Companies want to process large amounts of data in the cloud, use artificial intelligence and chatbots, to tap into new, unknown potential.
But despite all these big words, which are usually associated with little concrete knowledge on how to use them best in a business context, the most important basics are often forgotten. The assumption that data from a trusted provider’s tool automatically add value is unrealistic. To work successfully with data, I believe it takes three basic things:
- Tools: knowledge about data handling methods
- Knowledge of the domain: In what context do the data arise and how are they interpreted?
- Targets: According to which target values do you want to optimise? What is important for having more success?
Targets are the fundamental basis for everything that follows. Each company sets itself goals such as increased sales and growth. But to reasonably measure these targets, new orders, sales, and profits must be documented and then evaluated by controlling.
In this context, targets work well in most companies. In the online sector, positive examples can be found especially in the e-commerce sector. This is where focused, data-driven work takes place, and there is a very precise idea of monetary objectives.
However, if we look at digital environments that have less to do with e-commerce, we end up in the area of content or corporate websites. There, websites or apps usually exist as an addition to the company’s well-functioning offline business. Since digital products and services do not yet contribute to the critical part of business success in every enterprise, objectives and a clear orientation are often neglected. But without target values, you cannot actively control a change process.
At this point, it is important to consider how a digital presence can expand offline business to create added value. What results are valuable to us? These could include leads, newsletter registrations, downloads, or recurring visitors.
It is not easy to consider the meaning and purpose of the company platform and define it in measurable goals. In addition, these measurable goals must have target values to enable significant analysis.
Targets: The key to success
Clear targets with key figures and target values form the basis of data-driven work. Let’s use the example of creating a new blog post for this blog. We need to have a clear definition of why this blog exists, what goals it pursues, and what key figures we can use to measure concrete targets. In the best case, this should be part of the content strategy. Only by having targets can we begin to evaluate target groups, adapt data sources and media budgets, test structured new formats, text lengths and images, and thus continuously improve ourselves.
This requires, as described above, goals, knowledge of the domain and methods for structuring data.
- Targets: to know if the content you create pays for success
- Knowledge of the domain: e.g. to formulate test hypotheses
- Knowledge about data manipulation: e.g. clustering data as well as evaluating these clusters
The bounce rate is not a KPI!
Higher-level goals should be defined in a discussion or workshop with the product owners or product managers. The central question in these discussions must be: “Why does the website/app/campaign exist?” This sounds a little complicated, but it requires critical thinking and intensive conversation. Overall goals should be SMART.
For each higher-level target (objective), more specific targets (goals) should be defined. Goals represent more specific strategies that will be used to achieve a higher-level objective. Objectives can have several goals.
The next step is to define key performance indicators (KPIs) for the goals. These indicators help assess performance against the goals that have been set. It is important to exchange information with the data managers in order to jointly define the KPIs. The core content of this step consists of making the existing goals mappable and measurable on an abstract, technical level, and to answer the question “What key figures can I use to map my targets?”. To determine measurable KPIs, both knowledge of the technical product environment and knowledge of tools is necessary. However, not every metric is suitable as a KPI. The “bounce rate” is a number that is not a KPI. It is suitable as a supporting indicator, e.g. for landing pages; however, it is not significant enough to evaluate the success of the company. This is mainly due to how bounces are measured. A bounce is measured when the user’s visit only consists of one hit, i.e. only one piece of information is sent to the analytics server (e.g. opening a landing page) and then no further information is transmitted during the user’s session. So only one further hit decides whether the visit is counted as a bounce or not. I don’t think this information is sufficient to be considered as a KPI.
Target values must be put in a meaningful context
The next step is to define target values (targets) for each KPI. These are important in determining success or failure. For example, suppose a lead campaign has generated 80 leads. Is that good or bad? This question can be answered with previously defined target values.
You can derive target values from historical data. (How well have lead campaigns performed with what budget in the past?) If there is no historical data, try to use assumptions to define target values. An experienced partner can help.
The result should be a table with a similar shape as this. This was filled in using a specimen string for an objective.
Ray Sono recommends:
This process requires a lot of effort. However, it is essential for successful data-driven work to record these goals and optimise them in an ongoing process. Of course, targets and target values are not fixed for eternity. They should be reviewed at regular intervals and adjusted if necessary.
With this foundation, we can begin track these specific targets, base campaigns on them, and apply analytical machine learning methods to optimise metrics.
A solid target definition with key performance indicators and corresponding targets is one of the fundamental building blocks for driving business forward in a data-driven way. Thus, under no circumstances should this step be omitted.