Home Ecommerce analytics dashboard playbook Customer insights
Without a constant flow of contented and happy consumers, no firm can succeed. Better customer service results from understanding their demands. Customer insights dashboards track the current pattern of customer acquisition, retention, and churn rates as well as other customer segmentation options. It might be used to develop strategies that would increase revenue and profits by lowering acquisition costs, boosting retention rates, and offering more efficient and effective customer service.
The dashboard answers the below questions:
Once a business begins investing more in sales and marketing, management should ensure CAC does not rise to an unacceptable level. They should also pay close attention to customer attrition to make sure that new batches of customers are a good fit for the product/service and do not have a higher attrition rate than existing customers. If CAC, CLV, or customer attrition gets out of line, it’s time to stop and reassess the marketing channels and strategy. This report identifies opportunities to achieve the below goals:
This report tracks the KPIs related to sales perspective of business including:
Goals
Questions to ask
Dashboard views
Track cash flow
What are business revenues and expenses?
Gross Revenue, Discounts, Net Sales, COGS, Gross Margin,…
Track total accounts, new accounts
How have the number of new accounts, total accounts varied over time (compare TY vs LY)?
Total Accounts, New Customers
Track marketing effort
What is the cost of attracting new customers in comparison their contribution to business?
Acquisition Spend, CAC, LTV, Customer Contribution
The quick indicator view boxes at the top of the report make it easy for businesses to track the numbers they need. It can even be compared with the previous year to see if the situation is better or worse.
The waterfall chart shows a running total as values are added or subtracted. It’s useful for understanding how an initial value is affected by a series of positive and negative values. The columns are color coded so you can quickly tell positive from negative numbers. In this report, after deducting other expenses and losses from Gross Revenue, we obtain Customer Lifetime Value. Looking at revenue and expenses like this allows businesses to realize where they are doing well, whether they are over budgeted or not so they can adjust to fit their budget.
Finally, the ‘CAC Trend’ chart lets us compare the overall number of new accounts with the expense of acquiring new customers. Previously, it spent $40 CAC but received relatively few new accounts; but, recently, with the same $40, it received ten times as many new customers as in the last period. Businesses must reflect on why this is the case. Is it as a result of the occasionally carried out sales and promotional activities. The budget should then be divided correctly for each subsequent period.
This report allows you to track how your ecommerce business is performing and demonstrate the value of your online marketing efforts for the business.
Companies use cohort analysis to analyze customer behavior across the life cycle of each customer. In the absence of cohort analysis, businesses may experience difficulties in understanding the life cycle that each customer goes through over a given timeframe. Businesses use cohort analysis to understand the trends and patterns of customers over time and to tailor their offers of products and services to the identified cohorts.
Once the business sees trends in how different cohorts use its products, it can identify problems in its marketing techniques and determine when and how to best communicate with different groups or types of customers. The business also uses the broken-down data to design incentives that will motivate customers to keep using their products when they are likely to stop purchasing the products
The business could achieve the below goals based on Customer Trend report:
Goals
Questions to ask
Dashboard views
Track revenue & average order value of new/repeating orders
How much did customers spend each time they place an order and all orders? How much did customers spend each time they place an order and all orders?
Gross Revenue, Avg Order Value
Track percentage of customers are still active after a period of time
How many people who've purchased your products once before will decide to buy from you again?
Retention Rate
Track customer contribution over time
What is the real contribution made by customers after deducting expenses?
LTV/CAC ratio, Average Contribution
Track customer repurchase intention over order
What is the customer repurchase rate? On average, how many days will they come back?
Average Days Between Purchases, Repurchase Rate
Avg order value:
The average dollar amount customers spend each time an order is placed on your ecommerce website
Retention rate:
A metric that calculates the percentage of users who continue using your product or service over a given time period.
LTV/CAC ratio (Customer Lifetime Value to Customer Acquisition Cost):
Measures the relationship between the lifetime value of a customer and the cost of acquiring that customer. LTV/CAC is a signal of profitability. This metric tells you if the lifetime value of a customer is higher or lower than the marketing and sales costs to acquire that customer.
Average contribution:
The average customer contribution per year/month.
Average days between purchases:
The average days between customers’ orders
Repurchase rate:
Also known as repeat purchase rate is the percentage of customers who have purchased more than once in a time period.
Businesses can track Gross Revenue and Customer Contribution over time to see which periods sell the most. Looking at the report, we can see that in the second half of the year, the revenue increased many times compared to the first half of the year. This can be explained by 2 reasons: customers often have a habit of not spending much at the beginning of the year and since the beginning of July, the marketing team has run promotional programs related to discounts, free socks for pack orders,… We can see that the CAC in this period is also higher than in the first 6 months of the year. Although total revenue increased, the Average Order Value did not change significantly.
Business can also recognize patterns and changes in consumer behavior with the aid of Cohort Analysis. It provides information on customer behavior, including when they leave, when they buy more, and the existing marketing methods are effective or not.
Cohort year here represents the year that the customer created a purchase account, 2020 is the first year of sales, after 1 year, 2 years, how many people will still purchase? What will the LTV/CAC and Average Contribution of these customers be?
Looking at the matrix vertically and horizontally:
Rows 2020 → 2022: New customers who made their first purchase that year, then purchased in subsequent years
Columns Y0 → Y2: Show the maturity of the customer over time (line 2020: Y0 is 2020, Y1 is 2021, Y2 is 2022… – and similar to Cohort Month)
Cohort Avg days between purchases: Of the customers who joined that year, on average, how many days it takes for the customer to make a decision to buy the next order.
