A business case is commonly understood to be an analysis of a potential opportunity to evaluate whether it’s worth it for a business to invest in it. If we knew with 100% certainty that an opportunity pans out we would not need a business case.
In other words, “business casing” is about risk management.
The reason why there are almost no resources out there that show you how to do them is because they are highly situational. I’ll walk you through an example of a business step by step.
In my career, the skill of creating business cases was reserved for senior executives mostly and was not something that was ever demanded of me. I believe this to be a missed opportunity by companies and individuals.
Great business cases surfaced by operational teams (mostly orchestrated by their PMs) are extremely powerful and will be demanded in the future more and more, specifically from PMs.
If we assume it to be true that shipping something is becoming easier and easier then a differentiating factor for teams does not become who can ship faster but who can ship (and unship) the correct things.
However, aside from measuring, learning, and leveraging these learnings once you built a product business cases serve as an important first gate to avoid wasted effort by our research or product teams.
Great vs. bad business cases
Great business cases are not just highlighting opportunities we aren’t aware of they also highlight and examine high-risk failure points before we get to them. They focus on reducing risks through honest examination where it makes the most sense.
Simplicity
Another core component of great business cases is that they are simple. While business cases can be definitely in-depth examining opportunities their other purpose is to create alignment.
Alignment between people who do not have your in-depth understanding is created by keeping it simple and not overcomplicating it. Crafting a simple but well-founded case takes time and expertise but is a great way to start off on the right foot.
Context
Great business cases are placed in a context. An opportunity of 5mio ARR is not good or bad. It depends on the business context:
Is that affecting a good portion of the company’s revenue?
Is it the best opportunity available to us at this time?
Confirmation Bias
There is a particularly dangerous effect of having an ambitious company culture. It is extremely easy to bend a business case to look better than it should and just look for confirming facts to build something you just want to make happen.
I was told several times in my career to look for bigger opportunities and I do confess that there were instances where making a 2mio ARR case to 4mio ARR is way too easy as it often hinges on just dialing on one metric.
This is not just the problem of the PM doing this but also of the company of demanding it.
Reward honest assessments, not fairy tales that look good on a scorecard.
Learn with me
I have rewritten my Weekend course (7th/8th July 2023) at Maven to focus on Strategy and Business casing thanks to the communities feedback. This material is a result of that rewrite and will be explored more in-depth in the cohort.
My rough process:
Draft (We build the core assumption and its main value drivers)
Breaking it up (We break the main drivers apart)
We evaluate confidence/impact/effort for each risk
Confidence of Assumptions
Understanding Impact
Effort (Difficult vs. Complex)
We connect it to Value capture (Revenue etc.)
1. Draft - Business case “Collaboration”
Let’s assume we manage a product which is about handling digital documents (PDFs, doc, xls, etc.) and we want to evaluate whether it’s worth it to have our users collaborate around these documents. A “feature” which is completely missing from the product.
Key business assumptions
In order to have any kind of assumption whether this makes sense we have to assume some business numbers:
50mio monthly active free users
50mio ARR obtained purely through self-serve.
All of its revenue is coming from 1mio paying user
The average lifetime value is 50$
The product subscription costs ~4$ / month per user
Meaning our average customer churns after 1 year
We assume that we’re only investigating initiatives for our product that affect at least 10% of our yearly ARR (>5mio ARR)
The very first step is to assume which main channels we affect. In this case, we assume that adding collaboration to our product affects:
Conversion: Adding value to the product should influence our conversion metrics for users to convert to trial and then from trial to pro.
Network Effects: Because collaboration features build on people collaborating with each other they should have a big impact on people inviting users. This benefits users by inviting users which has a positive effect.
Retention: Adding this crucial feature will have an effect on our existing customer base. Some of them should have this need, ideally, it reduces churn and increases stickiness.
The “number” we already make an assumption on how big this opportunity is. In this case, I assume it’s 6mio ARR uplift.
Our initial assumption of this being worth 6mio ARR is within the threshold (after all we wouldn’t have touched this opportunity otherwise). But it’s standing on very shaky legs. In order to evaluate this number we are breaking now the affected channels up.
2. Breaking up the Draft
Our initial draft looked like this. we have 3 main areas with conversion, network effects, and retention that we assume to be affected:
In general, it makes sense to also limit yourself to the most important affected subchannels so your case doesn’t get overly complicated.
Let’s assume we manage a product which is about handling digital documents (PDFs, doc, xls, etc.) and we want to evaluate whether it’s worth it to have our users collaborate around these documents. A “feature” which is completely missing from the product.
