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Seeing the Forest for the Trees – Cohort Analysis



Click to learn more about co-author Maarit Widmann.

Click to learn more about co-author Felix Kergl-Räpple.

A marketing campaign or publication of a new release can make numbers of customers boom for a while. But what are the effects in the long run? Do the customers stay or churn? Do any of them return at some point? Does the overall revenue increase?

Patterns can be detected in customer behavior: for example, patterns in the regular behavior of customers (i.e., behavior that is not affected by our marketing campaigns or other similar actions). We might want to try to find a critical contract duration that determines whether customers will become loyal customers or not. Another scenario worth analyzing is customer groups – to see if different conditions in contracts with different starting points cause these different customer groups to show equal loyalty (or not). 

To answer these questions, we can analyze our sales data over time by time-based cohorts.

Identifying Long-Term Customer Behavior

Cohort analysis provides long-term feedback on our business decisions and customer engagement. Therefore, we use time as one dimension in the analysis. The second dimension is the metric that we analyze: contract value, customer count, number of orders, or anything else that quantifies the behavior of our customers. This “customer value” is shown separately for different cohorts. Cohorts are groupings in the data that share similar characteristics based on time, segment, or size. Given these three dimensions, we can identify patterns and trends that wouldn’t be visible in the individual records, thus providing a more complete look at our business. 

Before starting the cohort analysis, we have to define:

1. The cohorts that we consider in our data. They could be, for example, customers who started doing business with you within the same time frame (time-based cohorts), customers buying similar products (segment-based cohorts), or medium- and large-size companies (size-based cohorts).

2. The information that we want to show for the different cohorts over time. It could be an established metric, such as annual recurring revenue or churn rate, or anything else that answers our questions about the customers and serves the final goal to improve our business.

In this blog post, we want to concentrate on time-based cohorts. In the next sections, we will introduce the steps you go through to build a cohort chart (Figure 1), from formatting the data to visualizing the selected metric by time and cohort.

Fig. 1: An example of a cohort chart to analyze customer count, or any other metric such as ARR, by time and time-based cohort.

Example: Analyze the Number, Value, and Duration of Contracts

Let’s start by having a look at an example cohort analysis and seeing how the company’s business is doing. The company issues contracts – software licenses, mobile contracts, or magazine subscriptions, for example. Based on the starting time of the contract, we assign each customer to a time-based cohort.

The results of the cohort analysis enable us to answer questions such as:

  • Can we detect a positive trend in terms of more customers and more revenue? Is the trend stable?
  • Do customers who entered into a contract in a particular year generate more revenue than customers who started in other years?
  • Which year(s) show the greatest customer churn?
  • Does the value of the contracts remain stable over time?
  • Has the average revenue per customer increased or decreased?


In our example, the data contains information about contracts upsells, downgrades, and churn events. There are 45 contracts and 12 customers. Contract periods range from January 2015 to December 2019. Each row in the data shows the start and end time of the contract period, the contract value, and an ID that identifies the customer. You can see a sample of the data in Figure 2. 

Fig. 2: Data containing information on contract IDs, values, and periods. In the first step of the cohort analysis, this dataset is transformed into time series by assigning recurring values to single months within the periods. 

The first step in the cohort analysis is to assign recurring values to the single months within the contract periods. Recurring values exclude one-time events; that is, they consider only the services that are constantly provided over a limited time period: subscriptions to software, support, content, etc.

Step 1: Calculate Recurring Values

When we calculate recurring values, we format the original contract data into time series data where each row contains a single month, a recurring value, and an ID. We can do this calculation with the “Calculate Recurring Values” component shown in Figure 3. The component is available for download on the Hub

Fig. 3: Transforming contract data into time series data where each row contains a single month, a value, and an ID. The “Calculate Recurring Values” component, which performs the calculation, is available on the Hub.

Input Table

An example of an input table for the “Calculate Recurring Revenue” component is shown in Figure 2. The table must contain two columns that define the start and end date of each contract period, one column for the contract value, and one column for the ID.

Output Table

The output table of the component shows each individual month within the contract period, and the recurring values for each month. For example, if we had a row for a contract with a value of EUR 30,000 and a contract period of 12 months, the output table would show 12 rows for this contract, one for each month, and a monthly recurring value of EUR 2,500 (Figure 4).

