam. Major players in contention are ApplePay, Google Wallet, CurrentC, PayPal, GoPayment, LevelUP, Softcard and Looppay. Adoption of these platforms is driven by actions taken by merchants, users, banks, credit card issuers, device makers and network operators. As Apple CEO put it “You are only relevant as a retailer or merchant if your customers love you.” User adoption of these platforms is driven by the core features available in the platform. Merchants are heavily influenced by what users like. Mobile platform features have been changing over time and seems to have arrived to a critical point. In the United States, Mobile-based payments are expected to reach $142 billion in transaction volume in 2019, according to a report from the research firm Forrester, from about $50 billion currently. We list below some of the reasons for users to adopt a platform.
Ease of use: In order for the consumer to change their habit and use their phone to pay instead of their credit cards, the new method must be much simpler than the old one. The dominant method today involves the customer reaching into their wallet and pulling out their card and swiping it on a reader. To change this habit the new solution should reduce the number of steps. With their NFC readers and their finger print technology, Apple has done just that. We now have a secure and easy way of paying. Since this is easy to activate and many merchants have begun to accept payments, consumers have adopted ApplePay.
Least disruption to ecosystem: the current ecosystem has the payment platform provider, network operator, bank, credit card issuer, device manufacturer, merchants and users. The solution provided by ApplePay keeps the same set of players and this is a good strategy to get adopted. Merchants must have NFC compatible readers that can be used with other cards. Similarly, users must have NFC capable phones that Apple provides as default.
Added value to a key player: Merchants control a choke point in the m-payment architecture. They are a key player in deciding which m-payment solution gets adopted. With m-payment, a large trove of data is generated and this can be captured by the platform provider. Furthermore, the platform provider can support loyalty programs that allow consumers to keep track of their purchases and get rewarded for repeat purchases. When these loyalty program has alliance partners, rewards earned can be redeemed for goods from other merchants. This adds to the attractiveness of the platform. ApplePay with its initial list of merchant adopter provides just that.
Information business and data privacy: Since m-payment generates a huge data exhaust, companies have to decide what they want to do with the data. ApplePay has decided to not use or share the data generated. With Bigdata and analytics in vogue, every company is collecting data on user behavior. This has resulted in companies using contextual information to suggest additional products for consumers to buy. Many consumers are feeling that this has been taken too far and amounts to invasion of privacy. Apple’s choice of forgetting the data may be a welcome relief.
APIs that help cross industry boundaries: Apple, in general, protects its core technology while opening up its APIs to developers that want to use their core technology in domains that they see fit. Platform providers that understand the multi-sided nature of their offering and learn to exploit the ensuing dynamics are likely to be adopted. Both GoogleWallet and ApplePay have sponsors that have played the multi-sided platform game before.
Overall, when we sketch out the entire ecosystem (merchants denoted by circles, platforms as diamonds, each link represents a relationship between merchant and platform), it is quite clear that Paypal has the most traction with customers. Many merchants are hedging by adopting multiple platforms. Given that the mobile OS market is split 85/15 between Google and Apple, we seem to have hit the upper limit for Apple. Other platform providers must make sure that they are compatible to both operating systems.
The battle between Google Wallet and ApplePay should be very interesting. ApplePay has won round one by making the solution easy to use. If Google is unable to come-up with a solution that is equally simple, it will fall behind in the m-payment platform adoption. Given these dynamics in platform adoption, we see three scenarios: one dominant platform, multiple-dominant platforms, one dominant with several smaller platforms.
In the one dominant platform scenario, Google may adopt ApplePay as their platform and make it available on over 95% of the smart phones. Alternatively, if GoogleWallet is able to get merchants to enroll, it can make GoogleWallet the default payment system and dominate the market just using its dominance in the OS market. Paypal users can be out maneuvered by the App Economy and its use of within app purchases. Since most of the within app purchases are currently driven by Google and Android apps, they will dominate this space.
In the case of multiple platforms dominating, CurrentC may get most of the retailers and using loyalty programs get customers to adopt. Google and ApplePay will use their presence in the App marketplace and continue to flourish there. Softcard with the backing of credit card providers will convert all their current customers to transition into the mobile payment market. Peer to peer payment may also have its own winner. The resulting marketplace will have islands of users on multiple platforms. Just as users choose to use different credit cards for different expenses today, they will use multiple payment platforms for their purchases.
