FAQ2019-12-28T10:49:22+08:00

FAQ

Data collection and analytics

How to evaluate the compatibility of data collection device?2019-12-28T10:52:24+08:00

The compatibility of data collection device refers to three aspects: printer type, printer port type and printing instruction type.

There are many types of printers, such as laser printers and inkjet printers that are common in offices.

Most of the receipt printers used in POS of retail industry are thermal receipt printers and dot matrix receipt printers, and some use wide-format dot matrix printers, even laser printers or inkjet printers used in offices.

Among the printers supported by Counect CUBE, we are compatible with almost all printing ports, including USB interface printer, parallel (advanced parallel) printer, serial printer and Ethernet printer.

The printing instructions of the printer are very complex, and the total number of protocols in use is more than 40. Counect CUBE supports more than 10 common printing protocols for POS receipt printers and wide-format dot matrix printers, and supports dozens of printer brand expansion instructions.

How to evaluate the stability of data collection device?2019-12-28T10:53:55+08:00

The stability of data collection device includes the degree of interference to the original device, the stable operation of the device itself and the integrity and safety of the collected data.

Counect CUBE adopts hardware bypass monitoring technology to eliminate interference and influence on monitored device in principle. Even if CUBE itself is powered off, it will not interrupt POS’s normal cashier business.

CUBE itself has the function of data local saving, which can guarantee to cache the collected data to the local in case of network failure, so as to prevent data loss.

CUBE contains optional battery backup. When the external power supply is disconnected accidentally, the backup battery starts to supply power, thus ensuring the integrity of data.

Main differences between hardware data collection and software data collection2019-12-28T11:00:02+08:00

Hardware data collection

Software data collection

Compatibility

Printers with common interfaces

In addition to printers with common interfaces, united printers can be supported

Print environment change

Reset required

It needs to be reset. However, some cash registers will initialize the system on a daily or regular basis, which is not supported by the software

Follow-printing

Support

Support

Installation convenience

No change of any settings of cash register

Administrator permission is required for installation, and relevant settings of cash register need to be changed

Network

Device’s own network

Need to use the network provided by the cash register or external network equipment

Safety

Unidirectional transmission, no impact on cash register

There are security risks. The software itself may be attacked. If the cash register needs to be connected to the public network to transmit data, the cash register will be exposed to the public network environment

Stability

Probability of hardware damage

If the external network device is not used, there is no hardware stability problem

Computational consumption

No impact on cash register

Using the computing power of the cash register, certain computing resources need to be occupied

Third party device access

Simple implementation, no impact on cash register

Need to occupy cash register ports, and need to do more debugging

Cost

High

Low

What is a retail smart gateway?2019-12-28T11:02:27+08:00

A retail smart gateway is a full-featured POS edge gateway. It integrates the traditional POS receipt printing function and supports wireless printing.

Through the cooperation of smart retail gateway and our various monitoring accessories, the complete sales data of POS can be collected, including: receipt printer, customer display, keyboard, scanner and almost all other devices.

A retail smart gateway is a wireless router that can support 2.4G/5G Wi-Fi access.

In addition to supporting Wi-Fi access, the smart retail gateway also supports BLE wireless access and ZigBee wireless access, as well as multiple wireless sensors. It can be widely used in store IOT applications such as customer flow tracking and product tracking.

By choosing a dedicated smart screen or wireless access to mobile devices, we can provide more new marketing interaction for consumers, or provide effective guarantee for mobile follow-up service for employees.

Operation index glossary2019-12-28T11:16:26+08:00

Sales Value

  • sales amount, sales, turnover or sales volume

  • The total amount of all transactions actually completed within a specified time period
  • Sum of the amount of all single transactions of the reporting object in a specified time period

Per Customer Transaction

  • ATV (Average transaction value)

  • Average amount of a single transaction in a specified time period
  • Weighted average value of each transaction amount of the reporting object within a specified time period

Sales Transaction

  • transactions, transaction amount

  • The number of times all transactions have been completed in a specified time period
  • Sum of the times of all single transactions of the reporting object in a specified time period

Customer Flow

  • traffic

  • The total number of people who have stayed in the mall or store in a specified period of time
  • Initial number of people + number of people entering in a specified time period

Sales per Sales-person

  • SPS

  • Sales completed by a single salesperson in a specified time period
  • Sales value/number of sales personnel within a specified time period

Sales per unit area

  • Sales per unit area in a specified time period
  • Sales value / store area of the reporting object within a specified time period

Tenant Data Coverage

  • The percentage of the number of tenants with valid data in the number of all tenants in the specified time period
  • Number of tenants with valid data / number of all tenants in a specified period of time

Purchase Frequency

  • The number of times a SKU appears in transactions in a specified time period
  • The sum of the number of transactions of the statistical object (usually a single SKU) in the specified time period
Report analysis glossary2019-12-28T11:27:59+08:00

Zone

The statistical range of data for operation analysis, usually with a single floor as a zone.

