2020 trends - FinTech (Financial Technology)

1.
Hyper-personalization via big data and AI
For many years, marketing experts espoused the benefits of personalization to attract customers and keep them loyal. Today, thanks to big data and artificial intelligence that helps us process, store, and drive insights from the data, hyper-personalization is possible on an unprecedented scale. Financial institutions now have information about their customers' behavior and social and browsing history. AI facilitates real-time omnichannel integration of these insights to deliver a personalized one-to-one marketing experience for their customers at the time when the information is most relevant and useful.

2.
Robotic process automation (RPA)
During 2020, robotic process automation (RPA) will continue to impact financial institutions to help them be more efficient and effective as well as help ensure they meet federal and state compliance requirements. Today’s advanced RPAs don’t have to be explicitly programmed to perform tasks; they can simply observe what humans do and then automate or suggest improvements to processes. This includes processes such as customer onboarding, verification, risk assessments, security checks, data analysis and reporting, compliance processes as well as most other repetitive administrative activities.

3.
Conversational interfaces
According to Gartner, by 2020, chatbots will interact with the customers of 85% of banks and businesses. By eliminating human involvement in these interchanges, productivity, and speed improve. In fact, according to one report, financial chatbots save over four minutes on every interaction. This is a booming area due to the progress made in natural language processing and speech generation. Customers of financial institutions have come to rely on conversational interfaces to provide 24/7 service, instant responses to queries, and quick complaint resolution to improve personal banking significantly. Conversational interfaces also provide an easy and economical way for organizations in the financial sector to receive customer feedback.

4.
Blockchain
Blockchain, a special immutable computer file that is decentralized and distributed, is disrupting financial institutions. Blockchain can make things more efficient in the financial services industry. Since fraud and identity theft cost financial institutions billions of dollars annually, blockchain has the potential to save the industry from experiencing these significant losses. Blockchain in fintech is expected to reach $6,700 million by 2023 in the United States. Financial institutions will use blockchain for smart contracts, digital payments, identity management, and trading shares.

5.
Mobile payment innovations
One of the latest “big things” in fintech is the growth of the mobile payments industry. Consumers want payments to be instant, invisible, and free (IIF). Mobile payment innovations might even do away with our traditional wallets as global consumers are less reliant on cash. Google, Apple, Tencent, and Alibaba already have their own payment platforms and continue to roll out new features such as biometric access control, inducing fingerprint, and face recognition. One of the most popular payment methods in China and used by hundreds of millions of users every day is WeChat Pay. Alibaba’s Alipay, a third-party online and mobile payment platform, is now the world’s largest mobile payment platform. Many mobile payment platforms are building programs and offers based on the user’s purchase history.

While many financial institutions are continuing to adopt new technology to enhance operations and improve customer service, these five trends will provide exciting avenues for innovation. Financial institutions realize they must learn how to use fintech to their competitive advantage.

6.
Crowdfunding platforms
 allow internet and app users to send or receive money from others on the platform and have allowed individuals or businesses to pool funding from a variety of sources all in the same place. Instead of having to go to a traditional bank for a loan, it is now possible to go straight to investors for support of a project or company. And while their applications range from family and friends funding to fan and patron funding, the number of crowdfunding platforms have multiplied over the years.

7.
Insurtech
Fintech has even disrupted the insurance industry. In fact, insurtech (as it's been so-called) has come to include everything from car insurance to home insurance and data protection.

8.
Robo-Advising and Stock-Trading Apps
Robo-advising has disrupted the asset management sector by providing algorithm-based asset recommendations and portfolio management that have increased efficiency and lowered costs. Since the rise of more advanced technology that can analyze various portfolio options 24/7, financial institutions have adapted to offer online robo-advising services - including the likes of Charles Schwab (SCHW) - Get Report and Vanguard. Additionally, other popular robo-advising services include Betterment and Ellevest. Perhaps one of the more popular and big innovations in the fintech space has been the development of stock-trading apps. When once investors had to go directly to a stock exchange like the NYSE or Nasdaq, now, investors can buy and sell stocks at the tap of a finger on their mobile device. And with inexpensive and low-minimum apps like Robinhood or Acorns, investing from anywhere with any budget has never been easier.

9.
Budgeting Apps
One of the most common uses of fintech in 2019 is budgeting apps for consumers, which have grown exponentially in popularity over the years. Before, consumers had to create their own budgets, gather checks, or navigate excel spreadsheets to keep track of their finances. But after the fintech revolution prompted the development of financial services apps, consumers can easily and efficiently keep track of their income, expenses and other budgeting tools that have revolutionized the way consumers think about their money. Budgeting apps like Intuit's (INTU) - Get Report Mint help consumers track their income, monthly payments, expenditures and more - all on their mobile device.