Under the one dominant platform with several smaller platforms scenario, all preexisting commercial transactions will converge onto one platform or be totally interoperable. Just like the operating systems market Windows dominated earlier but MacOS had its share of creative types using it every day and Linux had the hard-core developer community. In the mobile payment marketplace, Paypal, Softcard and CurrentC may form a coalition and become interoperable and GoogleWallent and ApplePay will have their app marketplaces and few other retailers, and Starbucks will maintain their presence for their stores.
What should consumers track? The first piece of information to track is network effects – direct and indirect. Consumers must know the number of users on each platform. Also, the number of applications on each platform will drive adoption.
Users should look for exclusive links. If Walmart decides to use Softcard exclusively, chance are that many other retailers will follow suit. These exclusive links may be a precursor to a domino effect that would follow their announcement.
Unique value propositions that are being created via experimentation in emerging markets are important to follow. Many times it could be a smaller platform that does this experimentation but a larger platform would acquire the smaller platform and introduce that idea to the NA market.
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My colleague, Sal Parise, recently asked me what my strategy was in using Twitter. I think that my response may be helpful to others.
In order to understand my use of Twitter, it is very important to see the role of digital profile. I use LinkedIn, G+, my Babson Profile and SlideShare to create a digital profile. This feeds well into the Twitter strategy.
A key decision in using twitter Twitter is in determining who to follow. I think of this list as my filter to the world of technology and strategy. This list is always in churn and is comprised of who I consider as though leaders and influencers in the field. Most of the people on my list are experts, on topic and also willing to share what they know. Since they are influential, they are aware of most interesting conversations in the space or they actually initiate these conversations.
As for people who follow you, it is very important to stay on topic. My feeds are mostly on technology and strategy and rarely about topics like football or cricket. As I read articles, I find myself asking the question – why is this relevant to a technology entrepreneur or a person working in the technology field. If it is, I share it with a brief note on why it is interesting. Every now and then, I use the tool to contact companies. For example, when I taught a class on MOOCs, I wanted a few companies to talk about their products. My tweet was picked up by a few influencers on my list and they did a RT that caught the attention of several companies. In a couple of weeks, I had three companies visit Babson based on that trigger. These influencers are shortcuts to the people we are trying to connect with.
While this network can provide a great flood of information, and people with influential networks can become brokers, the skill still at a premium is sense making. Clicking and browsing can be a total waste of time, if one does not pause, reflect, connect, analyze and make sense of all the signals. Successful knowledge workers use the sense making task to abstract and apply what they are learning to their work tasks and personal lives.
In summary, it is important to have a digital profile, well curated list of people to follow and making time to make sense of information for your context. Articulation of this sense making and sharing with your constituents will help in the emergence of a useful digital identity.
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Some critical ingredients for software entrepreneurs are not fully developed in India. During a recent conversation, Rajesh Srivathsa pointed out a few.
A key ingredient that is lacking is the presence of well developed, uniform local markets. Most entrepreneurs are looking to outside markets (EU and NA) for selling the product but designing and developing them out of India. There is too much inefficiency in having such a schism. Proximity to the market will give them a better ability to sense and respond to demands in a timely manner.
Social ties that exist across organizational boundaries are essential in fostering the development and launch of new ideas. Network ties help entrepreneurs to find and hire the right talent at a low risk. These same ties can result in the identification of alpha users.
Existence of practices on the employee front stymie movement. It is quite normal for companies to ask for a three month notice from employees. For start-ups that is too much time to wait around. Also, companies try to retain senior staff at all costs. Instead, movement back and forth should be accepted. This will encourage more professionalism in people behavior.
The talent pool is too focused on efficiency and very little on innovation. The current crop of companies have high service demands and are focused on order taking and very little new product development. As a result, the environment is full of highly talented process managers and not many product managers. Understanding decision making across engineering, sales and finance requires prior exposure to these tradeoffs.
One personal note that I would like to add is that successful entrepreneurs have to deal with the Indian bureaucracy in addition to dealing with markets and competition.
Entrepreneurship is the engine that drives the knowledge economy. The optimism that one feels in knowledge centers like Boston, SF, Mumbai and Chennai is largely fueled based on the "can do" attitude of entrepreneurs. An ecosystem needs more than just entrepreneurs; it needs entities like VCs, large vendors, competitors, infrastructure providers, vibrant and knowledgeable labor pool, incubators and other bodies. These exist in all the hubs that I mentioned earlier. Then why are some more productive and vibrant than others?
