Translating technical impact into business metrics

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Are you wondering how to show the business impact of technical projects? Or maybe you want to learn how to translate technical metrics into business metrics. Well either way, you are in the right place! In this article, we provide advice on how to demonstrate the impact of technical projects, such as data science and software engineering projects.

Specifically, we focus on how to use business metrics to demonstrate the impact of technical projects. First we discuss why it is important to relate technical projects to business metrics. After that, we discuss different types of business metrics that can be used to demonstrate business impact. We also provide advice on which types of business metrics are most effective at conveying business impact. Finally, we discuss a framework for converting technical metrics into business metrics.

Why translate technical impact into business metrics?

Why should you translate the impact of your technical project into business metrics? Here are some reasons that you should always translate the impact of a technical project into business metrics.

Easier for business stakeholders to understand. The best way to communicate technical results to non-technical stakeholders is to speak their language and utilize concepts they are familiar with. Business stakeholders are generally much more familiar with high level business metrics than low level technical metrics such as model accuracy. They will much better understand the impact of your project if you explain that impact in terms of business metrics.
Easier to compare to other projects. Additionally, it is much easier to compare the impact of a highly technical project to other projects if the impact is described using business metrics. This is because business stakeholders are already aware of the impact that other projects are expected to have on key business metrics.

When to translate technical impact into business metrics?

At what point in the project lifecycle should you translate the impact of your technical project into business metrics? Here are the main stages in the project lifecycle where you should estimate the impact on business metrics.

When prioritizing projects. The first time you should translate the impact of your technical project into business metrics is before you even start working on the project. Specifically, it is when you are prioritizing different project ideas against one another and deciding which project should be worked on. Assuming a similar level of effort, you should generally prioritize projects that will have a large impact on the most important metrics.
When you need to get buy-in from stakeholders. The next time when you should translate the impact of your project into business metrics is at the beginning of the project when you need to get buy-in for a project. Translating the potential impact that a technical project might have into business metrics can help to convince your stakeholders that this is an important project that they should dedicate resources to.
Showing off the results of a project. Finally, you should also translate the impact of your project into business metrics when you are ready to show off the results of your project. This will help to show the organization that your team is providing value to the organization.

Types of business metrics

There are a few different types of business metrics that you can use to show the business impact of a technical project. Here are the main types of business metrics that you should consider.

Bottom line metrics. The first type of business metric you should consider is a bottom line metric. A bottom line metric is a metric that directly shows the financial impact of a project.
North star metrics. The next type of business metric you can consider is a north star metric. This is a metric that has been declared as the most important metric that a company or department is optimizing for. Different types of businesses and departments will have different objectives, so different companies will have different north star metrics.
Accessory metrics. The final type of metric that you can optimize for is an accessory metric. An accessory metric is a more granular metric that contributes to your north star metric. If you improve an accessory metric (and hold all else constant) then you should expect to see an improvement in your north star metric. Since accessory metrics depend on the north star metric that is defined, they will also look very different from company to company.

Using bottom line metrics to show business impact

Examples of bottom line metrics

Bottom line metrics directly show the financial impact that a project has on the company. They are generally measured directly in dollars (or a percent increase in dollars). There are two main types of bottom line metrics that you should consider when deciding how to show the impact of a technical project.

Dollars saved. The first way to show the amount of financial impact that a project has is to show the number of dollars that a project saved the company. There are many ways that technical projects can save a company money. For example, a technical project that automates a manual task that was previously performed by a human might reduce the amount of money that needs to be spent on employee salaries. A project that aims to create an internal tool that will replace a costly third-party vendor may reduce the amount of money the company spends on external tooling.
Incremental dollars earned. The other way to show the amount of financial impact that a project has is to show the number of incremental dollars that will be earned once the project is in use. For example, if you work on a project that aims to improve the conversion of leads in your sales pipeline then you might estimate the amount of incremental revenue that would not otherwise have been attained.

Advantages and disadvantages of using bottom line metrics to show impact

The main advantage of using bottom line metrics to show the impact of your project is that it is easy to compare the impact of different projects that take place in different business units. The number of dollars that a project contributes to a company is a standardized metric that can be applied to all projects that happen across the entire company.

There are a few disadvantages of using bottom line metrics to show the impact of your projects. The first disadvantage is that estimating a dollar amount associated with a project often requires strong assumptions to be made. That means that these metrics are only as good as the assumptions that they rest on.

Using with star metrics to show business impact

Examples of North star metrics

What are some examples of north star metrics that departments might use to measure their performance? The exact north star metric that is used by a company or department will depend on factors like the industry the company is in and the function the department performs. For example, if you work in a department that aims to grow the size of your customer base, then your North star metric might be the number of active customers that use your product. If you work in a logistics company that provides fulfillment services, then you north star metric might relate to the average number of days it takes to deliver a package.

Advantages and disadvantages of using north star metrics to demonstrate impact

The main advantage of using north star metrics to demonstrate impact is that they will generally resonate best with stakeholders in the same department. Stakeholders are used to thinking about these metrics and likely use the same metrics to evaluate their own performance.

