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Retail Analytics Tools Comparison for FMCG

in Urban city of Pakistan

INDEPENDENT STUDY – II LONG REPORT

By Pardeep kumar

Fall 2015 / MS (Software Engineering) / Reg No. 1271107 Email: Pardeep_kumar@outlook.com

IS Advisor

M. Ejaz Tayab

MS Program Coordinator

Dr. Husnain Mansoor

Computer Science Department

SZABIST, Karachi Campus December, 2015

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Abstract

In Pakistan, retail analytics is new. Big retailers in Karachi, like, Imtiaz super market, Hyperstars,

Dolman Mall, Macro, Ocean mall, Aga Super market, all have data but not in real-time and

insufficient to analyze and deduce strategies. Also, they don’t have analytical tools to work on

customer profiling, inventory insight, customer shopping engagement, etc. for analytics.

On the retail analytics system to practically deduce results. Data comes in Excel format which

was analyzed in a reputable analytical tool, such as, Qlikview 11.0 version, IBM Cognos Insight

noncommercial and Tableau 9.0. Version.

This paper is based on experimental work to prove the importance of Retail Analytics Tools.

Design Dashboards and compared various commercially available analytical tools. We have

tested practically how data is load in these different tools, how they process and analyze data,

designed three different Dashboards and compared the results as well with same data.

We have also worked on the first challenge using Estimate’s Bluetooth based beacons. We have

captured real-time data and used MS Excel Graphs to understand importance of retail analytics.

Keywords: Retail Analytics, Dashboard, Qlik view, IBM Cognos insight, Tableau.

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Contents

Introduction .................................................................................................................................................. 4

Problem Statement: ...................................................................................................................................... 5

Research Methodology ................................................................................................................................. 5

Research Structure and Tool ......................................................................................................................... 6

Stage 1 ................................................................................................................................................... 6

Stage 2 ................................................................................................................................................... 6

Research Scope ............................................................................................................................................. 6

Field of the Invention .................................................................................................................................... 6

Background and Prior Art.............................................................................................................................. 6

Comparison Analytics Tool ............................................................................................................................ 7

Data Analytics on Qlikview............................................................................................................................ 8

Dashboard on the Tableau .......................................................................................................................... 19

Dashboard on the IBM Cognos Insight ....................................................................................................... 28

.................................................................................................................................................................... 28

Data Analytics on Excel Sheet ..................................................................................................................... 37

.................................................................................................................................................................... 39

Compare the Technically These Three Different Tools. .............................................................................. 39

Retail Sooper Market device can a used Estimate Beacons. ...................................................................... 41

Retail Data Analytics on Display in Graph ................................................................................................... 42

Conclusion ................................................................................................................................................... 43

Future Work ................................................................................................................................................ 44

Acknowledgement ...................................................................................................................................... 44

Appendix A –Important contributors .......................................................................................................... 44

References .................................................................................................................................................. 45

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Introduction We are live in the competition in the world where manufacturers, distributors, and

retailers they take all the market present people and business community and FMCG supply

chain are dependent on the they have data present in the Databases and look for retail

Analytics that we are weekly shows[1] in the meeting showing the Dashboard for here

concern is that here data is not present in the not in the organize form first problem face here

is that data must be filter wise and data must come from multiple source that must be in

uniform then we are able to design the Dashboard for higher management then we are also

look into the issue full fill the requirement of the consumer every time his need present in

the [2] Shopping Mall or Sooper store in which event or which Season what are product more

demand they need more in the market if we are fail to the not full fill the requirement then

you are lost the customer you are lost the customer trust so very difficult to gain again.

Mapping of the product also important [3] which product where is present then we attract the

customer also. In Pakistan market competition also face the more issue against the product

here is Data Collection also problem not any sooper market any retail analytics tools present

for these kind of Analysis is present. In our research we are focus on the Data gathering and

how we are Retaial Analytics is Present in the organization old data is present how we are

utilize the old data that is also effective. we are used the Sample data applied on the Tools and

also look itno the see the estimote beacon that device used the via Bluetooth connect Android

App then we are get the data apply then in retail analytics.[4]

Where we have worked on multiple commercially available analytical tools, such as, IBM

Cognos, Tableau, Qlikview and did comparison. We have also worked on the first challenge

using Estimate’s beacon system practically to deduce data in real-time and analyze retail

inventory movements.

