Seery is a unique and very useful app for all merchants, any size or type of business. Seery can help merchants to monitor the most important KPIs. KPIs can be monitored on a weekly basis, for example, monitor sales forecast, or on a daily basis, what to expect today, tomorrow, in the next 30 days, 6 months from now? Besides that, Seery can help merchants to understand customer segments.   Using Seery, merchants will know who are their best customers, or customers likely to churn or to know customer lifetime value. Another possibility that merchants can find out is how many customers return to purchase a second time and who they are. Seery can provide answers to many questions about customers’ lifetime value and many more.  Machine learning-based Customer Segmentation provides an effective analysis for decision-makers to target their customers and develop appropriate marketing strategies according to their previous behavior.   Seery can tell you all about your past, present, and future sales figures and KPI values. Not only that, Seery will automatically segment your customers and calculate their value to your business!  The prepared view and metrics used in reports are based on the scientific work we read. Based on all information we collected, Seery reports are optimal for small & medium businesses.

Seery is an app that can help merchants to monitor the most important KPIs. KPIs can be monitored on a weekly basis, for example, monitor sales forecast, or on a daily basis, what to expect today, tomorrow, in the next 30 days, 6 months from now? Besides that, Seery can help merchants to understand customer segments.

 

Using Seery, merchants will know who are their best customers, or customers likely to churn or to know customer lifetime value. Another possibility that merchants can find out is how many customers return to purchase a second time and who they are. Seery can provide answers to many questions about the customer’s lifetime value and many more.

„If You Can’t Monitor It, You Can’t Manage It“!

 

 

Seery for Shopify, you can get on the Shopify app market.

To install the app, log into your Shopify account and look for the Seery app logo on the app market, and add it like any other app on the market and follow the instructions.

 

 

 

Usual parts of various Seery reports

Most of the reports in the Seery app have been built from several parts that are repeating.

The Report title and description box

Each report has a name, a short explanation (basic information about the report), and the label (to which group the report belongs). Read more icons will forward you to the Seery FAQ page, where you can find much more helpful information about each report available in Seery.

 

Period selection

The quick selection represents the period in the report; the default value in most of Seery’s reports is Today. If you want to change the time period, open the quick selection and choose the period you want or select a special period by defining the start and end date of the period on the calendar.

 

Attributes section

In this section of the report, you will find the attributes (parameters) for that specific report. Depending on the report, they may vary. The most usual are: 

• Customer country
• Customer city
• Monetary Segment Name
• Recency Segment Name
• Frequency Segment Name
• RFM Segment Name
• Vendor
• Product etc. 

All the values from the attributes are selected as the default values. In case you do not want to take into account all the values of certain attributes, select the desired values and click on the “Refresh” button; the data in the graph and the detailed table will change accordingly. 

 

Chart section

For most of the reports, the chart section is the heart of the app. Charts are a quick and visual way you get essential information. Charts vary from report to report, but also from period to period.

 

Usually, each report has several metrics that you can choose, such as Gross Sales, Number of Orders, or Repeat Purchase Rate. One of them is the default metric: each graph is based on that default value. At the same time, you can select and compare multiple metrics on a graph.

For additional use, each graph can be printed or exported into several formats. To activate that option, just click on the icon above the chart, at the right corner.

Details section

 

Scrolling down through the report, at the bottom of the report, you will see a detailed table. Data in the table vary from report to report, always presenting values according to the selected attributes and metrics. The table can be exported as an excel file by clicking on the “export icon” above the table, as marked on the picture.

Seery  provides four  Plans that you could choose from

• BASIC
• PROFESSIONAL
• ULTIMATE
• ENTERPRISE

Since each plan has access to all the reports, you choose your plan based on the number of orders per month. Regardless of the chosen plan, after installation, the first 14-day, you will be in a free trial.

Here is the list of all plans with available features and prices:

 

During installation, you can choose the plan that most relates to your need (Number of orders per month). As all reports are available for all plans, you can choose any of the available tiers.
In case your average order number will be different from your plan, we will inform you about it – so, you don’t need to bother with it, we will do it for you.

During installation or at the end of the 14-days free trial, you can choose the plan that most relates to your need (Number of orders per month).

If you want to change your chosen plan, you can do it easily by going to the Profile page.

How to: Uninstall Seery App

As we are 3rd party developers for Shopify and we can’t either install or uninstall Seery App for you. You will need to do it yourself by going to the Shopify dashboard.

In Shopify Dashboard go to Apps/ Installed Apps. In the list of your apps, find the Seery app.  In the right corner, you will see the “Delete” icon.

Click on it. In the new window that will appear, choose why you are uninstalling the app, and confirm deleting. That is all. All customer data that was used in the app will be deleted from the app database.

In the Seery app, we use many terms and abbreviations that are new to some of you. To help you understand different reports and analyses, we prepared a short glossary with definitions and explanations that you could find useful while using Seery or other apps too.

Besides classical terms from financial sales reports, here you will find explanations of terms like RFM or AOV. And what is that? Let’s go and find out more…

AOV – Average Order Value
The average order value (AOV) tracks the average amount spent each time a customer places an order.
The metric is defined over a specific period and is calculated by dividing the total revenue by the number of orders over that time interval.

ARPU –  Average Revenue per User
ARPU is the equivalent of total revenue divided by average users during a period.

Brand
The term brand refers to a business and marketing concept that helps people identify a particular company, product, or individual.

Brands are intangible, which means you can’t actually touch or see them. As such, they help shape people’s perceptions of companies, their products, or individuals.

CAC – Customer Acquisition Cost
Customer Acquisition Cost is the cost of acquiring a new customer to purchase a product or service. Customer acquisition costs are often related to customer lifetime value (CLV or LTV).

