KPI Dashboard Design: Metrics, Charts and Reporting [EN]

Obtaining benchmark data from the market

Which cost metrics are worth analysing and which are noise

Assessing competitiveness against the benchmark

Evaluating whether an incentive scheme actually works

Preparing a budget and analysing it afterwards

Which metrics genuinely reflect performance

Evaluating training investment

Measuring development and progression over time

Metrics for high-potential groups and succession pools

How turnover is calculated, and the ways it gets calculated wrongly

Analysing it by segment rather than as one company-wide number

Forecasting it

Finding real causes with regression rather than with opinions

Exit interviews and retention surveys as data sources

The difference between the four types of performance indicator

What distinguishes a KPI that works from one that is merely measured

How indicators connect to strategy rather than sitting beside it

Past, present and future performance, and why most systems only track the past

The 10, 80, 10 rule and why critical success factors come first

Case studies of KPI systems that worked and that did not

The four stages of the process

How to run a two-day workshop on success factors

Impact mapping to find the factors with the greatest leverage, using an airline example

The three laws of productivity and why they explain resistance

The emotional factors that make people accept being measured

Preparing a short pitch for the change

A proposal aimed at executives, at managers and at staff, which are three different documents

Kotter's eight steps applied to a measurement rollout

The common measurement traps, most of which look sensible at the time

Deciding what actually needs measuring

Formulating the metric precisely enough that two people compute it identically

Eliminating metrics that cost more to collect than they return

Reformulating and categorising a set of existing indicators

Using success factors to derive the indicators rather than guessing them

Calculating the indicators, including the awkward ones

Working through indicators for several key success factors

Improving performance reporting using Stephen Few's principles

Graphic display best practice, and the chart types that mislead

Getting more out of the software you already have

The hierarchy of reports: what staff, managers and executives each need to see

The seven fundamental building blocks

Introducing agile methods to raise the chance of success

Stand-ups and Kanban applied to a measurement project

The concrete next steps for the first five weeks

"Link KPIs to pay and productivity rises" — what actually happens

"More indicators mean better performance"

"All performance indicators are KPIs"

"Monthly monitoring improves results"

Financial and non-financial indicators, and the reward system myth

Where surveys fit when the system does not hold the data you need

The most commonly run surveys

Automation tools

Market benchmarks and what they let you compare against

Satisfaction, loyalty and engagement surveys, which are three different things

Experience and value proposition surveys

Burnout and cross-department interaction surveys

Management performance surveys, pulse surveys and exit interviews

Types of question and when each is appropriate

Response options and the use of scales

How long a questionnaire can be before quality collapses

Open-ended questions, and the platforms available, including Google Forms

Setting the task the survey is meant to answer

Designing the instrument around that task

Promoting it so people actually respond

Reminders, and the point at which they start hurting

The report formats available and what each suits

Chart types and which distortions to avoid

Which sections a report needs

Preparing recommendations, and analysing open-ended answers as text

What conclusions the data supports and what it does not

Planning actions against them

Interim surveys to monitor whether anything moved

Exporting data from any source system

Cleaning it with AI: duplicates, blanks, formats, validation

Building a single dictionary of metric definitions

Producing a golden file that every later analysis runs on

Getting a descriptive portrait of the organisation from one prompt

Breaking the population down by unit, level and location

Building pivots and charts through AI inside the spreadsheet

Calculating top-line rates and funnel conversion

Moving from what happened to why it happened

Finding anomalies by manager, unit, level and tenure

Identifying gaps where you spend above or below the market

The question, sub-query, slice pattern for interrogating data

Building a risk score with interpretable rules

Forecasting the next quarter by unit

Calculating cost and capacity needs from the forecast

Explaining every prediction, which is what makes it usable

Combining four analytical outputs into one presentation

Moving between spreadsheet and slides without manual copying

Speaking to executives in the language of financial impact

Defending each conclusion to a sceptical audience

What analytics is, and the 4S model

Solving business problems by defining the question properly first

Which data is genuinely useful and which is collected out of habit

Selling the importance of analytics to management

The ABC model, and the old versus new way of working with data

Segmenting a population by its basic characteristics

Visualising the segments

Analysing metrics within each segment rather than across the whole

Grouping segments, and finding the insight that survives scrutiny

What lifetime value is and how to calculate it

Calculating return on investment for a workforce

Finding the most valuable segments

Making decisions from that number, and measuring the increase