Fig. 4: Example input and output data of the “Calculate Recurring Values” component that converts contracts data into time series data: Records by contract period and ID are expanded to monthly recurring values by single month and ID.

Now, after formatting the data, we are ready to move on to the next step, where we build the cohort chart. We can subsequently use the chart to answer the questions about the state of our business. 

Step 2: Inspect Revenue and Customer Count by Time and Cohort 

In this second step, we calculate the selected metric separately for each month and time-based cohort. For example, we could have two customers who started in 2018, one with a monthly recurring revenue of EUR 2,000 and the other with monthly recurring revenue of EUR 3,000. These two customers would then constitute a single time-based cohort called “Started 2018.” The monthly recurring revenue for this cohort is therefore EUR 5,000 until at least one of the two customers upgrades, downgrades, or churns.

The time series data could also come from any other source. It could be the daily sales coming from subscriptions or grocery stores, for example. Regardless of what the data actually show, note that in order to perform cohort analysis, each record must contain a timestamp, identifier, and a value. 

The workflow in Figure 5 (which you can download from our Hub here) shows two components that enable you to analyze time-based cohorts using the following metrics:

  • annually/monthly recurring revenue (ARR/MRR)
  • annually/monthly recurring revenue relative to customer count
  • customer count
  • churn rate
Fig. 5: Analyzing time-based cohorts with the ARR and MRR by Time-Based Cohort and Customer Count and Time-Based Cohort components available on the Hub. Both components produce an interactive view showing how the selected metric develops over time for each cohort.

Example outputs of these components are shown in Figure 6. The line plot on the left shows the ARR for each cohort over time. The stacked area chart on the right shows the cumulative customer count for the different cohorts over time. The metric, the granularity of the cohorts, and the chart type can be defined in the configuration dialogs of the components.

Fig. 6: Cohort charts as produced by the ARR and MRR by Time-Based Cohort and Customer Count and Churn Rate by Time-Based Cohort components. The line plot on the left shows the ARR for each cohort over time; the stacked area chart on the right shows the customer count for each cohort and in total over time. 

From the line plot on the left in Figure 6 we can see that the ARR develops differently for the four time-based cohorts: 

  • The customers who started in 2015 (blue line) increase their ARR value in the first year, but reach a low in the second half of 2016. Their ARR starts increasing again in 2017 and returns to its original value at the beginning of 2018. 
  • The customers who started in 2016 (orange line) show an increase in ARR by the end of 2017. Their ARR starts to decline through to the beginning of 2019, where it then settles down to a constant value. 
  • The customers who started their contracts in 2017 (green line) have a constant ARR value over the whole time period from the beginning of 2017 to the end of 2019. 
  • The customers who started in 2018 (red line) increase their revenue until it sets to a constant value at the beginning of 2019. 

From the stacked area chart on the right in Figure 6 we can see that the decreasing ARR of the “Started 2015” cohort (blue area) causes a low in total ARR at the end of 2016 but it starts increasing again due to the additional ARR coming from the “Started 2016” cohort (orange area) and the constant ARR coming from the “Started 2017” cohort (green area). The decreasing ARR for the “Started 2016” cohort cannot be compensated by the ARR coming from the “Started 2018” cohort (red area). This means that the maximum total ARR is reached at the beginning of 2018 before the ARR of “Started 2016” starts to decline. 

Shared Components

Now, it’s your turn to analyze your own customer data and build the cohort charts. Drag and drop the components from the Hub, and follow the steps as described above. You can use the configuration dialogs of the components to customize your cohort analysis: Create time series with daily recurring values, extract cohorts based on the starting month, calculate the churn rate, or some of the other available metrics. If you want, you can also change the functionality of the components for your purpose: Add new metrics and charts, for example. 

Check out these videos – What is a Component?Component Configurations, and Sharing and Linking Components – for more details on components.


Cohort analysis gives us a robust and comprehensive view of the state of our business. It also gives us feedback over a long cycle of business. It smooths occasional fluctuations, giving us perspective on our customers’ behavior in the long term. Cohort analysis can reveal patterns in customer behavior that are only visible when we analyze customers by groups. For example, an increase in actual numbers could still mean a decrease in loyalty.

The steps in building a cohort chart from contract data include blending data, filling gaps in time series and checking for zero values, sorting, and pivoting, along with other data preprocessing operations. The components introduced in this blog post automate these steps, yet they let us define the key settings, such as the granularity of the cohorts and the metric to analyze.

