Dusting Social Archives and Mashing Analytics

twitter_0cf6779cb9

Social media is fair to whoever uses it. Almost every feature offered is universally available to every participant – individual or brands, small or large. It is the effort invested on the platform that decides the outcome. It is not the volume but the quality of content that creates a connection with the platform’s audience. A large number of brands already invest significant resources on popular social media platforms

Twitter has features to retrieve the archives, one can refer the below link for more details.
http://blog.hubspot.com/blog/tabid/6307/bid/33963/How-to-Use-the-New-Twitter-Archive-to-Analyze-Your-Marketing-Tweets.aspx#sm.00000tlz2w0qhbecgtvdyuuvwojy4
Companies that regularly post on social media sites can actually analyse their posts to understand the seasonality and trends of their followers, etc.

This can help to see the trend and alter it accordingly. Interestingly, it is also possible to analyse the trend of positive and negative sentiments amongst the users, about their products and also about their competitors.
In a pre-digital era, clearing archives and dusting of physical files creates sneeze, in digital era, dusting social archives can rain gold instead.

Mashing Up:
Marketing is like politics only, more the crowd for meetings does not mean more voter turnout and casting votes supporting one. More viewers and followers need not necessarily translate into selling of products and improvement of revenues.
With the improvement of analytics technology, it is easier today to mash up the digital social content analytics with the customer behavior, customer traffic, sales revenue etc.
Some analytics that are possible in such scenarios are:

a) Peak revenue period Vis-à-vis Peak social media traffic period (to see whether it is instantaneous or there is a constant lag or there is no relationship).
b) High net worth customers vis-à-vis their social media involvement.
c) Possible Revenues triggered through social media channel vis-à-vis mapping customer profiles for product or service enhancements.
d) KPI of Net Score of Sentiments (Positive or Negative) Vis-à-vis Net Revenue Score.
e) Trends of above across Season, Calendars and any irregular patterns.

The above are just indicative analytics, pre-built social analytical set-ups help to derive the hidden data converted into information in few minutes.

– Dr.S.Jayaprakash

Advertisements

Insurance Analytics – The need for Common Fintech analytical platform and Continuous Audit Investigative Analytics

 facebook_e5cbe64e42

It’s a double whammy for insurers, both, on the domain side and distribution side. Domain wise, the craze for wealth management products have offset the prime objective of insurance and therefore investment returns have taken center stage in insurance products. This has led to withdrawal of policyholders, in more than expected levels, thus, affecting the actuarial calculations and premium.  The actual product life cycle has changed a lot from an insurer’s perspective. This is also adding to increased IT budgets.

On the distribution side, 2 key players of distribution are always agents and banks. Both of them are affected due to the stupendous growth of technology. Online insurance policies are challenging the existence of agents. Similarly, the growth of payment technologies like ‘Paytm’ and other money wallet companies are challenging the way banks operate in the payment spectrum. Already the footfalls are dropping down drastically in the banking premises, thereby, reducing the personal interface between the customers and the bankers. Competition to offer loans (be it personal or commercial) at more competitive rates are available at the click of the button.

 For insurance, physical interaction is very important to underwrite the moral hazards, and hence, agents or banks play the role of first level ‘underwriters’. With the growth of technology and social media, combined, with methods like Tele-Underwriting, the need for first level underwriters have also come down.

In this scenario, there are various types of risks that any insurance company will be facing, that includes governance risk, product risk, operating capital risk, solvency risk, asset-liability mismatch risk, cross-sector business risk, reputation and consumer protection risk, fraud risk, compliance risk in major lines of business, and internal control risk including cyber security.

There is a need for any insurance company to monitor, quantify , consolidate, various risks. Data warehousing is dead, or rather, it may be a longer route. The growth of technology has enabled good analytical platforms, that can do many wonders, more than a data warehouse could ever do. Like the concept of Continuous Mortality Investigation (CMI), there is a need for continuous audit investigation, across all the risks and it cannot be just a daily MIS or Weekly MIS. The analytics should have the capability to investigate on its own and trigger alerts to the stakeholders on breaching, or reaching any threshold limits.

