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’ behavior 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 analyze 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 be­hav­ioral 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 analyze, for example, the de­vel­op­ment of the birthrate and the change in consumer behavior (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 be­hav­ioral 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 analyze 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 analyze 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 behavior 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 be­hav­ioral 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 behavior 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 behavior 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 be­hav­ioral changes.

The benefit of cohort analysis in marketing

Analyzing 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 behavior of the average user and want to organize the visitors depending on specific criteria, you should def­i­nite­ly take advantage of cohort analysis. For observing the behavior 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 cat­e­go­rize 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 less 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 behavior. 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 cat­e­go­riza­tion. You can also access the in­for­ma­tion you need, so that you can respond to the needs of in­di­vid­ual customer groups.

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