bi retail industry v1.1

8/7/2019 BI Retail Industry v1.1

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Business Intelligence forRetail

Submitted by:

Vivek Sharma

Ashutosh Sinha


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Retail Industry

1.Introduction

Retail is the most complex industry handling a wide range of

products from large numbers of suppliers and servicing the

highest number of customers.Retail Industry market trendschange most frequently than any other industry. To remain

competitive in the retail Industry, keeping an eye on all the

operations in retail business is very crucial.Understandingcustomer requirements and providing them with what they

want and still maintaining profitability requires valuable and

highly analysed information on management part to deal

with ever changing dynamic market conditions and to stay

ahead of the competition.

As retail markets become more competitive, the ability to

react quickly and decisively to ever changing trends and tocustomize products and services to individual customer

needs is more critical than ever. A business intelligence

system can be a very effective means of organizing and

analyzing the vast amount of information generated in a

retail business, and helps you to generate a more effective

business model for keeping your business profitable.

2.Retail and Business Intelligence

Successful retailers strive to accomplish three basic

objectives:

to align their business with client needs

to differentiate from competitors; and

to optimize product mix and space utilization.


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To achieve these basic goals, retailers must be able to

successfully manage inventory, product mixes, promotions,

advertisements, supply chain dynamics, and a host of other

factors. Furthermore, As retail markets become more

competitive, the ability to react quickly and decisively to

ever changing trends and to customize products and services

to individual customer needs is more critical than ever. Lack

of information is not the problemdata to assist in making

these kinds of decisions is readily available but the problem

is that the complexity and volume of information available to

organizations is overwhelming.

Technology plays an indispensable role in supporting thebackbone of retail businesses. In a retail environment,

transactional systems, such as Point of Sales (POS) systems

are efficient in what they are intended to do - record and

retrieve large volumes of transactions and operations.

Embedded in the Point of sales systems is a "treasure trove"

of dormant, unclassified and often unused information about

what happened in the business in the preceding period like

last week etc. Legacy reporting systems often presenthistorical information in the form of standard static layouts.

These reports can neither be viewed from different

perspectives at different times nor can they provide critical

insight for retailers in answering questions such as

How can inventory levels be optimized

What are the most profitable products

Who are the most valuable customers

Increasingly, successful retailers will be those that can

effectively categorize and utilize these data and use it for

decision making.


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3.Retail Industry analysis through BI

BI can provide useful insights into the retail sector:

Sales and profitability analysis. Store operations analysis.

Customer analysis.

Merchandise management

Supplier performance measurement

Marketing and e-commerce analysis.

Brand and marketing research.

Market Share analysis.

4.Key performance indicators of retail sector:

Sales per hour this statistic tells us about the speed at

which each individual salesperson is selling or attending to

customers compared to everyone else on the shift.

Average Sale the average selling price of each individual

salesperson compared to everyone else on the shift higher

averages show a greater knowledge of product as the

salesperson is able to sell higher ticket items. Low statistics

reveal the salesperson lacks skill in either product knowledge

or effective probing.

Items Per Sale tells us about the ability of the salesperson

to add-on to a sale.

Conversion Rate tracks how many visitors to the store are

turned into customers.

Wage to Sales Ratio compares a salespersons hourly

wages to hourly sales. This KPI differentiates clearperformers from underperformers


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Average sales per customer or transaction Total sales

for a given period divided by the number of customers or

transactions for the same period

Sales per square foot / meter Actual sales for a given

period divided by the total floor area (in sq.ft. or meters) of

the store.

Inventory Sales per selling hour Actual sales for the

store divided by the number of selling hours during the same

period

Sales per labour hour Actual sales for the store divided

by the number of labor hours used during the same period

Inventory Turns This KPI tells us how often the average

inventory over a given period of time (usually a year) is sold

in that same period of time

Inventory Store conversion rate The number of

transactions in a given period divided by the total number ofcustomers who entered the store during the same period

Coupon conversion percentage Percentage of coupons

that have been used by customers

Profit per Customer Visit Profit obtained from each

customer visit. This way you can easily set goals for your

sales team in order to increase profits.