Credits:

Forbes.com/sites/bernardmarr/2020/12/30/the-top-5-fintech-trends-everyone-should-be-watching-in-2020/#5d15555d4846
TheStreet.com/technology/what-is-fintech-14885154
PaymentsJournal.com/fintech-trends-everyone-should-look-for-in-2020/

Pricing a Good which goes from a Manufacturer to Wholesaler to Retailer to Customer (class 2 of 2)

There are a lot of factors that affect the decision of price-pointing any given product. We studied some of the major these factors in the 1st part of the story here:

https://saurabhkautilyagupta.blogspot.com/2020/03/pricing-factors-market-competition-quality-cost-supply-chain-hops-profit-margin-product-management.html

After collecting the above data points, let us start pricing calculation...

******************

From the Manufacturer's perspective:

Manufacturing cost = $100

Innterests & taxes = $50

Supply chain cost (warehousing, packing, shipping) = $20

Assuming we want to keep a net profit margin of 10%, we should price it at =
Cost + 10% margin.

Cost = Sum total of all costs =
($100 + $50 + $20) = $170

So, we would price our product at =
$170 + (10% of $170) = $170 + $17 = $187

So, the price at which the Manufacturer will sell the product to the Wholesaler =
$187

******************

From the Wholesaler's perspective:

Cost of Good = $187

Interests & taxes = $3

Marketing cost = $5 (initially - this will/might go down as sales increase)

Supply chain cost (warehousing, packing, shipping) = $5

Totals costs = $200

Assuming s/he wants to keep a net profit margin of 5%, we should price it at =
Cost + 5% margin =
$200 + (5% of $200) = $220 + $10 = $210

So, the price at which the Wholesaler will sell the product to the Retailer =
$210

******************

From the Retailer's perspective:

Cost of Good = $210

Interests & taxes = $5

Supply chain cost (warehousing, packing, shipping) = $5

Totals costs = $220

Assuming s/he wants to keep a net profit margin of 5%, we should price it at =
Cost + 5% margin =
$220 + (5% of $220) = $220 + $11 = $231

So, the price at which the Retailer will sell the product to the Customer =
$231

******************

Let us assume, that the competitors' product's price = $250

So, the MRP of our product can be kept at = $240

At $240, we are cheaper and better (assuming we are offering better features) than the competition, and the wholesalers & retailers are also making a decent margin. The retailers can offer the remaining $9 (=$240-$231) to the customers as a discount.

******************

Additional pointers:

If the product is also expected to generate revenue (this logic is not valid for all physical products, but for a product like Kindle which is not just a product - it is also a platform for selling more products), the Manufacturer can decide to reduce its initial Margin by $10.

After launching the product, we can change the prices multiple times (in the name of discounts, flash-sales, promotions, offers, etc) - Lower the prices if expected sales do not happen (or if customers give us feedback/suggestions about lowering the pricing) & increase the prices if the sales is increasing - Take note of the demand at each price point and decide the best price-point of our product.

a sample Sales-ad by Jabong (now part of Walmart group)

Pricing a Good which goes from a Manufacturer to Wholesaler to Retailer to Customer (class 1 of 2)

There are a lot of factors that affect the decision of price-pointing any given product.

Some of these factors are:

Pricing of similar products in the market by competitors?

Pricing of Similar products in the market by us?

Are we okay with launching a product at a drastically-different (high/low) pricing than our other products?

What is our usual product pricing & profit margin% for all other products that we have so far?

All the costs that went into manufacturing, marketing, etc.?
Ideally here we will include the operating expenses (like packaging costs, supply chain costs, etc.), the interests we pay on liabilities, and the taxes we pay to the government.

Length of the distribution chain - How many hops does it take before the product reaches in the hand of our customer?

Net Profit Margin% that we want to make?

Are we willing to take a hit in profit?

Are we willing to sell it at loss, for some time, to capture the market?

Is our product better than the competitors' products?

Who is our TG?

What price-ranges does our target audience usually buy?

What our customers perceive of us?

What kind of brand are we - new & popular, new but not-popular, old & popular, old but not-popular?

Do we plan to launch this product under our existing brand-name or with a new brand-name?

