Modern business is impossible without the use of the Internet. It doesn't matter if you're selling or making something. Consumers need information, and the easiest way to get it is to search the web. In addition, the effectiveness of the use of various communication channels is not always amenable to study, but for the Internet it is quite easy to do. Cohort analysis is considered to be one of the most popular and visual methods. Like other methodologies for studying causal relationships in consumer behavior, it requires the accumulation of statistical information. The Internet allows you to do this "imperceptibly" for the performer. After all, almost every action of a site visitor is recorded here - from the date of the first visit to the amount of time spent on each page.
Statistics at the service of marketers
It is unlikely that today there are still those specialists who interpret the word "marketing" as "advertising" and "sales". Undoubtedly,these are two important components of marketing activities. But the basis still lies in the study of demand and consumer behavior. And then everything is transformed into a search for opportunities to meet these needs.
And since we are talking about study and analysis, then statistics will help us. Careful accumulation of a database on the characteristics of buyers allows you to thoroughly study the demand and use the results of the analysis to the maximum advantage for yourself.
Most often marketers use correlation and regression analysis; they are interested in descriptive and predictive consumer research methodologies. All this requires the selection of the most indicative (or interesting for business) customer groups by some criteria. This is exactly what cohort analysis offers us.
Statistical analysis and commerce
In sales, you need to have a pretty clear understanding of the cause-and-effect relationship in the actions of customers. Cohort analysis allows you to do this by grouping consumers according to several criteria. Most often, a segment with a common characteristic (a visit to a store, a purchase, etc.) is singled out, united by the date of the event. In statistics, it is customary to talk about a group of people (objects) demonstrating similar behavior and signs. A simple example of a cohort would be customers who first entered a store the week before New Year's Eve. By studying their behavior, it is quite possible to draw conclusions about the effectiveness of advertising and commercial efforts.
Analytics
BGoogle developers have long come to help marketers. They offer a variety of services for learning about ecommerce statistics. Now you can conduct a cohort analysis in Google Analytics. Previously, it had to be done through forced audience segmentation. It was quite laborious and inconvenient. However, cohort analysis is now performed automatically. The analyst only needs to configure the report parameters according to his requirements.
Report data is displayed as a timeline and table. In the settings, you can change the four groups of parameters that the cohort analysis uses.
Cohort type is a general characteristic that unites a specific group of site visitors. Size can be grouped by time: exact day, week, month. If you select the "week" parameter, for example, the report will group all first-time visitors to the site for a given week into one cohort.
Next, you can change the "indicator". The variability here is about page views, session duration, number of users, and so on. And the last parameter is “date range”. With this feature, the analyst has the ability to track the activities of the cohort over the time period from the set starting point to the current date. When choosing grouping by days, you should remember that cohorts will be formed in rows, and the dynamics of visitor behavior - in columns.
How to use the analysis results
After examining the reports, one can trace the frequency of return of consumers towebsite. And comparing quantitative indicators with the plan for placing content on the pages of the site will provide an opportunity to understand what exactly interests and attracts customers.
For example, according to the analysis, a group of visitors is identified, which returns to the site with "enviable constancy". By raising the plan for placing promotional materials about holding some kind of promotions or presenting new items in the assortment at the time these customers first visit your pages, it is quite possible to draw conclusions about what exactly attracted the attention of potential customers. This information helps to improve the efficiency of the company. This is how cohort analysis is used in marketing. It makes it possible to distribute the advertising budget even more purposefully and efficiently and create effective communication channels.
What to look out for
Effective use of any statistical tool requires preparation. After all, a correctly posed question in a problem guarantees a quick solution.
What needs to be done before using cohort analysis? Ask yourself a few questions:
- Why is there such a sales dynamics?
- Which time period to choose (for an advertising campaign, for example)?
- How to determine when to send a big response?
The proposed hypotheses will help to more clearly define the parameters of cohort analysis.
Conclusion
Familiarizing yourself with the possibilities of the basic cohortanalysis in Google Analytics and getting an idea of its functional features, a marketer is quite capable of not only increasing website traffic, but also turning a potential client (random visitor) into a consumer.
Creating individual reports will give a clear idea of the characteristics of the target audience, will allow you to understand its reaction to your activity, regardless of whether you post an informational article or a promotional commercial offer on the site. Any marketing efforts must be economically justified. Cohort analysis, CLTV, Unit Economics - any methodology for studying consumer behavior is aimed at identifying the cost-benefit ratio and optimizing it.
But don't go too far. Daily monitoring will give the wrong idea about the motives of consumers visiting the company's website. It is long-term monitoring of representatives of one cohort that will allow you to track and correctly interpret changes in customer behavior.