With CAC, any company can calculate how much they’re spending on acquiring each customer. It shows the money spent on marketing, salaries, and other things to acquire a customer. Keep an eye on CAC so it doesn’t get out of control.

CLV- Customer Lifetime Value
The CLV represents the total amount of money a customer is expected to spend in your business, or on your products, during their lifetime. This is an important figure to know because it helps you make decisions about how much money to invest in acquiring new customers and retaining existing ones.

Cohort
Cohort usually refers to a group of customers or users segmented by acquisition date (i.e. the first time a user is buying a product, or service, or visits a website.

Cohort Analysis
Cohort analysis is a study that focuses on the activities of a particular cohort over a certain period. In other words, Cohort analysis allows us to identify relationships between the characteristics of a group and that group’s behavior.

Correlation
Correlation is a measure explaining how well our model fits your data. Correlation can be from 0-1. If the correlation is more than 0.5, it means it is a strong correlation between your data and the forecast we are showing. As correlation is closer to 1, that much correlation is stronger and you can rely on and trust in forecasted data.

Customers
The total quantity of customers.
Customers are the individuals and businesses that purchase goods and services from another business. To understand how to better meet the needs of its customers, some businesses closely monitor their customer relationships to identify ways to improve service and products.

Although consumers can be customers, consumers are defined as those who consume or use market goods and services.

Discount
Sum of discounts, product discount, and the product’s proportional share of a cart-wide discount.

Gross Sales
Equates to product selling price x ordered quantity generated by your customers within the selected period; does not include discounts, returns, taxes, or shipping.

New Customers
Customers who placed their first order within the defined period (only the first order counts, eg. If there are two orders on the same day, only for the first order this will be a new customer).

Net Quantity
Net quantity relates to the number of sold items – the number of returned items.

Net Sales
Equates to gross sales – discounts – returns discounts generated by your customers within the selected period; does not include shipping charges or taxes.

Ordered Quantity
The total quantity of all items ordered by customers within the selected period (net quantity + returned quantity)

Orders
The total number of orders that were generated by customers during a selected period.

Return Amount
The value of goods that the customer returned to the merchant within the selected period

Returning Customers
A returning customer is simply someone who has bought your product or service once before and has returned to make another purchase (eg. the Second order of a new customer in a day will be placed as the order of an existing customer).

RFM Analysis
The RFM score actually consists of three separate scores:

• Recency – how long it’s been since a customer bought something from you, or visited your website
• Frequency – how often a customer buys from you, or how often he visits your website
• Monetary – the average spend of a customer per visit, or the overall transaction value in a given period of time

RFM analysis scores every customer on each of these three factors, on a scale of 0 (worst) to 4 (best). After that, we assign an RFM score to each customer, by concatenating his numbers for Recency, Frequency, and Monetary value (for example, RFM = 444 are the best customers in all regards, RFM = 000 are the worst).

RFM segment name	Description	RFM SCORE
Lost customer	Made the last purchase a long time ago and didn’t engage at all in the last 4 weeks.	000, 001, 010, 020, 030, 040
Hibernating customer	Made their last purchase a long time ago but in the last 4 weeks, neither visited the site nor opened an email.	221, 211, 122, 121, 112, 111, 021, 012, 011, 101, 100
Can-not-lose customer	Made the largest orders, and often, but haven’t purchased anything for a long time.	044, 043, 033, 103, 104, 004, 003, 002
At-risk customer	Similar to “Can-not-lose customer” but with smaller monetary and frequency value.	144, 143, 134, 133, 142, 141, 132, 131, 124, 123, 114, 113, 042, 041, 034, 032, 031, 024, 023, 022, 014, 013
About-to-sleep customer	About-to-sleep customers with not too long-ago purchases.	220, 210, 201, 110, 102, 120, 130, 140
Need-attention customer	Need-attention customers are those whose last purchase happened more than 4 weeks ago.	424, 423, 332, 323, 232, 223, 214, 213
Promising customer	Potential loyal customer a few months ago. Spends frequently and a good amount. But the last purchase was several weeks ago.	414, 413, 412, 411, 410, 404, 403, 402, 314, 313, 302, 303 ,304, 204, 203, 202
New customer	Customers that bought most recently. The first purchase is calculated for new customers.	401, 400, 311, 310, 301, 300, 200
Potential loyal customer	Recent customers, and spent a good amount.	442, 440, 441, 430, 431, 422, 421, 420, 341, 340, 331, 330, 320, 342, 322, 321, 312, 242, 241, 240, 231, 230, 222, 212
Loyal customer	Loyal customers are ones that order regularly from your store.	432, 333, 324, 244, 243, 234, 233, 224
Champion customer	Bought most recently, order often, and spend the most from all.	444, 443, 433, 434, 343, 344, 334

Revenue
Sum of revenue generated by all customers

SKU – Stock Keeping Unit
An SKU is a unique alphanumeric code that’s used to identify a product or variant of a product, based on details such as its model, size, or color. SKUs can vary in length and can include letters, numbers, or both. SKUs are used to distinguish product variants, track inventory, and ship the right item to customers.

Seery Dashboard is a place that you don’t want to miss!

Here the most important information about sales forecasts and customer information is found. It is a collection of all the most important parts from all available reports in Seery. This should be a single daily point for merchants! Everything from the sales forecast, different KPIs, and different metrics can be found here.

Don’t miss this dashboard – this is your new morning coffee friend!

 

 

 

 

On the Seery Welcome page, a few basic information can be found. In a carousel, you will see what type of data you will find in the app. Besides the carousel, there is a section with your report activity. Not only that you can see which report did you use last, but this can also be used for quick access to reports.

The forecast section is the heart of the Seery app.

In the forecast section, you will find details on the revenue forecast for today, tomorrow, the next 7 days, and the next 30 days. Statistics and averages report will provide you with details about orders, customers, and much more.