Where will we be selling it - on own website, on other online stores, on offline stores?
If we sell offline, a price multiplier will have to be added to make sure that all the people - including wholesalers & all the retailers in the chain can make a profit by selling our product.

Is it a necessity or a luxury or a premium product?

Is there such a stiff competition that the final price is already decided?

Is there a scarcity of this product in the market?

After you collect the above data points, you will be able to start pricing calculation - that we have done in the 2nd part of this story here:
https://saurabhkautilyagupta.blogspot.com/2020/03/pricing-cost-expense-tax-gross-net-profit-margin-markup-product-management.html

comprehending a Bearish Market & a Bullish Market

The names, "Bearish Market" or "Bullish Market", come from for the way that these particular animals - a Bear & a Bull - attacks its victims.
A Bull swipes its victim upward during an attack while a Bear swipes its target downward during an attack, thus becoming a metaphor for market activity under these conditions.

Bull markets are defined by the market going up aggressively over a period of time.
As the market starts to rise, there becomes more and more greed in the stock market.
You see more and more people thinking, “Oh yeah let’s put money into the market because it’s going up.”

The Bear market is exactly the opposite of a bull market.
It’s a market where quarter after quarter the market is moving down about 20%.
And when that happens people start to get really scared about putting money into the stock market.

One of the most famous examples of a bear market takes the form of the 1987 market crash, which saw a 29.6% drop that lasted roughly 3 months - Often called Black Monday.

Credit: MarketVolume.com

Another infamous example is the The Wall Street Crash of 1929, also known as the Great Crash, was a major stock market crash that occurred in 1929.
It started in September and ended late in October, when share prices on the New York Stock Exchange collapsed.
It was the most devastating stock market crash in the history of the USA, when taking into consideration the full extent and duration of its aftereffects.
The crash, which followed the London Stock Exchange's crash of September, signaled the beginning of the Great Depression.

Credit: MarketVolume.com

a newspaper cutting of 24 Oct 1929

a newspaper cutting of 28 Oct 1929

Credits:

En.Wikipedia.org/wiki/Black_Monday_(1987)

RuleOneInvesting.com/blog/stock-market-basics/whats-the-difference-between-a-bull-and-bear-market/

MarketVolume.com/analysis/stockmarketcrashes.asp

En.Wikipedia.org/wiki/Wall_Street_Crash_of_1929

unleashing the power of Google Analytics' Cohort

Google defines 'COHORT' as a group of users who share a common characteristic; like 'Acquisition date'; identified by an Analytics dimension. A cohort analysis, hence, is the process of analyzing this behavior of groups of users.

sample Cohort-analysis report

Cohort Type:

GA, only gives the option of Acquisition date, as of now.

This is what goes on the vertical axis.

Cohort Size:

Days, weeks or months

Metric:

This is the metric that is being measured for each cohort.

This could be User retention, Revenue, Session duration, Pageviews, Goal Completions per user, Pageviews per user, Session Duration per user, Revenue per user, Sessions per user, Transactions per user etc.

Date range:

This is the range of data that you want to analyse.

*************************

Cohort report:

the Vertical section:

Shows the count of users, grouped as per their acquisition dates (cohort-type), grouped into weeks (cohort sizes), for last 6 weeks (date range)

the Horizontal section:

Shows last 6 weeks (date range)

the colorful Matrix:

Each row of the matrix shows how did the users acquired (visited your site/app for 1st time) in a given week, behaved (Mertic's behavior) in the coming 6 weeks.

So, in the image above, because we chose to analyze the User Retention metric, in the 1st row, for the visitors acquired in the "26 Feb to 4 Mar" week:
Week 0 is; obviously/always/for-all; 100%
Week 1 is 3.71%, which means that 3.71% of the visitors acquired in the "26 Feb to 4 Mar", got retained ie. came back the following week.

Credits:

Medium.com/the-data-dynasty/the-single-most-overlooked-report-in-google-analytics-yet-most-powerful-6ec90eba243a

Neilpatel.com/blog/cohort-analysis-google-analytics/

Youtube.com/watch?v=N02uDh-7Kcg

understanding the Tech behind the Push Notifications

demystifying the Mathematics behind Multivariate Experiments

Imagine your site/app to be a bag full of balls of two colors - red & black - in unequal proportions.

Because you can't look inside the box to count the number of balls of each color, you ask a couple of your friends to pick one ball each.

The reason for doing this activity is that, by checking a sample of balls, you want to estimate the entire count.

This is the basis of Multivariate Testing/Experiments as well:

Your site/app has a button whose existing color = red.
While the color you think the button should be = black.