A friend of mine, Rajan Srikanth, recently opined that what was missing in India (particularly) was the lack of a diverse set of options for an exit strategy. When building a company, entrepreneurs typically have many options to cash in. They can go for venture funding, test the capital markets, or make a trade sale to a financial buyer or a strategic buyer. Exits are just not happening in the Indian ecosystem at the speed and volume that is necessary to support the vibrancy that we are seeing in the early stage firms. With the "small board" for IPOs not really taking off, we are left with exits to financial/strategic acquirers and exits to investors as the only options. And the latter cannot sustain it indefinitely. What we also need is a "strategy" on the part of the bigger companies, who can fuel a win-win by seriously considering acquisitions of startups/scaleups in India
When I talked to LatentView's Venkat Vishwanathan about this he had a very interesting take on his situation. He is not really thinking about exits. He thinks that the point of being an entrepreneur is the excitement generated by the journey of building a company that lasts the vicissitudes. Venkat, as I wrote earlier, is cut from a different cloth and I look at him as an outlier, albeit a very successful one. As for the rest, what should happen in the macro environment?
The major IT vendors have now plateaued as far as scaling their existing services. They must now look outside to spur innovation and sustain the circle of life for the entrepreneurial life-cycle. Once it is clear to the ecosystem that these vendors have a strategy and an earmarked budget for acquisitions that will act as a motivator for planning an exit for many of them. These large company buyers should have an idea about what their customer acquisition costs ought to be, what is a tolerable churn rate, what is a good top line growth rate, etc. They should also have a track record for post-merger integrations. The major IT vendors should also have a preference on what is the right size of the acquisition for them. Once each vendor is able to communicate these through their actions, individual entrepreneurs know that there is a prize at the end and what they should target as metrics.
An healthy ecosystem will have acquisitions, divestitures, and mortality. In order to chart the course of industries, innovative entrepreneurs imagine a better future and make it happen. I agree with Srikanth, we do need tangible exit options for the IT economy in Chennai.
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A new wave of IT service providers from India are ready to flood the market with fresh services. It has been variously labeled knowledge process outsourcing, business intelligence or analytics. These companies are ready to satisfy the big data and analytics services needs of companies in healthcare, financial services, consumer packaged goods and others. Service providers come in many flavors: traditional full-stack IT companies with analytics, new entrants that service all verticals, new entrants specializing in just one vertical and, finally, pure-play technology specialists.
How can clients prepare for and benefit from this new wave of services? We have identified the following requirements.
Prepare the organization: Becoming an analytics-driven organization requires more than the addition of software. The culture of the organization needs to be changed to one that is data driven in its decision making. Companies must be ambidextrous, with one leg exploiting the present and the other looking to explore the future. Companies like Google, GE, Netflix and others can be good examples to follow.
Understand the problem: If the problem is well understood and the techniques for solving them exist, then one can hire a single vertical vendor or a full stack vendor for end-to-end services. Examples include TCS, Cognizant, Tech Mahindra, HCL, iCreate, ZS or Fractal.
If the problem is an unstructured one - no clear problem statement or solution technique - it would be wiser to use a vendor that services all verticals. Examples include Mu Sigma, LatentView or the full stack vendors.
Sourcing intent: If the intent is to complement existing initiatives, clients need vendors that can provide them with analytics capacity on-demand. Most of the full-stack service providers have built a deep bench of data scientists that can be put to use on projects.
When setting up an R&D or experimentation center, it is better to form a strategic partnership with a multi-vertical or full-stack vendor and take a holistic approach to problems.
Finally, when creating your own centers for excellence on analytics, technology vendors can be great partners. This coupled with your own ability to recruit data scientists would be a good approach. Examples of such technology vendors are Tableau, QlikView, Splunk and Cloudera.
Build absorptive capacity: In order to make use of the vendor provided capabilities, companies should be building their own internal capabilities. This could be in the form of small, centralized centers for excellence. Lessons learned by this group should be widely publicized.
What to look for in vendors? Just as in traditional IT projects one needs to look for references. In addition, certain capabilities can be vital.
Lab environments: Many vendors are setting up visualization labs to demonstrate what your decision making environment would look like. This could be very helpful in assessing their capabilities before selection.
