If you own an online store with a large customer database, and you’re actively collecting customer data, you’re probably in one of these situations:
- Your conversion rate is good, but you’re losing customers after the first order.
- You have a lot of customer data that could tell you why this is happening, but you don’t know what insights to look for.
- You can navigate your customer base with ease, but you don’t have enough time or resources for compiling data and analyzing reports.
These three challenges can be tackled by looking at your customer data in a layered manner.
- The first layer implies defining the metrics that can give you actionable insights. This saves time and gives you direction.
- The second layer involves finding patterns in your customer base by segmenting it. This keeps you focused on what moves the needle.
- And the third layer is the optimization of your funnels based on these findings.
We’ll take them one by one and show you how to go through these steps in a structured manner by using our customer retention framework and our customer value optimization tool, REVEAL.
The 5 key metrics to look for in your customer data
Since you’re starting from the big picture, you first need to find the macro metrics that matter for your retention rate. After that, you will dive deeper into each of them and analyze micro metrics to find patterns.
Let’s start with the macro metrics:
- Customer Acquisition Cost
- Customer Lifetime Value
- Average Retention Rate
- NPS Score
- Product Return Rate
Customer Acquisition Cost (CAC)
The CAC can be calculated by dividing the cost you’ve spent on acquiring new customers in a given period of time by the number of customers acquired in that interval.
CAC = Cost spent on new customers/ Acquired customers
This metric shows you IF the acquisition costs are worth the investment.
While there’s no standard CAC that’s considered “good”, you can benchmark your acquisition costs by comparing them to the LTV. The ideal LTV/CAC ratio is 3:1.
What insights can CAC offer you?
CAC is an important piece in customer segmentation, because it shows you the profitability of your clients.
Gross margin = Revenue – CAC
If you focus your marketing efforts on customer retention, CAC should improve over time.
Although you cannot calculate CAC in REVEAL, you an add it in your account settings, so that the LTV and profit are accurate.
Customer Lifetime Value (CLV)
One of the key growth metrics for e-commerce businesses is the Customer Lifetime Value. The formula for CLV is:
CLV = Customer Value x Average Customer Lifespan
To find the Customer Value you need to calculate the Average Purchase Value (Total Revenue/ Number of Orders) and multiply it by the Average Purchase Frequency Rate (Number of purchases/ Number of unique customers).
Customer Value = Average Purchase Value x Average Purchase Frequency Rate
To calculate the Average Customer Lifespan you need to know the Average Number of Years a customer continues to buy from you.
REVEAL automates these calculations for you, so in your dashboard, you will see both the CLV and the customer lifetime.
How to interpret CLV in the context of customer retention
This metric tells you which are your most valuable clients and how much time they stay with your business in average. These are the ones you need to focus on and your assortment and marketing messages should be tailored for this segment.
Average Retention Rate
The Average Retention Rate calculates the percentage of customers with more than 1 order out of the total number of customers. An average retention rate of 20% means that only 20% of all customers came back and placed the 2nd order. In other words, 80% bought something and never returned.
For most industries, average eight-week retention rate is below 20%, while for products in the media or finance industry, an eight-week retention rate over 25% is considered elite.
SaaS and e-commerce companies have a RR under 35%, anything above this value being considered great.
How Average Retention Rate helps you to find out where are you losing customers
This metric in itself doesn’t do much, so in this case you need to dig deeper and find out why customers are not returning.
You can understand what stopped them from placing repeat orders by running surveys or organizing interviews with your lost customers.
While REVEAL helps you find your retention rate, Survey comes in handy for understanding the why.
Net Promoter Score (NPS)
The NPS is a questionnaire that can be answered on an 11-point rating scale, ranging from 0 (not at all likely) to 10 (extremely likely). Depending on the score, people are segmented into:
- Promoters = respondents giving a 9 or 10 score
- Passives = respondents giving a 7 or 8 score
- Detractors = respondents giving a 0 to 6 score
The Net Promoter Score is calculated as the difference between the percentage of Promoters and Detractors.