(This blog post was first published on the KNIME blog.)

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Big Data

Proximity labeling: an enzymatic tool for spatial biology



In this Forum, we highlight how cutting-edge, proximity-dependent, enzymatic labeling tools, aided by sequencing technology developments, have enabled the extraction of spatial information of proteomes, transcriptomes, genome organization, and cellular networks. We also discuss the potential applications of proximity labeling in the unexplored field of spatial biology in live systems.

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Synthetic biology applications of the yeast mating signal pathway




Central carbon metabolism (CCM)

as the main source of energy, CCM oxidizes carbon through glycolysis, the pentose phosphate pathway, and the tricarboxylic acid cycle.


a cell host or an organism for the production of biochemicals such as enzymes by introducing synthetic modules or devices into the cell.


an assembly of biological parts that enables cells to perform logical functions, such as genetic switches, oscillators, and logic gates.

Convolutional neural network

a class of artificial neural networks with multiple building blocks that automatically and adaptively learn spatial hierarchies of features through back-propagation.

Clustered regularly interspaced short palindromic repeats (CRISPR)

a genome-editing tool in which CRISPR-associated nuclease 9 (Cas9)–guide RNA (gRNA) complexes recognize a protospacer adjacent motif through base-pairing and then cleave the target DNA,

CRISPR activation or interference (CRISPRa/i)

a tool that uses dead Cas protein and gRNA to activate or repress genes, resulting in gene upregulation or downregulation, respectively.

Cubic ternary complex model

an equilibrium model that describes the interactions between receptor and ligand. This model simulates the interactions of G proteins and receptors in both their active and inactive conformations.

G proteins

heterotrimeric G protein complexes are composed of α, β and γ subunits. Replacement of GDP by GTP in Gα causes a conformational change that dissociates the Gβγ subunits, leading to the activation of downstream signaling.

G protein-coupled receptor (GPCR)

a generic class of versatile, seven transmembrane-domain proteins that regulate a diverse array of intracellular signaling cascades in response to hormones, neurotransmitters, and other stimuli.


a cascade of molecular events that finally lead to fusion of the nuclei and the formation of diploid cells.

Metabolic engineering

a new scientific field that combines multi-gene recombination technology with metabolic regulation and biochemical engineering to overproduce desired products.

Mitogen-activated protein kinases (MAPKs)

a family of serine/threonine kinases that convert extracellular signals into a diverse range of cellular responses.


studies include genomics, transcriptomics, proteomics, and metabolomics that characterize and quantify pools of biological molecules, and together give rise to the field of integrative genetics.


a genetic circuit where oscillation is generated by the inhibition and activation of transcriptional/translational feedback loops.

Pheromone-response element (PRE)

a cis element that is present in multiple copies in the promoters of a variety of pheromone-responsive genes; PREs interact with Ste12 to initiate the transcription of pheromone-induced genes.

Quorum sensing

a cell density-dependent phenomenon in which cells adapt their behavior by synthesizing, secreting, perceiving, and reacting to small diffusible signaling molecules termed autoinducers.

Scaffold protein

proteins that recruit other proteins to form a functional unit, thus enhancing signaling efficiency and fidelity.


a Ste5 mutant that lacks the Gβγ-binding site because its N-terminus has been truncated; Ste5ΔN-CTM is no longer recruited to the plasma membrane following pheromone treatment.

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Biotechnology of functional proteins and peptides for hair cosmetic formulations



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    VW’s 9-month electric vehicle deliveries to China more than triple



    FRANKFURT (Reuters) – Volkswagen’s deliveries of battery-powered electric vehicles to China more than tripled in the first nine months of the year, the carmaker said on Friday, less than two months after it flagged the need to change its e-car strategy there.

    Deliveries of battery electric vehicles (BEV) to the world’s largest car market stood at 47,200 in the January-September period, up from 15,700 in the same period last year.

    “As planned, we significantly accelerated the BEV market ramp-up in China in the third quarter, and we are on track to meet our target for the year of delivering 80,000 to 100,000 vehicles of the ID. model family,” Christian Dahlheim, head of group sales, said.

    Volkswagen Chief Executive Herbert Diess in July said the carmaker had to change its approach to how it markets its BEVs in China after first-half deliveries stood at just 18,285.

    (Reporting by Christoph Steitz; Editing by Maria Sheahan)

    Image Credit: Reuters

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