Today, retailers gets data from various sources which are very different from each other, i.e. they are all

mixed up. One needs to filter and do data mining to get meaningful data from the big data a retailer

gets.

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Problem Statement: In the Retial Analytics there are two main challenges;

That we face how we get the Meaningful data and how we are filtered the data that we

are get the data in one uniform.

In our research, we have focused on the second challenge and have worked on the first challenge

where we have used Estimote’s beacons to get data in real time and deduce inventory movement

using MS Excel. Data comes in Excel format which is analyzed in three reputable retail

analytical tool: Qlikview 11.0 version, IBM Cognos Insight none commercialized and Tableau

9.0. Version.

In Pakistan, retail analytics is new. Big retailers, like, Imtiaz super market, Hyperstars, Dolman

Mall customers, Macro, Ocean mall customers, Aga Super market all have data but not complete,

not in real-time and they don’t have much tools to implement retail analytics.[3]

Research Methodology I compared three different analytical tools, QlikView, IBM Cognos Insight, and Tableau, I have

used same data to compare results from these three different available tools. I have designed

Dashboards as well to compare results outcome for managers and decision makers. And have

explored different features. I have experimented on real-time data using beacons where Estimote

beacons using excel to analyze the acquired inventory movement data in real-time. Our

Methodology will be both qualitative and quantitative research with experiment.[4]

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Research Structure and Tools used I have conducted the research study in two stages:

Stage 1

In this stage I have researched on three tools available commercially in Pakistan. I learned how

to use these tools, how to Extract data, how to design Dashboards. I read Tutorials, saw online

videos, read white papers, articles and forums. [2]

Stage 2

I selected Qlikview, IBM Cognos Insight and Tableau. I practically used Data sample from my

office data, designed different Dashboards on these three different tools. I experimented with

different scenarios and analyzed my results. I also used beacon system to acquire retail

inventory data and analyzed it on MS Excel. [3]

Research Scope Here is scope is that Retaial Analytics how we are get the data here is data is used in

the tool in the market data combined in the one in the platform in the one uniform data

also very difficult in one format data also gathering issue when we proper Dashboard

then we are solve the problems of the organization also that they grow the business also and

satisfied the customer also that is very challenging task also.[5]

Field of the Invention In Pakistan, retail analytics is new. Big retailers, like, Imtiaz super market, Hyperstars, Dolman

Mall customers, Macro, Ocean mall customers, Aga Super market, have data but not complete,

not in real-time and they don’t have much tools implement for analytics. Here my invention is

the I applied three different tools, Qlikview, IBM Cognos Insight and Tableau. Used Estimote

beacon systems for retail inventory data in real-time. [6]

Background and Prior Art Data is Available in the different format in the different organization and different Super Mall

but not applied on the Analytical Tools like Qlikview, IBM Cognos Insight and Tableau. Data

not showing in the Dashboard in real time so [7] organizations take very long time to take correct

and effective decision to compete the market and also not able to fulfill customers’ requirements.

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Comparison Analytics Tool Feature Qlickview Desktop Tableau Desktop IBMCognosInsight

Desktop

Visualizations Visualizations are wizard-driven; colors have to be selected

Visualizations are drag and drop, and have vibrant automatically generated colors.

Graphs and charts very old school type and visually flat.

In-memory BI platform Mean tools used the own memory fast processing of data for quick result showing.

Mean tools used the own fast processing of data for quick result showing.

Mean tools used the own fast processing of data for quick result showing. Engine used the own memory

ETL Process Tools has own ETL engine to break the data into single data structure.

Blending data from the different sources here is ETL to not repeat the data.

Here is ETL used doing proper reporting also perform.

Self-service platform User has own ability to use the tool no need of any expert the tool used

User has own ability to use the tool no need of any expert the tool used

User has own ability to use the tool no need of any expert the tool used

OS Only supported Windows and not supported Linux and Mac

Support for Mac, windows and Linux.

Supported for windows and Linux Mac not supported for.

Data Set Large enterprise-wide deployments with IT oversight and governance.

More commonly used as departmental vs. enterprise wide BI solution.

Government data is complex and enormous- so are the challenges facing those who work with it. Drop to visualize any dataset.

Data Source We are get the data from multiple data source also.

We are get the data from multiple data source also.

We are get the data from multiple data source also.

