I learn by doing and get to understand the double-click on challenges, nuances etc better when implementing these quick hacks.
For the next experiment, I wanted to build a solution which would do a sentiment analysis and draft a possible response for any inbound customer communication.
Why sentiment analysis for Customer Service?
As someone who has been on both sides of a customer-care channel, I do understand the need to triaging. Not every customer communication is the same. Some are high priority, and some may present an opportunity to convert a customer and so on.
Imagine all incoming messages are auto-mapped by
Emotion
Urgency
Key Concerns
Relevant Product/Service
Would this help the team-leader to smartly allocate cases (or one could even automate case-allocation basis rules built on these fields).
Customer Experience guiding Product Development
I am a big fan of marrying the aggregated analytics information (funnel metrics) with the granular feedback from customers for guiding the product roadmap.
As the cases grow, imagine a tag-cloud of issues that visually guide the product management and customer experience teams to the opportunities – my bet is there would be clear insights around bugs, possible new features and broken customer journeys.
Sample tag cloud for a hotel basis demo queries recieved
GenAI powered responses to Customer Queries
Just for fun, also added a draft response. This was super interesting, because
in most cases the prompt I used was over-promising. Almost like an instant resolution to the customer’s issue.
While the language was great, in some cases it assumed a negative experience when there was none (refer to the first one)
Identified cases where it needs to ask for more information for the resolution.
Imagine no longer having standardized email templates or holding responses. AI drafts generated for each case as it lands in the agent’s queue.
While this is not even scratching the surface of AI driven customer support, it was a good to see it in action rather than imagining the possibilities.
For most organizations (startup or otherwise) building momentum might be the #1 goal. Increasing momentum on revenues, margins, customers, transactions, data-footprint etc etc.
A big part of it comes down to choosing which opportunities to go after and which one’s to drop (even if for now). Of course there are multiple other factors too – the team, the why, the market, the GTM etc etc etc. But today, I am focused only on choosing your projects/initiatives wisely.
I was thinking of what could be a very simple framework for Building Momentum within a venture or team – Filter by Scale, Sort by Velocity
Given its origin in Physics wherein momentum is the product of mass and velocity its about maximizing for scale (impact or size of the opportunity) and speed-of-execution (or feedback-cycles, gestation-period etc).
I have found myself doing a lot of non-scale projects. Sometimes its the engineer and the problem solver in me that gets excited. Other times its just me rushing head-on rather than taking a pause and checking if this is really a good problem to solve. Is this a big problem?
So a quick 2-step framework :
Filter by Size. This is critical because even a small problem sometimes needs the same amount of time, money and commitment to solve. So choose wisely. One could sort the growth opportunities in a descending order and start with the first one, but in my experience it may not be very wise for two reasons. a) The biggest impact/scale opportunity may be daunting, really tough nut to crack. b) It may be a slow-track. Hence, I suggest you identify a cut-off and any opportunity that has a higher scale is in the consideration set.
Sort by Velocity. Within this filtered set of opportunities, sort by the ones that have higher velocity. And pick the top ones. As Reid says in Blitzscaling, prioritise speed of execution. The short feedback loops usually lead to quicker iterations and open up a path to those key metrics we chase – customers, transactions, revenues, margins etc.
I do understand, that this may be over simplification of how to choose projects, but I like simple frameworks as a starting point.
Lemme know what you think ?
Note that it is velocity and not speed. So there really is such a thing as positive and negative momentum !
Spent half a day tinkering with GenAI (OpenAI) APIs to solve a real-world problem. Felt good to be coding after so many months.
This was the 2nd attempt at understanding GenAI better with a real world problem statement. First one was this.
The Problem Statement – Opportunity to deploy OpenAI
At mTuzo we have collated all the card-based-offers for customers in multiple partners. We then provide these offers as B2B APIs to our fintech partners to showcase relevant targeted offers to their end-customers.
The most powerful value that mTuzo provides is that we have taken multiple unstructured data sources and made into a standardized uniform structured data.
Many offers have a set of Terms and conditions (T&Cs) around who or which transactions qualify for the offer. We do capture this in multiple fields across expiry, available outlets , card-level-eligibility etc.
One of the partners asked us if we could provide a separate data-field only for “minimum spends” needed to unlock the offer. In our APIs this is available prominently for showcasing to end consumers, but the partner’s use-case needed this as a separate field.
And we have 1000’s of live offers.
In the past we have used regex (& related techniques) to do something like this, but this time around I raised my hand to try & see if we could solve this via OpenAI.
Setting up GenAI API + own db
I can code only in PHP, so that decision was simple.
Next, pulled out a sample code to do a CURL to OpenAI APIs from the official website
Plugged in my API keys (created a new set just for this)
Model used – gpt-3.5-turbo-instruct (is this a good choice?)
Create a prompt to train tool (am sure this can be optimized further. Need to understand how) – "This AI tool helps credit/debit card customers understand terms and conditions(Tnc) of offers to identify any minimum spend criteria to avail the offer TnC- Get flat 20% discount on minimum spend of Rs. 2,499/- Should Reply- Rs 2,499\ TnC- Amazing offer of 1+1 when you spend of Rs. 4500/-\n Should Reply- Rs 4500\n\n TnC- Get upto60% discount upto Rs 100 Should Reply- NA"
Wrote code to connect to the db, pull one record at a time, extract the text field that has the minimum-spend data.
Create a dynamic prompt for each record by appending the text, at the end of the generic prompt script above
Call the OpenAI API for each record, with its unique prompt
Capture the JSON object returned, which looks like below. The real- response is in array[‘choices’][0][‘text’]
Use the response to do an update into a new column (for minimum spends) in the db/table.
OpenAI API response to the prompt asking it to find the MinSpend criteria in an offer tnc
The final result
Got almost accurate results. The model returned “No minimum spend” etc kind of response instead of “NA”. Maybe a fine-tuned model will help here
Speed – Ran through the whole db across all the offers in no time.