Here you will find reports like:

• Revenue Forecast
• Revenue Forecast Details
• Statistics and Averages

What is a Sales Forecast?

A sales forecast is a prediction of future sales revenue. Sales forecasts are based on different historical data taking into account various effects. Businesses use the sales forecast to estimate weekly, monthly, quarterly, and annual sales totals. Just like a weather forecast, your team should view your sales forecast as a plan to work from, not a firm prediction.

Machine learning algorithms are using your historical data, taking into account weekly and yearly seasonality, holiday effects, and anomalies detection. Based on that information, the report will give you a gross sales forecast for periods like today, tomorrow, the next 7, and the next 30 days.

Besides the gross sales forecast for defined periods (today, tomorrow, next 7 days, next 30 days), in this report you can find two charts and one detailed table, like:

• Gross sales Overview chart
• Next 30 Days Gross Sales chart
• Forecast for the next 90 days table

 

Gross Sales Overview

In this chart you can see actual sales data, predicted sales plus the trend. In the chart, you will see historical actuals and forecast for the next 365 days that is aggregated per month. With the help of this chart, merchants can see what they can expect in the next 12 months and they can be prepared for what is coming. If you know, there is no surprise! You can prepare yourself and even make something to change the trend.

 

The chart can be maximized for a better view in a shorter period. Mouseover on each graph will show you data on a day you wish to check.

 

Besides maximization, the chart can be exported and printed.

 

Next 30 Days Gross Sales chart

 

If you would like to get detailed insight into the next 30 days’ gross sales prediction, this is a chart for you. Here you can see for each day, what was actual sales last year, what is a prediction for this year, and what is the difference. Again, with detailed knowledge of what you can expect, you can prepare yourself and create some actions accordingly.

Using mouseover, for each day you will get information on actual sales, prediction, and difference between last year’s data and this year’s prediction.

 

Forecast for the next 90 days

In the Forecast, for the next 90 days detailed table you will get much useful information on prediction for the next 90 days.  The detailed table offers information like the actual date, predicted data, last year’s data, and the difference between last year (LY) and prediction for this year. Besides this data, in this table, you will find lower and upper prediction uncertainty. Each column can be sorted and exported to excel for future usage.

In the Forecast Seasonality report, you can find all details that are relevant for forecasting. Here you will see several blocks with information like:

• No of days -> Number of days show you how many days from your historical data we are using for calculation
• First date -> Date from which Seery started to take the data
• Last date -> Last date of data input
• Correlation -> A measure explaining how well our model fits your data. Correlation can be from 0-1. If the correlation is more than 0.5, it means it is a strong correlation between your data and the forecast we are showing. As correlation is closer to 1, that much correlation is stronger and you can rely on and trust in forecasted data.
• Anomaly dates -> Number of anomalies dates we detected in the past.
• Anomalies % -> Percent of anomaly dates in the past.

 

Besides the section where you can see some values that are important for creating a forecast, in this report, you will see several graphs as well.

Weekly and Yearly seasonality graphs, give the possibility to get in a super easy and quick way to sport trends or animalities. In this report, you can see data from the weeks or years combined into one single graph. In Weekly Seasonality, like this example shows, we can see that Friday and Saturday are days with above-average sales, while Tuesday and Wednesday are under average.

Similar information is available in the Yearly Seasonality graph. In this example, we can see that in all years, sales were lowest on January 22^nd, while March 23^rd is the highest. Since in this example, data are taken from 2132 days (almost 6 years), we can say this is pretty accurate and reliable information.

 

The next two graphs, Overall trend and Animalities last 90 days are could be very useful…

Anomalies’ last 90 days is a very cool graph. Here you can see how Seery was predicting and what was in reality achieved. For most of the days, Seery was on track with actual data. This is not a graph that will help you in preparing for the next action. On the contrary, it is the one to show you how good is Seery in her job. You can trust and you should trust in Seery.

 

 

Statistics and Averages report is where you will find much useful information because important metrics for most businesses are presented simply. It tracks one of the most important metrics for your business and monitors overall business health.

To be sure we are on the same page, here are definitions of some parameters used in this report:

• Gross Sales: Equates to product selling price x ordered quantity generated by your customers within the selected period; does not include discounts, returns, taxes, or shipping.
• Orders: The number of orders that were generated by customers during a selected period; in this report total number of orders generated by all customers from the start of data input
• Customers: the total quantity of customers
• AOV: Average order value
• ARPU: Average Revenue per User
• ANO: Average Number of Orders per Customer 

 

The gross revenue trend will show you how was your revenue from the day you start working. In the revenue trend, you can see the values for each month.

 

A very nice and useful chart is the Average Order Value. From this one you can see what was the trend for order value. In this example, we see that orders for the last couple of months are of higher value than it was in the past. At the same time, if we look at the Average Number of Orders chart, we can spot that last month’s number of orders was smaller than in the past.

 

 

Are you wondering at which day in a week your average sales are the highest or lowest? If not for the day in a week, maybe you would like to know which week of the year was your best? 

No matter what industry you’re in, any sector that deals with customers need to keep track of their sales. Using average sales across various periods can give you a greater idea of how well your sales strategies and marketing campaigns are performing, which tactics are connecting with consumers, and how successful your sales team is at converting leads.

Calculating your average sales depends on two factors: a period or frequency you want to analyze and the total sales value for that period. Average sales can be measured on a much smaller scale, such as daily or weekly, or a larger scale like monthly and even annually. 

Of course, you can calculate average sales by yourself, but why waste time when you have Seery for that! Knowing that, let’s looks at Seery’s Advanced Sales Trends report where you will get your average sales in a selected period. 

As you can see in the below picture, the Advanced Sales Trends report can be found in the Time section.

