On August 25th, Identify and Disrupt passed both houses of the Australian parliament with support from both major parties, just one day after it was first debated.

Officially dubbed Surveillance Legislation Amendment (Identify and Disrupt) Bill 2021, the bill empowers the Australian Federal Police (AFP) and the Australian Criminal Intelligence Commission (ACIC) with three new and egregious powers when investigating federal crimes, or state crimes with a "federal aspect":

• The police can surveil any networks or devices that are used, or likely to be used, by a suspect (Network activity warrant).
• The police can assume control of an online account in order to conduct investigation (Account takeover warrant).
• The police can now disrupt data, which is defined as the copy, deletion or modification of a suspects data (Data disruption warrant).

In spite of the justification for these bills being the battle against child exploitation and terrorism, these new powers are both extreme in degree (technology companies are obligated to comply) and broadly applicable (i.e., literally any crime with a so-called "federal aspect"). The legislation was rushed through in less than 24 hours, ignoring the advice of the Parliamentary Joint Committee on Intelligence and Security (a bipartisan committee) that recommended significant changes.

"Every increase in state surveillance has a democratic cost. Overbroad surveillance powers impact the privacy of all Australians and have a chilling effect on journalists and whistleblowers.

Given the powers are unprecedented and extraordinarily intrusive, they should have been narrowed to what is strictly necessary and subject to robust safeguards. That is why the Committee unanimously recommended significant changes.

It is alarming that, instead of accepting the Committee’s recommendations and allowing time for scrutiny of subsequent amendments, the Morrison Government rushed these laws through Parliament in less than 24 hours."

— Kieran Pender, Senior Lawyer at the Human Rights Law Centre.

This legislation comes after years of encroaching authoritarianism, including raids on journalists, efforts to force Facebook and Google to compensate the Murdoch empire for content shared on their platforms, the introduction of one of the most "intrusive data collection schemes in the western world", the deregistering of minority parties, and the granting of broad internet censorship powers to the Australian eSafety Commissioner. Most of these mandates have come with support from both major parties, leaving voters with little say, in spite of widespread criticism in many cases.

At this point, the Australian government is sinking into the technologically-enabled authoritarianism of its undemocratic rivals, foregoing fundamental western principles along the way. And, most alarmingly, there is no significant political movement against this trend. This is obviously resulting in significantly diminished freedoms amongst the people of Australia. Additionally, I predict this will further cripple the technology industry, with entrepreneurs fearful of what will come next from the heavy-handed parliament of Australia.

To make effective decisions when developing products, engagement with customers is critical. Building this into your way of working should be the priority of any product leadership. Along the way, data should be captured and analysed.

There are two broad categories of customer engagement data that should be integrated into your product development process: qualitative data, which is narrative in nature, and quantitative data, which is numerical in nature. Each of these categories have their strengths and weaknesses and should be invoked in different circumstances.

Building a great framework for qualitative data

Examples of qualitative product development data in a B2B SaaS context include:

• User interviews and documentation, often regarding pain points, requirements and business operating procedures.
• Case studies.
• Focus groups.
• User testing of prototypes and interfaces.

Qualitative data should rarely dictate (though it may inform) decisions about what features you should build. Rather, qualitative data is more useful for informing how features should work. The reason for this is simple: qualitative data is by nature narrow in scope and sample size. Relying on qualitative data for high-level strategic decision making often results in source bias, which leads to companies building for the customers they talk to (i.e., the most accessible or the loudest customers) rather than the broader market.

Generally, a good rule of thumb is to lean on qualitative feedback (e.g., user interviews, case studies and user testing) after you've decided you want to tackle a problem and during the solutioning process. When anecdotal feedback is nudging you in a specific strategic direction, try to find some quantitative data to validate your ideas.

A major challenge faced by many B2B SaaS product teams is that the majority of the data they receive is qualitative in nature (e.g., feedback on lost/won sales, support cases and churned customers). This can make it very difficult to determine what to build next and often results in features being built that only satisfy a small subset of the overall user base. The best solution to this is to try to adjust your business operations to convert what is traditionally anecdotal feedback into quantitative data.