Cost – this was a token-heavy process (as seen above – spending almost 140 tokens in each record). Most of the tokens are used by the prompt. And since this is a API call without context, the prompt has to be repeated every API call. Nonetheless, it took less than 0.01 USD for 50 calls (tested before running on whole db). The cost may get reduced by using a finetuned model
Next Steps
Build a fine-tuned model and explore if the accuracy improves and the cost comes down significantly
Run similar models for extracting other information from the offer-tnc-text
Check if we can ask the model to return multiple info in one go. If yes, and the response comes as a text, how do we split the text (can we ask for a separator in the response) and extract multiple fields. This may be very efficient as most of the tokens in each call are just for prompt. Might as well get a richer response back.
Game theory provides a strong framework for strategic options in a dual player game.
Options available both to self and the other player(s). E.g. When you are in a war.
What would the enemy do?
If they do that, how can I respond?
Will the outcomes be different if I moved first. (This one was super critical in the Cold War nuclear build-up era.)
It is said that Game Theory played a critical role in understanding how the Cold War era was headed towards a mutually assured destruction. Simplistically put, it allowed the political and military leaders to understand the options and their consequences very clearly.
In today’s highly volatile and dynamic business world, it is easy to feel intimidated and overwhelmed by the possibilities and hence the resulting choice of options.
Leaders feel comfortable finding no-regret options, that help their company keep moving ahead without compounding the risk.
But what are no-regret plans and how do we find one?
An interesting story from the World War II may help … Something I came across on Amazon Prime the other day
World War II, Iceland and Hitler’s plan to bomb America
Prior to Pearl Harbour when the war in Europe was picking up, Hitler had clearly called out his intention not to engage with America – the clear super power.
The Americans did not believe him. They just didn’t trust what Germany said or would do next.
Hitler meanwhile was building special long distance bombers that could fly across the ocean and bombard multiple targets.
The Germans faced one issue, the planes would need to refuel. Somewhere.
The US Intelligence had no clue about Hitler’s Long Distance Bombers Project.
Source: Wikipedia
While the US intelligence failed to get wind of the German plans, their strategic planners identified this as a possibility that Germany may explore.
Moreover, they understood that the viability of such a plan would critically depend on the option of refuelling being available.
And Iceland seemed like the perfect place to do that.
Hence, US occupied Iceland in July 1941. Rather took over the defence of Iceland from Britain.
A classic no-regrets example.
US didn’t know if Germany would attack mainland US.
But if such an option was denied to the Germans by controlling the air and water routes via Iceland, it sure must have seemed like a smart thing to do.
A no-regrets move.
My 2 cents watching this documentary was
Put yourself in your opponent’s shoes and map out the possible options they have
Identify options that can be “easily” denied to your competitor
If you don’t know whether the option is a high-viability one (for the opponent) find low-cost ways to deny it e.g. patents, licences etc.
a16z podcast has had two very interesting episodes on the past, present and future of audio. This episode goes deeper into understanding the trends, why audio as a content format looks promising and some great insights into Spotify’s journey of launching podcasts within the same app.
Here are my notes/summary of the episode.
Understanding Audio Vs Video TRENDS
Background vs foreground audio – E.g. music may be a background audio (music) or the main element (eg podcast). Video usually does not have such a concept
Active vs passive mode of content consumption – mode based framework to understand how audio or video is consumed
Audio is massively untapped – precisely because it can have both foreground/background or active/passive consumption. Unlike video which is mostly active consumption.
What all is audio – music, talk, podcast – multiple formats exist and new ones will emerge
Learn from the video evolution – Video is like a cheatsheet for what may happen/needs to happen in audio
Understanding AUDIO Trends
Uniqueify – A new term that the guest used to refer to what Tiktok did. If you just heard the Tiktok videos you wouldnt get it You have to look at the video to understand the uniqueness that the creator brought to it. This zone where the consumers start using the standard music/video and add their own uniqueness to it, opens up a whole new world of opportunities.
Tiktok is consumed one video at a time (? don’t know, never been on Tiktok). This is super helpful for the machine learning algos. Unlike a feed which has multiple posts that you as a consumer is exposed to simultaneously, Titktok product team knows exactly what you saw, for how long. What you skipped etc. The AI learns faster. Audio learns same.
Skipping and fast forwarding is a reality. Consumers tend to skip tracks in a playlist. If the first few seconds don’t sound exciting, they are on to the next one. Music creators have noticed this and its influencing what they create e.g. Songs used to start slow, would build up gradually. Not so much now.
Role of hardware – with airpods, home speakers there is a clear increase in the consumption of audio.
More consumer journeys (I listen to podcasts during evening walks) and content formats (audio books) are evolving.
Hardware is a strong signal. What situation are you in. Different jobs to be dine. It is used as a proxy to understand which job is expected. E.g. when the music (from the phone) is played on the car speakers, it is very different context from music on a home speaker. Spotify uses this signal to predict what you may expect from the app
Phone vs speakers. Phones are inherently more interactive. But the audio based interaction has opened up a new set of possibilities.
UI (interaction) and content – those are the two aspects of hardware signal that Spotify considers important
Impact of Covid – e.g. the Daily drive on Spotify was for the daily commute. With WFH that’s gone, how do we identify a different set of content for a similar regular consumption.
China leading the audio trend . Engaging with fans. Supporting a creator economy.
Audio creators will grow exponentially in future
Return on Discovery – what is the value a consumer derives from spending time in the discovery mode.
Why and how Spotify included Podcasts in the same app
Trade-off was between a clean-podcast-only-app with zero starting distribution vs existing app with phenomenal scale available on day zero
Data showed that music listening was significantly predictive of the podcast taste of consumers.
Build a super-app. Build something that serves that segment of consumers really well. Goes deeper into their requirements. E.g. podcast creators are hugely under-served. While multiple options exist, any platform that be the super-app for podcast creators has a good chance to scale (my assumption)
Steve Jobs( We know what is needed) vs Jeff Bezos (let’s see what sticks) approach to building podcasts. Spotify has an approach of Distributing decisions (find more) . Opinionated decisions. Run Experiments.