Category

The business characteristics of a tenant, usually divided based on sales target and the characteristics of products. Common categories include F&B, retail, life service, leisure & entertainment, kids, and supermarket.

Changes (Tenant)

Tenants with the largest increase or decrease in sales value in a specified period of time.

Key tenants

The tenant with the highest sales value in the specified time period.

Relevant purchase (SKU)

SKU combination purchased in a same transaction.

Hot/Active SKU

Products with actual sales in a specified time period.

New SKU

SKUs purchased for the first time in a transaction. New SKU is regarded as normal goods 30 days after its first appearance, and is no longer regarded as a new product.

Special offer

SKUs with promotions within a specified time period.

YoY Analysis2019-12-28T11:34:33+08:00

In this report system, year-over-year analysis is defined as historical data analysis in the same period. The daily report is the same day of last year, the weekly report is the same week of last year and the monthly report is the same month of last year.

Deviation value processing – data missing

Data may be missing due to external forces. In order to ensure the continuity and readability of the analysis, it is necessary to calculate and restore the data. The data calculation rules are as follows:

Scenario 1: Calculate historical data of tenant’s store

1. Basic calculation object: Tenants
2. Calculation of basic reporting period: Daily Report
3. Maximum time span of data calculation: 90 days
4. Calculable index: sales value, sales transactions

Step 1: accumulate one month’s data of tenants (Data-Store-MonA), and calculate the average data of that month as the base (Ave. Data-Store).
Step 2: calculate the average data (Ave. Data-Cat) of the same month based on the data of the same category of the shopping center (Data-Cat-MonA).
Step 3: take the average data of the same category of shopping center in the current month (Ave. Data-Cat) as the base 100%, calculate the data change index (Cat-Index_N) of the same category of shopping center in the past three months (n value is 1-90).
Step 4: use the data change index (Cat-Index_N) of shopping center’s category and the tenant base (Ave. Data-Store) to deduce all sales data of tenants in the past 90 days.

 If the opening date of the tenant is less than 90 days, the data will be calculated to the actual opening date.

Scenario 2: calculate the occasional missing data of tenant’s store in the current period

1. Basic calculation object: Tenants
2. Calculation of basic reporting period: Daily Report
3. Maximum time span of data calculation: 14 days
4. Calculable index: sales value, sales transactions

Step 1: calculate the average historical daily data of the tenant.
Step 2: use the average historical daily data of tenants as the current data.

The average value of daily data is calculated based on the same period of each week, that is, the average value of Monday is the average value of Monday historical data.
Tenant historical data usage is 4 weeks average.
When the historical data is less than 4 weeks and more than 1 week, it can be calculated according to the actual historical data.
If the historical data is less than 1 week, it is considered as scenario 1 to calculate the historical data processing of tenant’s store.

For the lack of data, this report system adopts the method of multi value interpolation to plan and calculate. The idea of multivalued interpolation comes from Bayesian estimation, which considers that the value to be interpolated is random, and its value comes from the observed value. In practice, it is usually to estimate the value to be interpolated, then add different noises to form multiple groups of optional interpolation values, and then select the most appropriate interpolation value according to a certain selection basis.

Link Analysis2019-12-28T11:40:59+08:00

In this report system, the definition of link analysis is to analyze the data of two adjacent periods. The daily report is the previous day, the weekly report is the previous week, the monthly report is the previous month, and the annual report is the previous year.

Deviation value processing – excessive data fluctuation

In daily operation, the business data of tenants will change a lot. Therefore, this report system sets a threshold value for data fluctuation. When the threshold value of data fluctuation exceeds the preset value, a reminder will be generated and abnormal data will be corrected.

1. Data correction object: Tenants
2. Basic report period of data correction: Daily Report

Scenario 1: the number of historical daily transactions of tenants is more than or equal to 15

When the sales value of a tenant exceeds 200% of the average value of the same period in the past four weeks, the system will automatically check the tenant’s data. During the checking, the relevant data will be displayed in the report at 200% of the average value of the same period in the past four weeks. After checking, tenant data will be displayed in the system with actual value.

Scenario 2: the number of daily transactions of tenants is more than or equal to 8 and less than 15

When the sales value of a tenant exceeds 300% of the average value of the same period in the past four weeks, the system will automatically check the tenant’s data. During the checking, the relevant data will be displayed in the report at 300% of the average value of the same period in the past four weeks. After checking, tenant data will be displayed in the system with actual value.

Scenario 3: the number of daily transactions of tenants is less than 8

When the sales value of a tenant exceeds 400% of the average value of the same period in the past four weeks, the system will automatically check the tenant’s data. During the checking, the relevant data will be displayed in the report at 400% of the average value of the same period in the past four weeks. After checking, tenant data will be displayed in the system with actual value.

For any ε>0, there are: . When , if the population is a general population, the degree of dispersion between the statistical data and the average value can be reflected by its standard deviation, so there are: .

This report system calculates the hierarchical standard deviation base according to the categories and operating conditions of different tenants, which is used to test the deviation degree of tenant sales value.