Vendor Qlik Tableau IBM

1st Release year 1993 2003 2008

Latest release version. Version 11.0 version: 9.1.0 10.2.2

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Table1. Comparison of three Analytic Tools

Data Analytics on Qlikview

Fig1- Dashboard Complaint Calls Analytics on the Qlikview

In the above Snaps shot shows that Dashboard source of data is ATM Complaint log

Management system which log the Every ATM call log on the system from all over the Pakistan.

Here above Result shows that most complaint comes from Karachi, second most comes from

Lahore. No of calls per region vise is shown. Other bar chart shows that No of Calls per

engineer. Junaid engineer from Karachi region attended most complaints and Usman attended

most calls from Lahore region. Here company can immediately know from where most calls

come and which engineer Performance is better. Also, call Priority can be established, which

calls are high Priority and which calls are low Priority as shown in the chart. [12]

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Fig2- Dashboard to show Details of the Complaint Calls

Above chart shows that No of Calls per bank and Location. Here we find which complaint calls

from which bank with high ratio. How many ATMs in the Company, Detail report like Banks

name, ATM issue, which engineer was assigned for the calls, at what time, response time,

Resolved on, Duration of calls, Remedy and Type of workdays. So here the Performance

measure the Banks Complaint how quickly we were able to resolve the Calls.[11]

Fig3. Dashboard for Top Ten issues in Complaint Calls.

Above chart shows which issue comes mostly, why it came, mostly what is the reason behind,

which issues are Top ten that available for the organization to improve performance?

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Fig4. Dashboard for the No of Calls per Month on the Complaint Calls

Above chart shows No of Calls per month, so here we Show data in month wise report, and

branch wise details. Which call mostly comes from which branch to see which branch suffer

most from ATM Problems, how to reduce the Calls and how to perform better to Increase the

Banks confidence.

Fig 5. Dashboard for the Parts Management on the Compliant Calls

Above chart shows Parts management graph. Parts issued for the Compliant Calls, How we are

managing the Parts for the Company. How these limited parts are used for very important

Complaint Calls as these are also very critical for the Bank’s ATMs.

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Fig 6. Dashboard for the Response Time of the Compliant Calls

Above Charts Shows the Average Response time for the Compliant Calls in days, also in the

Whole Year or in the Month to see Performance. Overall Calls Performance, Average

Response Time. Support Calls management System Running shows Actual Performance and

How we to improve the Response time and improve the Banks reputation and increase Satisfied

Client.[8]

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Fig 7. Dashboard for the Parts Issue Management on the Engineer Compliant Calls

Above Snapshot Shows which engineer has used Manual Parts and which Engineer used Auto

parts and how effectively the Parts were utilized.

Fig 8. Dashboard for the Periodic Maintenance for the Compliant Calls

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Above Charts shows the averages, Average Month for the Periodic Maintenance for the

average for Complaint Calls, how calls were managed effectively, the important calls and

Periodic Maintenance and how effectively resources were used they also for the Complaint

Calls.

Fig 9. Qlikview Associates Different Table Structure

Above Snapshot shows how Qlik view Associates different Tables with Associate keys, how

Relationship between Tables were created, how to extract the data and how Dashboard Design

associated with the Table.

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Fig 10. Qlikview: the Edit, Script and Load Data

Above Snapshot shows how to load the data from the Sql server OLE DB connector, Table

Files, Qlikview Files, Web Files, Field Data and Excel Sheet data. Also, how to edit the Script

for Association Tables.

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Fig 11. Qlikview: Edit the Script Connection String and what are the Tables Selected

Above Snapshot shows how in the Qlikview Connection string Build with database. How to

create the connection string build to load the data into Qlikview. OLEDB connection build with

SQL server.[7]

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Fig 12. Qlikview: Edit the Script Load the Table Data in the QlikView

Fig 13. Qlikview: Edit the Script Load to Update Table Data in the QlikView

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Above Snapshot show here we are required data Standard time requirement so we are the

Change the time format here we are some function apply also change the time format and change

the date format for our requirement accordingly so we apply here by default function also for

our need and also Extract the date also so mix data comes in the table date and time mix the

data so our requirement only date so we are extract the date only.[9]

Fig 14. Qlikview the Edit the Script Load the Joins Table Data in the Qlik View

Above Snapshot shows that here similarly Joins in the table multiple column used from the

multiple table from so we apply here joins concept in the sql server so here also Qlik view joins

load data accordingly.[8]

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Fig 15. Qlikview the Edit the Script Load Multiple Table Data Load in the Qlik View