Build for one person. Different mindsets
Challenges around allowing different interactions within the same app e.g. users skip to next track in a playlist and jump 15 or 30 seconds in a podcast. One interaction, two different results depending on the context.
A new format of music+podcasting being launched where talk is interspersed with licensed music. Works well for both the music and the podcast creator. And it seems consumers were always used to this format, thanks to the radio.
Finding Signals – With 3 min songs and high levels of skip, music picks up signals (on consumers taste) very fast. Podcast is slow. really slow and will need other types of signals. E.g. hosts, guests, topics – what is the user really interested in.
Product and strategy lessons from Spotify
Algotorial – Discovery of content via a combinattion of editors and algos. The guest takes a great example of Songs to sing on a drive. A machine does not understand this. An editor, a human can. So the way Spotify built such lists was to get the editor to select 1000 such songs and then let the ML take over to find and recommend other such songs. Algo based scaling. Human powered data-wireframe.
Mood graph – Mood is one of the biggest vectors for the billion+ playlists on Spotify. People club and consumer music by their moods. (mood based advertising has been around for a while)
Fault tolerance is very different in discovery vs consumption modes. A user who is just browsing may be ok to find a good song after n skips. Not so much when she just needs the music. Hence finding which mode the user is in is critical. Also ML training is limited to the discovery mode sessions.
Product Management needs contrarian hypotheses. While ML looks at past data to predict a straight line ahead into the future. Any innovative company will continue to take contrarian views and run tests
Prioritization is critical. But needs to happen top down. At Spotify, the top bets are decided and rank ordered by Daniel. The logic is simple. In any big company, the CEO cannot keep track of clashes. But whenever there is a clash of projects, everyone knows which one is more important.
Subscription is a very strong signal. The customer is voting with their wallet. Much powerful signal than likes, comments etc.
Go to the episode but before that, what’s your take on the future of podcasting?
These are my notes of interesting Podcasts. The inferences I have drawn may not be what the host/show/guest intended for. So beware.
Susan (YouTube CEO) was raised in the valley area as her dad taught at Stanford. She decides to not pursue her PhD but get an MBA instead. She gets married, buys a house and decides to rent it out to manage the mortgage better.
A common friend connects her to a startup looking to rent-out office space. The cofounders were still living in their dorms. And guess who move into her garage – Sergey and Larry!
The Google team expands, moves out of her apartment, and are looking for a marketing director – who do they hire as employee no 16 ? Their ex-landlady – Susan Go to the episode
Source: Masters of Scale
There is no True North, really. Just agree on one.
One of the things I love about Masters of Scale, is when Reid goes geeking into the depths of a concept. E.g. in this one a physicist from Oxford shares that
As a concept the north for any rotating body was assumed by the right hand thumb direction when the curled fingers are aligned in the direction of rotation
Since the earth, solar system and even the Milky way Galaxy all rotate the north poles of each could vary (and they do – the galaxy’s north is inverse of ours)
But since this discovery (about the galaxy’s direction of rotation) happened much later, it is now accepted that the earth’s north is assumed the standard. (watch the cool graphic below)
Same applies for business, What works for one company or one stage of the business may not work for another. Decide your own true North and stay the course. Go to the episode
When there is no map, use the compass
The North Star is so close to the North Pole (which in turn is along the axis of earth’s rotation) that it almost stays constant. Providing a good proxy for navigation.
Finding the North Star (could be values, metrics etc) is critical at any stage of the business. Knowing it clearly can aid in smarter decisions and better alignment.
Google had a very clearly articulated mission – be the best in search. This made most of their product decisions easier even when they were contrarian. E.g. no sponsored results in search, showing relevant/targeted ads, building own advertising system etc.
While, I didn’t manage to reach a level that would allow me to play my chosen sport(s) even at the district level competitively. I did manage to learn a lot from sports. A lot !
For one, it taught me how to deal with failures.
It also taught me patience and commitment – How to improve yourself gradually (sometimes too slowly to even notice)
But this post isn’t about me, its about this absolutely fantastic series on Netflix – The Playbook – A coach’s rules for life
Source – readysteadycut.com
If you haven’t yet seen it, I strongly recommend you to do so tonight itself ! And you can thank me tomorrow morning.
Summary of The Playbook – A coach’s rules for life
I have a habit of taking notes, and couldn’t resist noting down the amazing wisdom that the series has for each one of us.
These life lessons, as shared by some of the greatest sports coaches of our times, and are as true in the corporate world as they are on the sports field or our individual lives.
What’s even more interesting, is that each of these life lessons are presented with a story from their lives.
Given the level at which these coaches operate – i.e. when the stakes are really high and you are coaching the best in the world – the lessons have so much more punch and relevance.
bring your own ball (watch the episode to get the drift. She bought her own ball to boys games so as to play with them)
growth takes place outside of the comfort zone
create a home court advantage ( build a community of supporters/fans)
when you make someone feel better, they want to help you succeed
24 hrs rule- move on from a win or loss in 24 hrs (I loved this – can sure be mantra for our lives too)
what is delayed is not denied. It will happen. (amazing how rituals, symbols can become so powerful – she was gifted a part of the net by someone who believed in her. Kept it in her purse all the time.)
Thanks to Sanjay for pointing me to this series on Netflix.
Search Experience on Netflix, Amazon Prime and You Tube
Across both audio and video formats, the online media consumption in our generation is higher than ever before.
This growing consumption by an ever increasing base of consumers will also mean varied consumer journeys – i.e. how the content gets discovered and consumed may have multiple paths.
I believe SEARCH plays a critical role in enabling many of these consumer journeys.
Yet, many of the leading players have a highly sub-optimal search.
Let me start with a few examples. (NOTE: these are mobile-app-experiences, the on-TV-experience may vary)
I heard this song – Chal chalen apne ghar (don’t ask me why this song? I loved it until I saw it. ) – on the radio while driving. As soon as I reached home, opened up Amazon Music (if you are asking why Amazon Music? wait for the later sections) to search for it. And I just couldn’t find it. Whereas on YouTube, it came up instantly and under multiple spelling combos (critical for non-English language content)
I had similarly stumbled upon Fauda – an Israeli series that I really liked. And went looking for other Israeli series and movies across both Netflix and Amazon Prime. The Netflix search popped up the kinda results I was looking for, while those on Prime.. well.. have a look for yourself.