 

Again, the quick selection represents the period in this report; the default value is Today with Hour as a scale. That means once you enter the report, in the graph and detailed table, you will get data covering sales from all of your customers, products, and vendors only for today. If you want to change the time period, open the quick selection and choose the period you want or select a special period by choosing a date on the calendar. For example, if you change the period to the Last 30 days, it makes sense to see the scale for Day in a week, Date, or Week.

As in other reports, you can find the same attributes such as Product Type, Vendor, Tags, etc. All the values from the attributes are selected as the default values.
You can easily remove certain values by unselecting them and by clicking the Refresh button: the data in the graph and the detailed table will change accordingly.

 

Definitions
In case you are not familiar with our metrics, we will briefly define each:

• Gross Sales: Equates to product selling price x ordered quantity generated by your customers within the selected period; does not include discounts, returns, taxes, or shipping.
• Discount: Summ of all discounts like product discount, and the product’s proportional share of a cart-wide discount.
• Return Amount: The value of goods that the customer returned to the merchant within the selected period
• Net Sales: Equates to gross sales – discounts – returns discounts generated by your customers within the selected period; does not include shipping charges or taxes.
• Ordered Quantity:  the total quantity of all items ordered by customers within the selected period (net quantity + returned quantity)

By default, the main metric is Gross Sales, meaning, once data loads, the graph is based on that metric. You can change it to another or even select several metrics at once (sometimes it’s good to compare different metrics: you can come to some additional conclusions).  As soon you change the metric, the graph will be updated.

Additionally, you can break down the metric by the scale as well. You can analyze your period by:

• Hour
• Day in a week
• Date
• Week
• Month
• Year

As you change the scale, the graph changes as well accordingly. Here are few examples of how the same data changes based on the different scales.

 

A detailed table with all metric values ​​for the selected period and scale can be found at the bottom of the report. As you change the scale, so does the table.

Wouldn’t be great to identify groups of customers? That way you can tailor your marketing strategy to target each group with personalized offers, increase profit, improve unit economics, etc. In that case, the RFM Segments report is what you need

RFM analysis quantifies customers based on three key factors:

• Recency – how long it’s been since a customer bought something from you, or visited your website
• Frequency – how often a customer buys from you, or how often he visits your website
• Monetary – the average spend of a customer per visit, or the overall transaction value in a given period of time

RFM analysis scores every customer on each of these three factors, on a scale of 0 (worst) to 4 (best). After that, we assign an RFM score to each customer, by concatenating his numbers for Recency, Frequency, and Monetary value (for example, RFM = 444 are the best customers in all regards, RFM = 000 are the worst).

RFM segment name	Description	RFM SCORE
Lost customer	Made the last purchase a long time ago and didn’t engage at all in the last 4 weeks.	000, 001, 010, 020, 030, 040
Hibernating customer	Made their last purchase a long time ago but in the last 4 weeks, neither visited the site nor opened an email.	221, 211, 122, 121, 112, 111, 021, 012, 011, 101, 100
Can-not-lose customer	Made the largest orders, and often, but haven’t purchased anything for a long time.	044, 043, 033, 103, 104, 004, 003, 002
At-risk customer	Similar to “Can-not-lose customer” but with smaller monetary and frequency value.	144, 143, 134, 133, 142, 141, 132, 131, 124, 123, 114, 113, 042, 041, 034, 032, 031, 024, 023, 022, 014, 013
About-to-sleep customer	About-to-sleep customers with not too long-ago purchases.	220, 210, 201, 110, 102, 120, 130, 140
Need-attention customer	Need-attention customers are those whose last purchase happened more than 4 weeks ago.	424, 423, 332, 323, 232, 223, 214, 213
Promising customer	Potential loyal customer a few months ago. Spends frequently and a good amount. But the last purchase was several weeks ago.	414, 413, 412, 411, 410, 404, 403, 402, 314, 313, 302, 303 ,304, 204, 203, 202
New customer	Customers that bought most recently. The first purchase is calculated for new customers.	401, 400, 311, 310, 301, 300, 200
Potential loyal customer	Recent customers, and spent a good amount.	442, 440, 441, 430, 431, 422, 421, 420, 341, 340, 331, 330, 320, 342, 322, 321, 312, 242, 241, 240, 231, 230, 222, 212
Loyal customer	Loyal customers are ones that order regularly from your store.	432, 333, 324, 244, 243, 234, 233, 224
Champion customer	Bought most recently, order often, and spend the most from all.	444, 443, 433, 434, 343, 344, 334

Here is one example where we can see all customers divided into segments.

Depending upon their RFM analysis scores, customers can be segregated into the following categories:

• Lost customer
• Hibernating customer
• Can-not-lose customer
• At-risk customer
• About-to-sleep customer
• Need-attention customer
• Promising customer
• New customer
• Potential loyal customer
• Loyal customer
• Champion customer

What Are the Benefits of an RFM Analysis?

Using RFM Analysis, you can:

• Identify specific groups of customers
• Target them for marketing activities
• Promote repeat purchases and loyalty
• Acquire new customers

Even more than that, an RFM score:

• Helps you focus on and improve customer retention and customer lifetime value.
• Helps you lower customer acquisition costs
• Teaches you which of your customers are the most worth retaining, and which you shouldn’t spend too much time and budget on keeping.
• Helps you understand which of your customers you can’t afford to lose, and which are most likely to churn.

This is just a part of the things you can do with RFM analysis. Once you figure out how to best use an RFM score, you’ll know exactly what each segment of your customers needs, and what message to send them. Let’s see how you can target each group…

How to use RFM analysis in marketing?

Now when you have all the data at your disposal, it’s time to move on to the marketing part. By grouping customers by RFM values, you can immediately get a complete picture of what’s happening with your customer base.

