Business Intelligence in Retail Industry

Retail industry is a highly competitive market with new players entering the fray every once in a while. Rapidly changing customer demands and mounting pressure from suppliers, effective management of information is imperative to make good business decisions.

Target Corporation is the second largest retailer in the US having its sales through store fronts as well as digital channels. It differentiates itself from other discount retailers like Walmart and K-mart stores by offering more trend-forward, upscale merchandise at lower costs. A typical Target store offers clothing, beauty products, electronics, health products, groceries, home and hardware supplies.

Along with popular brands Target also sells its own branded labels such as Archer Farms, Market Pantry, and Simply Balanced; Sutton & Dodge, their premium meat line; Threshold, their premium furniture line; and the electronics brand Trutech. Target also has a separate e-commerce operations through the target.com domain. From its aesthetically designed store fronts to an award winning i-Phone app, Target aims to make their customer’s shopping experience memorable and unique.

Target with its operations across 1,801 locations throughout the United States has large volumes of data, right from its supply chains, warehouses and store operations. With this myriad information at their disposal Target wishes to make leverage this data to develop actionable insights and deliver value. The CEO of the company Mr. Brian Cornell wishes to use Business Intelligence to have insights into the various facets of his business. At a very basic level he wishes to see the performance of the companies from the perspective of the following parameters:

• Customer : Who are the type of customers that typically prefers to shop at Target stores.  What would be their average annual income? How many customers are enrolled for reward programs and how can they be segmented based on their spending. What is the age group of people shopping at the stores and the number of people preferring store fronts vs online shopping. 
• Sales Channel: Which are the different channels that are more profitable to the company. While their standalone retail stores are their main source of income, alternate channels such as online shopping, TV shopping networks, mall stores etc. are also channels that Target is interested in.
•  Promotional events/Discounts: What is the increase in sales during special promotional events such as Black Friday or Cyber Monday. How much discounts is to be provided as part of the reward program, which are the products on which discounts should be offered, for what duration should they be offered are some of the parameters the CEO would like to look into.
• Vendor/Supplier Payments: Which are the vendors and suppliers that provide the best raw materials and at what price? what has the performance of the vendor been in terms of timely delivery of raw materials and if quality of the products have been consistent.
• Employee Payments: Employees include clerks at the checkout counters, warehouse workers, delivery workers and other managerial and administrative employees. Information on the number of employees at each store, customer to employees ratio, salary paid to employees are different location and the working hours of employees are some of the parameters that the CEO would be interested in looking into.

      How Dimensional Modelling helps?

a     Using Data Dimensional modelling will help Target to understand its revenue model better by giving valuable insights on the sales it generates. By defining various dimensions such as store, product, date, promotion etc, the CEO will be able understand the granularity of its sales generation. Data dimensional will help understand that for a particular product, at a particular store at a particular location, what was the daily or monthly sales that occurred.  By observing the high cardinality, numeric measure of entries in the fact tables we would be able to get clear measure of various business event such dollars sales for a particular transaction by a customer. Additive and semi-additive facts will also help in aggregating various facts together to give the stakeholders the necessary level of granularity.

      Accumulating Snapshot Dimensional Model:

      Considering that there are many milestones in the entire process of obtaining the inventory, storing it in warehouses and delivering it across various stores across the country, an accumalting snapshot would be the ideal type of dimensional modelling for understanding the sales revenue for Target corporation.

  

     Below is a sample dimensional model that can be used for the Retail Industry. Some of the key dimensions are Product dimension, payment dimension, store dimension etc.

Thus through the use of dimensional modelling for data warehousing, key stakeholders of the business can get an idea of how Target is performing through implementation of various parameters.

References:

• http://flylib.com/books/en/4.65.1.20/1/
• https://www2.microstrategy.com/download/files/whitepapers/open/Business-Intelligence-and-Retail.pdf
• https://en.wikipedia.org/wiki/Target_Corporation

Business Intelligence & Analysis Tools

In today’s fast-paced business world, Business Intelligence (BI) tools are being used by organizations to make improved business decisions. It enables business users and analysts to access company data in an efficient and intuitive manner. During the past few years many BI tools have come to the market. Some of these are traditional stand-alone tools while some are enterprise wide, decentralized tools. This blog tries to look into some of the leading and well-known BI tools used by most organization. A weighted scoring model has been used to identify key evaluation criteria for these BI tools.

From the various BI tools available in the market, 5 tools have been selected for evaluation. These are as follows:

1)   IBM Cognos:

IBM Cognos provides an integrated BI platform that provides good flexibility and the ability to query packages in an ad-hoc way. It is ideal for large deployments across an enterprise that are typically centrally managed. 

IBM Cognos Analytics also provides good collaboration across teams, organizations, and ecosystems to amplify value and offer good scalability, governance, security and overall performance.

Unique selling proposition:

IBM Cognos Business Intelligence is a web-based, integrated business intelligence suite that provides tools right from reporting and analysis to score-carding and monitoring of events.

2)    SAS:

Business Intelligence and Analytics suite provides excellent predictive modeling with innovative visualization. Its interactive capabilities along with its diverse set of use cases, caters the analytic requirements right from an enterprise level organization to traditional user such as data scientists, IT developers and power users within these organization.