3. Searching for Ben Affleck movies. This one is interesting. While one can put the phrase “Ben Affleck Movies” in the app and see a list (don’t miss the 4th row in Netflix), its interesting how the cast section is leveraged across Prime and Netflix. Prime links it to a separate IMDB powered trivia section on the star, but no quick way to list ALL the Ben Affleck movies on Prime. Wouldn’t it be cool to just click on any of the cast or crew and see all their movies immediately.
You get the picture, right?
It got me thinking, these are big companies with millions of dollars in revenue and having the best technology and product talent, why then is the search experience sub-par?
There are a few hypotheses I could come up with.
Possible search/indexing challenges:
User-generated-uploaded-content vs original/copyright content – One big advantage YouTube has that the same song (as in the example above) is uploaded by thousands of users, each giving it their own vernacular spelling etc. This creates a rapid directory of possible variations all leading to the same underlying song. On the other hand, on Amazon Music there is just one instance of this song – the original version that is available. If the cataloging team failed to populate variations, search will always be failing on those cases. And there is clearly a tradeoff – while the chances of discovery are high on YouTube, the quality may not match that on Amazon Music or other such services.
Vernacular content – The search challenge becomes enhanced with vernacular content, because you are typing a non-English word in English – something that is prone to variations. Should I write it as Chalen or Chalein? So what’s the best solution? Start prompting possible matches as early as possible. But that’s easier said that done. Requires a very robust mapping of such variations and a link back to the content directory
Understanding the search context – Ben Affleck has been an actor, producer, director and even a writer. So when one is searching for “Ben Affleck movies” what are we really looking for? Maybe the best option is to ask the user in such instances or show all results – with tags – actor, writer, producer,director etc
What all are we indexing – while the above point is about understanding the consumer’s context during search, it is equally critical to decide what is powering the search. E.g. if a movie’s story has a Mossad character, does it qualify to come under “Israeli movies”. I guess we can argue it both ways.
Maybe it’s deliberate – It could be that the platforms have been intentionally designed to build suggestions as the primary driver for discovery. By the time I had seen my 3rd Israeli movie/series, my feed had a whole list of similar recommendations.
Search is not a boundary case, but…
Mapped a few dominant journeys for content discovery (as shown below) and few aspects become clearer
A few Consumer Journeys in content discovery
Search is NOT an edge case. But it is hard, especially in a non-UGC environment with vernacular content
It is super easy to implement the recommendation workflow. It carries a deep link and is highly scalable
Algos are scalable and provide a more reliable path towards solving for the key metric – # of hours watched on the platform. The algo recommendations can uncover titles that the user may never knew existed and hence would have never uncovered during the search-powered flow.
Maybe that’s why there is so much focus on optimizing the recommendation engine. The web is full of articles decoding the Spotify recommendation engine.
I am pretty sure that once the platforms hit an algo-powered-plateau of discovery and views , someone would decide to double down on solving for search.
Until then, let the bot and friend recommendations keep flowing.
For the last 2 weeks, I have been learning Spanish on Duolingo. It’s amazing.
The Duolingo app is just phenomenally well designed for helping one go deeper into the world of a new language – one chapter/session at a time.
Thanks to its regular in-between-session nudges I have been super regular. Built a 13 days streak.
Last week, I got a notification that I was in the Top 10 of the Silver League ! If I could retain the top 10 position by Sunday, I would progress to the next League – The Gold League.
And, guess what – I spent more time than usual going through the chapters and exercises. Collecting more points, just to ensure that I entered the Gold League. Which I did !
Duolingo Leaderboard
This got me thinking. What motivated my behavior to strive for this League membership?
I had no clue, what the Silver or the Gold League is? It’s just a leader-board in the app.
Is it across all learners, in which case its HUGE. Or is it for just Spanish learners? Or is it across a small cohort – algo picked to get all of us to do little more.
There wasn’t even any prize or any thing.
Whatever it is, the leaderboard got me motivated enough to invest more time in my Spanish lessons. I learnt faster. Got better. Got into the Gold League. And a bigger fan of Duolingo !
Its basic gamificationman !! – you must be thinking.
And hence I decided to uncover what drives Gamification .
Interestingly, last week itself I was listening to the Masters of Scale podcast featuring Ahn (Duolingo & Captca & reCaptcha founder). The two geeked out on how gamers influence product management.
Reid and Ahn discuss gamers product management
The Gamified Concoction
How we feel and what emotional decisions we make, are all governed by a few chemicals.
But like a good mock-tail, it works only when mixed well. Most businesses struggle to deploy game mechanics into their products and offerings
Why do so few businesses succeed at Gamification?
Finding the right user at the right time.
E.g. Leaderboards are considered as effective motivators, if there are only a few points left to the next level or position, but as demotivators, if players find themselves at the bottom end of the leaderboard. Competition caused by leaderboards can create social pressure to increase the player’s level of engagement, and can consequently have a constructive effect on participation and learning [Source: ScienceDirect]
This seems to be bang on – I could see how another 10 mins daily could keep me in the league. The Duolingo app never mentioned the leaderboard until I was almost there.
Choosing the right tools.
Whether it is leaderboards (competing with others) or performance graphs (improving on your past self) or avatars or meaningful stories (articulating the narrative for the user), choosing the right tool set makes a huge difference.
E.g. Insight Timer – a meditation/wellness app – has a subtle nudge to help one maintain-the-streak and build-the-habit.
Insight timer milestones
It’s more than just reward points.
“The biggest pitfall in using gamification is thinking that all you have to do is drop in some game elements. [For example, saying], “We will give people points for something , and they will get really excited just because the points are there.” – Kevin Werbach and Dan Hunter authors of The Gamification Toolkit.
Banks have long believed in the power of their reward-programs to steer consumers away. As bankers we obsess over the earn and the burn rates of various competing products in the market. But do our customers really care?