The main disadvantage of using north star metrics to show business impact is that it can make it difficult to compare the impact of projects that are being worked on across different departments that have different north star metrics. This can make it difficult to determine which department is contributing the most value to the company.

Using with accessory metrics to show business impact

Examples of accessory metrics

Accessory metrics are metrics that contribute to your North star metric but don’t necessarily tell the whole story. So what are some examples of accessory metrics? Let’s continue with the example that we laid out in the previous section on north star metrics. Imagine you worked in an organization where the north star metric was the number of active customers that are using your product.

In this scenario, a few examples of accessory metrics that might contribute to your North star metric are the number of new leads that are sent to the sales pipeline, the conversion rate of lead in the sales pipeline, and the churn rate of existing customers. All of these are metrics such that if you saw an improvement in one of these metrics (and the rest of the metrics remained constant), you would expect your north star metric to improve.

Advantages and disadvantages of using accessory metrics to demonstrate impact

The main advantage of using accessory metrics to show the business impact of your project is that they often feel closer to the problem that is being solved. This is because individual projects often aim to improve one specific accessory metric. For example, if your north star metric is the number of customers on your platform, then you will likely have to implement different projects to improve churn rate and lead conversion.

There are a few disadvantages of using accessory metrics to evaluate the business impact of your project. The main one is that accessory metrics only tell part of the story and sometimes an improvement in one accessory can lead to a detrimental effect on another metric. If the positive and negative impacts cancel one another out, you can end up in a situation where the impact on your north star metric is net neutral. Additionally, decision makers are often more interested in seeing the impact on north star metrics.

What type of business metrics should you use to show impact?

Now that we have talked about the different types of business metrics that can be used to demonstrate business impact, we will talk more about which types of metrics are most effective to use. Here is our main advice for deciding what types of metrics to use to demonstrate the business impact of technical projects.

Use North star metrics over accessory metrics. Whenever possible, you should use north star metrics over accessory metrics to demonstrate the impact of your technical projects. This is because North star metrics are ultimately the metrics that are used to evaluate the performance of the department. They are the metrics that are top of mind for other stakeholders and they are the metrics that they will be most interested in seeing.
North star metrics and bottom line metrics are suited for different situations. Whether you should focus more on bottom level metrics or north star metrics will depend on a few different factors. One is the audience that you are speaking to. If you are presenting to an audience that is trying to optimize for the same north star metrics that you are, then you may be better off focusing on north star metrics than bottom line metrics. If you are presenting to an audience that has different north star metrics, or if there are not clear north star metrics defined for your company then bottom line metric may be better. Another factor that should influence your factor is company culture and values. In a successful company that values user experience above all else, bottom line metrics might not resonate as well. In a company that puts more emphasis on profits and efficiency, business metrics will resonate better.

How to translate technical metrics into business metrics

Are you wondering how to translate metrics from your technical projects into business impact? Here are some steps you can take to translate technical metrics into business metrics. Note that this framework is most useful towards the beginning of the project when you are trying to assess the impact that a project might have on the business before the technical work has been started. Later in the project when the technical work has been completed, it is often easier to just run an experiment to determine the impact that the project has on business metrics.

Decide which business metrics you want to target. The first step is to consider all of the different metrics you might use to showcase the value of your project and decide which metric you want to use. As a very simple example, imagine you were implementing a change to the sales process that leads go through and you wanted to know the number of incremental dollars that would be gained in the next year.
Figure out components needed. The next step is to figure out what components you need to translate from the technical and accessory metrics that are closest to the project to the high level metrics you want to show an impact on. In this case, you might need to estimate the difference in conversion rate that you will see after implementing the change to the sales process, the number of leads that will go through the sales process in the next year, and the average number of dollars that is brought in per lead. By multiplying all of these numbers together, you can get an estimate of the number of incremental dollars the project will bring.
Assess which components you have. After you list out all of the components you need to translate from technical metrics or accessory metrics to business impact, the next step is to evaluate which components you already have a good estimate for. Perhaps you already have a good estimate of the number of leads that will go through the pipeline from a company forecast. You might also have a good estimate of the average number of dollars brought in per lead from historical data. This means that the only component you do not have a good estimate for is the difference in lead conversion rate that you expect to see after implementing your change.
Estimate the other components. Once you determine which components you do not have a good estimate for, you need to come up with an estimate for those components. You are generally best off deriving estimates from previous experimentation and data whenever possible. For example, if you have tested similar changes to the sales process before and have only ever seen a difference of 1 – 2% in your conversion rates then you should assume that you will see a similar difference after implementing your changes.
State your assumptions alongside your results. The final step is to make sure to state any assumptions that you made alongside your results. This is an important step because other stakeholders might have information that you do not that might help you improve your assumptions. For example, a stakeholder who has been at the company for a long time might remember that a similar change was tested years ago and the difference in lead conversion rate that was seen then was close to 5%. This might give you reason to change your assumption about the difference in lead conversion rate.

Check out our article on data science best practices for all of our best recommendations on how to increase the efficacy of data science teams.