The NPS is not expressed as a percentage but as an absolute number lying between -100 and +100.
+50 is considered an average score, while a value between +70 and 100 is considered an excellent score.
NPS tells you why you’re losing customers
Reveal offers two options for calculating the NPS: pre-delivery and post-delivery NPS. This means that you take the pulse of your customers before receiving the purchase and after, so you can easily see in which step you have to act.
The NPS score is fed automatically and constantly into your reports, and you can extract personalized trend reports with custom attributes, such as:
The seller who facilitated the order
- Brands bought
- Categories bought
- Products bought
By knowing who your detractors are (the ones who can drag your business down by leaving negative reviews), you can easily contact them and find out what was the problem.
In the same way, by knowing who are your promoters, it is easier to make them brand ambassadors and reward them.
You can install Reveal in your Shopify store here, to see your shop’s NPS.
Product Return Rate
The last of the five key metrics for e-commerce growth is the Product Return Rate, which tells you how many products are sent back to the store. The formula for the product return rate is the following:
Product Return Rate = (Products returns in giver period / Products sold in a given period) x 100
According to a study conduced by eMarketer in 2018, the Average Product Return Rate in eCommerce was 20% and 50% for “expensive” products.
What the return rate says about your retention strategy
An overview of the Product Return Rate can reveal different aspects, such as:
- your toxic products or brands,
- the most profitable categories that fuel your business,
- which customer segments are more likely to return products,
- which delivery methods are linked to the highest return rates.
In your REVEAL dashboard, you can analyze the Product Return Rate by looking at different attributes:
- Customer type (new vs. returning)
- Brands, categories and products
- RFM groups
Can I see these metrics in Analytics?
GA shows you a lot of data, so if you’re wondering if you can see such insights in Analytics, the short answer is no. That’s exactly why we’ve built REVEAL!
Google Analytics only tells you what is going on with your visitors, not your customers. It allows you to measure your advertising ROI and acquisition channels, but by itself it does very little in terms of revealing insights about customers.
However, there are 3 reports that vaguely tell you something about your user – not customer – retention:
- Cohort Analysis, which shows you groups of users that share a common characteristic. The downside of this report is that you can see data only for the last 30 days.
- Lifetime Value report, which measures the LTV of users acquired through different channels.
- Enhanced eCommerce, which provides product impression, promotion, and sales data.
As said, these metrics apply to all users, not only your paying customers.
Of course, there are workarounds: you can export you GA reports, import them in a BI tool or a data warehouse, and mix them with your CRM data. Without in-house data analysts, the full process is very time consuming and repetitive.
Our framework for finding out why an online shop is losing customers
We use this framework for all our clients who request a full retention audit, so it’s a tested and proven method.
Although we mostly work with webshops with more than 10.000 orders, you can apply it for smaller e-commerce businesses as well. The principles and steps apply to both small and large stores!
Our typical client profile is an e-commerce owner who cannot point out what exactly isn’t working. He has tons of customer data and reports, but cannot make sense of all the numbers, and cannot figure out which metrics are actually relevant.
The common pain point of our large e-commerce customers is the lack of actionable insights. They see the big picture and think they understand their customer base, but in reality they only see the tip of the iceberg.
The result is poorly interpreted data, or numbers interpreted in the wrong context.
Our framework for analyzing customer data in order to extract actionable insights has two pillars:
- Ongoing monitoring of key metrics, such as NPS pre- and post-delivery, Retention Rate, CLV, group fluctuations, Customer Acquisition Cost and Gross Margin.
- Ongoing optimization: Continuous cohort A/B testing, loyalty programs, customer acquisition, e-mail orchestration, website experimentation and personalization.
If you’d like to see a product tour and discuss REVEAL’s use cases more in-depth, book a Demo below.
Step 1. Customer segmentation using RFM model
The first step is to segment your customers in Reveal using RFM groups, and to identify your most profitable clients.