Building a great framework for quantitative data

Most product teams have a torrent of anecdotal feedback, related to the needs of specific customers or trends that coworkers or partners have noticed, coming at them every day. Finding a signal amongst the noise can be very challenging. The best way to do this is to work towards building business operations that make it easy to record feedback from individual customers in a way that can later be analysed as quantitative data.

For example, product managers who are receiving a lot of feedback from their support team should empower their team to record this feedback in a structured, analysable way. The same can be done for other customer touch points.

Turning anecdotes into real data

Below are the key touch points during the customer journey that product companies should build business processes around for insight collection:

• Sales opportunity closure → Product teams tend to hear from sales colleagues that if we only had this feature, we'd close way more deals. Often, after building these purported silver-bullet features, the uptick in victorious deals doesn't come. When declaring a sales opportunity as won or lost, key reasons for the outcome should be recorded. Your team should be able to tag areas of the product that the customer liked and disliked (so that you can later report on this) and record notes on what the ultimate deal winner, or breaker, was. This will allow you to quantify exactly how many deals were actually lost to the lack of the feature your sales team is requesting.
• Support case closure → When requesting support, customers will often give feedback on new features they want and existing features they found difficult to discover or use. By empowering your team to record this feedback in a quantifiable way, product teams can better keep their fingers on the pulse of what existing customers are struggling with. Sharing insights from this data with your support team can really help to keep them aligned with your product strategy as individuals may have very different ideas about what customers are asking for. This is especially useful in support teams where support agents are aligned to specific areas of specialisation, as everyone will end up hearing feedback pertaining to their area of specialisation and nobody else's.
• Success interactions → Your customer success team is a critical source of customer insights. They are proactively calling customers every day and valuable information is inevitably shared with them. To get the most value out of your customer success team, it's important to give them the tools to record any feedback they receive, in a way that will be easy for you to report on later.
• Churned customers → The cancellation process should always include the collection of any feedback customers may have on your products and services. Understanding why customers are leaving for a competitor can inform good product decisions.

Try to aggregate all of the data above into a single database in your CRM, alongside other feedback like CSAT and NPS (further reading on requesting feedback).

Additional quantitative data

Beyond the above, there's a lot more quantitative data you should be tracking. Fortunately, these data points are, by nature, much easier to quantify:

• Product/feature utilisation (i.e., how many people are using each feature).
• NPS.
• CSAT, which should be surveyed after each support interaction and the completion of key in-app operations (including wizards).
• Funnel analytics (i.e., how many people are completing certain in-app operations? When do people typically drop off?).
• Surveys to the customer base.

In conclusion:

• It's important for product teams to establish a strategy for the collection and analysis of data.
• Quantitative data is best for guiding product strategy, while qualitative feedback should be consulted during execution/development.
• Feedback should be recorded in a quantitative way that can later be reported on and analysed for the purposes of strategic decision making.

Today, software-as-a-service (SaaS) is the standard business model for monetising business-to-business software. In the SaaS model, software is licensed to customers through an ongoing recurring subscription (e.g., a customer might pay $25 per month for continued access) or another form of operational monetisation (e.g., transactional fees on application usage) which also includes customer services.

Traditionally, software was sold just like any other product: as a one-off purchase (sometimes on CDs!). This usually meant software had to be run on hardware owned by the buyer and updates had to be purchased separately. SaaS subscriptions are all-inclusive: the service provider includes hosting, upgrades and customer service in the ongoing subscription fee. Customers aren't purchasing a one-off product (i.e., a single version of a software), they are subscribing to an ongoing service which includes software.

What makes SaaS special?

On the surface this may seem simple: customers are paying for a subscription rather than a one-off purchase. But, this difference has significant implications for both the service provider and the customer.

So, what makes the SaaS business model unique?

Revenue is recurring

The power of the SaaS business model is that it gives product companies the ability to monetise their efforts on an ongoing basis. Instead of worrying about having to produce entirely new products to sell to new and past customers, SaaS companies can continue to make money from their investments in research and development for as long as their customers are getting value from the product. Meanwhile, traditional product companies have to constantly manufacture new goods to be sold.

