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Cohort Analysis: The Secret to Perfecting B2B Go-to-Market Strategies

By August 20, 2023March 26th, 2024No Comments

Running a cohort analysis is a valuable data analytics tactic that enables companies to gain deeper insights into the behaviors and trends of certain groups. By grouping people or companies with shared characteristics within one or more time periods, one can clearly understand the impact of certain events, strategies or decisions.

Today, when resources must be deployed efficiently, cohort data is particularly helpful to gaining actionable insights for sales, marketing, and customer success optimization. For example, a cohort analysis can be used to increase customer retention, optimize marketing campaigns, or improve sales training strategies.

Customer Cohort Analyses

Today, B2B companies are being heavily evaluated on their ability to retain and expand with existing customers. By grouping customers based on periods of time such as acquisition or implementation dates, companies can gain deeper insights how sales strategies, customer retention programs, and product engagement efforts impact customer behavior. Doing so helps companies understand how best to not only retain, but also grow active users.

Customer Cohort Analysis Scenario 1: Customer Retention Cohort Analysis

By grouping customers by acquisition date or onboarding date, you can more clearly understand customer retention rate trends as time goes on. The resulting customer retention table is a critical tool for companies who want to test and evaluate the impact of various strategies on customer engagement.

For example, let’s say your company wants to test whether increasing customer support representatives in a given region will ultimately reduce churn in that area. By comparing churn rates for customers prior to the initiative with customers acquired after the new reps were added, you can gain insight into whether adding more staff will drive customer satisfaction – ultimately increasing your customer retention rate (gross dollar retention), user retention, and total ARR. From there, you’ll have the confidence to dial up or down headcount investments as needed.

Customer Retention Cohort Analysis

Customer Cohort Analysis Scenario 2: Product Usage & Engagement

Testing behavioral analytics with new product features or enhancements is key to product evolution. By grouping users based on characteristics like function or company type, you can test to see who a new feature or enhancement impacts the most. Depending on the success of various releases with distinct audiences, you can then narrow your positioning to emphasize the features most used by each audience.

For example, let’s say you announced the recent launch a mobile app for your company’s product. By grouping active users into profile-based cohorts, you can understand how usage and engagement evolves. Does the app launch:

  • Increase usage by new users in one cohort more than other cohorts?
  • Cause new users within an existing customer account to sign up for the platform?
  • Increase total product engagement?
  • Help you retain customers?
  • Reduce customer churn rate?

Let’s say the app increases usage by your younger customers, you can target prospect contacts with similar demographics.

Sales Cohort Analysis

Sales leaders can run a number of different types of cohort analyses. For example, a sales cohort test can be based on groups of AEs, opportunities, or accounts. Since sales resources are expensive, a cohort analysis is incredibly powerful as it makes it easier to focus efforts and ultimately optimize the return on sales investments.

Sales Cohort Analysis Scenario 1: AE Hiring Cohorts

AEs that take longer to become productive and close deals could indicate inefficient hiring, onboarding or training programs. However, given the risk of outliers, grouping AEs by hiring cohort can aggregate the data in a way that reveals more meaningful trends. As time goes on, running an AE hiring cohort analysis can indicate whether sales training and enablement have been effective; essentially, whether or not the newest sales reps meeting, exceeding, or missing standard ramp expectations.

AE Ramp Cohort Analysis

Sales Cohort Analysis Scenario 2: Segment Efficiency Cohorts

Understanding the ICP is a critical exercise for growth-stage companies to master. While many will “set and forget” their ICP, the companies that dominate the product-market-fit and go-to-market fit stages are the ones that regularly evaluate their ICP by running prospect cohort analyses.

By grouping opportunities by various characteristics (for example: industry, company size, region, etc.), you can evaluate segment efficiency based on key metrics and sales KPIs. Are you seeing average deal sizes, sales cycle lengths, and conversion rates increasing or decreasing for these distinct groups? Depending on the trends, you’ll want to update your ICP and focus efforts on targeting the profiles of your best and most efficient customers.

Marketing Cohort Analysis

Running a lead cohort analysis is a great way to evaluate the efficiency of marketing tactics, channels, campaigns, messaging, etc. By grouping leads into cohorts based on distinct lifecycle stage entry dates and various types of engagements, one can understand how conversion rates and marketing cycle lengths are trending.

For example, an MQL cohort analysis is an important exercise because it helps reveal the impact of certain user acquisition strategies and marketing campaigns on stage advancement and conversion rates. One can pinpoint where in the funnel contacts drop the most or spend the most time. By identifying these trends, it is possible to pinpoint and ultimately address inefficiencies in the marketing and sales funnels.

This information empowers marketing leaders and demand generation teams understand whether they should continue, change or stop certain marketing efforts. Doing so should ultimately improve marketing KPIs while increasing the return on marketing investments.

Marketing Cohort Analysis Example 1: Nurture Campaign Optimization

Let’s say you want to evaluate the effectiveness of recent changes made to a drip campaign designed to nurture contacts associated with deals in the “No Decision” stage. If you group the contacts based on the date they moved into the “Nurture” stage, there are many ways you can then test messaging effectiveness.

In terms of leading indicators, you can evaluate behavioral analytics such as whether users are becoming more or less engaged with your email content (i.e. opens, clicks and replies), or whether you are seeing higher unsubscribe rates. Or, you can monitor how leads advance through your nurture funnel, pinpointing where users drop the most and what kind of leads keep getting stuck in the same stage.

Then, for lagging indicators, you can evaluate whether the time it takes your contacts to reengage in a sales cycle is increasing or decreasing. Furthermore, do these contacts ultimately become new customers?

Marketing Cohort Analysis Example 2: Google Ads Targeting Optimization

Google Ads does a great job of optimizing in ad content, based on engagement with your different headlines and descriptions. However, tracking ad efficiency down both the marketing and sales funnels can be tricky. That’s where an MQL cohort analysis can come into play.

By grouping contacts based on MQL creation date and ad campaign association, you can test whether increasing ad spend in certain regions or with certain demographics increases stage advancement rates and conversion rates. Tracking this ensures that your latest campaigns not only feed the top of the funnel but are also creating revenue for your business.

MQL Cohort Analysis

Cohort Analyses are More Important than Ever

In the current environment when companies are being challenged to do more with less, testing is critical. Surrounded by an abundance of vanity metrics, a cohort analysis provides companies with truly actionable insights for efficient growth. A cohort analysis helps make it easy to monitor the impact and outcome of certain strategies, tactics or ideas on certain related groups. Identify cohort criteria, define the KPIs and let the data speak for itself.

If you’re new to cohort analysis, and want to learn more, continue reading on page 2 for the basics.

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