Suc­cess­ful marketing campaigns all have one thing in common: they are perfectly tailored to target groups. But finding and reaching these target groups can prove tricky for online marketers. Without in­tens­ively studying users’ behaviour as part of a com­pre­hens­ive web analysis, you can only guess whether your planned marketing steps are creating the desired effect. For example, a complete data set usually acts as a basis where you can find out which devices visitors use to access the website. A different approach to web analysis is known as cohort analysis. Here, instead of col­lect­ing different in­form­a­tion to analyse at once, different groups (cohorts) are allocated for analysis. The criteria of the cohorts can vary quite con­sid­er­ably, which we discuss below.

Cohort analysis: defin­i­tion

For decades the concept of cohort analysis has played an important role in stat­ist­ic­al surveys in social science and demo­graph­ics. Cohorts (from the Latin 'cohors' meaning 'crowd') are groups of people who share a common demo­graph­ic. For example, this could be the birth year or the year they started working, or certain his­tor­ic­al events such as a president’s in­aug­ur­a­tion. The term 'gen­er­a­tion' is often used. When a cohort analysis (also referred to as a 'cohort study') is carried out, the be­ha­vi­our­al changes of the defined groups of people over the time period they are examined. Once you’ve collected the data, you can either:

  • Obtain an accurate picture of the un­der­ly­ing cohorts (intra cohort study), in order to analyse, for example, the de­vel­op­ment of the birthrate and the change in consumer behaviour (either over a long period, or on a random basis).

  • Make a com­par­is­on with at least one other group of people (inter cohort study), in order to obtain useful insights into be­ha­vi­our­al dif­fer­ences.

At the end of the 19th century, stat­ist­i­cians Karl Becker (1874) and Wilhelm Lexis (1875) laid down the found­a­tion for the analysis of certain pop­u­la­tion groups. Through ad­vance­ments made by demo­graph­er Pascal Whelpton (1949), these ap­proaches known as cohort analyses finally obtained in­ter­na­tion­al notoriety. The aim of Whelpton’s research was to analyse the increase in the US’s birthrate after WWII. Today the process is in­creas­ingly used for studies in medicine, politics, and the market economy.

Im­ple­ment­a­tion and in­ter­pret­a­tion

Cohort studies can be carried out in two different ways: you can arrange the cohorts together and accompany them in future (pro­spect­ive cohort study), or you can access data from the past so that you can analyse the present (ret­ro­spect­ive cohort study). In order to be able to implement one of these types of cohort analyses, the following steps need to be taken:

  1. Define the research question and aim: to obtain relevant in­form­a­tion, you have to ask the right questions. Only when you have concrete ideas about the content and purpose of the in­vest­ig­a­tion, can you create the necessary structure of the study.

  2. Define cohort events: the second step is to define the events in which cohorts occur, as these can lead to an answer to the research question.

  3. Determine relevant cohorts: now you determine which and how many cohorts are to be parts of the study. It is also possible to split or specify the formed cohorts.

  4. Perform the cohort study and evaluate it: if the desired cohorts have been found, you can carry out the re­spect­ive type of study (pro­spect­ive/ret­ro­spect­ive, inter/intra cohort study) and interpret the data received.

The changes in behaviour you want to obtain by carrying out the cohort analysis are de­term­ined by three factors or effects. The eval­u­ation and weighting of these are the main tasks of in­ter­pret­a­tion:

  • Cohort effects
  • Age effects
  • Period effects

Cohort effects are the be­ha­vi­our­al dif­fer­ences and changes between different cohorts. They can be generally explained by the existence of different social and en­vir­on­ment­al in­flu­ences. Age effects, on the other hand, are the changes that can be at­trib­uted to the in­creas­ing age of people and their related attitudes. Lastly, period effects represent behaviour changes that result from changing en­vir­on­ment­al con­di­tions – re­gard­less of gen­er­a­tion­al and socio-demo­graph­ic factors.

From these three effects, you can notice any clear trends regarding the behaviour of in­di­vidu­al groups. On the basis of these trends, you can use them to develop future prognoses or solution strategies. The main task is to separate age, cohort, and period effects, which can occur in every result, from each other. If you include these as iden­ti­fic­a­tion problems in the cohort analysis, you can find a clear reason for the be­ha­vi­our­al changes.

The benefit of cohort analysis in marketing

Analysing the market and the as­so­ci­ated target groups is an important part of strategic planning that precedes every marketing campaign. In online marketing, the focus is in­creas­ingly becoming more about the behavior of users. The millions of data that have already been collected serve as a strong basis for further planning, but this in­form­a­tion first needs to be ex­tens­ively evaluated. If you want to go a step further than just gaining knowledge about the behaviour of the average user and want to organise the visitors depending on specific criteria, you should def­in­itely take advantage of cohort analysis. For observing the behaviour of new and existing customers or re­cog­niz­ing regional trends, this procedure has been an in­dis­pens­able tool in e-commerce for a long while.

Example: cohort analysis in e-commerce

Cohort analyses enable you to check how suc­cess­ful your marketing campaigns are in a very precise way, as the following example shows:

You, an online store owner, decide you want a total redesign and layout change. To check how the new design is fairing with customers, you should look at the recorded trans­ac­tions and cat­egor­ise your customers into existing customers (cohort 1) and new customers (cohort 2). After two months, you look at the results and notice that the number of trans­ac­tions has decreased. Without further in­form­a­tion, you could say that the new layout wasn’t very well received. A look at the separate figures of both cohorts could reveal two other scenarios:

  1. Cohort 1 (existing customers) completed more trans­ac­tions than before the store redesign. In contras,t there were fewer purchases made by cohort 2 (new customers).

  2. There were more purchases made by cohort 2 (new customers) than before. Cohort 1 (existing customers) has carried out fewer trans­ac­tions.

Cohorts: the more specific, the more mean­ing­ful

The example above shows the ad­vant­ages of im­ple­ment­ing a cohort analysis, which is that it is much more flexible and specific than a mere analysis of average user behaviour. Thanks to the powerful features of current tools such as Google Analytics with regards to data col­lec­tion, it’s now possible to dif­fer­en­ti­ate between new and existing customers; the tools help you to check the behavior of more complex cohorts. You can include, for example, the age and location of customers, or the device being used in the cat­egor­isa­tion. You can also access the in­form­a­tion you need, so that you can respond to the needs of in­di­vidu­al customer groups.

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