Above Snapshot shows that how we are multiple table data in the Load accordingly our

requirement we can edit the script modify the Column name and modify the what are needs

multiple table load script very easy in the Qlik view also for convince for the developer that

we want that type of data Load. Here also Syntax checker which check the script syntax so easy

for the developer also for the not making any mistake during the data loading. Here in the Qlik

view also display in web also for the convince for the user also. [10]

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Dashboard on the Tableau

Fig16. Dashboard on the PS Call logged Management on Tableau

Above Snapshot Shows that here highlight from 2014 Calls and 2015 here Calls also so we

are differentiate the which calls from 2014 year and 2015 year also here is small report also

ATM model and Banks Call is assigned from the other Department or Direct from Banks so

here conclusion report . [11]

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Fig17. How we are Data Load on the Tableau

Above Snapshot shows that How we are connect the Data, text Data, Excel data , Access,

Statistical Files and other files from server Also Tableau Server, SQL Server , Oracle Server,

My SQL server , Amazon Red shift and other Server Also IBM server , Google Analytics, SAP

, Teradata and other Server Also mostly come server below snapshot shows that also.[12]

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Fig18. How we are Data Load from the Server on the Tableau

Fig19 Dashboard bar Chart PS Call Assigned Different PS Consultant on the Tableau

Above Chart shows that which calls assigned for him where complain came from which date

issue Escalate which ATM Model issue description which year here the conclusion information

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for the Quick PS Consultant. For him how manage the Calls very effectively for the Resolution

of the Calls.[5]

Fig20 Dashboard Pie Chart Call is assigned on the Quarter wise on the Tableau

Above chart shows that Call is assigned on the Quarter wise that different calls shows in the

every unique calls shows in the Pie Chart that PS Consultant another view the Calls on the

different Angle also for the also which call look into which PS consultant also for resolution

of the call also.

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Fig21 Dashboard Tree Map chart Call is assigned on the Quarter wise on the Tableau

Above chart shows that on the Tree Map chart all the on the one Snapshot as well as also for one

tree view and or one Hirechcy of the call for very quick information how effectly feel the

difference that chart also PS consultant more easy and more friendly for that kind of chart one

consolidate information in one Cell or one Tree map chart.

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Fig22 Dashboard on the Bubble Chart for the PS Consultant on the Tableau

Above Chart shows in the Bubble chart this chart also very unique every different PS

Consultant Bubble chart different Color Shows very easy for the PS Consultant whole

Information display for one consultant same color used very easy for Consultant For whole

Call information Display this chart for the that type of chart not available in the Qlik view

and IBM Cognos Insight that chart also very useful PS Consultant as well also.

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Fig23 Dashboard on the Line Chart PS Consultant on the Tableau

Above chart Shows that Line Chart and Also very Unique Chart for every full information

display for the every PS Consultant that information display unique Chart Display very unique

information for the PS Consultant display the information every click on Symbol for the

Assigned PS Consultant. Here other Detail also you can here set the data on the Dimension and

Applied formula and Calculation apply filter the data for more Explorer the information here

sheet, workbook, and Story also you are design for the Dashboard.

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Fig24 Data Loading and Filter the Data Tableau

Above Snapshot shows that How we data load in the Tableau here we have two option live

connect the data and Extract the data in the Tableau so live connect the data we have the

benefit that very less time connect the data where in the live information any change so we are

also change the information also update dashboard available for the user also other Qlik

view or IBM Cognos insight that option lack and very beneficial for the developer and End

user for the developer not repetitive over load the load again and again the data for the End

user beneficial is that Update data present for the and update the new information available for

the user and other here we are doing here filter the data also modification also done here and

other column we are not required so we are here delete the column also and filter the data also

according our requirement and need of the End user also and apply function and change the

name also for the according our need. Apply the Script and query also for the developer for full

fill the requirement. [8]

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Fig25 Data Loading and Filter different option the Data Tableau

Above Snapshot shows that how we are choose different way filter the data likewise the

wildcard contains, Start with , End with and Exactly with Matches and Include all values and

empty here also Exclude the data other option are apply the condition by filed, by range , by

formula and reset the values that above filter why we are change the data and filter the data

because we are not required to the extra load the to the Tableau and also full fill the requirement

and End user need also for very clean the data see the End user full fill the need. Here other

option are Top by field and by formula also we are also that way also filter the data according

our need. Filter the data best option not extra load and extra memory consume and very difficult

restrict the data on the when we design the Dashboard also so very difficult also. Firstly filter

the data very important for the End user also.

