6 New Product Development Strategies Worth Exploring
Any PM with more than a year of experience will tell you that new product development tactics and strategies pop up all the time. Being innovative people by nature, we, of course, want to adopt the latest and greatest of these new strategies.
But is it really worth the time and effort to learn and adopt every one of these strategies?
As a senior product manager, I've learned a lot about product development through sheer trial and error.
So, here's what you need to know
so you can do more trials with fewer errors.
What Is a New Product Development (NPD) Strategy?
If you are among the lucky product managers who get to build a product from scratch, you first need to figure out the game plan or the New Product Development Strategy.
The NPD strategy is a set of steps that you and your team take to ideate and build your product (as well as the tactics that you need to employ to handle each step in the plan).
What the New Product Development Process Looks Like in 2024
The way people created new digital products 5-10 years ago and the way they do it now are slightly different.
I don’t want to talk about the way you form your NPD strategy and what the modern version of it looks like (we have a separate guide for that).
Instead, let me highlight the key changes in the way folks are building new products nowadays versus a decade ago.
Product Ideation / Researching Existing Products
Then
You would spend a significant amount of time researching your competitors, hearting their customer feedback, figuring out your competitive advantages, brainstorming your product concept, doing idea generation, coming up with new product ideas, and more.
Now
With the advent of ChatGPT (especially GPT-4), any research that would take weeks for you to complete can be done within a couple of days.
Deciding on Commercialization, Pricing, and Package Deals
Then
Most users purchased software once and would be responsible for downloading and installing updates as they became available. Enterprise software was bought and sold by a sales department (sales-led growth), who would work with the organization and client to develop custom solutions and pricing.
Now
When it comes to figuring out how you will price and sell your products, the key trend that you need to take into consideration is PLG.
The modern product-led growth strategy assumes that you will rearrange your product roadmap in a way that your focus stays on initiatives that create fantastic user experiences inside your product to make sure that your product is marketing itself.
Moreover, PLG business strategies usually consider a free pricing tier or a free trial that will let your users get the value proposition and customer experience of your product directly before making a decision to buy it.
Formulating your Marketing Strategy
Then
Just like the ideation process, conducting market research and building your strategy has traditionally been very time-consuming. It includes a wide variety of tasks, such as:
- Conducting market research and selecting a target market.
- Identifying new and existing markets
- Working with focus groups representing your demographic and performing market testing to identify your customer needs.
- Calculating the market share of similar products in both existing and new markets.
Now
Today, we have a lot of emerging AI-driven tools (including ChatGPT) that can significantly reduce the time it takes for you to research and formulate your marketing strategy. There's also Similarweb, Browse AI, Brandwatch, and many others that you can consider adding to your stack to help with this.
To sum up, AI tech has made it much easier for entrepreneurs to formulate an NPD strategy, build their business growth plan, and move forward with it. However, AI research is not the only thing that has changed in the product development process.
In fact, there are 6 more new strategies that you can consider using for your next product.
Strategy #1: AI Helping With Decision-making
We live in the golden age of AI and this technology is all over the place. Apart from using it to enhance your product functionality, you can also rely on machine learning algorithms to help you with your product decisions.
The first application of AI in the product decision-making process that might immediately come into your mind is, of course, advanced data analytics. Great product teams rely on data and analytics to track the performance of their products/features, identify areas of improvement, and extract valuable learnings.
You usually do this by using an event-based analytics tool or by hiring a dedicated analytics team. Both options will take the quality of product management in your company to a whole new level. However, they come with a range of limitations such as:
- Time and resource-heaviness: Your development team will have to integrate these analytics tools and implement all of the event triggers. Your dedicated analytics team, on the other hand, might take days to weeks to perform complex analyses.
- Inability to perform complex analyses: Event-based analytics tools will be able to handle lightweight and mid-level tasks for you. Your analytics team, on the other hand, can do complex tasks. You cannot, however, cover the cases when the task at hand is super-complex with lots of unknowns.
Luckily, modern AI models and algorithms have become smart enough to handle the analytics tasks for you. Their ability to handle high-complexity tasks within mere minutes is simply jaw-dropping.
Example
Datarobot, for instance, is able to dig into the performance data of your product and give you an estimate of your Customer Lifetime Value (LTV).
LTV is among the hardest metrics for product teams to measure, especially if your product is fairly new and you have not accumulated enough acquisition and churn data to calculate it. Datarobot’s AI model, however, can extrapolate your existing data and predict your LTV no matter how little existing data you have.
Strategy #2: AR/VR Prototyping
One of the great sides of software development is your ability of rapid prototyping. My CTO once built a prototype/MVP version of one of our products overnight and launched it the next morning.
The product was a flop and did not pass the idea validation phase. But my point is that we were able to build and do a product launch of a prototype version within a single day.
We could not do anything close to rapid prototyping, however, if we built any physical type of product. The problem is that you will have to get into the manufacturing process and work with multiple suppliers to be able to build a single unit of your product.
It means that you are wasting a significant amount of time and money on getting your hands on an product samples that you can distribute among your target audience for them to use and criticize.
The great news is that the latest developments in AR and VR tech have made it possible for your users to interact with your prototypes digitally, without the need for you to go through the hassle of producing prototypes.
Example
One of the most prominent examples of this is the Ford Motor Company which builds and tests its cars in VR space first before manufacturing the first physical copy for updating their product line.
Thanks to this process, the engineers and designers at Ford are able to find and fix issues on their prototype early on by actually seeing, “touching”, and interacting with the car.
Only after a couple of virtual iterations of testing and fixing the product design will the PM team give a green light to the manufacturing teams to start working on the physical prototype of their car.
Strategy #3: Fantastic DevOps Processes
I will not be surprised if you wonder what DevOps has to do with product development strategies.
