Answer

Business metrics are quantitative indicators that reflect the effectiveness of a business solution in practice. The business analyst first collects information about the strategic goals of the company and then identifies which key performance indicators (KPIs) relate to the tasks at hand.

Next, the analyst formulates specific, measurable, achievable, relevant, and time-bound metrics following the SMART principle. Examples include: a 15% revenue increase in one year; a 5% decrease in returns in one quarter; a 10-point increase in NPS, etc. The analyst describes the collection procedure, measurement frequency, calculation formulas, control tools, and the boundaries for deviations when analyzing metrics.

It is important to integrate metrics into the company's processes and to define data sources in advance: CRM systems, BI, reports. For automatic processing, the analyst may use Python/pandas:

import pandas as pd
# Calculation of customer retention metric
retention = (customers_active_month2 / customers_active_month1) * 100

Key features:

• Metrics stem from business goals and reflect value for stakeholders.
• They are described in detail: data source, formula, period.
• Regular review and adjustment based on results.

Trick questions.

Should all metrics be quantitative?

No, there are also qualitative metrics (for example, customer satisfaction based on interviews or focus groups).

Does a business analyst need to develop calculation formulas for all metrics themselves?

Not always: formulas can be provided by specialized experts; the analyst arranges them into a clear structure, automates collection, rather than invents each formula.

Is it appropriate to use only one metric to assess the success of a project?

No, it is recommended to use multiple metrics (for example, to assess both user and financial indicators) to obtain a comprehensive picture.

Common mistakes and anti-patterns

• Formulating vague metrics like "increase efficiency" without clear criteria.
• Lack of alignment of metrics with specific business goals.
• Ignoring the sources and correctness of data.

Real-life example

Negative case: The analyst implemented a new order processing system, selecting “reduced processing time” as the only metric.

Pros: Quickly obtained a measurable result Cons: Did not account for the increase in order errors and a decrease in customer satisfaction

Positive case: The analyst proposed a set of metrics: average processing time, error rate, customer feedback (NPS).

Pros: Comprehensive assessment, timely identification of problems Cons: Increased complexity of data collection and analysis