Units per customer or transaction Total number of

units sold in a given period divided by the number of

customers or transactions for the same period

Shelf space profitability measured per product

Price premium The relative price of a product compared

to a benchmark price (average retail price)


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Promotion share Share of promotion products in

percentage (%) of total sales

Shortages/overages in cash registers

Sell-through percentage (%) Is a percentage of units

sold during a period and it is calculated by dividing the

number of units sold by the beginning on-hand inventory (for

that same time period)

Percentage of perishable items with past due date

Number of perishable items with past due date as apercentage of all items in store

Product visibility on shelf Measures the amount of frontal

views of a single product-package on a fully stocked shelf

Sales Gross Margin Return on Inventory Investment

The GM ROII multiplies Inventory Turns (which tells us how

healthy our stock is) by Gross margin (which tells us the

percentage of profit we make on each sale)

5. Business Intelligence software for retail: Major

vendors

1.

The IBM Cognos BI suite serves more than23,000 customers in more than 135 countries. Complete corporate performance managementsolution. Supports enterprise planning, scorecarding,and business intelligence.

2.


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Exceptionally strong in advanced analyticanalysis. Full analysis power revealed in the hands ofuses with knowledge of SAS programming.

Subscription-based pricing model.3.

Widely established and respected vendor ofbusiness intelligence solutions.

Market leader for data warehousing tools. Gartner recognized the Siebel BusinessAnalytics platform as one of the most comprehensiveand visionary BI platforms available.

Oracle solutions are designed to integrateseamlessly with any existingdatabase systems.

Hosted and on-premise B.I. solutions available.

4.

As one of the largest business softwarecompanies, SAP can integrate BIthroughout SAP applications and your company.

Strong data scalability and performance thanksto in-memory analytics and column-based vectoring.


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Acquisition of Business Objects makes SAP the

largest BI vendor.

5.

6.

6.Sample retail Business Intelligence

dashboards:


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7. Designing a Business Intelligence data ware house for

Retail Industry

This section would deal with the question of how to design a

business intelligence data ware house for retail industry. Wewill explain this step by step through a small retail case

study to make it simple to understand.

Case study:

We are running a retail business which has 100 retail

stores spread over five states.

Each of the stores has a full complement of departments,including grocery, frozen foods, dairy, meat, produce,

bakery, floral, and health/beauty aids.

Each store has roughly 60,000 individual products on its

shelves.

About 55,000 of the SKUs come from outside

manufacturers and

have bar codes imprinted on the product package.

These bar codes are called universal product codes

(UPCs). UPCs are at the same grain as individual SKUs.

The remaining 5,000 SKUs come from departments such

as meat, produce, bakery, or floral. While these products

dont have nationally recognized UPCs, the grocery chain

assigns SKU numbers to them. Data is collected at two points:

Point-of-sale(POS)-when customer make purchases.

Data collection point-when vendors deliver materials.

Management concern: maximizing profit.

Charging as much as possible.


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Lowering cost for product acquisition and overheads.

Attracting more and more customers in the highly

competitive pricing environment.

Other managements major decisions revolve around:

Promotions.

Pricing.

We shall use dimensional design process and it consists of

following four steps:

1. Selecting the Business process.

2. Declaring the Grain.

3. Choosing the dimensions.

4. Identifying the facts.

Step 1: Selecting the business process

In our retail case study, management wants to better

understand customer purchases as captured by the POS

system.

Thus the business process were going to model is POS

retail sales.

This data will allow us to analyze what products are selling

in which stores on what days under what promotional

conditions.

Step 2: Declaring the grain

Grain here is defined as the modularity level of the data.

In our case study, the most granular data is an individual

line item on a POS transaction. To ensure maximum


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dimensionality and flexibility, we will proceed with this

grain.

While users probably are not interested in analyzing single

items associated with a specific POS transaction, we cantpredict all the ways that theyll want to cull through that

data.

