At the end of the 19th century, German philosopher Georg Hegel coined the term Zeitgeist to describe an invisible agent or force that characterizes an epoch.
The Zeitgeist of the 2020s might be captured in the terms attention economy, digital-first thinking, permacrisis, hyper-individualism, conscious consumption, and the up-and-coming AI economy.
Democratized access to information, technologies, and capital has made product engineering faster and more accessible to growth-driven entrepreneurs.
We’ve also seen that small teams beat bigger players in terms of speed. Instagram had 13 employees (and a high-growth product) when it was acquired by Facebook. Mojang (the company behind Minecraft) had 37 employees when Microsoft acquired them for $2.5 billion.
At the same time, larger enterprises are successfully reinventing themselves with new digital products. Nokia pivoted from hardware (smartphones) to software as a service (SaaS) products for the telecom industry. Amazon went from being an online bookseller to building out a platform business with stakes in eCommerce, cloud computing, advertising, media, and entertainment. Walmart is no longer just a grocery store chain. The company also operates a profitable retail media ad network (Walmart Connect) and offers financial and insurance services.
New product development has never been easier… and has never been harder.
Launch rates for digital products are at an all-time high, but failures are frequent. –market fit, overly optimistic revenue projections, and shifting consumer demand are common reasons behind unsuccessful launches.
According to CB Insights, 35% because of insufficient need or demand for the product. Another 20% get outcompeted.
Large or small, new or veteran, companies across the board are pressed to show strong financial performance to stakeholders and investors alike. With recession looming, profitability becomes a more important metric than growth at all costs.
The other side of faster product engineering is a lower tolerance for time-to-value intervals. And productivity growth in mature economies including the UK, Germany, and the US has been slowing down or in decline since the early 2010s, despite ongoing digitization efforts.
The abundance of competing products makes it hard to carve out a niche. In the words of Paul Graham, you want to launch to a market where there are “not just people who could see themselves using [your product] one day, but [who] want it urgently.”
In reality, however, you must always balance between creating a product that a lot of people want a bit of or a product that a few people want a lot of. The latter is always better, as it indicates a stronger market fit.
The goal of effective product engineering is to first help you figure out what the market wants and then to provide it with the least risks and the most rewards.
Software product engineering: Key concepts
Before we dive in, let’s recap the key concepts.
Product engineering is an iterative process for producing an item (product) for sale. It includes steps such as ideation, design, development, launch, and subsequent scaling.
In our case, we’re primarily talking about software product engineering — aka the process of transforming a rough concept (a meal kit delivery service) into a market-ready digital product (HelloFresh).
A product, in general, is something that people want or need. Great products are built for specific audience segments and fulfill a particular need (or create one!).
The process of figuring out what people want from new products is called product thinking.
The simplest way to define product thinking is that it is the skill of knowing what makes a product useful — and loved — by people. As with all skills, it can be nurtured and developed; it’s not just an instinct one does or doesn’t have (and even instincts are trained, after all).
Product thinking is often confused with the product vision.
A product vision is a mental image of the aspirational future: your concept of the value the product will generate. Having a clear vision of the outcome is great, but it’s not enough for creating a feasible and detailed product roadmap.
A product vision typically lacks a clear sense of direction. Markets send mixed signals on the size of demand and users’ preferences. Drilling down to the most promising segment of the total addressable market may force the team to chase conflicting priorities.
To figure out the optimal path forward, leaders rely on product thinking — a continuous, data-driven evaluation of your idea across different phases of the product development cycle.
The guesswork of product development
The product success strategy resembles a black box.
Whenever you try something new — be it holding a fork with your non-dominant hand or building a gaming system for self-driving cars — you’ll get several types of reactions.
First, you’ll have people who think that your idea is weird. They’re not impressed and probably won’t be an ideal target audience (at least not right now).
Then, you’ll have people who would probably love your innovative approach but aren’t aware of it yet. For example, meal kit providers like HelloFresh struggled to scale because their target audience had a problem (same old boring meals) but didn’t yet know about a new type of solution (new recipes delivered to their door every week).
