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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.

What is Seery app? #

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“!



Installing Seery #

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.




Choosing Seery Plan #

Seery  provides four  Plans that you could choose from


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 30-day, you will be in a free trial.

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



Uninstalling Seery #

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.


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

Revenue Forecast #

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 revenue forecast for periods like today, tomorrow, next 7, and the next 30 days.

Besides the revenue 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:

  • Revenue Forecast Overview chart
  • Next 30 Days Revenue Forecast chart
  • Forecast for the next 90 days table


Revenue Forecast 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 Revenue Forecast chart


If you would like to get detailed insight into the next 30 days revenue 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.

Revenue Forecast Details #

In the Revenue Forecast details 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 good 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 22nd, while March 23rd 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 the track of actual data. This is not a graph that will help you in preparing 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 #

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

Here you will find:

  • Revenue -> total revenue achieved
  • Orders -> number of orders from start of data input
  • Average order value (AOV) -> general average order value
  • Average Revenue per User (ARPU)
  • Average No of Orders per Customer (ANO)

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.




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….

Customers segments #

Customer segments report will give you a lot of valuable information related to your customers. Merchants don’t need to do anything; no data should be input. Seery is automatically segmenting everything!  You only need to click on the report and read the data Seery prepared for you.

Based on predefined formulas, Seery is automatically segmenting all of your customers so each one of them is segmented depending on:

  • Recency,
  • Frequency,
  • Monetary value.

Depending on recency, for example, you can have

  • Active,
  • Lapsing, and
  • Lost customers.

Lost customers are those that didn’t buy anything for more than 2 years.

Depending on a monetary value, we are segmenting customers to

  • High,
  • Medium, and
  • Low-value customers.

All this combined gives merchants a very powerful tool to use for plans and activities.

Let us see one real situation. In this example, we can see that a particular merchant had 88985 customers, from which 16995 are repeating customers. Repeating customers are those that purchased at least 2 times on different dates. In percentage, this is 19,10%.



Based on all those information and segmented customers you can immediately choose which of those segments you want to focus on. In this example, you can see there are 98 lapsing customers. Just one click on a number in the Count column will give you a full list of customers in this segment.


In the customer detailed table, you can find a very extensive list of metrics. Not only that you can see the name of each customer, but you can find out what was the first day of purchase, what was the last day or purchase, how much they spent on average. Above all that information, in this table, you will even get the information about predictions, like the probability of activity and monetary value in a period of 90 and 365 days, and even the expected average order value.

All the information are here, you only need to use it so you can benefit from it.



With all those detailed information, merchants can prepare activities for specific customers at a specific time – target your customers so they don’t churn.




One Customer Details #

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.



Customer List #

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.

Cohort Analysis #

If you would like to check and analyze the behavior of each Cohort (a group of customers) this is a report you should use.

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

Let’s check our examples, as this is the best way to explain the needed information.  When doing Cohort analysis in Seery, you can look at data as a percentage or number of customers. Besides that, you can define which monetary segment you would like to see, all or a specific one.









Here is one example of Cohort analysis. We defined to see the number of customers, all monetary segments together.  If we look at March, for example, we can see that from 1661 customers that we had in March, two months after (May) we had only 88 of those left. 5 months after, there are 33 left as active.


Let’s now check another example. We want to see data as a percentage but only high spending customers.

From 100% of new customers in January, after 6 months there were 50% of them with us. Only in the 7th month, we lost 25% and still had 25% of them buying from us.

Looking into this data, we can see that churn is no so high like it was when we looked at all monetary segment customers. This could mean

Based on this information, you could decide what is your target Cohort group for some actions.


For better visualization, the same data you can see in a chart view. Here are the same data as in the pivot table.


Customer Lifetime Value #

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.


Retention #

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 in the same day, only for the first order this will be a new customer).
  • Existing customers: All customers who place at least their second order within the selected period (eg. the Second order of a new customer in a day, will be placed as the order of an existing customer).
  • Orders: Sum of orders within the 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.

No. of Orders New Customers vs. Returning Customers report

Similar to other reports, this one enables you to see the Number of orders per type of customer, new vs returning.


Coming soon

Frequently Asked Questions / Troubleshooting #

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




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