Prod Mgmt in the age of "ChatGPT" & "Threads"

Well, the above is my first step into the world of click-bait headlines !!! Let's see if it is effective.

In case you haven't noticed, there is a new dopamine dealer in town. And instead of slinking in dark alleyways, this dealer is loud and proud and calls itself "Threads an Instagram app" by Meta. And, unless you have been on digital detox or off-the grid for the past few days, you would have also heard that Threads has been the fastest app/service to cross the fabled 1 Million user mark, surpassing ChatGPT. On July 10th 2023, it crossed 100 Million user signups, 5 days after launch !!

This graph from Statista is quite poignant on the lightening pace that apps and services are growing at nowadays.

This pace, I am sure can be explained due to various factors like growth of internet access, growth in smartphone use, growth in connected platforms, the growth of social media itself etc. But it is still important to reflect that Facebook the mother app of Meta took 10 months to get past the 1 Million user mark approx. 14 years ago.

This article does not focus on the reasons on why Meta launched Threads. Experts have mentioned points such as, the tapering of growth in other Meta platforms, seeking additional advertising real-estate, and even providing a text heavy platform that can be used to train Meta's LLM model "Llama".

Here, I would like to take a stab at examining the launch of such fast growing products like Threads and ChatGPT through the lens of product management.

The existing literature on Product Management uses the product life-cycle bell curve as the bedrock for most of its theories. As we all know, the introduction stage is supposed to be slow, followed by the growth and maturity stages where product uptake starts tapering down into decline. This is when the product team is supposed to be looking at product extensions to extend the life of the product.

Now, which stage of the above, do you slot a service that has 100 Million users in the first 5 days of launch? If this service is still in introduction stage, then using a linear growth rate, the service will have the entire 7 Billion people in the world as its subscribers in little over a year (350 days to be exact). So, growth, maturity and decline for this service will all happen within a year. I know, I am making a broad range of assumptions and simplifying calculations, but you get the point.

Or, would you consider Threads to be a product extension of Instagram. As you login with your Instagram credentials, import your contacts and followers from Instagram. Off course, in the case of ChatGPT it was an entirely new service. which encountered competition very quickly in the form of Alphabet's "Bard" and CharacterAI. So, you can consider it to be in Growth Stage as per the life cycle.

Threads and even ChatGPT still have a lot of missing features, which if you are a believer of Agile Product Management, follow the tenet of quick good enough feature roll-outs, with future functionality based on product roadmaps, performance of the service, and user feedback. Thus, in the feature aspects you can consider these services to be following the traditional Product Management playbook.

Then consider user engagement, the hallowed metric of digital products and services to grab user eyeballs and advertising dollars. You can have many user signups, but if those users do not engage then all is moot.

The digital world has had many examples of services launching with a boom and then fizzling out later. Example, Google+, this image from Techcrunch back in 2011 indicated 10 Million users on Google+ in 2 weeks. Google even claimed to having 540 Million users on Google+ in 2013. But, these users were not engaging on the platform and hence not making the platform attractive for users or advertisers. Eventually, Google killed it in 2019.

Thus, sign ups (I would consider it to be the equivalent of Sales from the Product Management Life Cycle) needs to be considered separately from user engagement on the product or service. And it should be considered as the primary determinant of success, as the more user engagement there is, due to the "network effect" more users will signup to the service. So, product performance, feature sets, support, accessibility etc. should all be geared towards increasing user engagements.

And how about Saturation/maturity? With a product getting a million users within an hour, saturation also comes early. For example, ChatGPT is already seeing a bit of a cool off, as can be seen from the graph on the right. This does look eerily similar to the life cycle graph from above. Thus, while getting users to try out your service can be quick, keeping them there and getting new users once the hype has died down is not so easy.

In conclusion, Product Management practitioners and teams need new ways of thinking about their products and services and how these are managed through the different life cycle stages, in this age of hyper scale and growth. User acquisition/signups are important, but user engagement is even more important where advertising dollar follows eyeballs. Metrics like DAU (Daily Active Users), MAU (Monthly Active Users), Average Session Duration, Customer Retention Rate and Bounce Rate can help in uncovering product health, user experience, product feature use, drop-off points, content engagement and others to turn a user into an advocate.

Action points for Product Managers/Teams:

1. Be prepared for scale from Day 1.
2. Be ready with the future product roadmap from Day 1, as with scale you might have to introduce new features on Day 3 or 4.
3. Make sure that the tech-stack is ready to handle scale from Day 1.
4. Make sure that ancillary services like Support, Sales, are ramped up and ready as quickly as possible.
5. Be ready with strategies to convert sign-ups into active users by making the process seamless.
6. Have solid strategies in place to increase user engagement by measuring metrics.

Further reading

• https://www.thespl.it/p/threads-the-fastest-growing-product
• https://aatir.substack.com/p/why-meta-launched-threads-to-take
• https://www.digitalinformationworld.com/2023/07/chatgpt-sees-10-dip-in-usage-could.html