Google Pay brought in an interesting twist to the cash-back fever that ruled India, with its seemingly simple scratch-card-like experience. That one simple insight (of riding on an existing gamified experience) has changed the way cash-back was communicated in the industry now.
Problem Framing is considered a crucial step in the development of a new solution/product or in problem solving.
Yet, we miss spending enough quality time on this step, many a times.
Disclaimer: I will be over-simplifying a few aspects, to drive home the key point so please humor me.
Since early March, as the Corona virus spread to Europe and other countries in Asia/Americas, there was a lot of noise around two things.
Or rather lot of noise around the shortage of two things – hand sanitizers and ventilators.
In India, sanitizers were completely sold-out, thanks to panic buying and stocking up.
The demand for ventilators was projected basis the statistics in Italy and it was obvious that we did not have enough ventilators for the population. Even if one assumed that 1-2% of the infected population would need ventilators, there weren’t enough ventilators.
Not just India, it was the same case in UK, USA and most other countries.
If you read the news, or noticed your Linkedin or Twitter feed, it seemed the problem statement was:
We need more hand sanitizers and ventilators, fast !
Within a few weeks, there were many solutions popping up.
Hand sanitizers made at home, being made by FMCG companies etc.
For ventilators, multiple paths were being proposed and explored:
I was inspired by the determination by so many, to find a solution to the ventilator scarcity challenge. It was a phase, when I would spend almost 20-30 minutes looking at the new solutions and being amazed by the interesting paths taken by these innovators and tinkerers.
And then I read a Linkedin post that really shook me. (sorry – missed taking a screenshot )
It was from a doc, who asked a simple question to the ventilator innovators – he asked if the innovators would be willing to bet their own lives on an untested ventilator. He went on to say, the problem is not that we need a new design for ventilators, we need a higher supply of reliable/tested ventilators. Ventilators that medical teams are trained to use.
The problem framing for ventilators really was more like:
We need a higher supply of reliable ventilators. Fast.
Reading that post, it was clear that the challenge wasn’t so much about a new, easy-to-manufacture design but more about ensuring a faster supply of reliable ventilators. Reliability was non-negotiable. And most of us missed this in the early phase.
Many who co-created their solutions with medical practitioners, ensured that reliability wasn’t compromised.
This article, talks about it in the context of the UK:
Here, in the U.K., the government rather botched the whole ventilator thing quite royally. Instead of doing what industry leaders had suggested to the government, which was to repurpose factories to make ventilators using existing plans from ventilator manufacturers under license, the U.K. government took a different approach.
Boris Johnson hosted a call with 60 captains of industry from the U.K. — household names like JCB, Rolls-Royce, Airbus, Honda — and the focus was on making something from scratch from the bottom up. As has so often been the case with this virus, military metaphors were drawn. It would be a crowning moment in British manufacturing, akin to the Spitfire during the Second World War.
Medical advisors said, “Okay, but we don’t need those basic bag-and-a-bottle pumpjack machines. We need the product to be fit for purpose.” But it soon became apparent that what was being proposed was woefully under-equipped to meet what was needed. Instead of listening to experts, British politicians had felt that they understood the problem better and tasked people with a proven track record in an entirely unrelated field, the job of solving the problem.
So, how do we ensure we have an accurate framing of the problem? Some ideas include:
Get the key stakeholders (e.g. medical practitioners in the case of ventilators) involved in the early stages of problem-solving and solution-design. Co-create if possible. They will catch the missing adjectives
Define the conditions of satisfaction – again vetted by the user/stakeholder and not just the interpretation of the innovator
“Watch” the existing solutions in action. It is one thing to read about what a ventilator is and another to witness it in action in an ICU. One would know the stakes involved.
Slow down. As an engineer, I have witnessed the inherent nature of my mind to jump into the solution mode. This adrenaline surge that comes from building something new, or solving a problem, takes us away from spending enough time asking ” do we really know what the problem is “
Are you someone who used to drink a coffee (or two) everyday at the barista next to your office? If, yes spare a quick minute to tell us how WFH during COVID has impacted your coffee+work association.
[poll id=”2″]
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Read below on why this quick poll….
Let me explain why this quick poll.
There’s a lot of talk about how COVID and the resulting lockdown has resulted in a compression of demand and supply across sectors and across categories.
In most of the articles i read and discussions I am part of , people talk about how demand for most non-essential items will take a long time to pick up and reach the old levels.
No arguments there.
But I think for most non-essential spends, we need to ask what is the elasticity of behavior.
What is behavior elasticity?
I am defining this as the # of days it takes consumers to get back to the old patterns of spending.
Yes, it will vary by the spend-category . Essential and non-essential spends will have very different elasticities.
Hence I have chosen coffee for this poll.
Why Coffee for this poll ?
Few reasons:
For many like me, coffee is an essential spend 🙂
Its reasonably addictive to nudge consumers to find alternates – espresso machines, other brews etc
For years, marketing dollars were spent to leverage the power of association. For regular coffee drinkers, work/productivity may have a strong association with coffee. Is this association broken down easily
So the key question is – once offices open, will coffee drinkers be behaving as earlier?
If yes, how soon?
I think demand elasticity (not in the traditional price based elasticity) will follow behavior elasticity. And could be a big component of how fast demand recovers.
E.g. I think for the coffee shops, there may be loss in business due to
People losing jobs – say 10-15%
People working from homes post opening of lockdown – another 10%
People who will be at work but don’t buy coffee anymore – This could be the make or break – hence the poll.
I have always wondered how has Israel, given its challenges, managed to position itself as a leader in cutting-edge military tech. And it seems the answer lied in the months and years that followed Yom Kippur War.
Specifically the creation of the Talpiot Program within the IDF.
I just chanced upon Israel’s Edge: The Story of the IDF’s Most Elite Unit – Talpiot on Kindle and I was hooked on to the book from the very first chapter.
So what is the Talpiot Program?