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Dashboard on the IBM Cognos Insight

Fig26 Dashboard on the Bar chart on the IBM Cognos Insight

Above chart shows that Bar chart that most PS Calls from which Bank and how many total

calls for each bank and also shows is that other filter the data here also mention in the other

Department Also calls from and total of All Calls also come from describe shows in the above

chart that very analyses the Which calls from which Banks or other source so very easy for PS

Consultant.

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Fig27 Dashboard on the Bar chart for Particular Issue on the IBM Cognos Insight

Above chart Shows that For the Particular issue that most comes from which Banks that issue

comes for high in the range that’s PS Consultant analyze why issue come most from the

Particular Banks come from Most that shows in the Above more sure less Problem come from

the Bank.

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Fig28 How the Data Load from the IBM Cognos Insight

The Data Source Excel file , Text file, ODBC connection , IBM Cognos Report data and IBM

Cognos Package data that you are used for the here the IBM Cognos insight here very

Limited Data Source for used here we filtered the data which data import you want for your

Used here one option Not available that you are used for the connect the Excel data that you

are want .

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Fig29 Dashboard on the Bar chart different angle IBM Cognos Insight

Above Snapshot shows that Attribute , ALL Dimension and All import data and also display

Bar chart shows in the different angle very straight line view that most calls from which Banks

other issue you are can display as you want for the your information .

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Fig30 Dashboard on the Pie chart different angle IBM Cognos Insight

Above chart shows that on the display information Pie chart that information display different

charts facility available whole the same information but in the different for the Viewer for

different look into the feel and explorer the more information as you want for detail information

also calculate And other measure value you can also calculate.

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Fig31 Dashboard on the Pont chart different angle IBM Cognos Insight

Above chart very unique chart Pont chart that count all calls for the Bank that chart very

unique chart that also very unique chart that Pont chart not available in the Qlik view and

Tableau for the same information display that here we choose that same information display in

the very unique angel for the information that’s Gamble for the chart how we gamble the chart

as well as for the information.

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Fig32 Dashboard on the Scatter and Bubble chart different angle IBM Cognos Insight

Above Snapshot shows same information in the Scatter and bubble chart that chart not attractive

for the Tableau chart display in the different Color in the Bubble that chart very suitable for the

PS Consultant here IBM Congnos Insight as a Bubble chart but not Suitable for the PS consultant

not attractive that how I compare the chart as well as the Chart same information on the

different tool display and different angel want. Also.

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Fig33 Dashboard on the Tree chart different angle IBM Cognos Insight

Above chart shows that tree chart and tree chart also shows in the Tableau but in the Tableau

very attractive chart also tree chart so we are compare another chart also that tree chart here in

the IBM Cognos insight not the attractive chart so we are conclude the result is that Tableau

chart more suitable and more attractive chart as compare to IBM Cognos insight here same

information which calls from most which Banks so we are got different information and also

not attractive information.

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Fig34 How we Import the Data and mapping the data IBM Cognos Insight

Above Snapshot shows that how here we are import the data and also mapping the data Target

the items , Mapping source items mapping column according our need and required the data

here items from the source are dimension and measure in the target cube and also here define

the properties and relationship for the items according our requirement and need of the data

and here mapping also hierarchies of the data required or do not required hierarchies so here the

according our need what are the requires here other option are add Calculated items or clear all

mapping and set the properties also for the relationship of the data. Here other option also

Summary the data what are the identify here that whole cube design the data what are the

dimension and what are the measure values so accordingly we are set Dashboard. Here other

option apply also here define also dimension what values unique or measure the values and filter

also apply and apply the condition according our requirement also so the we here modify the

data also. So here properties summary are Cube, Dimension, Levels, Attributes, Measure

accordingly set the properties also for the need and requirement also.

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Data Analytics on Excel Sheet

Fig35 Excel graph on the “ISSUES”

Above Chart shows graph on “issue” that come in the software. Issues are opened and closed.

Above graph shows issue which are all open. These data are very helpful for the data Analytics.

0

0.5

1

1.5

2

2.5

15-Sep-15 16-Sep-15 17-Sep-15 18-Sep-15 19-Sep-15 20-Sep-15

Issues

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Fig36 Dashboard on the Excel Sheet Describe one of the issue

Above chart shows that one of the issue described “Network failure”, which was opened and

closed so here we are history of the issue which are close and open for the resolution we are

known in the depth also very clearly identification the issue so that are issue so we are more

focus on the close the issue so we are great efficient work on this very help for the Monitoring

the software and very close on the issue close and open so very helpful the chart.