Cohort Repurchase Rate: Of the customers who joined that year, what percentage of people come back to buy the next order.
The way your customers interact with your brand and products changes all the time—so you need to spot, predict, and react to changes fast to create customer delight. Customer trend analytics lets you better understand your buyers to improve their product experience and boost customer satisfaction and revenue alike.
Understanding who your customers are and what they want is a fundamental part of any successful business. Yet as a business grows, so does the customer base, and it can become increasingly challenging to create a one-size-fits-all customer profile. This is where the concept of customer segmentation comes in.
RFM is a marketing technique that allows you to segment your audience according to their relevance to your business. This is done based on three criteria: Recency, Frequency and Monetary Value. RFM analysis is one of the most efficient methods to identify high-value targets and increase conversion rates. In this post we’ll cover how to use and calculate this advanced metric in order to improve your marketing and sales results. RFM stands for:
It is a very useful method indeed! It provides answers to questions as…
The answers to these questions are the starting point for new strategies focused on reaping the following benefits:
To put it simply, RFM analysis takes three key customer variables into account to create a ranking that groups customers according to their business value. This means that if a client recently completed a purchase (Recency), they will get more points. If they made multiple purchases (Frequency), they will be placed higher. And if they spent a bigger amount of money on their orders (Monetary value), they will also have a higher score. When we combine these three factors, we can create a RFM ranking.
Customer
Recency
R-Score
Frequency
F-Score
Monetary
M-Score
R-F-M
Segment
C1
62
3
3
4
449
5
3-4-5
Loyal
C2
33
4
3
4
466
5
4-4-5
Champions
C3
119
1
3
4
310
4
1-4-4
Can’t Loose Them
C4
42
4
3
4
266
4
4-4-4
Loyal
C5
40
4
2
3
148
3
4-3-3
Potential Loyalists
C6
42
4
11
5
965
5
4-5-5
Champions
C7
28
4
1
1
75
2
4-1-2
New
Customers
C8
80
2
7
5
660
5
2-5-5
At Risk
C9
30
4
11
5
965
5
4-5-5
Champions
C10
116
1
7
5
812
5
1-5-5
Can’t Loose Them
In order to discover distinct customer categories based on their real behavior and to implement more successful tactics, RFM segmentation is particularly helpful for eCommerce platforms. For each audience group, the chart below displays segment terminology, behavioral specifics, and viable marketing approaches.
Segment
Questions to ask
Dashboard views
Champions
Bought recently, order often, and spend the most.
Reward them. Can be early adopters of new products. Will promote your brand. Most likely to send referrals.
Loyal
Orders regularly. Responsive to promotions.
Offer membership/loyalty program. Keep them engaged. Offer personalized recommendations.
Potential Loyalists
Recent customers, and spent a good amount.
Offer membership/loyalty program. Keep them engaged. Offer personalized recommendations.
New Customers
Bought most recently.
Provide onboarding support, give them early access, start building relationships.
Promising
Potential loyalist a few months ago. Spends frequently and a good amount. But the last purchase was several weeks ago.
Offer coupons. Bring them back to the platform and keep them engaged. Offer personalized recommendations.
About To Sleep
Below average recency, frequency and monetary values. Will lose them if not reactivated.
Share valuable resources, recommend popular products/renewals at discount, reconnect with them.
At Risk
Made biggest purchases, and often. But haven’t returned for a long time.
Win them back via renewals or newer products, don’t lose them to competition, talk to them.
Hibernating
Last purchase was long back, low spenders and low number of orders.
Offer other relevant products and special discounts. Recreate brand value.
Lost
Lowest recency, frequency and monetary scores.
Revive interest with reach out campaign, ignore otherwise.
RFM will give businesses the ability to comprehend their customers based on how recently they have made a purchase, how frequently they make purchases, and how much they spent per basket. This will assist decision-makers in classifying customers into various groups and making appropriate marketing efforts. It will increase customer happiness while simultaneously maximizing income and cutting expenditures.
Companies that rely on recurring revenue face particular difficulties in analyzing customer turnover and retaining customers. This complexity may be caused by a variety of factors, such as transaction records being added only for purchases and not for cancellations, historical measurement capturing users who have not yet had the opportunity to repurchase, or different repurchase cadences necessitating independent measurement.
This can mean a completely different approach from their brick and mortar competitors in terms of data modeling and formulation. This report for customer churn analysis offers analysis of customer growth and churn based on behavior by answering the below questions:
Dashboard view: Churn Trend
Comparing a company’s customer retention performance to that of other businesses in the same industry and its competitors’ customer turnover rates can be helpful. It is crucial for a business to have a low churn rate since a high churn rate indicates high attrition, necessitating the expenditure of money to acquire new customers in order to fill the void left by departing customers. Because the churn rate affects a company’s profitability and growth, it is crucial to comprehend it. Looking at the chart, we can compare the number of customers churned with the number of customers acquired as well as the churn rate. In which the churn rate is calculated as follows:
Dashboard view: Customer Waterfall
The waterfall chart shows a running total as values are added or subtracted. It’s useful for understanding how an initial value is affected by a series of positive and negative values. The columns are color coded so you can quickly tell positive from negative numbers. In this case we have the calculation: Initial Customers + Acquired Customers – Churned Customers + Returned Customers = Active Customers. This change can be easily tracked over time with the Time Frame slicer.
Dashboard view: Customers At Risk
While calculating churned customers, the product line that the company sells must be taken into consideration. Since this is underclothing, the life cycle is quite short. To identify whether a customer is about to leave, we shall compute the ratio:
Using Customer Churn Analysis, business could minimize churn and identify potential causes of customer loss.
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