Talpiot is this super elite, highly secretive unit within the Israeli Defence Force which hires geeks – the ones who have an unusual acumen in maths, science and computers. And not just any geeks, geeks who could work together to take on impossible challenges. The ones who have strong leadership skills and the mental and physical ability to endure even a paratroopers course.
But then again, its hard to imagine that the Iron Dome ( and its other avatars – David’s Sling, The Arrow) was first designed and proposed by a group of undergrad students to counter the threat of the incoming rocket attacks on the settlements.
The Program Highlights :
The soldiers of Talpiot begin their military service at Hebrew University but are housed separately
Study for their bachelor’s of science in physics, mathematics, and computer science,
The courses are taught at an accelerated rate, almost 40 % faster
Also trained in military strategy and complete an officer’s training course.
They spend their summers doing 12 weeks of basic training – the one given to the paratroopers.
Talpiot soldiers take special courses rotating with each force of the army: intelligence, navy, and air force – to learn about the weapon systems from the inside. They sit in cockpits of fighter jets and shoot off weaponry to gain a real understanding of its operational and technological needs.
During the second year, they devise a project of their own choosing for three months. This is where a lot of early versions of innovative tech has come from. The professors who proposed the Talpiot program insisted that innovation was possible only by young minds !
Talpiot graduates undergo military training alongside their academics, they observe first hand the theater of war, the need for technology to create an edge and how and where they can help.
A common theme emerging across multiple graduates that the book interviewed, is that the graduates learnt to take on the impossible projects. That they got trained to not be afraid.
Another Talpiot innovation came from Amir Beker, who turned down medical school to attend the program. During his military service under Talpiot in the late 1980s, Mr. Beker learned that Israeli helicopter pilots were suffering from severe back pain from vibrations during flight. To build a better seat, he first had to determine how to measure the effect of vibrations on the human vertebrae.
Together with a Talpiot classmate, Mr. Beker led a team that installed a custom seat in a helicopter simulator, cutting a hole in its backrest. Training a pen on a pilot’s back, the team used a high- speed camera to photograph the marks caused by a range of vibrations. The researchers analyzed the computerized data to come up with a way to redesign the seats.
The story behind its creation – Post the debacle of the Yom Kippur war, it wasn’t just witch-hunting that happened, there was serious soul searching. And it was two profs who articulated what was needed – Israel needed to establish itself as the leader in advanced weaponry.
The clarity of purpose – The Founding profs were clear, that the tech supremacy is not restricted to what weapons their traditional enemy forces have, but what they could get from the super powers. The USSR supplied weapons had led to significant tank losses for the IDF in the crucial war. Till then Israel had considered itself to be highly superior to the Arab armour. The conclusion was that Israel simply have to be better equipped with technology, better than anything else in the world.
Willingness to experiment – Can you imagine two profs walking into a meeting with the defence minister of army chief and suggesting why they should hire geeks and create a new unit? In most countries the defence organization wouldn’t take them seriously. Not so in this case.
Following through with the commitment to support the experiment – Getting access to the best talent was difficult in the early days of Talpiot, since no one knew about it. The young minds obviously wanted to go other elite units wherein the possibility of post army career benefits are much higher. The IDF stood by their commitment to see the program get what it needed – the best minds with most promising leadership skills
Letting the program evolve – As with most new initiatives, the first version is just a rough template for the subsequent ones to follow. The Talpiot program also evolved on multiple fronts – what attributes to look for, how to structure the course, who the instructors should be (most courses now have ex Talpiot graduates mentoring the batch)
Balancing military’s process-discipline with flexibility for nurturing innovation – The course does not prescribe missions or objectives for its graduates. They have the flexibility of choosing what appeals to them. Given that the course exposes them to the realities of each wing of the military, the graduates are able to identify problems on their own. This is probably how you stoke the passion of a young genius. Choose your own problem worth solving.
Flexible career paths – the graduates are free to choose active combat roles or pursue PHDs specializing in their chosen fields or even venture out in the business world. This wide choice of career paths post the extensive Talpiot program ensures that the talent pool is out there creating multiple down-stream ripples. There are some who joined the Air Force and now teach dog-fights to pilots. There are others who have founded billion dollar worth ventures around security/cyber tech and many more went into research.
Development of a feeder eco-system – As the Talpiot program grew in popularity many schools started focusing on training their students to qualify the rough entrance test. This is exactly how IIT coaching evolved, and creates a larger talent pool to tap into.
With so many bschools, governments and companies focused on innovation, am sure there is a lesson or two to be learnt from the Talpiot story.
Very simplistically put, payment is the movement of money from A to B.
And the world is becoming increasingly comfortable with money flow going digital. Whether its a consumer paying another consumer (P2P) or consumer paying a merchant (P2M) or business paying its vendors/suppliers (B2B) – the levels of digitization of these use-cases is very impressive.
But, what happens when the speed of digital adoption is very different at point A vs point B.
What does that mean for the digitization opportunities, challenges and product related nuances for the send and the receive side.
Let’s take domestic remittances as an example. In most of S Asia be it India, Bangladesh, Nepal – many blue collared workers send their monthly wages/salaries back home to their families/dependents.
These blue-collared workers are typically operating in urban or semi-urban areas and hence are exposed to an inherently more digitally savvy ecosystem. These workers have access to affordable smartphones, low cost reliable data-connectivity and also have multiple opportunities for assisted-on-boarding for any digital solution. Think of 4-5 workers staying in a house and one young digitally savvy showing others how to use the smartphone for voice or video chatting.
Their families (the receive side of this payment flow) on the other hand, may not have a similar eco-system. The data connectivity may be poor, opportunities for learning from others may not exist etc etc.
It might be safe to assume, that the digital adoption by the send side is expected to be much faster than the receive side.
And if that really happens, what does it mean?
Historically, domestic remittances was an agent assisted business. Remittance providers had extensive agent networks for cash-in and cash-out. Because the old flow has been :
Worker gets the salary (most probably in cash)
Goes to the remittance point (an agent outlet)
Shares details of the recipient, validates himself and pays the amount (in cash)
The recipient gets notified (usually on SMS) and
Goes to a nearest cash-out-point for withdrawing cash. Or if it was a transfer to her bank account, would go to an ATM/branch for cash withdrawal.