Continuous improvement and radical focus

Because SaaS customers are paying a recurring fee to use a single product, SaaS service providers can focus on continuously improving their core products, rather than build entirely new products. This enables SaaS service providers to create exceptional products, as their investments in research and development compound over time. This also enables a very high degree of focus on improving products that are already selling well in the marketplace.

In contrast, most traditional product companies cannot rely on their existing customers to continuously and frequently purchase from them. This forces product companies to constantly expand their product offering so they can target new customers.

Retention, and therefore quality, is critical

While most of the revenue for traditional product companies comes from new sales to new customers, the majority of revenue coming into a mature SaaS company is from existing customer subscriptions. This makes retention critical. It is far cheaper to keep a customer than to sell to a new one, therefore the first priority of any SaaS company is to retain their existing customers.

This makes the quality of both the product and customer service far more critical to a SaaS company.

Lifetime value is high, so continuous growth is important

For SaaS companies, the cost to acquire a new customer, compared to the lifetime value of the average customer, is very low. This motivates SaaS companies to constantly grow and invest the majority of their capital into the acquisition of new customers.

Successful SaaS companies retain their average customer for a very long time. This makes them very valuable compared to a customer who purchases a product once, and may take a long time to upgrade (if ever).

(Effectively) Zero marginal costs

While each new SaaS customer pays the same as the last, they do not cost the same as the last — they usually cost less! This is because cloud-hosted software has fantastic economies of scale. While traditional businesses need to manufacture and entirely new product for each sale, SaaS businesses are essentially selling the same bits and bytes to multiple customers.

Imagine if, for every new sale, we had to re-code the whole product from scratch? That's essentially how traditional manufacturing-led businesses work. Software is extremely scalable because it is effectively free to replicate and distribute.

Not only does this make SaaS companies highly profitable, it makes them much quicker to grow because the money that would usually be spent on the manufacturing of each unit can be spent on sales and marketing.

Key SaaS metrics

While all businesses conform to roughly the same accounting standards, SaaS businesses are typically managed with some unique metrics that are critical to determining the health of the business.

Below is a collection of metrics I typically keep a close eye on, though there are many more metrics relevant to operators of SaaS businesses.

Monthly Recurring Revenue (MRR)

This is the most important metric in a SaaS business. MRR is the amount of revenue you get from your customers on a monthly basis. It only includes revenue that is recurring, so excludes any professional services revenue.

Related metrics include:

• Annual Recurring Revenue → The amount of recurring revenue you get from your customers on an annual basis.
• Monthly Recurring Revenue → The amount of recurring revenue you get from your customers on a monthly basis.
• New MRR → MRR gained from new customers.
• MRR Expansion → Additional MRR gained from existing customers (i.e., plan upgrades, additional products).
• MRR Contraction → MRR lost from existing customers (i.e., downgrades and removal of products).
• MRR Churn → MRR lost due to cancelled customers.
• MRR COGS → The cost of your MRR — this is the total of your cost to support your customers and the cost of any infrastructure, hosting and licenses for your software.
• MRR Gross Margin → The profitability of your MRR. This is your MRR COGS divided by your MRR (MRR COGS / MRR).

Customer Count

Simply put, your customer count is the number of customers subscribed to your SaaS product.

Related metrics include:

• Gross New Customers → The number of new customers you signed up in any given period.
• Churned Customers → The number of customers who cancelled their subscription to your SaaS product in any given period.
• Net customer movement → The net movement of your customer count over any given period. This is your Gross New Customers less the number of Churned Customers in the same period (Gross New Customers - Churned Customers).

Average Revenue per Account/User (ARPA/ARPU)

Your Average Revenue per Account value of the average customer for any given period (usually monthly or annually). This is calculated by dividing your MRR or ARR by your Customer Count (MRR / Customer Count).

Related metrics include:

• New ARPA → The value of the average new customer. This is important for tracking whether your current sales strategy is leading to bigger or smaller deals than your current average customer.