Champion customer

These customers are your most loyal base. They purchase often, spend more than average, and have purchased recently. These are the customers you want to keep happy and you should cherish them.

Loyal customer

They may not always spend the most, they may not have shopped with you recently, but they always come back. These are customers who love your brand and products. Cherish them, offer your loyalty programs, reward them, ask them to make some revies.. in short, keep a good and steady relationship.

Potential loyal customer

They are your recent customers with average frequency and they spent a good amount. Keep them close by offering membership or loyalty programs.

At-risk customer; Need-attention customer

Those customers may not purchase often, but these are your biggest spenders. These customers should be nurtured because they already love your products. Be sure to offer product recommendations based on what they’ve already purchased or other most bought products.

New customer; Promising customer

New customers are very important for any business. You’ve done the hard work to buy something for the first time, now it’s time to nurture that relationship. Retaining these customers can be the best way to find new loyal customers and champions.  Start building relationships with these customers by providing support and special offers.

At-risk customer, Hibernating customer

At-Risk Customers are your customers who purchased often and spent big amounts but haven’t purchased recently. Reactive those customers by sending them personalized reactivation campaigns, offering renewals and helpful products to encourage another purchase.

About-to-sleep customer

You know you have a lot of About-to-sleep customers. They are disengaging from you. Think about a re-engagement campaign that would bring them back to your website. Send them a personalized email, or run a survey asking what happened that made them stop visiting you.

Can-not-lose customer

Can-not-lose customers are customers who used to visit and purchase quite often but haven’t been done any activity recently. Bring them back with relevant promotions, and run surveys to find out what went wrong and avoid losing them.

The Matrix RFM report shows the number of customers, monetary sum, average days since the last purchase, average frequency, and average monetary broken down by Recency, Frequency, and Monetary segment (RFM).

 

Coming soon

Want to know more about which product variant is sold the most or see how a certain product variant is sold in different cities and countries? Or maybe you want to check only specific vendors and sales of theirs variants. All that is possible in the Variant Sales report.

In that case, the Varian Sales report is exactly what you need. This report is in the Product section in Seery, just like the similar Product sales report.

A product variant analysis gives a deep insight into what your customers are buying the most in the selected period – use this data to make timely business decisions that give you a head start over your competitors.

Same as in many reports, the Quick selection represents the period in this report; the default value is Today. That means once you enter the report, in the graph and detailed table, you will get data covering sales from all of your customers, products, and vendors only for today. In case you are interested in another period, you can change it by opening the quick selection and choose the period you want (e.g. last 7 days, last month, etc.). Also, if you are interested in a specific period, you can define it by selecting the start and last date of the period.

You can select some or all the attributes (parameters) like:

• Product type
• Vendor
• Tags
• Product
• Customer country
• Customer city

All the values from the attributes are selected as the default values. In case you do not want to take into account all the values of certain attributes, select the desired values and click on the refresh button: the data in the graph and the detailed table will change accordingly.

By default, the main metric is Gross Sales, meaning, once data loads, the graph is based on that metric. Besides, you can also define how many top products you want to see in the graph. It could be 1, 2, or even 100. To define how many top products you want to see in the graph, type the number into the limit box. In case you want to change the number of products, you can change it by using arrows, or by re-entering it.

Like in most of the reports, even though the main metric is Gross Sales, you can switch it on another one or even select several metrics at once. To choose another metric, just click on the “Show” icon box and you will see all the available metrics. As soon you choose it, the graph will be updated.

The detailed table in this report will give you all available metrics that correspond to selected products. Using this table you get deep information about Variant, Ordered Quantity, Gross Sales, Refund, Discount, and Net Sales per variant.

Want to know more about which product is sold the most or see how a certain product is sold in different cities and countries? In that case, the Product Sales report is exactly what you need. You can find it in the Product section in Seery.

 

A product analysis gives a deep insight into what your customers are buying the most in the selected period – use this data to make timely business decisions that give you a head start over your competitors.

Same as in many reports, the Quick selection represents the period in this report; the default value is Today. That means once you enter the report, in the graph and detailed table, you will get data covering sales from all of your customers, products, and vendors only for today. In case you are interested in another period, you can change it by opening the quick selection and choose the period you want (e.g. last 7 days, last month, etc.). Also, if you are interested in a specific period, you can define it by selecting the start and last date of the period.

You can select some or all the attributes (parameters) like:

• Product type
• Vendor
• Tags
• SKU
• Customer country
• Customer city

All the values from the attributes are selected as the default values. In case you do not want to take into account all the values of certain attributes, select the desired values and click on the refresh button: the data in the graph and the detailed table will change accordingly.

By default, the main metric is Gross Sales, meaning, once data loads, the graph is based on that metric. Besides, you can also define how many top products you want to see in the graph. It could be 1, 2, or even 100. To define how many top products you want to see in the graph, type the number into the limit box. In case you want to change the number of products, you can change it by using arrows, or by re-entering it.

Even though the main metric is Gross Sales, you can switch it on another one or even select several metrics at once. To choose another metric, just click on the “Show” icon box and you will see all the available metrics. As soon you choose it, the graph will be updated.

In this example, you can see the same graph when three out of five metrics are selected.

The detailed table in this report will give you all available metrics that correspond to selected products. Using this table you get deep information about Products, Ordered Quantity, Gross Sales, Refund, Discount, and Net Sales per product.

 

In the customer section, you will find reports that will give you much valuable information on your customers. Here you will see customers divided into segments by recency, frequency, and monetary value plus divided into active, lapsing, and lost customer groups. Above that, each customer can be checked and dug deep into history purchases together with forecast value….

What is Cohort?

In business analytics, a Cohort usually refers to a group of customers or users segmented by acquisition date (i.e. the first time a user is buying a product, or service, or visits a website). Cohort analysis is a study that focuses on the activities of a particular cohort over a certain period. In other words, Cohort analysis allows us to identify relationships between the characteristics of a group and that group’s behavior.