2.1. Conversion
For the conversion channel I have identified 4 different areas that will be affected by this new feature:
SEO, the company’s main strategy to get new users on board is through SEO. This existing stream will definitely be affected due to its existing size.
Paid, It’s possible that we add paid advertising to advertise the new collaboration feature to test this adoption
Free to Trial / Trial to Pro, We have a hypothesis that the funnel from free to paid is definitely affected. Moreso on trial to pro when users see how useful it is to collaborate with others around their documents.
LTV Impact, The business has a current lifetime of 50$ per user which seems to be low, if a portion of our users start to adopt collaboration and invite others they are locked in to the product and LTV should rise as a result. Users who come also because of the collaboration feature into the product should have also naturally a higher LTV compared to those that don’t.
Let’s visualize it:
Looks good enough, let’s move on to network effects:
2.2 Network effects
For network effects we have 2 mainly affected areas:
External users: we have people inviting others to the product due to them being able to collaborate. These people would not have come into our product otherwise. We should visualize how we assume that they move through the product and at which rate
LTV: Network effects are some of the strongest drivers for retention if you count being in a Team as a network-related effect. Since we already identified it as an effect from our conversion channel we simply visualize the connection so we don’t consider it double.
This is how it looks now:
2.3. Retention effects
For retention, we have a myriad of effects to consider but when we think about them they are all connected with each other. If this was a new business we wouldn’t have to think about them as there’s not much to defend and we would focus on our conversion channel and new customer acquisition even more.
Existing Customers: We have an existing customer base of 1mio. users. They will definitely be affected by our new feature addition. If we assume that our feature affects them it will also have a direct effect on our churn rate but those are implied and connected to each other.
We also have to be careful when considering churn as churn is directly affecting LTV which we already opened up in our first channel. Let’s keep it in mind for the moment.
Our broken-up case looks like this now:
Simplify and sense check
Paid vs SEO
As I’m looking over this case I’m starting to think that the “Paid” channel doesn’t make a lot of sense in relation to the others. While we could definitely have some effect there in relation to all the pull we have from our other channels (the main business is driven by SEO) I will delete it from the graph.
Another reason is that we never proved that we can run a good paid acquisition channel for this example business, most of it comes from SEO. Delete.
Let’s add color to it
I use a very simple system which I’ll elaborate more about in the next article but for now:
Red: Wild guess, unclear impact
Yellow: Somewhat based guess, medium to high impact
Green: A well-educated guess, high impact
The color should reflect how certain we are that something will be affected by our feature addition and whether it’s going to be a lot. It’s for the moment a mixture of impact and confidence. At this point, we already should only have assumptions in the case that can drive enough uplift, that’s why nothing so far should be low impact. (If there is, delete it) The point of this exercise now is less to be sure but to think about each point:
Let’s think it through:
SEO: So much of our business comes from SEO that even a small change should have a big impact but SEO is also notoriously difficult to move. After all, collaboration is not a core use case of our users but more of an added benefit. Yellow
Free to Trial: Same reason as above. Most people might not sign up for the feature mainly, they don’t look for a collaborative document solution, most of them look for a document solution.
Trial to Pro: People will love the collaboration aspect of the product if they start using it which is likely if they enter into the trial, that should have a big impact. Green
LTV: We know from experience and other products that LTV is driven a lot by collaborative team use cases. collaboration is at the heart of this. Green
Externals: Collaboration is a huge network effect driver. It’s maybe the main reason for looking at it from far away, we also identified it in our strategy as a core hypothesis to fix our bad LTV. Green
Existing Customers: We know from the market that collaboration features are having a huge impact on LTV and retention in product usage. It also fits our product but we’re unsure of how many of our existing customers really “need” it. They didn’t sign up for it after all. Let’s put it at yellow for the moment.
Churn: For the existing customers that ARE affected the churn improvement should be substantial for sure though. I know from experience that in some cases you can drive LTV up by almost 200% for the affected customers. Green
This is a good starting point to now put in the actual work and connect our metrics as we know them to the estimated effects.
3. Impact & Confidence
When we think about what can influence the outcome of a business case we have 2 dimensions that are crucial to consider without overcomplicating things:
Impact: How much does a specific change mean to a specific channel/user group compared to what they already have? “Impact” is the delta between the most likely alternative of what someone could do otherwise and what we give them.
Confidence: How confident are we that our assumption about Impact is correct? The less confident we are the bigger the range of potential outcomes is.
For the following sections keep in mind, business cases are not science, and therefore too much validation is counterproductive. It’s a tool to evaluate quickly whether something could be worth it before making substantial investments.
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