How to prioritise the IT backlog with economic impact
In the day-to-day of any tech company or IT department, there is a constant tension we all know: the backlog. That infinite list of tasks, improvements, and above all, errors (bugs) that seem to grow faster than we can resolve them. And at Luce IT, this is something we do not allow.
Traditionally, the way to decide what gets fixed first has been somewhat subjective. Tags like “P1” or “P2” are used, there is talk of “technical severity,” or, in the worst-case scenario, what the most important client—or the most insistent boss—asked for that morning is prioritized. The problem with this approach is that it usually fails when the volume of incidents grows. The result is that we end up underinvesting in reliability while dedicating resources to visible tasks with very low actual return.
What if we could change the debate to data? What if, instead of arguing about “perceptions,” we talked about economic impact? Today we will see how the automatic quantification of errors allows us to prioritize a bug that costs us €345,000 a month over a cosmetic one that only “annoys” to the tune of €800.
Why we need the language of money in IT
For Business and IT to stop speaking different languages, we need a common language. That language is the euro. When we convert each incident into an economic hypothesis, the conversation changes completely.
There are three fundamental findings that justify this change in mindset:
- Outages are extremely expensive: According to recent reports, more than half of major outages cost over $100,000, and 16% exceed one million.
- Fixing late multiplies the cost: It is not an urban legend. Correcting an error after delivery can be up to 100 times more expensive than having detected it in the design phase.
- The backlog is a financial liability: If an error in production is not fixed, its cost usually grows over time due to revalidation, change control, and the necessary coordination.
Importance of the Cost of Delay
The spirit of this new prioritization is summed up in the Cost of Delay: how much does it cost us to delay solving a problem? It is not just about lost revenue; we also include opportunity cost and assumed risk.
Imagine you have two errors on the table:
- Error A: A 1.2% drop in checkout conversion.
- Error B: A failure in internal reporting that shows an incorrect sales KPI but does not affect the end customer.
Without economic data, IT might think Error B is “urgent” because the sales team uses it daily. However, if we calculate that Error A takes away €40,500 a month and Error B only €1,500, the decision makes itself.
How to automatically quantify errors
We do not need “magic AI” to start, but an intelligent data flow that combines observability, product analytics, and financial data. We can follow an incremental approach:
1. Deterministic Rules
These are simple formulas based on data we already have. For example, for a shopping cart failure, the formula would be:
Affected sessions x Conversion drop x Average order value x Margin. This gives us a monthly direct impact figure that everyone understands.
2. Statistical and Causal Models
For more “silent” errors or when there is high seasonality (like Black Friday), more advanced models like CausalImpact are used. These models estimate “what would have happened if the bug didn’t exist,” allowing us to separate the noise of real traffic from the impact of the error.
3. Key metrics to monitor
For quantification to be useful, we must look beyond direct loss:
- ARR at risk (Annual Recurring Revenue): Vital in SaaS models. If the error affects “core” accounts, we calculate how many could leave (churn) due to that failure.
- Support cost: Every bug generates tickets. Multiplying the number of extra tickets by the unit cost of attention gives us the real operations expense.
- Reputational Risk: Although difficult to monetize, we can use the drop in satisfaction (NPS/CSAT) as a multiplying factor of the impact.
Go from intuition to revenue
Moving from a backlog management based on “scares” to one based on economic data requires a cultural shift, but the roadmap is clear. Start with simple rules, integrate them into your ticketing tools (like Jira or ServiceNow), and make economic impact a mandatory field on every ticket.
When IT stops being a “cost center” and starts being seen as a team that prevents millionaire losses and maximizes return, the relationship with the business transforms. At the end of the day, prioritizing well is not just a matter of technique; it is a matter of efficiency and profitability.
Do you know how much money your business is losing right now due to errors in the backlog?
Do not let intuition guide your technical priorities. At Luce IT, we help you take control with Session Analysis Intelligence, allowing you to quantify the real impact of each incident.
Calculate here how much you could be losing and start prioritizing with economic impact.
Frequently Asked Questions about the economic impact of the backlog
What is the Cost of Delay applied to IT errors?
It is a metric that quantifies how much money a company stops earning or loses for each day or month an error remains unresolved in production. It includes lost revenue, support costs, and the risk of customer loss (churn).
How can the impact of a bug be calculated if I don’t have exact financial data?
You can start with deterministic rules using proxies. For example, if we know the average conversion rate and cart value, we can estimate the loss by multiplying the affected sessions by the drop observed in analytics.
What is the difference between prioritizing by technical severity and by economic impact?
Technical severity measures the complexity or scope of the flaw in the code, while economic impact measures the real consequence on the business. A “critical” error in a process nobody uses can be less important than a “minor” error in the payment process that affects thousands of users.
How does the Error Budget help improve the relationship between Business and IT?
It establishes an acceptable failure limit. When it is exhausted, both teams know beforehand that the absolute priority becomes stability. This eliminates arbitrary negotiations and tensions in the face of service outages.
