Introduction

Metrics act as one of the most important indicators to understand the development and performance of your product. They track product health and performance. These help product managers make better decisions. It is also called KPIs, and it indicates what exactly is working in a product and what is not. 

In product terms having a good KPI score is called a green flag, which means that the company and the team are doing things in the right way and customers are also happy about it. But having a low KPI symbolizes a red flag, which means that things are not going exactly the way it looks like. 

These red flags initially appear to be a red dot, but if not paid enough attention by the responsible parties, the dot soon turns into a flag, and it can impact the business negatively.

Let’s look at some of the commonly used metrics:

 

Metrics used commonly

 

1. Customer Acquisition Cost:
   Customer Acquisition Cost refers to the actual cost incurred in order to acquire a customer. This includes the cost of marketing, sales, advertising, promotions, etc.
    
2. Customer Lifetime Value:
   Customer Lifetime Value is the total revenue a customer brings to your business in his/her/their entire customer lifecycle. It is extremely important for businesses to know the value of the Customer's Lifetime in order to decide how much and in which channels should they bear the cost to acquire customers.
    
3. Customer conversion rate
   Customer conversion rate measures the number of customers that you have actually converted. 
    
4. Retention rate
   The retention rate measures the number of customers that the business has been able to retain. It also gives a measure of customer loyalty.
    
5. Churn rate 
   The churn rate refers to the number of customers who leave your service. This can be either customer churn (drop in the number of users) or revenue churn (losses made by the company due to lower customer retention)
    
6. Active users
   Active users, as its name suggests, are the number of people using your product or service for a particular time period. It is extremely important for the company to know how many active users it has in order to understand the performance of its business. It is calculated on a monthly, weekly, or daily basis, each having its own significance.
    
7. Monthly recurring revenue (MRR)
   Monthly recurring revenue is the total revenue generated by the product or service in a month. This is an important metric to check your business’s performance in order to analyze how much the customers are willing to pay and for how long.

 

Green metrics vs red metrics

Green metrics are positive performance metrics that indicate that the product is on the right path and is performing well and meeting its goals. Examples of green metrics include revenue, customer satisfaction, user engagement, and conversion rate. These metrics provide insight into the success of the product and help product managers make data-driven decisions to continue to improve and grow the product.
 

On the other hand, red metrics are negative performance metrics that indicate that the product is not performing and has room for improvement. Examples of red metrics include high churn rate, low user retention rate, high customer acquisition cost, and low conversion rate. These metrics may indicate that the product is not meeting customer needs, there are issues with product design or functionality, or there is strong competition in the market. Red metrics require product managers to take action to address the issues and make improvements to the product.

 

What are these red flags and how does AI helps fix them
 

1. High Churn Rate:  A higher churn rate can impact on the overall success of a product. It can result in decreased revenue, increased customer acquisition costs, and harm to the company's reputation. Additionally, a higher churn rate can indicate that users are not finding the product valuable or that competitors are offering a better solution.
   
   How can AI fix this: To address this issue, product managers can use AI-powered customer analysis tools to gain insights into customer behavior, preferences, and pain points. These tools can help identify which customers are most likely to churn and why. By understanding the reasons behind the high churn rate, product managers can make data-driven decisions to improve the product and retain customers.
   For example, AI can be used to analyze customer feedback and identify common issues or pain points that are causing customers to leave. Product managers can then use this information to make improvements to the product, such as adding new features or improving existing ones.
    
2. Low User Retention Rate: Low user retention is a warning sign that the product may not be meeting the market demand or that there may be problems with the product itself. As such, product managers should view low user retention as an opportunity to identify the root causes of the issue and make improvements to the product to increase user engagement and retention.
   
   How can AI fix this: Product-led teams can use AI and product intelligence to analyze user behavior and identify opportunities to improve user engagement. These tools can help identify which users are most engaged with the product and what features they are using the most. Product managers can then use this information to personalize the user experience and provide targeted recommendations to users, which can increase retention rates.
   For example, AI can be used to analyze user behavior and provide personalized recommendations based on their preferences or past behavior. Product managers can also use AI to create personalized messaging and promotions to keep users engaged with the product.
    