Around 100k people visit your site daily.

On average, 3% of daily visitors have been clicking the red button, for the last 30 days.

Technically speaking, the red button's CTR = 3%.

Note that "3%" is an average, which means that it is the MEAN of daily-CTRs of the last 30 days (i.e. there would have been days when the CTR would have been 9% and other days when the CTR would have been 0% as well).

Now, you want to test how the black color button will perform.
Though you are confident that that the black button will get a better CTR, you can not just replace the red button with the black button and show it to all users, as there is a chance that users might not like it at all, and hence not click it at all.

So, you let a couple of your visitors view the black color button.

The reason for doing this activity is; just like the story that I told at the start of this post; that, by checking a sample, you want to estimate the entire click-count.

Now, let's say you showed the black button to 1k (1% of total) visitors for 30 days, and the following are the results that you get:

We can see from the above image that on 1st & 2nd day, the CTR of the black button was 0 (no one clicked), while on 26th, 27th, 28th, 29th, 30th day it was 0.09 (9 out of each 100 visitors who saw the button clicked on it).

Also, at the bottom of the 4th column, we have calculated the Mean of the CTR's data = 5

Also, in the 5th column, we have calculated the Square of (CTR data - CTR Mean) for each day.

And, at the bottom of the 5th column, we have calculated Sum of Square of (CTR data - CTR Mean) = 0.026, which we will now use to calculate the Standard Deviation (sometimes, also called the Standard Error), whose formula is:

Standard Deviation
= Square-Root of [{Sum of Square of (CTR data - CTR Mean)} / {Impressions total count}]
= Square-Root of [{0.026} / {30000}]
= 0.0009 or 0.09%

*** So, the Mean of CTR data is 5% with a Standard Deviation of 0.09% ***

Now, we will apply the 68-95-99.7 rule of Statistics, assuming the data is normally distributed (the rule is depicted in the image below):

Rule 1

68% of the data falls within ONE standard deviation (=0.09 in this case) of the mean.

So, 68% of our CTR-data would be between 4.91% (=5%-0.09%*1) and 5.09% (=5%+0.09%*1)

Rule 2

95% of the data falls within TWO standard deviations of the mean.
Actually, precisely, it is not TWO, but 1.96

So, 95% of our CTR-data would be between (5%-[0.09%*1.96]) and (5%+[0.09%*1.96])

So, 95% of our CTR-data would be between 4.82% and 5.18%

Rule 3

99.7% of the data falls within THREE standard deviations of the mean.


So, the final result of the experiment will be:

1. We are 68% confident that the CTR of the black button is between 4.91% to 5.09%
2. We are 95% confident that the CTR of the black button is between 4.81% to 5.18%

the '68-95-99.7' Rule

============================

Notes/Tips:

1.

These exact coefficients; for example, 1.96; are called the Standard Error or Z-score; denoted by 'Z'; of Standard Deviation - It can be calculated using the NORM.S.INV function in the excel sheet.

2.

The +0.18% & -0.18% are called the Margins of Error

3.

The 68%, 95%, etc. are called the Confidence Level

The Significance Level is calculated by subtracting the confidence level from 1.
So, if we choose to use the 95% confidence level, the Significance level = 1 - 95% = 0.05

4.

The ranges; 4.91% to 5.09%, and 4.81% to 5.18%; are called Confidence Interval

5.
p-value is the measurement of Statistical Significance of any given experiment's result.
It is also called the measurement of the Uncertainty of any given experiment's result.

6.
The 95% confidence level is the most preferred one for declaring the results of Ab tests.
The 99.7% is used where you are testing something which is extremely-sensitive-for-business.
Similarly, 0.05 is the most commonly used p-value to check whether the result is Statistically Significant or not.

7.
The existing red color button is called the Control.
The black color button, that we wanted to test, is called the Variant.

8.
In AB testing we start with 2 hypotheses:

Null Hypothesis (H0) - This says that the Control & Variant have no impact on the KPI (CTR, in our case)
Alternate Hypothesis (Ha) - This says that the Control & Variant have different impacts on the KPI.

AB testing, hence, is used to check which hypothesis is correct.

If the p-value is less than the Significance level, i.e. we have got a high Confidence level, we say that our experiment has been successful i.e. Ha has come out to be true i.e. we reject the H0.

Similarly, if the p-value is more than or equal to the Significance level, i.e. we have got a low Confidence level, we say that our experiment has been a failure i.e. Ha has turned out to be false and we reject the same.

9.