For example, they business users may want to

understand the difference in sales on Monday versus

Sunday. Or they may want to assess whether its

worthwhile to stock so many individual sizes of certain

brands, such as cereal. Or they may want to understand

how many shoppers took advantage of the 50-cents-off

promotion on shampoo.

While none of these queries calls for data from one

specific transaction, they are broad questions that require

detailed data sliced in very precise ways.

Step 3: Choosing the dimensions

The dimension table contains the textual descriptors of thebusiness.

Major primary dimensions for the our grain are the date,

product, and store.

We assume that the calendar date is the date value

delivered to us by the POS system. Later, we will see what

to do if we also get a time of day along with the date.

Within the framework of the primary dimensions, we can

ask whether other dimensions can be attributed to the

data, such as the promotion under which the product is

sold.

Step 4 : Identifying the facts


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A fact table is the primary table in a dimensional model

where the numerical performance measurements of the

business are stored

The facts collected by the POS system include the salesquantity (e.g., the number of cans of chicken noodle

soup), per unit sales price, and the sales dollar amount. In

some cases it may include the dollar cost.

Three of the facts, sales quantity, sales dollar amount, and

cost dollar amount, are beautifully additive across all the

dimensions. We can slice and dice the fact table with

impunity, and every sum of these three facts is valid andcorrect.

Whereas, dimensions like gross profit and unit price are

non additive and can be calculated through query.

Figure: Measured facts in the retail sales schema

Date Dimension

The Date dimension is the most frequently used dimension of a

datawarehouse. Some data in this table can not be calculated.

For example, whether the date was a holiday or not has to be

calculated through holiday calander input from the company.

Moreover, fiscals can be cutomized (say the financial yearextends from January to December) ending A typical date


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dimension is shown below.

Date dimension is most frequently used for slicing operations,

and most reports are prepared on time basis. Drilldown is also

done on the basis of this dimension.

If the rows in a fact table are coming from several timezones, it

might be useful to store date and time in both local time and a

standard time. This can be done by having two dimensions for

each date dimension needed one for local time, and one for

standard time. Storing date in both local and standard time, will

allow for analysis on when facts are created in a local setting

and in a global setting as well.

Product dimension

Product dimension helps in slicing and dicing through the

various reports that can be prepared using this dimension.

The important function of this dimension is to hold as many

descriptive attributes of each SKU as possible. Typically

each SKU rolls up to brands, brands to categories, and


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categories to departments. Generally, 50 attributes is

considered a reasonably good descriptive dimension. This

dimension is one of the 3 primary dimensions in data marts

on retail.

Store dimension

Store dimension is very similar to the product dimension, and

is subject to slow change.


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Promotion dimension

The promotion dimension describes the promotion conditions

under which a product was sold. Instead of having a single

attribute of promotion like discount/ free product etc, thisdimension Promotion conditions include temporary price,

media name, free product name etc. Each promotion flags

appropriate flags in the row to discribe itself including

reductions, end-aisle displays, newspaper ads, and coupons.

This dimension is often called a causal dimension (as opposed

to a casual dimension) because it describes factors thought to

cause a change in product sales.

It may be difficult for users to comprehend, but is very useful

in protection against changing dimensions. Here, another tip

is to avoid null key in fact table, even if the product is not

under any promotion as it helps to get right results from

joins.

Rationale for the approach

1) Selecting a single business process to model at a go:


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We model processes instead of trying o pursue a single

requirement/report. This is because most the

needs/requirements for the end users is on a case basis.

One can not economically use time & effort to set up

new reporting routines, everytime a new requirement

comes up. Also, the scaling of an ad hoc reporting

system could take toll on the OLTP systems, for getting

the data feed. Hence, it is wise to approach the problem

in a more systematic way, even if the immediate

requirement seems small.

Moreover, the Process approach cuts through the

department barriers and makes it easier to see thelarger pictures. Another need of production DW

designers is to see the clarity in relation to availability

of data, ie. What data is already there in the DW (A

large DW is usually replenished by multiple data feeds,

and it gets increasingly difficult for a designer to keep

track of what is already there) and what needs to be

fetched, when implementing a new requirement.

