How DTC brands retain customers with a loyalty program
By understanding your customers’ needs and wants, you can create loyalty programs that reward repeat business...
Relationship marketing becomes an essential strategic tool in an environment where customers' needs and preferences are changing rapidly. Since customers have considerably gotten more power in the digital era, it has disrupted and redefined customer experiences and brand management --- 86% of consumers will pay up to 25% more for a better customer experience. Now, organizations can anticipate these changes by managing their relationships more effectively through customer analytics. The fundamental purpose of customer retention efforts is to ensure maintaining relationships with value-adding customers by reducing churn rate.
Furthermore, a recent study by Ketchum analytics showed that 45% believe COVID-19 has already changed their brand preferences while 62% expect permanent shifts. Another study by McKinsey & Company puts this number at 75%. As covid-19 is and will continue to shift consumers' shopping habits and behaviors, brands need to focus on developing long-term profitable relationships to enhance the value that they deliver to their customers. Thus, understanding your retention metrics & customer loyalty analytics within the scope of your retention marketing strategy could help you identify opportunities to get your customers to come back, and find new ways to retain them.
Retention marketing tactics have several advantages for a business because engaged and recurring customers increase their spending at a faster rate, buy at a full margin rather than at discount prices, and create operational efficiencies. Moreover, acquiring new customers is more expensive than retaining existing customers, and customer retention efforts can reduce marketing expenses --- A 2% increase in customer retention has the same effect as decreasing costs by 10%. Besides, customers who are satisfied with your products or services are more likely to recommend your business to others and are less responsive to competitive offers and price appeals.
Customer retention analytics can be used for understanding - How loyal your customers are to the brand, which customers you should focus on to retain them, and what customer retention strategies should be implemented.
E-commerce KPIs (add-to-cart rate, conversion rate, revenue per session), by shopper loyalty segment. - Source
Customer retention is the process of keeping existing customers by ensuring that they remain satisfied with your products and services.
Customer retention works by identifying the factors that drive satisfaction, engagement, or dissatisfaction among current consumers to maintain a strong relationship with them via tailored offers and efforts from marketing campaigns. The aim should be to prevent customers from switching to a competitor's product or service.
To calculate your customer retention rate, you can use this simple equation :
Customer Retention Rate = ((number of customers at the end of the period - number of customers acquired during the period) / number of customers at the beginning of the period) X 100
“The probability of selling to an existing customer is 60–70%; while the probability of selling to a new prospect is 5–20%”
Customer retention analytics is the measurement of customer loyalty and activity to identify areas for improvement. It provides details on how customers behave, what they want, what is important to them, and how they make decisions. It also helps in understanding customer actions (or inaction) associated with the purchase of goods and services. Customer retention analytics focuses on the value of customer relationships over time, and how to identify which customers are at risk for leaving so that brands can develop strategies to retain them.
For example, when analyzing customer retention analytics data, an organization may find that only 20% of their customers regularly use loyalty programs, but this set tends to spend 30% percent more than other segments. Brands can develop strategies to increase usage across all customer segments. Another example is a customer who has an account on the website, purchased in the past but hasn't purchased in six months. These customers are at risk for leaving and can be targeted with marketing campaigns to bring them back into the sales funnel.
The analysis of key retention metrics gives brands the tools to better understand the changes in customer behavior and their buying patterns over time so that they can develop acquisition, retention, and cross-selling strategies. Customer retention analysis is not just about measuring customer loyalty or determining the quality of a relationship between an organization and its customers, but also about understanding why customers behave in certain ways. It helps businesses to identify trends that can help them predict future behavior from existing clients as well as new prospects --- what makes your brand stand out from competitors, and what unique value you can offer customers.
It depends on what kind of business you're in. For example, a food and beverage brand might want to conduct customer retention analysis daily or weekly, while a financial services company might only need it once or twice per year.