A low standard deviation tells us that the data is closely clustered around the mean (or average) - and hence produces a skewed/pointed graph, while a high standard deviation indicates that the data is dispersed over a wider range of values - and hence produces a flattened/spread graph

10.

If the confidence intervals of your original page and variation b overlap, you need to keep testing even if your testing tool is saying that one is a statistically significant winner.

Further reading:

KhanAcademy.org/math/ap-statistics/tests-significance-ap/idea-significance-tests/v/p-values-and-significance-tests

VWO.com/blog/what-you-really-need-to-know-about-mathematics-of-ab-split-testing/

ConversionSciences.com/ab-testing-statistics/

YouTube.com/watch?v=cgxPcdPbujI

YouTube.com/watch?v=hlM7zdf7zwU

YouTube.com/watch?v=-MKT3yLDkqk

New Coke - the epic 1985 Product failure of Coca-Cola (lecture 2 of 2)

in this post, we will try to do the RCA-postmortem to understand why did the 'New-Coke' fail.

You can read the failure-story here:
New Coke - the epic 1985 Product failure of Coca-Cola - part 1 of 2

Following are some of the failure reasons, that I've gathered from multiple sources:

1.
The Coca-Cola Company's apparently sudden reversal on New Coke also led to conspiracy theories, including this one: The company intentionally changed the formula, hoping consumers would be upset with the company, and demand the original formula to return, which in turn would cause sales to spike. Keough answered this speculation by saying "We're not that dumb, and we're not that smart."

2.
Later research, however, suggested that it was not the return of Coca-Cola Classic, but instead the nearly unnoticed introduction of Cherry Coke, which appeared almost simultaneously with New Coke, that can be credited with the company's success in 1985.

3.
The Coca-Cola Company concluded that it had underestimated the public reaction of the portion of the customer base that would be alienated by the switch. The company failed to consider the public's attachment to the idea of what Coke's old formula represented.

4.
Brands are more than a list of individual physical characteristics and so it is highly dangerous to focus on a single product attribute (e.g. taste). Our brains respond to implicit/psychological goals such as reassurance and conformism that we associate with a brand. These implicit goals help differentiate brands that may be very similar to each other in terms of physical characteristics. Coca-Cola had been telling consumers that Coke was “it” and “the real thing” for many years and now New Coke completely undermined this strategy by changing the formula and discontinuing old Coke. Little thought appeared to have been given to the attitudes of customers and that they might prefer tradition and stability over novelty. In the USA in-particular, Coca-Cola has a strong symbolic meaning and is seen as a cultural icon by some consumers. This contributed to the sense of loss when old Coke was discontinued.

5.
People are motivated to buy brands by implicit/psychological goals that conventional market research struggles to identify. Conventional market research relies on responses from the conscious mind & hence, usually fails to trace the implicit/psychological goals, because it relies on direct questioning which generates a response from our slow, rational mind - However, our attention is largely activated by our quick, intuitive brain. People don’t have full access to their psychological motivations and instead post-rationalize decisions when asked to explain a choice.

6.
The blind taste test completely ignored the influence of brand perception as participants weren’t informed of the brand until after they had stated their preference. Indeed, a 2003 study using the implicit research technique fMRI found that the results of the Pepsi Challenge were reversed when respondents were shown the packaging of the product they were drinking.

7.
Psychological research has repeatedly shown that people are more concerned about avoiding a loss than making a gain. By withdrawing old Coke customers felt an emotional loss of a brand they had probably consumed since childhood and scarcity magnified this loss. When old Coke was withdrawn there were stories of people going around and buying up old stock and selling it on for up to three times the normal price.

8.
Coca-Cola was such an established and well-known brand that many loyal customers saw it as part of their identity. Conforming to your tribe or in-group is an important motivator of behavior.  The market research ignored the importance of identification and herd-instinct with relation to brand loyalty.

9.
The mere-exposure effect means that people associate familiarity with safety. Coca-Cola was such a familiar brand to so many people that New Coke was always going to struggle to replace such a strong brand. People perceived them to be separate brands as old Coke was so entrenched in their psyche.

a 1930s print-advertisement of Coca-Cola

Credit:

Coca-colacompany.com/news/the-story-of-one-of-the-most-memorable-marketing-blunders-ever

Motherjones.com/food/2019/07/what-if-weve-all-been-wrong-about-what-killed-new-coke/

Vox.com/2015/4/23/8472539/new-coke-cola-wars

Alistapart.com/article/what-the-failure-of-new-coke-can-teach-us-about-user-research-and-design/

Conversion-uplift.co.uk/new-coke-market-research-fail/

the FinTech-Disaster story of Stock-Trading company Robinhood

A Must-Read FinTech Disaster story (case-study):

Robinhood; the no-fee trading app with 10M+ users & $7B+ valuation; crashed on Monday (2nd March 2020), & after experiencing 17Hours of downtime, recovered on Tuesday.