 When the banking sector faces challenges, Insurance sector, obviously, has to face it’s ripple effects and hence, it is time to reinvent  the sector in sync.

– Dr.S.Jayaprakash, Ph.D

Digital Money & Analytics in 2016 – Growing Gold Mine

Digital Money & Analytics - Growing Gold Mine
Digital Money & Analytics

With the growth of plastic cards, online-wallets, the spending pattern among the consumers have changed, and as expected, it is predicted that within next 4 years, the transactions may touch $500 billion.

Especially for India, the growth would be twofolds. The surge of smart-phones may push many customers to use online money wallets, if not plastic cards, and on the other side, many retailers may be forced to accept such transactions to improve their revenues.

This can be a big boon for the growing small and medium scale industries across the world. The small & medium scale industries have already started using the same tools like software and analytics. Thanks to cloud computing and SaaS, the cost of software has decreased drastically. With the growth of digital money, the SME segment can leap into the next level of cognitive analytics, fine-tuning their critical parameters like stocks, productivity, distribution networks, making their plans towards zero hour.

Cognitive analytics is seeing big growth in the contemporary times that allows the software to think like a human brain. With the growth of cognitive analytics and the possibility of customer behavior data, through digital money transactions, combined with the robustness of public data in terms of GDP… purchasing power, weather data, festival calendar etc., the small and medium enterprises can equate the supply and demand data more accurately, thereby, improving their profits.

Time to Act

As the competition is becoming complex, meaningful statements may lose precision, and precision statements can become meaningless. Cognitive analytics and prediction cannot be instantaneous, it is just like a human brain. It takes time for the human brain to get trained on the complexities, to get alerted, to avoid, or defend its decisions. Hence, when digital money grows, it would be ideal to invest in advanced analytics. Coaching the predictions, and keeping the customers loyal to your business, as you continue to expand.

Gold mines, eventually, are drained and exhausted due to extensive mining over time. However, in certain conditions, gold mines are invented that continue to grow and expand. For example, when the Internet came into existence, companies like Facebook and Google, tapped into a figurative gold mine, by becoming world renowned tech giants. Digital Money is another gold mine, just waiting to be explored, which will sooner rather than later sweep the world in an exceptional way. First come first serve, to the early bird goes the worm.

– Dr.S.Jayaprakash; Ph.D

Irresistible and Momentous Loan Portfolio – Reports that get read!

It’s been an old story when loan portfolio analysis was churned using excel features and boring pivot tables. The loan portfolio reports were generally overlooked at times because they were not visually delightful to the eye. The trend is changing as financial lenders are now turning towards more and more robust analytical software platforms for better insights.

These days the lending managers are constantly worried about the risk of the interest rate, Loan growth and also the strength of member retention. Trying to have a magnified view to this maze, a series of financial data sheet is often a pile of complicated reports which makes it a tough job to draw together the comprehensive health and wealth of their affiliates. Now here is a great news for the financial institutions, that most managers can expect to pour through mountains of reports in any given month that too without missing any important lending metrics due to the innovative way the data is presented, treated and executed.

The lending managers are now relaying on analytical tools that promises a strong valuing collateral, one that is able enough to bring out the wider picture of their affiliates performance (forming well or poor) with a static analysis, they are also on a look out for a tool that is solely in-charge of showing the trends of delinquencies, build a clear graph of the credit scores and give apt risk ratings.

So here is an overview of what an ideal loan portfolio report consists of:

An ideal loan portfolio report has a clean, simple and user- friendly interfaces.

The loan portfolio report is visually appealing with detailed tables, graphs, charts.

Bringing data to work is another characteristic of an ideal loan portfolio report as it gives actionable insights.

A smart loan portfolio report is uniform at all levels and is made available to the decision makers so they all can be on the same page.

– Shrutika Bansal