Likewise, early market entrants like Uber and Airbnb paved the way for new product concepts and had to persuade their audiences that Ubering was better than hailing a cab on the street and that staying at an Airbnb was cheaper than staying at a hotel. Granted, their gambits paid off.
But countless companies fall prey to consumer reluctance.
How come? Because though most consumers say they want new products… they aren’t necessarily interested in using them.
Customers with a lot of expertise (experts) may not search, because they think they already know what’s best. At the other end of this spectrum are customers who are clueless. They do not search because they do not know which questions to ask, where to find the answers, or how to interpret the information if it arrives.
Source: WIPO — Global Innovation Index 2022
…And yet a bigger R&D budget doesn’t always translate into better product development and innovation.
A PwC analysis of the world’s largest corporate R&D spenders found that high rollers with massive annual R&D budgets like Roche ($10B), Novartis ($9.5B), and Johnson & Johnson ($9B) trailed behind relatively thriftier companies like Siemens ($5.8B), Oracle ($6.8B), or Facebook ($5.9B) in the Innovation Index.
Of course, the level of R&D intensity (investment measured against sales) is much higher in companies in industries like healthcare versus digital companies. Still, as industry analysts have concluded time and again, it’s not the size of the R&D investment that counts but rather how it is used.
So how can you maximize the value of your investments in new product development and reduce the risks of taking the wrong turn?
At Intellias, we do this by adopting the glass box product engineering strategy.
The glass box digital product engineering strategy
At the concept stage, product development resembles a black box.
Your team has more questions than answers:
- What problem will our product solve? What’s our unique value proposition (UVP) going to be?
- Who’s the ideal target audience for our product? What features would they like to have?
- How do we organize the product engineering process to accelerate time to market?
- How do we generate awareness, interest, and sufficient demand for our product to break even and then make a profit?
- How are we going to measure success? What metrics should be in place?
- What should we do if we get outrun or our market share is eroded by a bigger/faster competitor?
To help our clients get answers to these questions, our product team has developed a framework for turning that black box into a glass box.
Our glass box product engineering process is based on industry best practices such as the Double Diamond process model, lean product development principles, and continuous feedback loops, as well as on our field expertise in engineering innovative products.
The glass box strategy has four key stages:
- Data-driven product idea validation
- Lean software engineering
- Metrics-driven go-to-market strategy outlining
- Safe product scaling
Product idea validation
To determine if there’s a bright future for your product, you need to understand the market readiness by determining the product–market fit.
Andy Rachleff, CEO of Wealthfront; and Don Valentin, founder of Sequoia formalized most of the ideas behind product–market fit.
According to Rachleff, there are some early indications that the market thinks your product is the right solution. The goal of product–market fit analysis is first to generate those indications, then to analyze and transform them into product backlog items.
To get started, you need to develop several value hypotheses:
A value hypothesis is an attempt to articulate the key assumption that underlies why a customer is likely to use your product. It identifies the features you need to build, the audience that’s likely to care, and the business model required to entice a customer to buy your product.
How do you articulate a value hypothesis?
Our product engineering company relies on:
- Market analysis: Total addressable market, serviceable addressable market, primary target audiences, and competitive landscape
- User analysis: Business/target user feedback, support tickets, open-source data mining
Each value hypothesis is then recorded as an experiment — an idea we want to test and validate during the minimal viable product (MVP) stage to verify product–market fit.
For example, if you’re building a telematics insurance product, your value hypothesis could read like this:
For young drivers (aged 18–27, living in the UK) who prioritize safe driving, our product provides a dynamic policy premium that rewards them for their safe driving behavior. By continuously tracking and updating their driving record, users who have no recorded speeding, harsh braking, or collision events can expect to see their policy premium decrease over time.
A good value hypothesis defines the what (the main value proposition), the who (the target audience), and the how (the product feature).
