Driving business strategy through unit economics
Understanding the interactions between all drivers of a business is crucial to evaluate its success and effectively inform the product roadmap
Traditionally, when executives sit down to consider the health of a business, they tend to focus on high-level KPIs: revenue, growth, number of customers, and so on. In high growth environments however, these indicators can be misleading, they don’t always tell you what the driving force behind your results are. It’s easy to get distracted by how much revenue you’re generating, but if your customers aren’t making you a profit — even if there’s a lot of them — does that really matter?
Consider the following example. Which business is healthier?
Business 1:
£2.5bn funds under management
5,000 customers with 50,000 transactionsBusiness 2:
£1.5bn funds under management
1,000 customers with 30,000 transactions
It’s the relationships and interactions between what lies underneath the surface that matters most. High-level metrics are merely an output of many different combinations of these interactions.
To really understand these relationships, a business should be broken down into its smallest common denominator. Take customers for example, who are the biggest driver for a business: it’s easy to look at customers on an aggregate level. But acquiring customers is expensive…really expensive, so it’s much more valuable to focus on the interaction between customer acquisition cost (CAC: the full cost of acquiring each customer) and customer life time value (CLTV: the net contribution made from a customer during their time as your customer discounted to present value), to untangle what value your business is getting per unit — in this case the customer.
This is the approach we use here at Octopus Labs. I’ll use the example of Octopus Cash — our smart savings service built for financial advisers and their clients. It operates with a slim margin, so we have to think carefully about how we raise awareness and distribute. We have a dual focus on keeping our CAC low, while simultaneously doing everything we can to boost our CLV (we aim to operate at a CAC:CLV ratio of 3).
When it comes to prioritising new features during roadmap planning, we’ll consider how our decisions impact both CAC and CLV. Specifically, we’ll look at how our decisions impact the following metrics:
1) Number of financial advisers introduced per business development manager
Octopus Cash primarily operates as a business-to-business-to-customer model: our sales team build relationships with financial advisers & wealth managers who in turn if they feel it’s is useful, will recommend our service to their clients. Therefore, the number of financial advisers using the Octopus Cash platform is hugely important.
We have a small (but perfectly formed) sales team so we look at each member on a “per unit” basis: how many advisers has each of our sales reps introduced per month? How does this correlate to the volume and quality of calls each rep is doing? Looking at the actuals of each business development manager allows us to calculate their individual breakeven points and feeds into our business model.
2) Number of clients per adviser
Our biggest hurdle (as with a lot of businesses!) is getting a financial adviser or wealth manager to write their first case with us… it’s also directly related to the cost as our sales team get rewarded each time. We structure this cost with the other eye on the potential value we get throughout our relationship with that financial adviser: i.e. assuming that they introduce more than just 1 client over time. One of our key metrics therefore is the number of clients per financial adviser over time. And this is where the product needs to do the hard work for us. If the product isn’t frictionless and built with utter simplicity we are creating barriers for our customers and therefore ourselves.
We measure this through cohort analyses. This allows us to compare how many clients an adviser has introduced after a given number of months on the platform. Unlike an average, we can use cohorts so see how behaviours change and drill down in to why. Over time we aim to improve our product so that more recent advisers find it easier or simpler than they did in the past. If we get this right this should show up in our cohort analysis Currently we’re targeting a 6% month on month growth in the number of clients per adviser. I’ll let you work out what that is in a year.
3) Average deposit size
While the number of cases written is obviously important, their size matters too. Therefore, we keep a close eye on our average deposit size per customer.
4) Platform efficiency
Our slim margins mean that we need to keep operating costs down as much as possible. This is actually a great thing because it forces us to put every operational activity we do up to the ‘scale-test’: “Does this process work if I’m serving 500, 1000, or 100,000 customers?”. We track the time it takes to perform key operational activities to get a baseline for prioritisation. Examples of this are customer onboarding time and time spent on generating customer comms.
Key to both platform efficiency and customer experience is the ability for customers to self-serve. This doesn’t happen over-night. To get there, we track for (amongst other things) number of ‘simple inbound requests’, i.e. what answers to customer requests should be found on the website. Analysing these types of requests helps us tighten up our website copy and we then measure if these type of requests are reduced.
During our roadmap planning, we prioritise operational improvements by giving each item a score on a range of different categories including how much it will improve the customer experience to number of hours saved per week. The impact of any changes is measured against our baseline.
Tying it all together
By now it is hopefully obvious how these metrics interact: sales drive an increase in the number of professional advisers introduced to the platform. Advisers introduce more clients over time if the product works well and removes friction. Their clients’ deposits drive our funds under management (FUM) and then the more efficient we are at managing this, the better the outcome for us as a business.
While we could instead simply set an FUM target of e.g. £500 million, what would that actually reveal about the health of our business? How do we know what is driving our inflows? What is going well and where we do we have to intervene? If we’re achieving our FUM metrics because our sales team is doing an amazing job and introducing 100s of advisers each per month (remember, this is the cost bit), but none of these advisers go on to write more business (this is the customer lifetime value bit), then we’d have a happy sales team and a very depressed CFO.
Measuring is important, but what we do with it is even more so
The beauty of unit economics and cohorting is that they are living and breathing: we are constantly measuring them, updating our models with actuals and adding even more metrics as our product evolves and scales: we’re now looking closely at operational metrics as well as churn metrics including up- and cross-sell rates.
Once we understand what is going on where and why, we can influence it: how can we make the interaction with Octopus Cash more seamless facilitating advisers to add more clients? What can we do to increase retention rates? How do we get more advisers on the platform? How do all of these variables come together to accurately forecast inflows?
Our unit economics are the common thread in our business, tying up sales, operations, marketing and tech development. They interact with each other and make our business holistic allowing us to scale and operate as a slick engine, where taps can be turned on and off as required. In short, our unit economics are the main driver of our business strategy.