The RFM scoring is based on 3 attributes:
- Recency – how recently a customer placed an order in a given period of time
- Frequency – how often a customer placed an order in a given period of time
- Monetary Value – how much a customer spent in a given period of time
Based on the ADBT (average days between transactions) and the customer distribution for Recency, Frequency & Monetary values, we adjust & decide over the number, score levels & components of the RFM Groups.
The RFM scoring system in our software is done automatically, but it also allows you to customize the groups according to your business preferences.
Of course, you can do RFM scoring also manually by exporting your database on a given period of time and importing it in a spreadsheet for analysis, but that implies huge amounts of work.
Step 2. Segment and cohort analysis
Once the groups are defined, you can see the share of customers, revenue and margin per segment.
You can see different characteristics of your RFM groups. For example, what is the Product Return Rate for each RFM group, what is the NPS for each RFM group, customer count by RFM group and many other attributes.
The best part of RFM segmentation is that you can see exactly who those customers are by diving into the group data.
Install Reveal in your Shopify store to see your shop’s customer segments!
Step 3. Qualitative research for each RFM group
At this step, you have a better understanding of how your customers are distributed. However, it isn’t clear why some of them bring so much value while some of them did not buy from you after the first order and so on.
This is where Qualitative Research comes into play. This process reveals the reasons to buy and the barriers in relation to products and services. We translate the findings into insights that help us define the Ideal Customer Profile (ICP).
For each RFM group there is a set of questions we address: starting with demographics, reasons to buy, NPS, barriers, reasons for not returning and other custom questions based on the business case and goals.
We send the surveys via e-mail, however for smaller RFM groups we opt for phone interviews. The NPS scores are sent separate from all the other questions, for on-going monitoring.
Step 4. Extracting anomalies from customer data
While we gather insights from the previous step, we look at other parts of the story to spot gaps and anomalies.
Anomalies give us hints of what makes one group different from others, and allow us to discover toxic brands or categories, cities with loyal customers and so on.
Step 5. Building the Ideal Customer Profile
By mixing the qualitative and quantitative data, we define your Ideal Customer Profile (ICP):
- who they are – location, gender, age,
- buying habits – when they buy,
- what they buy – product assortment anomalies,
- why are they coming back – reasons & barriers,
- what they expect further – qualitative research.
A clearly defined ICP means:
- Improved ad targeting & lower CoCA, achieved by aligning the marketing efforts & budgets to the specifics of the segment.
- Date-driven assortment & merchandising decisions, based on the buying patterns and preferences of each RFM group.
- Better customer service – by providing priority support, faster response times and tailor-made return programs to the customers more likely to stick around.
- Personalized messages in your distribution channels, for acquiring ideal clients.
Step 6. Customer journey optimization
Armed with so many insights, we can finally look at your business and marketing goals, and analyze your Customer Journey by mapping the touchpoints.
We apply the customer findings across the entire journey to create an infinite loop that drives conversions and supports repeat orders, almost on autopilot.
In most cases we organize a workshop with the client’s team for going through the journeys and explaining our approach.
Step 7. Developing the retention strategy
This entire process has one main goal: to help you make sense of your customer data in order to retain more customers.
Developing an effective retention strategy is not a one-time shot, but an iterative process during which we experiment with tactics that work together to deliver:
- Better customer service for the best customers
- Higher acquisition of ICPs (custom & lookalike audiences)
- Product assortment adjustments to better serve your target segments
- Better nurturing campaigns – e-mail, ads, SMS, website personalization
It may look time-consuming or resource-intensive, but it’s the same methodology that Valentin, our CEO, used for growing his previous e-commerce businesses and for bringing Omniconvert where we are today!
Back then he was using Excel and GA, but today we’re able to do all these almost automatically with REVEAL, the first Customer Value Optimization software on the market.
Our tool is available for FREE on Shopify and on request for other platforms. It integrates with Klavyio, Sendgrid and Google Analytics.