Let’s look at what all is changing:
Many workers will start getting salaries into accounts/wallets/prepaid cards
Workers are digitally savvy now, comfortable doing transactions on mobile.
Some may start using their mobile wallets, cards for merchant payments – because in their locations (urban mostly) there are merchants accepting digital payments.
Many will not want to stand in line or physically visit an outlet to send money.
And this will mean that many mobile-originated un-assisted remittance origination services will flourish. All vying for this big base of consumers who are just becoming digitally savvy, just becoming comfortable enough to send their hard earned money digitally.
On the other hand, the receive side looks much the same as older times:
Even if the family in the village gets money digitally, they cannot spend it digitally.
ATMs may not be efficient for banks, so local shopkeepers are best way to withdraw cash.
And since this shopkeeper has been the usual cash-out point, one may see little value in changing how the remitted money comes in
Again, to make things really simple, lets assume that there are two distinct profiles on either side.
Send side – 1. Cash-first, feature phone user and 2. Digital first smart phone user
Receive side – A. Cash heavy spender and B. Digital spender.
And let’s assume that digital adoption is process of migration from the first profile to latter of a large enough pool of consumers. This would give us the usual 2X2 matrix. Here’s a quick visual model of what all this means –
Domestic Remittance : Varying pace of digital adoption
While this may be an oversimplified assessment, the bigger point I am making is the following:
For most transactions (not just in payments) , its a human at either ends. These individuals may have different environments, motivations, behavior and hence
The rate of digital adoption at both the ends may vary drastically
And this opens up a world of interesting opportunities as the use-case undergoes a fundamental change – bottlenecks will shift, old assumptions fail, new business models will need to emerge
And it in in these times that disruption works best. The incumbents may be too committed to the old model and the new players may have just timed it right.
While I have a long way to go as a programmer, coding does put me in a zen kind of a state. The mix of learning and building something new, is almost magical.
It does fuel and inspire the problem solver in me. And it is
a great teacher too.
I just learnt that ….
Finding out what is really broken, usually takes more time than fixing it !
Last week, I was trying to fix a bug on a piece of code that was first written few years back. The software itself has undergone multiple iterations and grown in its features and inherent complexity.
I spent almost the whole day digging deeper, going step by
step to identify where the error was coming from. This meant tracing the code
flow and checking the data integrity at each stage.
Once the exact source of the error was identified, the
solution itself was just 2 minutes of code-writing. And then came the critical
step of testing the updated code for all the test-cases, before moving it into production.
And as I ended my day and looked back on how my time was spent, it was an eye-opener to problem-solving.
Same is true for medicine also. The doc will suggest a few tests and look at the results to ascertain what really is wrong before suggesting any line of treatment.
“Building a visionary company requires one percent vision and 99 percent alignment.” —Jim Collins and Jerry Porras, Built to Last
And I believe the alignment needs to show not in meetings but on the ground – in customer interactions, in every process and the decision making across all levels.
A recent experience drove home the point very clearly.
Background: Over the years, I have consolidated all my credit cards to just two – one from HDFC and another from a MNC bank. And yes both are Visa !
I have also been using my HDFC Credit card as the primary one, with reasonably high and regular monthly spends. And the bank’s team has followed up with me to increase my credit limit. But I didn’t as I didnt feel the need.
For those who of you who are not from the payments industry – a line increase is a key action to get a higher share of a customer’s spends while balancing the associated risks.
On my other card, I just have a standing instruction where a small amount is billed every month. That card has rarely been used beyond this singular, recurring transaction. And almost 90% of the available limit goes un-utilized each cycle.
The story : Last month, I had to make a big payment and I was looking to split it across the two credit cards.
I used my other card first thinking that it has a significant limit available. My transaction got declined because I remembered my limit inaccurately and had attempted a transaction above the available limit.
I then checked my available limit on my HDFC card (in the last SMS notification) and decided to split my transaction on my HDFC card into two separate transactions – trying to avoid any risk-based decline.
Immediately got a call from the risk call-center of the bank – to confirm if these were genuine transactions. And post my confirmation, the lady at the other end mentioned that I have now exhausted almost 95% of my limit. She also asked if I wanted to double my limit in the next 1 minute – on the same call.
Given this is my primary credit card – I said yes and after responding to a few questions to verify/validate my identity my limit was doubled. Right there in the call.
I loved the contrast in the experience across the two banks:
Revenue focus – HDFC converted a cost center unit (the high value transaction confirmation call center) into a portfolio intervention team that helps drive positive revenue impact
Understanding consumer journey and needs – HDFC team knew that for a highly active card, a 95% limit utilization is probably when a customer needs line increase. One may argue that number of customers who may go through such a scenario is very low. But look at the efficiency and the high levels of conversion. Do you want to keep investing in emailers and calls/SMS for line enhancement but not look at specific instances in a customer’s journey where the conversion probability is highest. And friction is the least.
Lost Opportunity – The MNC bank missed a huge opportunity. For a customer who is not regular, it is tough to remember the total limit or track available limit. And here was a big ticket transaction attempt. A call-back/notification to confirm the limits, may have gotten them the transaction and may be higher spends in future.
These are the customer interactions that decide the winner in the hyper competitive world we operate in – how organizations understand and respond therein.
Factories and laboratories evoke very different images.
With a factory – I am usually thinking the industrial revolution in all its glory – machines, assembly lines, robots, workers, all working in a disciplined and predictable manner – churning out products that are all identical and meet the claimed specifications. Low room for error. Designed for scale.
On the other hand, when I think of a lab – I usually come up with a white coat wearing team of specialists pouring over data. Going deeper into a topic. Asking fundamental questions. Pushing boundaries. New ideas being discussed and prototyped.
While I know these are extremely simplified and probably exaggerated views, they are helpful in defining what I call as the Factory Mindset and the Lab Mindset. So bear with me.