Fig37 Dashboard on the Excel Sheet Describe Region wise the issue

Above chart shows that Now we are see the issue on the region wise which region wise issue

come high on the software then we are identify that what are reason behind this why issue

come very clear picture on this that so these are issue comes and so these are come now

0

5

10

15

20

25

30

15-Sep-15 16-Sep-15 17-Sep-15 18-Sep-15 19-Sep-15 20-Sep-15 21-Sep-15

Network Failure

open close

0

1

2

3

4

5

6

7

8

Sindh Punjab KPK

Issues

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region head headache so how we are decrease the issue from the region wise so we have clear

picture on this also.

Fig38 Dashboard on the Excel Sheet Describe ATM wise the issue

Above chart shows that Now we are issue region wise and in the region which ATM issue

high what are reason behind this that issue come from one of the ATM higher and in the

now focus on the Region head to the engineer what are activity perform and or that Particular

ATM has created the problem for us what are the hardware changes and what are the software

changes required to fix the issue so we are the decrease the issue so here we are clear that we

have the data then we are more focus on that we are clear more clear issue on the hand and

resolve the issue also so we are better perform the issue.

Technical Comparison of the three Different Tools. Qlikview Very user friendly tool and very nice Design the Dashboard so much popular in the

market and also so many client also So much data selection option very large Design the

Dashboard in the Data Loading here also Script the Data also so much option we are change

the Column name and also we are apply. we are require the data from the Quarter Data so in

the data Monthly data is present so apply the formula for the Quarter data get and weekend

formula apply here weekday data required so we are the apply the formula for get the data

according our requirement so we get our result and Analytics accordingly. Here we are also

change the Column the Name and those column also here delete here that we are not required

0

1

2

3

4

5

6

7

ATM1 ATM2 ATM3 ATM4

Issues

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from the table to get the data and change the data type also perform according our requirement

.we are required data Standard time requirement so we are the Change the time format here we

are some function apply also change the time format and change the date format for our

requirement accordingly so we apply here by default function also for our need and also

Extract the date also so mix data comes in the table date and time mix the data so our

requirement only date so we are extract the date only. how we are multiple table data in the

Load accordingly our requirement we can edit the script modify the Column name and modify

the what are needs multiple table load script very easy in the Qlik view also for convince for

the developer that we want that type of data Load. Here also Syntax checker which check the

script syntax so easy for the developer also for the not making any mistake during the data

loading. Here in the Qlik view also display in web also for the convince for the user also. IN

the Tableau also how we are choose different way filter the data likewise the wildcard

contains, Start with , End with and Exactly with Matches and Include all values and empty here

also Exclude the data other option are apply the condition by filed, by range , by formula and

reset the values that above filter why we are change the data and filter the data because we are

not required to the extra load the to the Tableau and also full fill the requirement and End user

need also for very clean the data see the End user full fill the need. Here other option are

Top by by field and by formula also we are also that way also filter the data according our need.

Filter the data best option not extra load and extra memory consume and very difficult restrict

the data on the when we design the Dashboard also so very difficult also. Firstly filter the data

very important for the End user also. In the IBM Cognos Insight how here we are import the

data and also mapping the data Target the items , Mapping source items mapping column

according our need and required the data here items from the source are dimension and measure

in the target cube and also here define the properties and relationship for the items according

our requirement and need of the data and here mapping also hierarchies of the data required or

do not required hierarchies so here the according our need what are the requires here other

option are add Calculated items or clear all mapping and set the properties also for the

relationship of the data. Here other option also Summary the data what are the identify here that

whole cube design the data what are the dimension and what are the measure values so

accordingly we are set Dashboard. Here other option apply also here define also dimension

what values unique or measure the values and filter also apply and apply the condition according

our requirement also so the we here modify the data also. So here properties summary are

Cube, Dimension, Levels, Attributes, Measure accordingly set the properties also for the need

and requirement also. how here we are import the data and also mapping the data Target the

items , Mapping source items mapping column according our need and required the data here

items from the source are dimension and measure in the target cube and also here define the

properties and relationship for the items according our requirement and need of the data and

here mapping also hierarchies of the data required or do not required hierarchies so here the

according our need what are the requires here other option are add Calculated items or clear all

mapping and set the properties also for the relationship of the data. Here other option also

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Summary the data what are the identify here that whole cube design the data what are the

dimension and what are the measure values so accordingly we are set Dashboard. Here other

option apply also here define also dimension what values unique or measure the values and filter

also apply and apply the condition according our requirement also so the we here modify the

data also. So here properties summary are Cube, Dimension, Levels, Attributes, Measure

accordingly set the properties also for the need and requirement also.