Cohort Analysis for Business

The cohorts run along the vertical axis, with the oldest cohorts on the top and the newest ones at the bottom. Across the horizontal axis are the periods since the start of the cohort. The cells in the middle have the corresponding values for the metric you’re plotting.

Let’s see how you can get and use Cohort Analysis in Seery… 

Seery’s Cohort Analysis

When doing Cohort analysis in Seery, you can deep analyze data by:

• Aggregation level
• Show as
• Monetary segment

Aggregation levels offered in Seery are: Week, Month, Year

Metrics by which you can analyze cohorts are:

• Percentage
• No of Customers
• No of Orders
• Gross Sales
• Gross Sales (Cumulative)
• Average Order Value (AOV)
• Customer Value (Cumulative)
• Customer Value

Detailed monetary segments are:

• Very low spender
• Low spender
• Medium spender
• High spender
• Very high spender

 

What really means each Customer Cohort group is defined by available metrics in “Show as“?

• The Cohorts per Percentage shows the average customer quantity in the percentage of your newly acquired customers after each month after their first purchase.
• The Cohorts per Number of Orders shows the average order quantity of your newly acquired customers after e.g. 1 month, 3 months, or 6 months after their first purchase.
• The Cohorts per Number of Customers shows the average customer quantity of your newly acquired customers after each month after their first purchase.
• The Cohorts per Gross Sales shows the average customer gross sales of your newly acquired customers after each month after their first purchase.

Let’s check some use cases and how you can interpret the data… 

After you run the analysis, the first tab that appears is the Cohorts Tab. Depending on the metric (the default is Percentage), the results are presented through a heatmap and her graphic representation.

 

Here is one example of Cohort analysis. For all our customers, regardless of the monetary segment, let’s track the percentage of customers.  If we look at March 2021, for example, we can see that from 100% of new customers that we had in March, two months after (May) we had only 6,56% of those left. 2 months after, there is only 1,84% left as active.

Let’s now check another example. We want to see data by metric No of Customers but only for high spending customers. Looking into this data, we can see that churn is not so high like it was when we looked at all monetary segment customers.

Looking more deeply into different monetary segment customers, you could decide what is your target Cohort group for some marketing actions.

For better visualization, the same data you can see in a chart view.

 

Check the short video to see how you can use Cohort analysis for getting the data you need!

 

Customer purchase latency, or latency, is the time between two customer events, such as a first and second purchase. We can say it is the average time between two purchases. A latency of 15 days means that on average, a customer is placing a new order every 15 days.

Why it is important?

It’s much easier to hold over someone who’s already made a successful purchase from you, than gain a new customer. Also, it is much cheaper too. The key is to treat customers like VIPs: once when you gain their trust and they get insider deals, it is more likely they will become loyal customers. This is where latency steps in!

How to calculate latency?

You can calculate latency by hand, or use a spreadsheet with some super-complexed formulas, but why would you do it manually when you have Seery? Seery calculates latency for you every day, based on each order and every customer: all you need to do is to open the report and analyze the data we gave you.
The most important latency is between the first and the second order. But, not only that, in Seery, you will see latency for all the possible other orders as well (between the second and the third order, etc.).

Let’s see how you can get and use data from Seery… to get latency, use the Customer Purchase Latency report.

Customer Purchase Latency report you will find in the Customer section.
When you choose the report, the values you will get are automatically prepared for all of your customers, regardless of the origin or RFM Segment, from day one in Shopify. Those data you will see in the graph and detailed table under the graph.

 

 

In the first section of the report, you will find the attributes (parameters) of the report:

• Customer country
• Customer city
• Monetary Segment Name
• Recency Segment Name
• Frequency Segment Name
• RFM Segment Name

For example, by Monetary segment, your customers are divided into three groups:

• high spender
• medium spender
• low spender

If you are interested only in your high spender, simply unselect the other spenders, more precisely, the low and the medium spenders. By clicking on the “Refresh” button, the data in the graph and the detailed table will change accordingly. As we are calculating latency from all your data, there is no period to select.

By default, the main metric on which the graph is based is Latency(days).

 

In the above graph, we can see that between the first and the second order, on average, it has passed 57 days. The latency between most other orders is quite smaller, with decreasing tendency, which is great. We can conclude that customers that have made their first order should have been targeted earlier. Then they would make their second purchase much earlier. In that way, latency between the first and the second-order could get smaller.

Except for latency, in the graph, you can select other metrics, such as Sales, Number of Orders, and Repeat Purchase Rate. At the same time, you can compare multiple metrics on a graph.

 

Last but not least, under the graph, you can find a detailed table where you can see the values of all available metrics for every existing order. Using this table you get deep information about order #, exact latency, gross sales, AOV plus the number of orders in one order group.

 

What is Purchase Frequency?

Purchase Frequency is the number of times that a customer purchases goods or services in a given period. For every business, understanding how often a customer purchases within a given category gives a sense of their engagement and contribution.

Why is Purchase Frequency important?

For most eCommerce businesses one of the most powerful ways of growing revenue is focusing on customer retention. Purchase Frequency alongside Repeat Customer Rate is one of the most used KPIs for tracking that. It is easier, but also cheaper, to focus on repeat customers than on new ones. Loyal customers who make frequent purchases are also more likely to promote your brand and recommend it to other customers.

Purchase Frequency helps you to understand your customers purchasing behavior. With that knowledge, you’ll better structure your marketing activities around your customers’ habits. There are different metrics that you can analyze within purchase frequency. Besides customer purchase frequency you can look deeper and check product purchase frequency, brand, category, product variant, and many other.