3. High Customer Acquisition Cost (CAC):  High CAC can be an indication of several problems such as a lack of product-market fit, ineffective marketing strategies, or inefficient sales processes. As such, it is important for product managers to closely monitor CAC and take action to address any red flags.
   
   How can AI fix this: AI can help identify the most profitable customer segments and the best channels to reach them. Product managers can then use this information to create targeted marketing campaigns that are more likely to convert.
   For example, AI can be used to analyze customer behavior and identify patterns in their purchasing habits. Product managers can use this information to create targeted promotions or offers that are more likely to convert.
    
4. Low Conversion Rate: A low conversion rate can be a due several problems such as poor user experience, unclear value proposition, or a lack of trust in the product or brand. If the conversion rate remains low, it can lead to decreased revenue, decreased customer acquisition, and a negative impact on the overall success of the product.
    

   How can AI fix this:  AI and product intelligence can help fix low conversion rate by providing product managers with valuable insights into user behavior and preferences, allowing them to make data-driven decisions to optimize the product and increase conversion rates.

   For example, AI-powered analytics tools can track user behavior and identify patterns in user engagement and conversion. This can help product managers to identify where users are dropping off in the conversion funnel and make targeted improvements to improve the user experience and increase conversion rates.

   Product intelligence tools can also provide real-time feedback on product performance and user behavior, allowing product managers to make informed decisions and quickly iterate on the product to optimize the conversion rate.
    

5. Poor Product Quality:  If a product has poor quality, it can result in negative customer feedback, lower user retention rates, and decreased revenue. Poor product quality can also harm a company's reputation, making it more difficult to acquire new customers and retain existing ones.
   
   How can AI fix this:  One way AI and product intelligence can help is through the use of machine learning algorithms that can analyze user feedback and identify patterns in product defects and bugs. This can help product managers to identify the root causes of quality issues and make targeted improvements to the product.

   Product intelligence tools can also provide real-time feedback on product performance and user behavior, allowing product managers to quickly identify and address quality issues as they arise.
   Additionally, AI can help product managers to anticipate quality issues before they occur, allowing them to take proactive measures to prevent issues from happening in the first place.

 

How to address these red flags to your team?
 

Seldom it is observed that companies view the metrics as a headache to present in front of the stakeholders and team. Sure, the stakeholders deserve to know the health of the company but they should know it unfiltered. 

While it is important to analyze all the metrics, it is even more crucial to present them in such a way that there is a way to find a solution. And product teams often tend to fumble at this stage. Let’s understand how to break this bad news in front of your counterparts 

 

1. Build a narrative
   There is no number without a reason. The dips in the indicators or even the ups in it, all have a reason and you need to understand where and how you can attribute that number. For eg - sales of a B2B product declined in USA, but if you closely look into it you can attribute it to the failure of compliance to data laws. Hence, it is important to build a narrative around the numbers so as to be able to comprehend them well.
    
2. Present the reason for the numbers
   While there are always reasons for dips in the numbers, it is important to note all the possible reasons for it. Following the PLETES model will help in understanding it
   Politics
   Legal
   Educational
   Technical
   Social
   Ecological
   Hence, there may be more than one reason your product is not able to perform or get promoted
    
3. Prepare an action plan
   Now that you have gone into the depth of understanding the shortcomings of the product, your customer’s response, and your cost dynamics, here is the stage where you prepare a legit action plan. And this is where the middle and top management can get involved. Because this is also the stage where the finance marketing and operations team have a discussion on where should the costs be cut, or how should the product be marketed well.
    
4. Use of AI for better analysis
   In today’s day and age data is held to be a superpower and true to its fact, your team should collect as much data as possible to understand how to make the changes better. Along with this, doing constant market research, and trend analysis helps in knowing the facts behind 

 

Conclusion  

Hence, with the growth of data, technology, and changing consumer behavior, staying in the market for a long is tough without making proper use of resources. And once you have studied and put insights into action your red flags can soon become green.