License cost can sometimes be a concern for SAS customers especially when there are wider deployments across an entire enterprise.

Unique selling proposition:

SAS meet both, the enterprise needs of IT, as well as the self-service needs of the business. This flexibility differentiates SAS from other vendors in the market.

3)   Microsoft BI and analytics:

The Microsoft BI and analytics suite supports different BI use cases and analytic requirement. Scalability is one of the major strength of this product. It ranks very well for data volume accessed, with an average data size of 62 TB, which is higher than any of its competitors. It also provides cloud-based services and collaboration capabilities through a subscription-based model using Office 365.

Microsoft BI and analytics also has a deployment with an average number of end users of 6,000 which in comparison to other products is higher.

Unique selling proposition:

With a strong customer base, the solution provides built-in connectivity to on-premises SQL Server Analysis Services, which will allow organizations to leverage existing data assets without having to move to in the cloud.

4)   Tableau

One of the most popular BI visualization tool, Tableau is the perceived market leader among all the other BI tools that are out there. Tableau supports a wide range of data sources from SQL to MDX, as well as a number of Hadoop distributions, support for Google BigQuery, Salesforce and Google Analytics.

Tableau also provides a robust mobile client. Visualizations are optimized for mobile devices and its touch-optimized controls makes accessing and viewing data easy and intuitive.

Unique selling Proposition

Tableau easy to use visual based data discovery capabilities enables business users and analysts to play with in data even without extensive skills or training with any previous BI platforms.

5)   Qlikview

QlikView, much like tableau, provides ease-of-use for both IT professionals and non-technical users. It provides a self-contained, tightly integrated development platform for building intuitive and interactive dashboard applications. It offers a highly interactive dashboard, centralized dashboard application development for an enterprise features to support governed data discovery.

Unique Selling proposition

Its key strength lies in the fact that it can combine data from diverse sources such as Salesforce.com, Oracle, SQL Server, SAP and Excel. QlikView’s also ranks high in customer loyalty, satisfactory performance, and is known for its product quality and overall market position.

Comparative Analysis

In order to compare these BI tools to see how they stand against each other, we have used a weighted score model to analyze their strengths and weakness. To do so we have identified the following evaluation criteria based on which we make our comparison. 

1. Reporting: Reporting help users convert data into actionable knowledge. They allow users to better understand the analysis within reports, and the underlying data those reports are based on, to support better decision-making. Some of the basic features should include drill down through reports, conduct slice and dice OLAP analysis etc
2. Data Source Support and Integration: This criteria tries to check the different data sources that the tool supports and ability to provide user-defined measures, sets, groups and hierarchies.
3. Analytic Dashboards & Content: The quality of visualization and exploration capabilities and the ability of the tool to create highly interactive dashboards and content with ease of use that can be later consumed by others.
4. Platform: The different platform that the tool can run on and its ability to deliver contents in an interactive mode on multiple computing environment such as cloud deployment, web-based, mobile or stand-alone platforms.
5. Customer Support:This criteria includes the ways customers receive technical or account support. It compares the type of support available (e.g. Email, Phone, FAQ), availability of user groups, service-level agreements etc.

Based on these parameters the weighted analysis table looks something like this. Evaluation criteria are given a weight-age based on their importance that is required in any standard BI tool. Each vendors is scored on a scale of 1 to 5 with 1 = poor, 2 = average, 3 = good, 4 = excellent and 5 = outstanding.

Criteria	Weight	IBM Cognos	SAS	Microsoft	Tableau	QlikView
Reporting	30%	4	3	3	4	4
Data Source Support and Integration	20%	3	4	4	5	5
Analytic Dashboards	30%	4	4	3	5	4
Platform	10%	3	4	4	5	4
Customer Support	10%	2	3	3	3	4
Score	100%	3.5	3.6	3.3	4.5	4.2
Rank		4	3	5	1	2

We have given scores to each of these features based on how predominant they are in these tools. We have also used the Customer Survey Metrics from Gartner Magic Quadrant for Business Intelligence and Analytics Platform to base our scoring on.

Once these scores were given we calculated the weighted score for each of the 5 BI tools. Based on this analysis we can see that Tableau has the highest score of 4.5 closely followed by QlikView with a score of 4.2.

SAS, IBM Congos and Microsoft BI have more or less the same score in all the criteria and they are ranked 3,4 and 5 respectively.

Final Verdict

Based on the selected features and the product scores given for each of these features we can see that Tableau is clearly the winner. As written in Gartner's Magic Quadrant report,

"Tableau has clearly defined the market in terms of data discovery, with a focus on helping people see and understand their data. It is currently the perceived market leader with most vendors viewing Tableau as the competitor they most want to be like and to beat"

References:

1. http://www.gartner.com/
2. https://www.wikipedia.org/
3. https://www.yellowfinbi.com/YFCommunityNews-6-Key-Features-of-any-Business-Intelligence-Solution-100207
4. https://www.yurbi.com/blog/straight-talk-review-of-tableau-software-the-pros-and-cons/