Let’s check Seery’s Variant purchase frequency presented in the “Variants on Nth Order report”…

The Variants on Nth Order report is under the Customer section. This report will inform you of the best-performing product variants. 

In the first section of the report, you will find several attributes (parameters) of the report:

• Customer country
• Customer city
• Monetary Segment Name
• Recency Segment Name
• Frequency Segment Name
• RFM Segment Name
  

When you choose the report, the values you see are automatically selected for all of your customers, regardless of the origin or RFM Segment, from day one in Shopify.

Among the attributes, there is a variable called Order number, which is the key factor of this report. Choosing by which order you want to analyze the data, you will be able to see variant sales from the first to the last order placed by your customers. Not only that you can analyze 1st or 2nd order, but you can also look at the 10th or even the 20th order. There is no limit to which order you will analyze it. As long there are data, you will get the result.

If you want to know more deeply which variant was bought the most in a specific country(s) or by any RFM segment, use the attributes to select what you need. Again, each option is not dependant on others, which means you can select any you need.

What is Purchase Frequency?

Purchase Frequency is the number of times that a customer purchases goods or services in a given period. For every business, understanding how often a customer purchases within a given category gives a sense of their engagement and contribution.

Why is Purchase Frequency important?

For most eCommerce businesses one of the most powerful ways of growing revenue is focusing on customer retention. Purchase Frequency alongside Repeat Customer Rate is one of the most used KPIs for tracking that. It is easier, but also cheaper, to focus on repeat customers than on new ones. Loyal customers who make frequent purchases are also more likely to promote your brand and recommend it to other customers.

Purchase Frequency helps you to understand your customers purchasing behavior. With that knowledge, you’ll better structure your marketing activities around your customers’ habits. There are different metrics that you can analyze within purchase frequency. Besides customer purchase frequency you can look deeper and check product purchase frequency, brand, category, product variant, and many other.

Let’s check Seery’s Variant purchase frequency presented in the “Variants on Nth Order report”…

The Variants on Nth Order report is under the Customer section. This report will inform you of the best-performing product variants. 

In the first section of the report, you will find several attributes (parameters) of the report:

• Customer country
• Customer city
• Monetary Segment Name
• Recency Segment Name
• Frequency Segment Name
• RFM Segment Name
  

When you choose the report, the values you see are automatically selected for all of your customers, regardless of the origin or RFM Segment, from day one in Shopify.

Among the attributes, there is a variable called Order number, which is the key factor of this report. Choosing by which order you want to analyze the data, you will be able to see variant sales from the first to the last order placed by your customers. Not only that you can analyze 1st or 2nd order, but you can also look at the 10th or even the 20th order. There is no limit to which order you will analyze it. As long there are data, you will get the result.

If you want to know more deeply which variant was bought the most in a specific country(s) or by any RFM segment, use the attributes to select what you need. Again, each option is not dependant on others, which means you can select any you need.

For this report, we can easily say it is “sugar on the top” 😊

In the Customer Lifetime Value (CLV) report you can get information on customers that in another way you will not know it.

Here you can see three main segments:

• All customer and information for all customer together
• Repeating customers
• One time buying customers

In each group, you can see the number of customers, the total amount spent by the group, predicted future spending amount plus CLV per each customer.

Customer Lifetime Value is calculated based on historical customer lifetime value (real spendings) and predictive customer lifetime value. In this example, we can see that the CLV of repeating customers at $83.96, whereas the CLV of one-time buyers is only $27.43.

Above this, for easier visualization of data, in this report, there are several charts with a detailed breakdown of segments.

 

Let’s see first what is Retention Rate and why it is so important…

What does Retention Rate mean?

Retention rate refers to the percentage of customers who continue buying a product or service over some given timeframe. This is a critical success metric for subscription-based businesses, such as SaaS software providers, and companies whose customers repeatedly buy the same products from them, such as milk and coffee brands.

For businesses that sell products to customers only once over a long period—car or refrigerator manufacturers, for example—retention rate is a less relevant success metric. But suppose these companies also sell an ongoing service to support their products, such as a warranty or maintenance agreement. In that case, the retention rate can be an essential metric to gauge success in selling these add-on sales.

Why is a high Customer Retention Rate so important?

Many entrepreneurs and business analysts believe customer retention is the most crucial metric to determine a company’s success. High customer retention means customers of the product or business tend to return to, continue to buy or in some other way not defect to another product or business, or non-use entirely.

Before we move to specific reports in this section, here are definitions for values in this reports:

• New customers: Customers who placed their first order within the defined period (only the first order counts, eg. If there are two orders on the same day, only for the first order this will be a new customer).
• Returning customers: A returning customer is simply someone who has bought your product or service once before and has returned to make another purchase (eg. the Second order of a new customer in a day will be placed as the order of an existing customer).
• Orders: The total number of orders that were generated by customers during a selected period.
• Revenue: Sum of revenue generated by the type of customer

 

In this section, you can find several reports. Each one is presented by graph in which you can choose what you want to see.

New Customers vs. Returning Customers report

In this report, you can see the number of distinct New Customers vs. Returning Customers that are detected in an analyzing period. Simply by looking into color, you can notice a number of new vs returning customers that bought something. 

 

Retention – % of Returning Customers report

In this graph, you can see % of returning customers each month.

Revenue New Customers vs. Returning Customers report

In this report, you can see total revenue made by new vs revenue made by returning customers.

Today, internationalization and its strategy are crucial for most online stores. For many merchants, the success of your business growth depends on the target group you select and the country you are trying to reach it in.

So let’s have a look at how Seery can help you in getting data on your sales per country…

Definitions
For better interpretation, the main result of the result Sales per Country report is visualized. Let’s go through some definitions that are used in this report and which are important for the visualization.