The Factory and Lab Mindset
The Factory mindset or culture is where the blueprint has been validated and chosen for at scale business. Think of this as the mindset needed to to do more of the same thing at the right cost, quality – a highly process oriented approach.
And this is relevant in the service sector too. Think of a bank branch, or the claims underwriting team of an insurance co. The rule book, processes, roles and responsibilities are all clearly laid out. Thereby ensuring that customer after customer can be duly served.
The Lab mindset is what triggers change and innovation. It requires the ability to challenge the status quo. To ask fundamental questions, build hypotheses. Be bold enough to experiment with some of those hypotheses. And be ready to fail.
Need for both mindsets to co-exist
In today’s fast changing business environment, its imperative to nurture both these mindsets/cultures concurrently. More so within the product team(s) at consumer/enterprise technology companies.
And it’s tough !
In any multi-product organization, chances are that the products are at various stages of their lifecycle. Some might be in a MVP or Pre-commercialization stage, while (hopefully) most are on a scale-up path in the commercialization stage and few others may be close to the end of their life cycle.
Sarah is a product manager focused on an early stage product. She needs to be like a scientist – doing experiments, tracking whats working and what’s not and getting the product to evolve rapidly. More importantly her manager needs to guide, motivate and evaluate like a senior scientist would. The Lab mindset needs to prevail.
Contrast this with Peter, who’s product contributes to almost 15% of all revenues. A big part of his job is to keep the commercialization machinery humming. From updating pitch decks, to reviewing the pricing model, understanding the sales pipeline – its a very different role he plays. He also does some of what Sarah does full-time. Peter needs to have a close pulse of the market – understand the current pain-points, evolving consumer needs and build or modify features/functionalities to keep the product relevant.
This ability to toggle across the Factory and the Lab mindsets is critical for organizations and individuals to keep innovating, evolving and staying relevant while still growing.
Do you agree?
If you do, how do you think organizations should be designed to nurture this co-existence of seemingly different cultures and mindsets? Share how your organization does it well already. Because I feel this is more fundamental than just having different kinds of performance metrics depending on the stage of the product.
If you have found a way to do this well at your individual level please do share.
Going back to the earlier visualizations, isn’t it hard to imagine a lab-coat wearing nerdie walking around the factory floor? I remember from my engineering summer internship – the R&D deptt was in an air-conditioned separatesection of the plant. The deptt infact had its own assembly line to make shock-absorbers!
Yesterday, I got to experience the WhatsApp payment flows. It surely felt like a neat friction-less experience both for adding/mapping bank accounts and for in-chat payments.
And in my excitement I forwarded it to a friend who didn’t have any UPI handle so far. And I was surprised by the reaction.
How does WhatsApp know my bank account ??!!
And frankly I had looked at it the other way round – they are showing me the specific account that I want to associate here.
And this got me thinking about friction in digital consumer experiences.
I remembered my Amazon experience.
I have recently changed my laptop and phone and each time I logged into my Amazon account from a new device/browser I got a security challenge. I had to enter a security code that was sent on my email.
This is inspite of me authenticating myself using my Amazon credentials – login id & password.
So why the additional step? Why add to the friction of logging in?
Its a friction-less way of doing XYZ !
We have drastically reduced the friction in each transaction
Our platform provides the most friction less experience for ABC
Am sure like me, you keep hearing how every venture and corporate is focused on reducing friction and there by making it a significantly better experience for their consumers/stakeholders etc.
And I get it.
If I almost always use an offers platform to look for offers near me on a mobile app, it should not ask me to choose a city, then location etc – it should just pick my location and show me the offers. I get it.
Similarly, if my online or in-app payment process need an OTP and there is a way to automatically read the OTP rather than needing me to toggle from the merchant app to the messaging app and back. It is definitely so much cooler and easier.
BUT, ALL FRICTION IS NOT BAD
What I don’t get is how suddenly friction has become such a bad thing.
Way back in my school days, we were taught in Physics that while friction caused wear and tear, it also was the main reason wheels work – friction prevents slippage and aids rotation. Snow chains for tyres – aid driver confidence by increased traction (apart from helping break the top ice layer).
My current thinking on friction less experiences is as follows:
All consumers are not same. What is a great experience for some may be a concern for others (elevators vs escalators) . Hence it may be best to have varying levels of friction available for consumers.
Friction can help build consumer confidence – esp amongst users concerned about security
Friction is also an industry level phenomenon. As an industry matures and consumer confidence builds, need for a faster, smoother way to do the same old task would become stronger.
This happened yesterday – We settled down for a discussion first thing in the morning and a colleague says- smile guys, its a good day!
But, it was a spirited discussion . And the smiles quickly vanished.
We shared our views and debated. And I realised I was talking with a lot of emotional energy.
I told myself, its ok – because I am committed and passionate about this. But there was clearly another voice telling me – its not ok.
And then something interesting happened later in the evening.
I was reading Pegasus Bridge June 6 1944 – By Stephen Ambrose and came across a section where it talked about the front in North Africa.
In particular the book was introducing Hans von Luck – a protege of Rommel – who agreed with his British counterpart to fight a civilised war.
Every evening at 5 p.m. the war would stop. The Brits would break for tea and the Germans for coffee. They would then get on the radio and share the details of captured personnel and any messages these POWs may have for their families – usually messages confirming that they were ok.
In one particular instance, the Germans learnt that the Brits had got a fresh supply of cigarettes. Von Luck offered to trade a captured British officer for a million cigarettes. The British countered with 600K. And a deal was stuck.
But the prisoner refused to be exchanged because he insisted he was worth the million initially asked for !!
I was shocked !
These are men at war. They are willing to kill and die. Yet they manage to keep humour and civility intact.
I am lucky to have read about this incident the same evening I was telling myself its ok to lose my cool. It has helped put things in perspective.
I went back to my colleague and shared this story. Thanked him for opening the meeting with a request to smile.
He has decided to make sure that every meeting he attends, he would put smile(s) as the first agenda item.
And I am inspired to go a step further. To keep humor intact at the workplace – the place where we spend most of our waking hours does not need to be such a serious place.