Growth metrics

There are a number of metrics critical to modelling your growth. These include:

• Sales & Marketing Costs → Simply put, this is your total costs of sales and marketing efforts, including fixed costs like salaries, over any given period (i.e., how much are you spending on acquisition?). This is an important dependency for other metrics.
• Cost of New ARR → This tells you how efficient your sales and marketing efforts are. It is calculated by dividing your Sales & Marketing Costs by your New MRR (Sales & Marketing Costs / New MRR). For every dollar you spend on acquisition, how much MRR do you earn?

Research and Development metrics

There are a number of metrics critical to modelling your investment in product development. These include:

• R&D Spend → Simply put, this is your total costs of product development. This is primarily the cost of your development team as well as any licenses/tools related to the development of product.
• R&D:MRR Ratio → This metric tells you how much you spend on R&D in comparison to your MRR and is a useful for benchmarking R&D spend against other SaaS businesses. It is calculated by dividing your R&D Spend by your MRR (R&D Spend / MRR). Mature SaaS companies typically aim for an R&D:MRR ratio of around 30%.

I am an advocate for simple and collaborative methods for defining strategy for a team, department or company. Many strategy frameworks are too complex and while they may seem democratic (by embracing voting, for example), they usually lead to the middling harmony of sticking to the status quo. Instead, your collaborative process should encourage rigorous debate to overcome the mediocrity of consensus.

Any strategic planning process should aim to:

1. Establish a thorough common understanding of the desired outcomes.
2. Identify, acknowledge and debate perceived obstacles.
3. Prioritise a small number of high-leverage opportunities to tackle.

Converging on desired outcomes

While high-level goals may be relatively clear (e.g., Our goal is to become the most loved Help Desk application for Retailers), individuals often aren't on the same page regarding what the ideal end-state actually looks like (e.g., are we targeting all retailers or retailers of a specific size? Online only or in-store?). For this reason, I think it's critical to start any planning session by defining a very specific view of the ideal end-state of the project/product/team/organisation.

To do this, I recommend a silent brainstorm. Essentially, each member of the team will independently describe their view of the ideal end-state. After this, they'll share their view and the team will discuss and debate the various descriptions in an attempt to converge on a common understanding of what we're trying to achieve.

Let's assume a company with industry-leading support wants to take better advantage of this by making their high-quality support an explicit selling point of the software. A description of the end state might be:

• Customers are happy and love us.
• Customers are happy to give us a testimonial or participate in a case study.
• NPS of over 80.
• First-response times are under four hours.

Each of these points can be debated and refined based on the diverse descriptions brought to the table by the team. In the example above, some may disagree with the importance of first-response times, instead believing that a quality response that actually solves the problem is better than a quick but relatively superficial response. By making explicit these differences of opinion regarding what the ideal state looks like, you can use debate to converge on a unified view.

Acknowledge perceived obstacles

When goals are set by management or even by a simple vote, it is common for individual contributors who are responsible for working towards these goals to have unspoken skepticism towards the achievability of these goals. Often, this skepticism is well-founded and based on obstacles that should be explored and tackled before undergoing the broader initiative. Alternatively, this skepticism can be based on perceived obstacles that are not valid and debating them can lead to stronger team confidence. Either way, acknowledging and discussing any perceived obstacles is usually very valuable.

If you have already got a description of your desired end state, this can be easy to do. Simply ask your team to identify some obstacles for achieving each aspect if the desired end state and discuss these obstacles. Try to converge on a set of legitimate obstacles that the team will need to consider along the way.

Prioritise a small number of key opportunities

Each aspect of your ideal state and obstacles will likely translate into one or more opportunities. For example, if one aspect of your nirvana is to improve customer response times, ask your team what they think they need to do to get there. Additionally, review each obstacle and try to find opportunities to eliminate these obstacles.

Again, the goal here is to achieve consensus through debate rather than a simple vote. Each opportunity should be scrutinised by the team and eventually prioritised with the goal to define an explicit, common understanding of what is the most important work to tackle first. These opportunities should be loosely defined initiatives that can later be further explored by the team.