Importantly, the process of validating and iterating on your product idea should happen in parallel with defining the optimal business model for it. The idea is to identify a target customer segment and their needs, develop and test product hypotheses, and iterate on both the product and business model based on customer feedback. This iterative process is often called getting out of the building, and it involves a continuous feedback loop between customers and product development.
The goal of experimentation is to help you reach the stage where the market accepts your product and shows clear demand for it.
Or in the words of Andy Rachleff: “Time after time, the winner is the first company to deliver the food the dogs want to eat.”
Lean software engineering
You’ve done your research. Now you need to put those theories into practice. To validate product–market fit, you need to bring a pilot product version (an MVP) in front of real users. Then you need to work through the market feedback, review the findings, and get back to building a full-featured product.
This may sound simple, but in practice companies run into ample problems due to:
- Corporate inertia and prolonged decision-making
- Ineffective operational and software development processes
- Missing technology expertise
- Lack of experience with interactive product development
- Limited time, human, and financial resources
When your top tech talent already has their hands full with core products and IT infrastructure support, new product development plans often get delayed (or shelved).
That’s why innovation-driven companies often rely on external product engineering partners. Your partner can act as your remote R&D arm, contributing the skills, knowledge, and operational leanness to accelerate your time to market.
At Intellias, we combine the baseline principles of the Lean Product Development methodology with Agile software engineering best practices.
For each project, we assemble a cross-functional team of product, design, development, and QA experts. Then jointly, we prepare for iterative development.
First, the team creates product prototypes to validate the problem–solution fit — in other words, securing evidence that a product could indeed solve a pressing problem in an underserved market.
A validated prototype becomes the cornerstone of the minimum viable product (MVP). At this point, our product engineers take center stage. We use Agile methodologies to create project plans and implement operational processes for fast, safe, and waste-free product delivery, namely continuous integration (CI), continuous delivery (CD), and automated release management.
The product delivery process is benchmarked against lean metrics and is designed to maximize team productivity and product quality (reducing lead times, the defect rate, and so on).
To achieve quality by design, our engineering teams practice test-driven development and regular code reviews, while on the design side, UI/UX specialists ideate and iterate on the product presentation layer.
This way, we can build an MVP version fast. Then we can securely scale the product by shipping one validated feature at a time, backed by customer and market feedback.
Unlike typical yearlong product development cycles that pre-suppose knowledge of customers’ problems and product needs, lean development eliminates wasted time and resources by developing the product iteratively and incrementally.
Metrics-driven go-to-market strategy
You have a limited amount of time, people, and money when developing digital products. The longer it takes you to realize that the selected concept isn’t (yet) a winner, the more resources you waste.
On the other hand, the faster you prove that your early version has a strong product–market fit and generates value for users, the faster you’ll be able to harness that growth momentum.
The purpose of a go-to-market strategy is to help you get in front of the right targets fast to prove (or pivot from) your product idea.
At this stage, you need to create a baseline target — minimal results your product needs to achieve to be considered successful. For that, you’ll need to decide on the metrics you’ll use to track the product’s success rate.
Product metrics worth tracking
| Customer-facing products | Products for enhancing internal capabilities |
|---|---|
| • Number of active users (daily, weekly, monthly) | • Time to completion |
| • Customer acquisition costs | • Adoption rate (for products to be launched cross-geography or cross-function) |
| • Customer lifetime value (CLV) | • Process cycle time |
| • Conversion rates (trial-to-paid, demo-to-sale, sign-up-to-purchase) | • Throughput (e.g. orders processed per day) |
| • Time to convert | • Error rate |
| • Time to value | • Resource utilization |
| • User retention stats | • Employee NPS |
| • Burn rate | |
| • User churn rate | |
| • Referral rate | |
| • User segmentation behavior and preferences | |
| Online marketplace specifics: | |
| • Gross merchandise value (GMV) | |
| • Average order value (AOV) | |
| • Repeat purchase rate | |
| • Seller retention rate | |
| • Platform engagement (time spent on the site, number of page views, number of searches) | |
| Subscription-based product specifics: | |
| • Monthly recurring revenue (MRR) | |
| • Average revenue per user (ARPU) |
The main goal of your go-to-market strategy is to identify, attract, and engage power users — people who use your product to its full potential, explore the advanced settings, use little-known features, or even create new edge use cases.