2) Declaring the grain

The general thumbrule in case of grain declaration is '

the smaller, the better'. For example, one could also

capture the denomination and currency number of the

notes used in a transaction, and that would allow for

very interesting analyses to take place. But there is a

huge chance that such level of details would mostly get

unused. Also, granularity takes a toll on the processingof cumuative results. Additional cumulative tables

would be needed to be maintained and updated for

every added level of granularity. However, with the

growing powers of hardware, this does not seem to be a

big concern.

Not providing the smallest possible level of granularity

would lead to problems of redesign, in case arequirement comes up. So, designing for a system to be


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working 10 years from now, the designer should take

the freedom of keeping the granularity as high as

possible.

3) Choosing the dimensions:

Choosing dimensions is the art part in the design of DW.

How business people describe the data they use, can be

learned from anticipation and expertise. Dimensions

help reduce the run processing time for queries, as

dealing with large databases is tricky.

Keeping the processing time in check is essential as the

database size for Data warehouses can exceed

terabytes. Also, the cost of query gets reduced if we can

use less of SQL functions.

Keeping as many dimensions helps in slicing and dicing.

However, with shifting IT contracts, vendors tend to add

new dimensions for every new requirement without

studying the system properly. Can lead to degenratedimensions resulting in higher query cost and less

friendliness to the user.

4) Identifying the facts:

One should avoid using ratios and percentage data in

fact table. This data needs to be updated every time an

entry is made to the affecting variables. This could

imply either inconsistency due to delay in updation orincreased load due to updation. Instead such data

should be calculated by the application logic.

Best practices explained


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1) Use of surrogate keys :

Though operational changes happen once in a decade

or two, it is always wise to be ready for the change.

Thus, it is recommended that surrogate keys be used toconnect fact and dimensions. This helps to remove the

dependency between the data stored in the row and the

key. The key must not hold any peice of information

that can possibly be used.

2) Design for extensibility

Ideally, the design should be able to accomodate new

requirements just by adding new dimension tables andan rows in fact table. In case of product launches, where

product description is not available till late, using

dummy data can be helpful. However, some

requirements can not be fulfilled this simply. For

example, if it is desired to increase the granularity of a

dimension table along with the fact table, it would be

necessary to drop the fact table and rebuild it. However,

existing applications would be unaffected.

Also, when a new data source is involving unexpected

new dimensions is encountered, it is wise not to force fit

the measurements into the existing fact table.

3) Snowflaking

Snowflaking refers to the practice of dimensional

normalization in the data warehouse design.Snowflaking makes the query design for users more

difficult. Also, the queries become more costly due to

the joins. Monetary value of disk space saving is not

very high and the higher cost of maintainance of

consistency is bore by the ETL process, which is rather

acceptable than putting the load on OLAP queries.

Moreover, datawarehouses are not meant for much of

data changes afterwards, as historic data keeping isdone here.


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4) Centipede dimensions

Keeping too many dimensions in the model implies that

two or more dimensions need to be combined togather.

The thumrule here is that if there are more than 25dimensions, you may need to revisit the design. If the

resulting dimension is noticably smaller than the cartesian

product of the seperate dimensions, then it could be a

good decision to combine.

5) Protection against slowly changing dimensions

Dimensions such as product and store are subject to

slow changes. For example, a store may change it'slocation and thus other related fields in address

columns such as city, state and PIN code etc. Similarliry,

a product may be renamed and it's cost of production/

acquisition may change over time.

It is generally desirable to preserve the historic

information as well as the warehouses being actionable

on the basis of current information. Author of DWtoolkit, Kimball suggests various ways to cope with the

problem of changing dimensions. Of these solutions,

there are 2 types that preserve historical data.

Type 2 preserves changing attruibutes by adding 2

more attributes, start date and end date. Type 6 also

works in a similar way, but also mentions the

immediately previous applicable value to the current

value.

References


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