Sunday, 29 May 2016

Digital Transformation in Banking - my POV

The digital banking landscape has never been more dynamic than it is today. 

The number of people going into branches to do their banking is falling dramatically. Customers are changing the way they bank, which requires banks to be flexible and agile.

A lot has changed about today’s customers. In this digitally connected world, customers search, learn, communicate and shop with technology. Easy access to abundant information, web connectivity & smart phones which are key characteristics of the digital age, may have impact on customer loyalty. 

On top of that new competitors & new technologies are impacting the banking industry faster and to a more significant degree than ever experienced.

With rising pressure from agile digital competitors, whether fintech startups
like Sofi, Billguard, Wealthfront and Moven, or larger entrants like Apple, Microsoft, and Google, every financial services organization must think like a tech company.

Today’s banks are facing various head winds that require shift to digital:
  • Channel overload – mobile, online, branch, ATM
  • Growth required – competition from non traditional players
  • Budgets are allocated to risk and compliance

Banks are introducing digital financial advisers called Robo Advisers, which helps customers to make more informed savings and investment choices.

Recent analysis shows that over the next five years, more than two-thirds of banking customers are likely to be “self-directed” and highly adapted to the online world.

Consumers already take great advantage of digital technologies in other industries like booking flights and holidays, buying books and music, and increasingly shopping for groceries and other goods via digital channels.

Banks are using fintech companies to co-develop new services that meet their business needs in areas such as money management, payments, lending and mobile on-boarding.

There are several challenges in digital transformation:
  • Data quality as it is coming from disparate sources
  • explosion of Big data sources like social, audio, video, beacons data
  • Lack of data integration across various lines of business
  • Resource shortage to develop and maintain digital solutions

Some of the trends in Digital Transformation:

How to start this digital journey:
  • Understand and assess what customers, partners, employees and other stakeholders want.
  • Map the customer journey for each touch point.
  • Analyze the quality of experience and identify the challenges to be resolved.
  • Prioritize and deploy social, mobile, analytics, cloud, IoT as necessary.
  • Test and move fast on failures to new innovations or business models

Banks must focus on humanizing the digital relationship, not digitizing the human relationship. They should use any technology or innovations which has to ultimately benefit the consumer.

Saturday, 21 May 2016

Digital Assistants – Siri, Cortana, Alexa, Google Now?

In October 2011, Apple gave voice to iPhone 4s through Siri, the digital assistant to whom you could talk, when in need of some information. Since then the Intelligent Personal Assistants have evolved across multiple platforms.

Later in July 2012, Google Now made a debut on Android smartphones. It was known for the ease with which it integrated with Google services and enabled tasks using cards that helped you understand what you need to do next.

The whole point of having a digital assistant is to have it do stuff for you. You’re supposed to boss it around. Typical tasks you can ask them are:
  • Make calls to your contacts
  • Send texts and emails
  • Create calendar events
  • Create reminders
  • Open apps
  • Play music
  • and many more

These Digital assistants have been catching on, offering a hands-free and more natural way to ask questions, find information and manage busy lives.
  
Digital assistants accessed via smart phones can bring together geo-location and online activities such as booking travel, checking in on social media, finding restaurants or shops.

They work by understanding as much about you as possible. This happens by accessing patterns and data from your email, track past GPS location patterns, as well as past requests made to it, by you. On top of that digital assistants continuously learn from the millions of requests it receives from users across the globe. Machine learning, as data scientists call it, enables them to adapt its responses depending on the nature of queries.

Siri is the best at understanding natural language. It can pick up on multiple ways of asking the same questions, so you don't need to worry about remembering the exact phrasing for commands, but for now it works only on Apple devices – iPhones, iPads & Apple TV but not yet on Mac.

Google Now knows you from collection of personal data and search you do all the time and it knows your daily commute, your interests, and details about your schedule. All that information coupled with artificial intelligence is used for the predictive "cards" feature, which shows you what you're interested in before you ask. Google Now works on Android, iPhones and even PCs.

Cortana is quite cheerful, and its location-based reminders ("Remind me to call a friend when I get home") worked the best. Cortana's fate is tied to Windows 10, which Microsoft expects to be on 1 billion devices in two to three years. Cortana will also be available on Android and iOS, and might be the most-used of the bunch by 2020.

Alexa is inside the Echo, a speaker you place anywhere you like. It's always listening for its name and a seriously impressive microphone can pick up commands in a normal voice from across the room.

Apple's Siri sometimes has problems with simple requests with accents, while Google Now is a personality less arm of the company's search engine. Microsoft's Cortana is trying to be both clever and useful, but it's virtually nonexistent on mobile phones where we need it most. Amazon's Alexa is gaining steam in the smart home, but you can't ask it anything complex.

Going into future we may not need keyboard and hands to type and everything will work on voice instructions. So get ready to meet some new husky interesting voices. Once you pick one, you might never break up!!

Friday, 13 May 2016

Sentiment analysis in the age of Digital Transformation

Sentiment Analysis is the process of determining whether an information or service provided leads to positive, negative or neutral human feelings  or opinions.

It is essentially, the process of extracting, identifying and characterizing the sentiments with the help of natural language processing, statistics, or machine learning methods and can be derived from various online mediums such as social media, review forums and blogs or as part of call center operations

Some of the sentiment prediction tools work just by looking at words in isolation, giving positive points for positive words and negative points for negative words and then summing up these points.

Sentiment analysis would help you to better plan your next course of marketing actions keeping in mind the existing tone.

Some of the questions that Sentiment analysis can help are:
  • Why don’t people like this new product?
  • Does this customer feedback look satisfied or dissatisfied?
  • What do consumers talk about my brand in online space?
  • Are they happy with the services?
  • What do our customers like about our competitors?
A typical process for sentiment analysis is as below:
  • Extract text or streaming data from various sources
  • Removing stop words, punctuation, slang words, special characters, numbers, extra white spaces
  • Stemming meaning putting word variations like "great", "greatly", "greatest", and "greater" all into one bucket
  • Converting text to lower case
  • Categorization using dictionaries (taxonomies) according to line of business
  • Identifying one word, two words or three-words combinations for more accuracy
  • Classify the words into positive, negative or neutral categories
  • Generate the word clouds
  • Provide further reports on most negative sentiments to be actioned by business

Today there are several products available to do sentiment analysis using Natural Language Processing (NLP) & Machine Learning.

They go across all the social media conversations like blogs, news, forums, videos, tweets, reviews, images, Facebook etc and collect the data streams for further analysis.

Several machine learning algorithms like decision trees, naive bayes classification are used for classification.

NLP provides ability to read and understand, as well as derive meaning from the languages that humans speak, and it is part of Artificial Intelligence.

Nestle, via their Digital Acceleration Team, tracks the sentiments of their 2000+ brands to know what their customers think and to deliver products that they want and to prevent crisis’s from happening.

Coca-Cola, the brand that built its marketing message around happiness and sharing, has built vending machines which sets the price of can based on how positive your tweets are. 

Consumers are always on their smartphones leaving the trails of their feelings in the digital world.

In the age of Digital Transformation, Sentiment analysis is all about helping companies gain better insights into their customers, and helping them to bridge the gap between insight and action.

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