The analysis of key retention metrics gives brands the tools to better understand the changes in customer behavior and their buying patterns over time so that they can develop acquisition, retention, and cross-selling strategies.
To conduct this analysis, you'll first need to map out what you want to measure, track customer purchases, implement a metrics system and set up key performance indicators. The aim is to help you recognize what you've done wrong as well as what you're doing well. Knowing where and why consumers churn can assist you in determining where and why your marketing and retention plans are ineffective.
The first step for any organization is figuring out what they want to measure. The goal of this exercise is to identify your key retention metrics. You can use ad-hoc analytics to see which ads are performing the best, where customers have a poor experience and what kind of customer interactions are causing the most repeat purchases.
Here are some questions that could help you map out what to measure :
- How many customers come to my store?
- What percentage of new visitors turn into paying customers?
- After a certain period, how many customers buy again?
- What percentage of purchases are recurring purchases?
- What percentage of revenue is recurring purchases?
For example, if the goal of your analysis is to figure out your repeat customer rate, you need to know how many unique customers buy something the first time they come to your store. You also need to know how many unique customers buy something when they come back again within a certain time frame, which could range from a season, a month to a year depending on your business type.
Knowing how many customers come to your store is important, but it's not enough. You need to know what they buy and do in your store as well. The next step is to track your customers' shopping behaviors. You can do this using Google Analytics or another analytics tool that tracks customer actions such as add-to-carts, transactions, and registrations. You can also track this information using your CRM or customer relationship management system to get a better idea of customer behavior.
“Referrals among repeat customers are 107% greater than non-customers”
After you have figured out what to measure and how to track customer purchases, you'll need to be able to extract, aggregate, and visualize your data. This is where you'll track customer behavior, define your key criteria and set up KPIs.
As part of your metrics system, you'll need to set up key performance indicators. KPIs are a way for you to track the progress of your business. The KPIs you choose should be relevant to what you're trying to achieve and help track how well or badly the situation is going. For example, if you want to increase customer retention by 20%, you should be able to track this change so that you know when you've reached your goal. You can get some inspiration from other industries, like SaaS, where companies innovate on their approach with a product-led growth strategy.
Conducting customer retention analysis requires a knowledge of certain metrics and key performance indicators. Analytic techniques include retention cohorts, churning date forecasting, and a/b testing to determine the impact on specific customers or groups of customers. To begin your analysis, you need to have collected the right data from your customers, using analytics and CRM software and customer feedback loops.
— Repeat customer rate :
This is the number of customers who return to shop with you within a certain amount of time.
To calculate the repeat customer rate, you need the number of customers that purchased more than once and divide this number by the total number of unique customers.
The equation will look like this:
# of Customers That Purchased More Than Once / # Unique Customers
— Repeat purchase rate or Purchase frequency :
This is the percentage of customers who make repeat purchases. This is similar to repeat customer rate except that it measures the percentage of customers who make repeat purchases rather than just measuring how often they come back.
To calculate the Repeat purchase rate, you need to divide the total number of customers who have purchased more than once by the total number of customers.
# of Customers Who Made a Repeat Purchase within the time frame / Total Number of Customers
— Purchase frequency :
The average number of purchases made by a customer within a certain amount of time. This is a crucial statistic to grasp since it represents how many times your clients buy from you. This KPI is generally measured in days, weeks, or months depending on your business type.
To calculate purchase frequency, you need to divide your total number of orders by the number of unique customers for the same time frame.
The equation will look like this:
= Number of Purchases with a period (days, weeks, or months) / the number of unique customers for the same time frame.
— Customer lifetime value (CLTV) :
This is the total revenue expected to be generated by a customer during their lifetime. Your company should aim for a CLV that increases or remains steady, as this demonstrates that you're retaining your customers and their purchases are growing as a result. If your CLV is diminishing, it means that your customer base is shrinking and you need to look at why.