During this given period the StockMarkets gained $1 Trillion.

As Robinhood users were neither able to buy or sell, this sparked furor & outcry on social media among users.

Some users are now also demanding compensation while others are threatening lawsuits.

This 'DOWNTIME' has not just threatened the exemplary reputation the company worked hard to build, but has also ripped off it's loyal users.

People want access to their money, and when they don't have it, the minimum expectancy is communication, which came very very very late in this case - Robinhood later pinned the outage on instability in a part of their infrastructure that allowed their systems to communicate with each other.

We are in 2020 and High-availability & Disaster-recovery should be the unbreakable backbones for any tech-organization, leave alone FinTech.

I end this article with the following comment by Lalit (founder/CEO, Oxylabs Inc.):
//
I think almost every technology company has gone through similar failures in service. However, as I see it, money is a proxy to Maslow's base layer. If we are asking people to trust us with their financial assets, contingency planning has to be sacrosanct. That being said, we have to make tradeoffs. On one end, there are banks, secure yet high inefficient, on the other are disruptors, moving fast, with a promise of a better, more inclusive future. There are no right answers, just hard tradeoffs.
//

Join the conversation on my Linkedin's post here:
Linkedin.com/posts/kautilya_mustread-fintech-disaster-activity-6641319719100088320-2IQJ

Founders - Vladimir Tenev & Baiju Bhatt

Credit:

https://www.linkedin.com/feed/news/fintech-darling-takes-a-major-hit-4516755/

Cryptocurrency Trading - Supreme Court of India vs Reserve Bank of India

The Supreme Court of India (SCI) has allowed trading in cryptocurrencies.

This nullifies the Reserve Bank of India (RBI)'s 2018 circular which barred banks & financial services from dealing in virtual currencies; including cryptocurrencies (such as Bitcoin) and crypto-assets; as this raised concerns of consumer protection, market integrity, and money laundering.

However, the crypto & blockchain industry in India still faces hurdles as a government panel, appointed to look into the matter, has recommended that India ought to ban all private cryptocurrencies. In July, the panel also recommended a jail term of up to 10 years and heavy fines for anyone dealing in digital currencies. On several occasions, the government along with the central-bank had cautioned the public about the risks of cryptocurrencies. If the government follows the panel's recommendations, it could signal the end of the road for these digital currencies in India.

What is Cryptocurrency?
Cryptocurrencies are digital currencies in which encryption techniques are used to regulate the generation of currency units and verify the transfer of funds, operating independently of a central bank.

Credit:

Timesofindia.indiatimes.com/business/india-business/supreme-court-allows-cryptocurrency-trading-cancels-rbis-2018-circular/articleshow/74470172.cms

New Coke - the epic 1985 Product failure of Coca-Cola (lecture 1 of 2)

In order to understand why the New Coke introduced by the Coca-cola company in 1985 failed miserably; even when the launch of a new drink is as logical today as it was back then for the company, given the acute market competition; we need to understand the chronology of the events...

1945 onward

Coca-cola was the #1 brand in the soft-drinks market with a 60% market share.

1961

Pepsi is a carbonated soft drink manufactured by PepsiCo. Originally created and developed in 1893 by Caleb Bradham and introduced as Brad's Drink, it was renamed as Pepsi-Cola in 1898, and then shortened to Pepsi in 1961.

1963

TaB; 1st diet cola soft drink; was launched by The Coca-Cola Company, introduced in 1963 - Tab was notably popular throughout the 1960s and 1970s, and several variations were made, including a number of fruit-flavored, root beer, caffeine-free, and ginger ale versions.

The Coca-Cola Company had a long-standing policy to not use the Coca-Cola name on any product other than the flagship cola - That's why the name didn't have Cola in its name.

Following studies in the early 1970s that linked saccharin, TaB's main sweetener, with bladder cancer in rats, the United States Congress mandated warning labels on products containing the sweetener. The label requirement was later repealed when no plausibility was found for saccharin causing cancer in humans.

 1964

Diet-Pepsi premiered into the cola market.