Retail Super Market can use Estimate Beacons. A beacon device system can be used to track the product via Bluetooth, we get location by

using various sensors such motion, humidity and temperature that we are get additional

information also in real time so we get inventory, visibility data for retail Analytics. Estimate

Beacons which location present. Beacons are the signal emitting device that transmits radio

signals in specific distance with required signal strength. Beacons are based on silicon casing and

ARM computer combined with Bluetooth device consisting a small battery and low level

software install in beacons.

Beacons are using for broadcasting small amount of data therefore Bluetooth containing only

257 byte data in each packet through the data cell phones are able to determine the signal

proximity. The other principal is advertisement to the available cell phone device in range its

transmits packet after one second with scanning devices but the problem is devices locked and

unlocked in case of locked device beacons required more power with high frequency therefore

beacons eliminate these devices and preserve the power but during the transmission if active

devices move out of rang the data should be distorted so we can design some other application

through SDK that’s help us to transmit two more packets in each scanning if we set it 490ms

give us two packets increase in frequency and in 330ms give us three packets and in 240ms four

packets give. These signals are transmitted with blinking and effect on a battery life. It’s

complicated to determine the exact position of beacon due to public place different types of

obstacles then calculated by RSSI (receive signal strength indicator) in smart phones but for the

precise position tracking we embedded the map on developing apps.

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Fig39 Estimate Beacons Device via connect the Android app the device shows in the App

Retail Data Analytics on Display in Graph

0

5

10

15

20

25

1 2 3 4 5 6

Shelf Temperature with Hour

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Fig40 Chart Display Shelf Temperature Varies in Hour

Above Chart shows that the Temperature varies in Shelf with the Hour going we are

recording the temperature as an experiment with our Bluetooth device “estimote beacon

indoor” device that also temperature varies above chart shows that in one hour temperature

20 Centigrade and 2 hour 19 C and so on the chart shows that how we product preserve that

temperature so much varies in the then accordingly we are product set in the shelf.

Fig41 Shelf Life with Product varies with respect to the Hour varies

Above chart shows that the who person know how much product stay in the shelf with

respect to the hour with different Pepsi id shows that that are duration we are calculate with

Time-in and Time out the product in the Shelf the above chart shows that very clearly in

the above chart. How we are the product set in the shelf according the demand of consumer.

Conclusion I have studied many tools and after studying them, I have selected three tools for my research

work which are, Qlikview, IBM Cognos Insight and Tableau. I have done technical comparison

and selected these Tools for my experiments. I have built three different Dashboards. With

Same data I have compared working of these tools, I have learned Data Loading types,

understand the method of filtering and data cleanup and which data required for analytics and

which data is not required. These tools require technically different method of Dashboard design

and different scripts to load data and different ways of applying filters. I have concluded that

Tableau and Qlikview are better. I have also worked on Estimote beacons to acquire real-time

0

20

40

60

80

100

120

1 2 3 4 5 6 7

Shelf Life with Hour

Shelf1 Duration in Hour Pepsi ID

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inventory movement data from retailer shelf and found its importance in terms of item shelf-life,

stock-outs and customer trends.

Future Work In this research study I studied three Tool that I used but more tools are also available so one can

work similar activities on these tools as well. We can also use more sample data and get other

real-time data. We can involve retailers, like, Dolman mall or macro or Hyperstar, can connect

with their database servers and work on their data for retail analytics.

Acknowledgement I would like to thanks and acknowledge following domain experts and personnel who have all

help me in completing my IS, without their help it would have been impossible to produce such a

good work:

Wasi UL Akbar

Syed Haris Hasani

Arif Ahmed

Appendix A –Important contributors Below are some of the domain expert’s names along with their designation who contributed and were

considered and helped me in this research.

Organization Employee Name Designation

Touchpoint pvt ltd Wasi UL Akbar Professional Service Consultant

Touchpoint pvt ltd Syed Haris Hasani Professional Service Consultant

PAF KIET Arif Ahmed Student

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