Decision styles and other biases: We like to think that we are rational in our decision making. In reality, there are many types of biases in our daily decision making. Look for expertise in cognitive psychology or at least an understanding of the work done by experts like Kahneman and Taversky.
Analytics organization: Vendors are still not clear where they should place their analtyics organization. Some have multiple units each within a vertical. Others have created a separate unit. In either case, talent sourcing and retention are proving to be difficult. The pay scales for data scientists, career paths and management of resources are quite different from the traditional programmers. Ask your vendor about their approach to managing data scientists.
Data scientists: Internal universities are being setup to train data scientists. It would make sense to assess their curriculum. In particular, check to see if they have good programs for statistical techniques, research methods and communication.
Model management Capabilities: In addition to the traditional capabilities for modeling, assess their ability to manage models that they create. Just as knowledge management helped with traditional IT projects make gains in productivity, model management will have a big role within analytics-driven organizations.
Connectors: Vendors (especially full stack and multi-verticals) have many divisions along with the organization. It is very important to bring to bear expertise from these divisions. Look for connector roles within these organizations that have experienced people with modeling, domain and communication skills.
Order takers vs. scientists: When solutions to problems are clear, it is easy to hire data scientists from these vendors. When dealing with unstructured problems, make sure to qualify the vendors on the scientific method and also good communication skills.
The business analytics market is still at its infancy. Like many other industries, much is yet to be learned. As others have opined, profitability and risk mitigation awaits the fast learners.
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A former student of mine stopped by to discuss an idea that he was planning to implement. This conversation got me thinking about the general idea that he was utilizing -- telemetry. A definition that I found on the web about telemetry states:
automatic transmission and measurement of data from remote sources by wire or radio or other means.
The idea is simple from a technology perspective. Build an architecture that has dumb sensory nodes that can communicate simple information to a centralized hub using wired or wireless communication technologies. These sensors send information on an entity of interest and transmit that to a central location. This allows entities to be globally distributed but for information to be centralized. This allows for scarce experts to be concentrated and better utilized in the hub while the spokes can have low-cost dumb sensors.
The data itself can be about the social interactions of the object, attributes of the object or the basic components of the object. It is a continuum between social and deep information. Statisticians can analyze this data and discover hidden patterns that can be forwarded to scientists or domain experts. These scientists can now create hypothesis to be tested and use the patterns purposefully
This core idea can be used to collect information about people, machines or other every day objects (like cars,) The collected data must be cleaned up for quality purposes and then be used for monitoring, gaining insights, or predictions.
Some questions to consider. Who owns the data? What policies should be set for information usage? Is it necessary to collect deep information? What behavioral information should be collected? What about data quality (timeliness, frequency, accuracy, etc.)? Is there a need to integrate with third-party data? What techniques to apply to get information from the data? Any societal benefits? What new roles and responsibilities need to be created?
This idea is being applied to healthcare, telematics, personal analytics, manufacturing and services. General patterns of use will emerge over the next few years. Big data specialists and data scientist are going to be very busy creating business models around the idea.
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Organizations are seeking to extract value from the large volumes of data that they are collecting. This data deluge has been spurred by the availability of cheap sensors, lower storage costs, explosion of social media tools and the drive to be data-driven or evidence-based.
Many of the success stories that we hear are centered around A/B experiments for product features or go/no-go decisions based on customer adoption. Developments in text mining has also resulted in computing customer sentiment for a product or company using unstructured data.
All these techniques result in innovation that are interesting and important. However, they are not of the breakthrough kind. GE may use the internet of things to decide on what product features to highlight or Mariott may choose to exploit a particular sentiment for short term gains. These are incremental in nature.
These are what one would term localized exploitation. I am still waiting to hear about data was used to redefine the business scope of an organization or new product introductions. In some sense this is not a fair expectation. Discontinuous or disruptive ideas are not a linear extrapolation of the past. As such, past data cannot be used for these ideas.
To paraphrase Ditty Harry -- "A Data Analyst has got to know his limitations."
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A new generation of Indian entrepreneur has arrived. They have a unique perspective on how to run a business. One such trail blazer is Venkat Vishwanathan of LatentView.
LatentView is part of the white hot business analytics space. This company is very representative of the knowledge economy in India. It has employees that have business, math and programming expertise. It has all the trappings of a modern day company – a great location, fabulous offices, creative workspaces and a learning environment. They even have their on 5F model (product, customer, employees, sales, and environment) model they use to find signals that support decision making.