1970

Pepsi introduced the Pepsi Challenge - a blind taste test which showed most Americans preferred Pepsi to Coke by a margin of 53% to 47% (because Pepsi was sweeter than Coke as it contained more sugar).

1980

The overall market for colas steadily declined in the early 1980s, as consumers increasingly purchased diet and non-cola soft drinks, many of which were sold by Coca-Cola themselves. This trend eroded Coca-Cola's market share.

1982

Diet-Cola premiered into the cola market (It was the 1st brand since 1886 to use the Coca-Cola trademark - It was after the long-term success of Diet Pepsi became clear to Coca-Cola that it decided to launch this sugar-free brand under the Coca-Cola name, which could be marketed more easily than TaB) - Diet Coke did not use a modified form of the Coca-Cola recipe, but instead an entirely different formula based on the TaB formula.

It quickly overtook TaB, in sales.

1983

Coca-cola was still the #1 brand, but only with a 24% market share - largely because of competition from Pepsi-Cola.

Pepsi had begun to outsell Coke in supermarkets.

Coke maintained its edge only through soda vending machines and fountain sales in fast-food restaurants, concessions, and sports venues where Coca-Cola had purchased the 'pouring rights'.

1984

Coca-cola was still the #1 brand.

Pepsi had used aggressive celebrity endorsements from the likes of Michael Jackson and hip advertising music to position itself as The choice of the new generation - It was this time that the phrase Pepsi Generation became popular - This helped Pepsi’s market share to gradually increase with a rate that and could have overtaken Coke by 1990.

Coca-Cola decided to conduct market research to better understand consumers’ preferences. This indicated that “taste” was the main reason for the decline in Coke’s popularity.

Coca-Cola's senior executives commissioned a secret research project dubbed Project Kansas headed by marketing VP Sergio Zyman and Coca-Cola USA president Brian Dyson to create a new flavor for Coke. Coca-cola CEO decided to develop a new formula; named New Coke; which would have more sugar than old-Coke and Pepsi. They then conducted over 200k blind taste tests, surveys, and focus groups to confirm that people preferred the new/sweet Coke over both old-Coke and Pepsi - and the results were overwhelmingly positive - Thought only about ~12% of testers felt angry and alienated at the thought, and said they might stop drinking Coke altogether.

23 April 1985

New Coke was launched - It used a version of the Diet Coke recipe that contained high fructose corn syrup and had a slightly different balance of ingredients.
Old Coke was discontinued - Production of the original formulation was ended (because coca-cola didn’t want to have two competing products at the same time).

In many areas, New Coke was initially introduced in old-Coke packaging - Bottlers used up remaining cans, cartons and labels before new packaging was widely available - Old cans containing New Coke were identified by their gold-colored tops, while glass and plastic bottles had red caps instead of silver and white, respectively. Bright yellow stickers indicating the change were placed on the cartons of can multi-packs.

The press conference at New York City to introduce the new formula did not go well - Reporters had already been fed questions by Pepsi, which was worried that New Coke would erase its gains - Goizueta, Coca-Cola's CEO, described the new flavor as bolder, rounder, and more harmonious, and defended the change by saying that the drink's secret formula was not sacrosanct and inviolable - A reporter asked whether Diet Coke would also be reformulated assuming is a success, to which Goizueta curtly replied: "No. And I didn't assume that this is a success. This is a success."

Coca-cola's stock went up on the announcement.

Coke's sales were up 8% over the same period as the year before.

Most Coke drinkers resumed buying the new Coke at much the same level as they had the old one.

Surveys indicated that the majority of old-Coke drinkers liked the new-Coke.

3/4th of the survey respondents said they would buy New Coke again.

Despite New Coke's acceptance with a large number of Coca-Cola drinkers, many more resented the change in formula and were not shy about making that known - Many of these drinkers were Southerners (where Coke was first bottled and tasted), some of whom considered Coca-Cola a fundamental part of their regional identity. They viewed the company's decision to change the formula through the prism of the Civil War, as another surrender to the Yankees.

Coca-Cola headquarters began receiving (40k) letters and telephone calls expressing anger or deep disappointment - One letter, delivered to Goizueta, was addressed to "Chief Dodo, The Coca-Cola Company" - Another letter asked for his autograph, as the signature of "one of the dumbest executives in American business history" - Their hotline, 1-800-GET-COKE, received over 1.5k calls a day compared to around 400 before - A psychiatrist whom Coke had hired to listen in on calls told executives that some people sounded as if they were discussing the death of a family member.

