In parts 1-3 of this series we explored how Service Monitoring can improve your bottom line by protecting revenue, reducing operational costs and enabling growth.
A quantified example
Now we will quantify how Service Monitoring can improve the bottom line, with a worked example. The numbers and calculations that we'll refer to here are captured in this spreadsheet [available as Excel or PDF]
ACME MACHINES INC. has launched its first connected machine, and the first 1,000 of them are live. Actually that’s not quite true: The production team says they have shipped 1,000 of them into the market, but in truth the company has no clear idea how many are truly “live” and delivering the service that the customer is paying for.
They’re getting anecdotal reports of technical problems, and unhappy customers, and realise that their old reactive approach to customer service (waiting for the phone to ring) is entirely inadequate. Their business plan calls for them to double the number of widgets for each of the next 5 years, to reach 16,000 devices deployed, and so the problems caused by their current “customer blindness” will only get exponentially-worse. The CEO decides it’s time to get on the front foot, and hires a Head of Customer Experience to lead the change to a more proactive mode of operation. That person will lead both the Product Management function (which defines what the customer experience should be) and also the front-line Customer Support team and the back-office Operations team (collectively known as COPS) who are responsible for ensuring that a quality experience is actually delivered.
The following is representative of the kind of conversation that DevicePilot might have with this new Head of Customer Experience. We capture the key numbers of the business in order to calculate the benefits that Service Monitoring will bring. To ensure a quick, tangible RoI, we’ll focus just on the first year of benefits - even though the benefits can be expected to grow further in subsequent years.
In the top section of the spreadsheet, we capture the key numbers of the business. From the top, the customer provides their estimates of:
- The average annual salary of a COPS person
- The average monthly revenue that we receive from the customer for each device (when it’s working)
- The number of deployed devices today - and in a year’s time.
- The revenue per device per month
- The device up-time achieved today, i.e. the percentage of time that devices are working over some period. This is an average figure, since a typical estate will have devices that are 100% up, some that experience occasional outages, and some that are either completely dead or in very poor health. One of the first things that Service Monitoring delivers is the ability to measure this number, but in its absence we’ll have to estimate it.
- We estimate the up-time achievable once Service Monitoring is in place, i.e. with DevicePilot. While this is an estimate, some confidence can be achieved by looking at similar historical use-cases. Based firstly on an accurate picture of the current situation, this benefits will be derived by taking several different actions, which often include: improving the quality of software and hardware, and improving business processes for identifying, analysing and resolving problems proactively, including business automation, which DevicePilot provides by integrating with other business tools as required, such as CRM and ticketing systems.
- The number of months of growth acceleration that could be achieve - once you’ve stopped the fire-fighting.
- The increased pricing opportunity that comes from offering a premium-quality service.
- The size of the COPS team today.
- The number of people that will need to be in the COPS team in a year’s time. If nothing else changes then this may simply be a pro-rata increase in proportion to the size of the device estate. In this case we expect a doubling.
- In contrast, the number of people that can be in the COPS team in a year’s time with Service Monitoring in place. Often, this can be a completely flat number, but in this case we’ve allowed one extra head.
- Now for the numbers around revenue risk due to losing customers. There are at least a couple of ways to calculate this.
- If you have a few large customers, then you can estimate the chances that at your current performance levels you will lose each of them, and multiply by the hit to your revenue. So e.g. a 20% chance of losing a customer who is bringing-in 30% of your revenue results in a 6% revenue risk. Add all these up for each customer, then multiply your current revenue by that number.
- Alternatively, if you have lots of smaller customers, then you may already be experiencing measurable churn due to poor performance, in which case you can use “actual” numbers for this - which is what we’ve done here by using a “devices lost per month” number.
- Most businesses need to build reports to summarise their performance over say the last month, for internal use. It is often common to have to produce reports for customers, detailing for example how many devices were deployed, the activity on those devices, summarising and explaining any deviations from a Service Level Agreement etc. One of the bad practices which tends to naturally build-up in every business is that reports are built manually. Numbers are pulled-together from multiple sources, analysed by hand, and then pretty charts are produced. Not only is this inefficient and error-prone, it also takes time, which means that the business is running itself using outdated information. DevicePilot ingests live data continuously from the device estate and produces up-to-the-minute reports on demand at any time, massively reducing the reporting load while massively improving timeliness. So here we quantify the time currently taken to generate reports manually.
- When things go wrong, the COPS team has to swing into action to make things right - and mollify the customer. In the early days of trials, this kind of manual response is exactly the right approach, as it allows problems to be deeply understood, and customers to be kept “on-side” so that the trials can continue. But it is an approach which fundamentally does not scale. Time spent recovering customers is time which would be much-better spent on building effective processes for the next stage of scaling.
- Revenue monthly today, and in a year’s time, both without DevicePilot, simply by extrapolating today’s numbers.
- To calculate the device revenue lost due to churn, per year, today, note that number of devices lost compounds monthly. If we lose 25 devices each month, then in month 1 we’ve lost 25 devices, but in month 2 we’ve lost a total of 50 devices, and so on. At some point we need to write this off, so here we’ve assumed that we expect customers to churn on average anyway after 1 year (for most businesses this is hopefully rather pessimistic, so you might make this number larger).
- Person costs are the sum of time lost due to manual report-generating and customer-recovery.
The nice thing about using a spreadsheet for this analysis is that it allows you to do a simple “what-if” analysis by changing these inputs. What if we only achieved half the up-time improvement? What if we were able to charge 20% more for a premium service? What if operational salaries go up? etc.
Bottom-line financial improvements
Having gathered all this information, we now can estimate the improvement to the bottom-line of the business, in each of the categories outlined earlier, as shown at the bottom of the spreadsheet. We are interested in the improvement that we should see today (left), and the improvement in a year’s time (right). Since business calculations are sometimes done per day, per month or per year, this spreadsheet calculates the results for each of these periods.
The people costs saved row represents new hires avoided because the team has been made more efficient. The saving for this today is $0 on the assumption that the current team will stay as-is.
Now by dividing these savings by the monthly cost of a Service Monitoring package such as DevicePilot, you can calculate your Return on Investment (RoI), either as a percentage or in absolute terms. For a company like this example, it's typical to achieve an RoI of 200% or more, immediately, and this can easily grow to 500% or more as the business continues to grow.
We’ve seen how Service Monitoring delivers quantifiable benefits across seven different categories - all of which contribute to a significantly-improved bottom line, i.e. a very positive RoI.
Your numbers will be different. For some businesses, the savings are mainly in people-cost. For others, mainly in revenue protection or acceleration. In the long term, your rate of growth is probably the magic factor that your investors care about the most. You may decide that some of these categories don’t apply to you - or that you have some extra categories that do. Work them out using this spreadsheet as a starting-point - or let us help you do that.