The simplest formula to calculate customer lifetime value is the following:
CLV = Average Order Value x Purchase Frequency x Retention Period
— Average revenue per user (ARPU) :
This is the average revenue that your company gets from a customer. If your ARPU is decreasing, it could mean that your customers are buying less from you and it could be a sign that the customer's relationship with your brand is deteriorating.
To calculate the average revenue per user, you need to divide your total number of orders by the number of unique customers in a certain time.
= Total Revenue Generated During Period / Number of Users During Same Period
— Average order value (AOV) :
The average amount of money that a customer spends when they purchase from your store. If the AOV of your customers is increasing, it means that you are acquiring high-value customers who will bring in a lot of revenue. If the AOV is decreasing, it could be an indication that your customers are spending less money than usual and your business is losing revenue.
To calculate AOV, you will need to divide total revenue by the number of orders.
= Total Amount of revenue made / Number of orders
— Churn rate :
This is the number of customers who have left your business divided by the total number of active customers. The churn rate represents an important KPI since it shows the percentage of customers who have stopped doing business with you. Keep in mind that churn rate can be influenced by several factors and you need to take all these into account before drawing any conclusions. Some common signals include :
It's always better to identify early signals of customer churn so that you can take pre-emptive actions.
To calculate the churn rate, then divide your number of users who have stopped doing business with you by the total of customers at the start of the period.
Churn rate = (# of customers who have left / total customers) * 100
— Churn Cohort Analysis :
This analysis is used to determine the number of customers who have left your business or stopped doing business with you, during a certain time frame. It's important to note that the drop-off rate is different for each cohort. For example, if you have 100 customers at the beginning of October, and 25 of them leave by the end, your drop-off rate will be 25%. Comparing cohorts and periods could allow you to bring this metric into context.
For example, you could pinpoint if certain customers acquired from a Google ads campaign during January stayed with your business until the end of February or if this campaign attracted customers with a higher churn rate. Alternatively, if your customer support is reporting a higher return rate in the same period, this might be an indication of why your churn rate is increasing.
To perform this analysis, you need to divide the number of customers who have left by the total number of active users at the beginning of that period.
= (# of churned / # of active users during the beginning of that period) * 100
— Net promoter score (NPS) :
This metric is used to measure customer satisfaction and loyalty, by asking your customers how likely they are to recommend your business. This metric is important for evaluating customer experience since it allows you to determine if your customers are satisfied with the interactions they have had on your site.
On a scale of 0 to 100, an NPS of -100 would mean that all your customers are extremely dissatisfied with your business and they wouldn't recommend it to their friends.
To calculate average NPS, you will need to divide the number of promoters (customers who score your business with a rating of nine or ten) by the number of detractors (customers who score your business with a rating between six and eight).
= Number of Promoters / Number of Detractors
— Customer Satisfaction Score (CSAT): This metric is used to measure how satisfied your customers are using a combination of technical tools, such as customer support surveys and feedback forms. This metric is important for ensuring customer satisfaction since it enables you to understand which customers are more satisfied and how they feel about your offerings.
The CSAT score is calculated using the survey data. These scores are most often presented in a percentage - ranging from 0% to 100%.
A simple way to calculate customer satisfaction score is to divide the number of satisfied customers by your total number of received responses and multiply it by 100
= (Number of Satisfied customers / total number of received responses) x 100
Customer retention analytics is one of the most important ways businesses can measure and achieve sustainability. The key to optimizing your customer experience from start to finish begins with understanding how customers think and what drives them away. With these insights at hand, you can create marketing campaigns or product offerings that keep customers coming back for more--even if they've churned before. By proactively working to keep customers engaged, you can prevent churn and build customer loyalty.
Customer retention analytics helps businesses understand their audience behavior throughout the customer journey. Customers won't always be engaged, but when they are - you want to make sure that the experience is a great one.
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Marketing Manager at Loyaly & eCommerce growth specialist. I believe that retention is the new acquisition, and online customer loyalty programs make it possible.
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