Well, the short answer is that a good DevOps process is indeed a product development strategy itself and it is something that I would definitely recommend you to pay attention to when working on your NPD process.
Now, here's the long answer:
The DevOps I am talking about here is not the trendy position in the engineering team, but the process and the philosophy that lets you quickly and seamlessly build, test, and deploy new versions of your application or website to the production environment.
We can quickly identify a number of engineering and operational benefits to implementing great DevOps processes in your company (such as better stability of the codebase, manageability of your collaborative coding processes, and more).
However, as Product Managers, we need to focus on the business benefits of a well-oiled DevOps machine in your company, including:
- Faster time-to-market for your new features and product versions. I have seen products, the testing and deployment processes of which took 1-2 months for each major release! That is too much time! You could build entire MVPs and validate them during the time it took to perform the release of these products!
- Better stability in terms of not introducing new bugs to your product after each release. You don’t want your product to look like a hot mess after each release and you don’t want your users to get mad at you and ditch you for a more stable alternative.
- More predictability in terms of your engineering team being able to honor their deadlines. I know, release dates change all the time. But with a well-organized DevOps process, you get much fewer unforeseen last-minute problems and much better chances to release on time.
Example
To illustrate the massive difference DevOps makes in terms of reliability and deployment speed, let’s compare the tech giants (that excel at DevOps) with typical enterprise companies.
Here, we can see that a behemoth like Amazon manages to deploy 23,000 per day with a lead time in the “couple-of-minutes” range while the frequency for a typical enterprise is (if you can believe it!) once every 9 months.
We can see this improvement in product lifecycle in other tech firms too, such as Apple, Uber, or social media giants like Facebook or others.
Strategy #4: API-First Approach
Sometimes the success of your product comes not from business/product decisions, but from the way your engineers and tech leadership design your architecture.
For instance, the product I am leading has on-device noise cancellation and transcription. This early architectural decision has led to us having significant advantages over our competitors. Namely, the on-device transcription is essentially free for my company. Thus, we can price it at a level that others (with cloud transcription that is costly) cannot compete with.
Another great architectural decision that you can consider to vastly improve your product’s capabilities in the future is building it with an API-first approach.
The logic here is: to make your website or mobile/desktop app communicate with your server, you will be building lots of APIs. Usually, you treat those APIs as something internal and build it in a way that you cannot use them outside of communicating with your own apps.
The API first approach dictates that the APIs you build for your own needs should be good enough for you to use as public APIs too (including the necessary security and structure).
This way, when you decide to add integrations with 3rd party tools or offer your API as a separate service, you will not have to spend a ton of time and effort on building public API from scratch.
Example
My favorite example of an API-first approach done right is Stripe.
The API that they offer to their users is just fantastic. It’s secure, robust, easy to work with, and includes practically every feature that you can find in Stripe.
Strategy #5: Edge Computing Approach
Computing is super expensive. Your infrastructure and computing costs are especially significant if your processing happens in the cloud.
LinkedIn, for instance, was notorious for spending most of its revenue on cloud computing before getting acquired by Microsoft. They famously reported a cost of $72 million for one of their quarters back in the early 2010s.
Having such significant cloud costs will directly affect the way you build and price your products and the way you position yourself in the market.
Let me return to the product I manage and the fantastic architecture that our tech leadership had designed for it. As I already mentioned, instead of running the AI models in the cloud, we decided to run them locally on our users’ machines, bringing the unit costs of these models to zero.
The approach my tech leadership had taken there has a name—edge computing. It is the structure where you are delegating all or part of your computing to your customers’ devices.
Example
A rather famous example of a company taking this approach is Tesla. I can argue that their cars are more software and IT hardware than cars. I mean have you seen the onboard computers in a Tesla car?
These twin chips come with supercomputer-level processing power and are able to run most of Tesla’s AI models locally and significantly save on infrastructure costs and bandwidth compared to what Tesla would have spent on running these models if they did it in the cloud.
Fun fact: A security expert friend of mine hacked into his Tesla’s computer recently. Apparently, it runs on Linux (of course, everything runs on Linux) and has Dropbox installed on it.
Strategy #6: Super-Personalization
If you ask any digital marketing expert out there about the most important trends in their discipline, the majority of them will give you the same answer—personalization.
This same logic applies to product management, too. Let’s not forget that not all of your users are the exact copies of your ideal customer persona. Your persona is simply the “mathematical average” of all the different people you intend to target.
Note: Personas are among the most important deliverables for PMs to create and manage. Check out our list of product development tools that will help you with this task.
In fact, practically nobody is an exact match, and real-life users have at least one characteristic or preference that is different from your persona. It means that the product you build around your persona will not be able to completely cover the pain points of real-life users and each one will have at least something to complain about.
Luckily, we live in the world of big data and AI. You can take advantage of the behavioral data you have about your users and offer them something called “super-personalized” experiences where your features and content exactly match their individual needs.
Example
Netflix and Spotify are among the top companies in the world that have mastered super-personalization. Just use these products for a couple of weeks and they will start suggesting perfect songs and shows to you.
The Product World Is Evolving—Keep Up!
The way your startup goes from a product idea to a successful product has changed significantly. New technologies such as AR/VR and AI can help you build better products and reach your market faster.
So, no matter if there is a current product you work on or you’re about to build a minimum viable product from scratch, I definitely recommend you take advantage of these innovative approaches to take your product-building game to the next level.
Adopting a successful product development strategy will definitely help you reach your goals, but you will also need to use the right tools to execute this strategy. Be sure to check out our curated list of product tools that will help you with this.
Finally, great PMs always keep up with the latest trends in the world of product management. We can help you with that too! Make sure to subscribe to our newsletter for more product management resources and guides, plus the latest podcasts, interviews, and other insights from industry leaders and experts.