The thing that stood out for me was its adoption of the Chennai value system. Venky, while very accomplished, is very humble and determined. They want to be known for their quality and integrity in the marketplace. If this means turning down projects they are not afraid to do so. Everything about them is measured and deliberate. They aim to grow at a predictable pace of between 80 and 120% year over year. They recruit from a small set of highly acclaimed schools and choose their associates to fit well with their value system. Another thing that makes them unique is they are self-funded. This gives them the freedom to grow at their own pace and not lose their values.
Their employees hold stock options and even have an internal market that enables the management to buy back shares and allow employees to gauge value. This company will go places in this marketplace and with their value system and capabilities. If I were an employee and holding stock in this company, I would hold.
There is so much to learn from this company. There is a saying that if you want to get anywhere you need to know where you are from. This company has deep roots and will be going places. I hope to make this a regular stop during my visits to Chennai .
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It was a glorious summer day in Bangalore and Mu Sigma was celebrating the class of 2010. The entire batch was being fed a traditional South Indian Brahmin meal cooked by the famous Arasuvai Natarajan. The servers were none other than the executive team lead by company founder Dhiraj (dressed in traditional South Indian garb).
What makes this company so successful at what they do? While the answer to that may be too complex to analyze, here are some things that stuck out during my conversations with the team.
While they understand the competitive landscape very well, they are not obsessed with the moves made by them. As their chief fulfillment office Ambiga put it – we are just focused on our company culture and executing to our plan. She feels that as long as they maintain a small company feel, while they expand and grow, they will be just fine. They have made investments in capabilities that allow free flow of information and allow associates to locate the right expert for solving problems.
They have created a unique cadre of knowledge workers that they label decision scientists. These are individuals who can move effortlessly between the business domain, mathematics, statistics and computer science domains. Their in-house university ensures that they have the ability to create and mentor this talent. Their selection process is tailored to detect talent who are good problem solvers with a curiosity and appetite to take on challenges.
When I asked one of their senior managers Anuj as to what they do, he replied “we solve customer problems.” They follow the scientific approach to problem solving and have adopted a structure for problems (referred to as DNA). They listen to the problems/symptoms/opportunities as described by the client. This is followed by a list of variables that they find critical to the exercise. They then perform a mind mapping exercise to breakdown the problem statement. They look at all factors that may affect the variables that they identify. This is followed by a pattern matching exercise, where they look for prior art that matches the current situation. This is followed by a similar search through the literature and conversation with experts to find approaches to solve the problem. It is now time to iteratively prototype a solution. There are two supporting elements to their approach. One, get the client organization ready for decision sciences. Two, use a holistic approach to problem solving.
This organization has continued to grow stronger over the years. It is being attacked from all directions. Traditional IT vendors are moving up the stack by introducing anaylytics to their portfolio. Management consulting firms are moving down the stack with these offerings. Other pure play vendors are taking them head-on. Finally, niche players are nibbling away at their positions one vertical at a time.
Emerging markets go through a lot of turmoil with new entrants, failures, mergers and acquisitions. The business analytics marketspace is no different, but Mu Sigma feels like it is built to last and the one to beat.
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My good friend Sukumar is the chief innovation officer at Cognizant. His group has done excellent work in the area of knowledge management and social networks and they won many an award to prove that. This time around, he explained to me how they have created an internal app marketplace to solve the legendary IT development backlog problem.
Organizations struggle with projects that run over budget, fall short of the required needs and exceeds the time allocated. In addition, project and resource allocation is a complex process, often shrouded in secrecy but alway a political process. Cognizant has solved many of these issues by going to an app based platform and governance model for their internal application needs.
Given their size and complexity, this is a great accomplishment. They now have a transparent model for project selection, approval and retirement. Users can propose ideas for apps in their internal marketplaces. Developers can choose to work on one or more of these apps. A governance body makes budgetary and go/no-go decisions based on data. Since every app is API based, CTSH can provide the vital statistics on each one of them.
A side benefit of this model of development (that follows many of the principles of lean thinking) is that each one is carefully scoped and covers bounded functionality. When many such components exist, users can repurpose them to create new applications as needed, using concepts like mashups. This reduces development and testing time by orders of magnitude.
In my opinion, this may be the largest collection of apps behind the corporate firewall. In addition, many organizations can benefit from learning about their governance model.
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