Many people say they don't care about what data Big Tech is collecting on them. "I've got nothing to hide" is a common explanation for this. But, just because you're comfortable with the ways your being tracked today doesn't mean you will be in the future, when more data points are available for aggregation.

Soon, many of us could be wearing an Augmented Reality headset from Apple, Google or others. There has been a lot of speculation around these devices over the years, but I haven't seen anyone talk about the user privacy implications.

Apple's rumoured AR glasses sound very similar to Google Glass. Essentially, a pair of glasses with cameras, sensors and screens. They can see what you can see, they can track your eyes and they can show you what you want to see, over the top of what you're looking at. These will likely be peripheral to your smart phone, with most processing and networking taking place on your phone.

In a world where you're using smart glasses, a phone and a smart watch from a single vendor, they'll have access to:

• Biometrics like heart rate, blood oxygen and blood pressure.
• Mobility data such as your location, walking speed and stability.
• What you're looking at (thanks to cameras on your AR glasses and object recognition).
• Your browsing and messaging history.

And a lot more. What is interesting is the kind of things companies will be able to infer from the combination of biometric data and the awareness of where you are and what you've been looking at. For example, they could infer:

• Fears.
• Taste in food.
• What kind of people you're attracted to.
• What kind of people you dislike.
• Specific people who you like and dislike.

This will be extremely valuable information to have in a targeted advertising context. If consumers and regulators allow company's to collect and use this data, they will. And this is only one example of how the breadth of data collection is likely expand with new technologies.

Given the inevitable expansion of data points, I think consumers should take this seriously today and choose privacy-first products today, even if they aren't too concerned about what Google or Facebook know about them today.

Big Tech companies have the tendency to swarm the same problems. iOS versus Android; Google Workspace versus Office versus iWork; Apple Music versus Spotify versus YouTube Music versus Prime Music; Netflix versus Apple TV + versus YouTube versus Prime Video; Instagram versus YouTube. On one hand, Big Tech going head-to-head in the same categories can lead to better consumer outcomes as competition drives innovation. On the other hand, Big Tech does tend to employ the exact same strategy when attacking upstarts.

I talk a lot about regulation. But, let's put this aside for a minute. Let's talk about what Big Tech should do. Not just for shareholders (though their needs should obviously be a priority), but for the broader technology ecosystem.

Some problems require a tonne of resources to solve. Some don't.

Though it may be an inconvenient truth to those positioned stubbornly against the mere existence of Big Tech, some problems are so big and complex that only extremely well-funded organisations can tackle them with any credibility. Moonshot projects, like advanced robotics, alternate worlds, self-driving cars, solving ageing, satellite internet, interplanetary colonisation and next-generation silicon, require billions of dollars of investment that may never pay off. There are very few organisations, outside of governments, able to make that kind of investment.

On the other hand, there are major problems faced by society that are seemingly being ignored by our richest companies. Additionally, they do seem to be spending a lot of time on things that other, much smaller companies could solve.

Sometimes, Big Tech will enter and revolutionise a stagnant product category (i.e., iPad revolutionised tablet computing; AWS revolutionised cloud computing). Other times, they blatantly enter existing categories where they can add very little additional value and flex their muscles to try and squeeze out truly innovative upstarts. Recently, Facebook did the latter by launching Facebook Bulletin.

Substack launched at an opportune time — mainstream media is reorganising itself massively and many individual writers are wondering why they should work and be compensated any differently to other content creators. Substack brought journalists and writers the modern content creator business model, allowing them to self-publish to their 1,000 true fans and make a lot more money along the way. Substack makes this business model extremely accessible to all writers and allows them to own their relationship with their customers.

While I welcome competition in this category, Facebook Bulletin is a heavy-handed attempt to squash an innovative startup tackling an interesting problem, previously neglected by Big Tech, by bringing their massive audience to a narrow group of A-list written-word influencers. Later, they'll use these success stories to position themselves as the best option for Substack's existing and future customers.

While playing chicken with regulators, product launches like this may not be good for Big Tech shareholders.