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Retail Marketplaces – Ramesh Krishnamurthy

Tools of the Trade

In today’s fast-paced business landscape, data-driven decisions are the key to sustainable growth. As a prelude to our Special Feature on Retail Marketplaces, Ramesh Krishnamurthy, CEO of Komerz explains why cohort modelling and analytics are powerful tools that can help businesses understand user behaviour, optimise strategies and drive success, moving us beyond “omni-channel” commerce to a more unified and targeted approach in e-commerce platforms.

Retail Marketplaces - Ramesh Krishnamurthy 1

What are Cohorts?
Cohorts are groups of users who share a common characteristic or experience within a defined time frame. By tracking cohorts over time, businesses can gain valuable insights into user retention, engagement and conversion rates – these insights will help the business make more efficient choices in targeting their media spend giving a lower cost per acquisition and improved retention rate.

How Cohort Modelling Drives Growth?
There are five core ways in which Cohort modelling and tracking can drive growth for the business, no matter if it is a retail store, online market place or direct to consumer web-shop.

Retention Analysis: Cohort analysis allows you to identify trends in user retention. By comparing how different cohorts behave over time, you can pinpoint areas where improvements are needed and implement strategies to boost retention.

Personalisation: Cohort data enables personalised marketing and product experiences. Tailoring your offerings to specific cohorts’ needs and preferences can enhance customer satisfaction and loyalty.
Optimising Acquisition: Understanding the lifetime value of different cohorts helps in optimising marketing spend. Focus your resources on acquiring customers who are more likely to become long-term, high-value users.

Product Development: Cohort analysis can guide product improvements. By identifying which cohorts find the most value in your product, you can prioritise features that resonate with your core audience.
Scaling Strategies: As your business grows, cohort modelling becomes even more critical. It provides insights into how new user groups behave compared to earlier cohorts, helping you adapt and scale your strategies effectively.

Cohort modelling is a valuable technique used across categories to gain insights into user behaviour, retention and growth. It is especially valuable for retail businesses who are seeking to understand behaviours and triggers across different use cases…

New vs. Returning Customers: Cohort modelling can help an e-commerce company understand the purchasing behaviour of new customers versus returning customers. By tracking cohorts over time, they can assess which group has a higher lifetime value and adjust marketing strategies accordingly.

Product Cohorts: E-commerce businesses can create cohorts based on the first product a customer purchases. This can help in cross-selling and upselling by analyzing which products are often bought together and tailoring product recommendations.

Subscription Services
Subscription Renewals: For subscription-based businesses like streaming services or SaaS companies, cohort analysis can reveal renewal patterns. Understanding when cohorts of subscribers tend to renew or cancel their subscriptions can inform retention strategies.

Feature Adoption: Cohort modelling can track how different cohorts of users adopt new product features. This helps in prioritising feature development and assessing the impact of feature releases on user engagement.

Mobile Apps
User Onboarding: Cohort modelling can be used to analyse how different cohorts of users go through the onboarding process of a mobile app. This can lead to improvements in user experience and engagement.
App Updates: By tracking cohorts, app developers can understand how updates and changes affect user engagement and retention. This information can guide future updates and improvements.

Content Marketing
Content Engagement: In content marketing, cohorts can be created based on when users first engage with your content. This can help in assessing the long-term impact of content and understanding which types of content resonate most with different audience segments. It helps leverage social media and generating the right engagements.

Lead Conversion: Cohort modelling can track cohorts of leads generated through different marketing channels. By analysing which channels produce higher-quality leads that convert to customers, marketing budgets can be optimised.

Credit Risk Assessment: In the financial industry, cohort modelling can be used to analyse the repayment behavior of borrowers. Different cohorts of borrowers with similar risk profiles can be tracked to assess default rates and improve lending strategies.

Investment Portfolios: Cohort modelling can help investors track the performance of different cohorts of investments, such as stocks or bonds purchased in specific time frames. This can inform portfolio adjustments.

Cohort Modelling is a powerful tool for making data-driven decisions and optimising strategies for growth and retention. It is also perfect for creating attribution models to ensure there is machine learning driven performance improvements.

Increasingly, Cohort modelling is being used by brands to systematically improve their media and targeting strategies – As an example, an agency managing a portfolio of brands can build around cohort modelling. It starts by using cohort analysis to identify the brands core audience (cohorts) and then determining in which channels these cohorts are most likely to be shopping. Using this data, they can design fully attributed marketing strategies to pinpoint conversion opportunities in each channel then build a channel and cohort specific P&L. Once the channel P&L is developed, a combination of on-platform marketing as well as upper and mid-funnel communication can be used to execute the communication plan with laser focus.

Cohort modelling is just one of the data trends which we, at Komerz see becoming increasingly important for retailers and brands – using data and insight to reduce cost and increase conversion, serving the shopper exactly what they want, when and where they want it.