You can identify your top countries based on the following metrics:

• Gross Sales: Equates to product selling price x ordered quantity generated by your customers within the selected period; does not include discounts, returns, taxes, or shipping.
• Discount: Summ of all discounts like product discount, and the product’s proportional share of a cart-wide discount.
• Return Amount: The value of goods that the customer returned to the merchant within the selected period
• Net Sales: Equates to gross sales – discounts – returns discounts generated by your customers within the selected period; does not include shipping charges or taxes.

• Ordered Quantity:  the total quantity of all items ordered by customers within the selected period (net quantity + returned quantity)

The quick selection represents the period in this report; the default value is Today. That means once you enter the report, in the graph and detailed table, you will get data covering sales from all of your customers, products, and vendors only for today. If you want to change the time period, open the quick selection and choose the period you want or select a special period by choosing a date on the calendar.

All the values from the attributes are selected as the default values. In case you do not want to take into account all the values of certain attributes, select the desired values and click on the refresh button: the data in the graph and the detailed table will change accordingly.

If you want to change any of the parameters, especially the period, open the quick selection and choose the period you want or select a special period by choosing a date on the calendar.

In the same way, you can select some or all from the attributes like:

• Product type
• Vendor
• Tags
• Item Variant
• Customer country
• Customer city

 

By default, the main metric is Gross Sales. It means, this is what you will see in the graph once data are loaded. Besides, you can also define how many top countries you want to view in the graph. It could be 1, 2, or even 100. To define a number of top countries, type the number into the limit box. To change it, use arrows, or just enter the number you want.

Even though the main metric is Gross Sales, you can choose another one or even select several metrics at once. To see other values, just click on the “Show” icon box and you will see all available options in this graph. As soon you choose it, the graph will be updated.

In this example, you can see the same graph when all metrics are selected.

 

Like in many reports, under the graph, you can find a detailed table where you can see all the available metrics that correspond to search results. Using this table you get deep information about Countries, Quantity, Gross Sales, Refund, Discount, and Net Sales.

 

 

The success of your company’s growth could also, besides country and target group, depend on the cities your customers are in. What goes well in one city, could do nothing in another.

So let’s have a look at how Seery can help you in getting data on your sales per city…

This report is very similar to the Sales per Country report. The difference in this report is that you can check results per each city your customers are coming from. Again, this helps you to make some decisions and define who you want to target at one point.

 

Definitions
For better interpretation, the main result of the result Sales per City report is visualized. Let’s go through some definitions that are used in this report and which are important for the visualization.

You can identify your top countries based on the following metrics:

• Gross Sales: Equates to product selling price x ordered quantity generated by your customers within the selected period; does not include discounts, returns, taxes, or shipping.
• Discount: Summ of all discounts like product discount, and the product’s proportional share of a cart-wide discount.
• Return Amount: The value of goods that the customer returned to the merchant within the selected period
• Net Sales: Equates to gross sales – discounts – returns discounts generated by your customers within the selected period; does not include shipping charges or taxes.

• Ordered Quantity:  the total quantity of all items ordered by customers within the selected period (net quantity + returned quantity)

The quick selection represents the period in this report; the default value is Today. That means once you enter the report, in the graph and detailed table, you will get data covering sales from all of your customers, products, and vendors only for today. If you want to change the time period, open the quick selection and choose the period you want or select a special period by choosing a date on the calendar.

All the values from the attributes are selected as the default values. In case you do not want to take into account all the values of certain attributes, select the desired values and click on the refresh button: the data in the graph and the detailed table will change accordingly.

If you want to change any of the parameters, especially the period, open the quick selection and choose the period you want or select a special period by choosing a date on the calendar.

In the same way, you can select some or all from the attributes like:

• Product type
• Vendor
• Tags
• Item Variant
• Customer country
• Customer city

 

By default, the main metric is Gross Sales. It means, this is what you will see in the graph once data are loaded. Besides, you can also define how many top cities you want to view in the graph. It could be 1, 2, or even 100. To define a number of top cities, type the number into the limit box. To change it, use arrows, or just enter the number you want.

Like in other reports, even though the main metric is Gross Sales, you can choose another one or even select several metrics at once. To see other values, just click on the “Show” icon box and you will see all available options in this graph. As soon you choose it, the graph will be updated.

 

Again under the graph, you can find a detailed table where you can see all the available metrics that correspond to search results. Using this table you get deep information about Cities, Ordered Quantity, Gross Sales, Refund, Discount, and Net Sales.

 

 

coming soon

The customer list report gives you a list of all of your customers. In this example, this list contains more than 88000 customers. Each customer here is listed with all relevant data and columns. Each customer is segmented by recently, frequency, and monetary segment. Above that, here you can find information like monetary amount sept, predicted total monetary value, prediction to be “alive” customer in the next 90 and 365 days, plus many more.

 

 

The table can be exported into excel and saved for future needs. Data are here for you, you only need to use it.

If you want to check details for a specific customer, this is the report you will love it.

After you select the name, you will get a report with many details about the customer, like recency, frequency, and monetary segment. Above that, here you will see the probability if the customer is still “alive” for your business, predicted monetary value in the future, as well as total customer lifetime value.

 

Several detailed charts follow after this general information.

The order history chart shows the gross amount per date for a customer in a historical timeline. In this timeline, you can see when this customer made a purchase and what was the value. In this example, you can see purchases of one very frequent and high-value customer. Many orders are high value, but besides that, there were many purchases for several years now.

 

On the other hand, if we check the next Amount per day in a weekly chart, to see when this customer was buying. In this example, we can see that this customer was buying on all days except on Wednesday, Tuesday, and Sunday. Besides that, here you can see that purchases made on Monday are with the highest value.

 

 

In the next sections, we will gather frequently asked questions we received at our support email address.
If those questions/answers haven’t solved your problem, please send us an email with your question to seery@qualia.hr