Columnists also ridiculed the new flavor and damned the Coke's executives for having changed it. Comedians and talk show hosts made regular jokes mocking the switch. Ads for New Coke were booed heavily when they appeared on the scoreboard. Even Fidel Castro, a longtime Coca-Cola drinker called New Coke a sign of American capitalist decadence. Goizueta's father expressed similar misgivings to his son.

Gay Mullins, a Seattle retiree looking to start a public relations firm with $120k of borrowed money, formed the organization Old Cola Drinkers of America on May 28 to lobby Coca-Cola to either reintroduce the old formula or sell it to someone else - His organization eventually received over 60,000 phone calls - He also filed a class-action lawsuit against the company (which was quickly dismissed by a judge because, in two informal blind taste tests, Mullins failed to distinguish New Coke from old or expressed a preference for New Coke).

Despite ongoing resistance in the South, New Coke continued to do well in the rest of the country.

Now, the Coca-cola executives were uncertain of how international markets would react, and when the executives met with international Coke bottlers in Monaco they were not interested in selling New Coke.

Pepsi-Cola took advantage of the situation, running ads in which a first-time Pepsi drinker exclaimed, "Now I know why Coke did it!". Pepsi took out a full-page ad in The New York Times proclaiming that Pepsi had won the long-running "Cola Wars".

But Pepsi actually gained very few long-term converts, despite a 14% sales increase over the same month the previous year, the largest sales growth in the company's history.

Coca-Cola's director, Carlton Curtis, realized that consumers were more upset about the withdrawal of the old formula than the taste of the new one. 

mid-June 1985

When soft drink sales usually start to rise, the new Coke's numbers were flat.

Coca-Cola's chemists also quietly reduced the acidity level of the new formula, hoping to assuage complaints about the flavor and allow its sweetness to be better perceived.

Couple of bottlers were also suing Coca-Cola which had argued in its defense that the formula's uniqueness and difference from Diet Coke justified different pricing policies from the latter – but if the new formula was simply an HFCS-sweetened Diet Coke, Coca-Cola could not argue the formula was unique.

The bottlers also saw great difficulty having to promote and sell a drink that had long been marketed as "The Real Thing", constant and unchanging, now that it had been changed.

Bottlers and their acquaintances/friends/relatives, particularly in the South, were also tired of facing personal opprobrium & ostracization over the change.

23 June 1985

Several of the bottlers took these complaints to Coca-Cola executives in a private meeting.

Talks about reintroducing the old formula moved from if to when.

the Coca-Cola board decided to bring back the old-Coke - Company president Donald Keough revealed years later, that they realized this was the only right thing to do when they visited a small restaurant in Monaco and the owner proudly said they served the real thing, it's a real Coke, offering them a chilled 6 and 1/2 oz. glass bottle of original/old Coca-Cola.

11 July 1985

Coca-Cola executives announced the return of the original formula, 79 days after New Coke's introduction.

The company hotline received 31.6k calls in the two days after the announcement.

The new-Coke continued to be marketed/sold as Coke until 1992, when it was renamed Coke II.

The old-coke was named Coca-Cola Classic, and for a short time it was referred to by the public as Old Coke.

Some who tasted the reintroduced formula were not convinced that the first batches really were the same formula that had supposedly been retired that spring. This was true for a few regions, because Coca-Cola Classic differed from the original formula in that all bottlers who hadn't already done so were using high fructose corn syrup instead of cane sugar to sweeten the drink, though most had by this time.

"There is a twist to this story which will please every humanist and will probably keep Harvard professors puzzled for years," said Keough at a press conference. "The simple fact is that all the time and money and skill poured into consumer research on the new Coca-Cola could not measure or reveal the deep and abiding emotional attachment to original Coca-Cola felt by so many people."

end of 1985

Coca-Cola Classic was substantially outselling both New Coke and Pepsi.

Six months after the rollout, Coke's sales had increased at more than twice the rate of Pepsi's.

New Coke's sales dwindled to a three percent share of the market, although it was selling quite well in Los Angeles and some other key markets.

1987

The Wall Street Journal surveyed 100 randomly selected cola drinkers, the majority of whom indicated a preference for Pepsi, with Classic Coke accounting for the remainder save two New Coke loyalists. When this group was given a chance to try all three in a blind test, New Coke slightly edged out Pepsi, but many drinkers reacted angrily to finding they had chosen a brand other than their favorite.

Credit:

En.wikipedia.org/wiki/New_Coke
En.wikipedia.org/wiki/Pepsi
Conversion-uplift.co.uk/new-coke-market-research-fail/