Power users are a strong sign of product–market fit. Moreover, they’re the best source of product development insights. They often provide wish list–style feedback, report bugs, and evangelize your product to others.
Even if power users account for a small percentage of your total user base, they are a strong indicator that there are more people like them on the market (likely interested in adopting your product). Moreover, they present a great base for developing a bottom-up product-led growth (PLG) strategy — a self-service model that enables customers to discover and adopt your product on their own without extensive selling.
Winning over power users can also pave the way further up the chain. Take it from Dropbox. The file sharing app heavily relied on PLG to win its early user base, and today the company still generates 90% . Back in 2008, its viral referral campaign led to 3900% growth within 15 months.
Strong traction with business users also paved the way for bigger contracts. Dropbox first sold its product to business users, who later introduced the solution to their companies, effectively forcing upper management to adopt Dropbox since it was already widely used.
Instead of trying to sell its product to tech executives, Dropbox created an engaged community of regular users first — and once teams got a sense of the product’s value, selling the technology further up became easier.
Safe product scaling
Scaling is good, and it’s virtually limitless for digital products.
But scaling also assumes getting your product in front of larger audiences — aka taking your product from an early market into the mainstream.
Source: “Crossing the Chasm” by Geoffrey Moore
And that’s when the risk of failure rises again.
Early traction doesn’t always translate to major growth. Mixer, a game streaming service acquired by Microsoft in 2016, had a steady cohort of active users. However, as the team admits, they were behind in total numbers compared to bigger players like Twitch or YouTube. The community wasn’t growing as well as anticipated. In 2022, Microsoft decided to shut down the service and partner with Facebook Gaming instead.
Profit margins will also determine your ability to safely scale. Serving a smaller customer group can make the economics work, while serving a larger market can prove cost-inhibitive if your monetization model isn’t optimal.
Omni, an on-demand storage and rental startup, had a hot start in the market, easily proving its product–market-fit. A few years into its operations, however, the startup realized that its business model wasn’t generating adequate ARR to cover operating costs. “The service was really great for the consumer but when they looked at what it would take to scale, that would be difficult and expensive,” a company source said. After several attempts at scaling and pivoting, Omni shut down operations at the end of 2019.
So how can you build a safe bridge for crossing the chasm? At Intellias, we always turn to data for answers. We can conduct a comprehensive assessment of your product context, covering your business model, product lifecycle stage, and sales funnels. An inward look can help you locate current inefficiencies in core processes (development, sales, marketing, support, etc.) and realign those processes with your business goals.
Then we can look at the wider market situation — trends, risks, and competitive pressure. How have market conditions changed since the initial launch? What’s emerging on the radar? Analyze and benchmark available product metrics against industry averages and competitors.
Such analysis, paired with customer feedback, can help you identify and address the weaker areas of your product. Or it can help you locate new opportunities for innovation or pivoting. Instagram was originally designed as a Foursquare lookalike — an app called Brnb for checking in at restaurants, parks, and other destinations, and the company pivoted to a photo sharing app. Meanwhile, Foursquare eventually shifted to the B2B segment and evolved into a location data sharing platform.
Sharpening your product engineering focus
Big ideas can turn into a big success or a big failure. The role of great product engineering is to increase your odds of success by minimizing the guesswork about the hows, whats, and whys behind customer actions.
With a strong product engineering expertise (and partner), you can rely on qualitative and quantitative feedback at every stage of the product development cycle until you build a business that is safe to scale.
Intellias always welcomes conversations about digital product development. Whether you’re looking for a partner to execute your product vision or ongoing consultancy for improving existing processes, we’d be delighted to assist you. Contact us.
