Optimizing Business Intelligence for the Retail Industry

OPTIMIZING
Intelligence for
the Retail Industry
BUSINESS
P A R T N E R S

TABLE OF CONTENTS
Ingredients...................................................................................................................................8
Cost Flow Models and Costs of Goods Sold (COGS)......................................................................9
True Costs (Unit-by-Unit Costing)................................................................................................10
Average Cost (AVCO)....................................................................................................................12
Visualizing Costs..........................................................................................................................14
MMU, IMU and CMU.....................................................................................................................16
Visualizing Cost of Goods Sold.....................................................................................................19
Demand, Sales and Demand Conversion Demand........................................................................22
Visualizing Sales and Demand Data............................................................................................24
Inventory and Velocity..................................................................................................................27
Visualizing Inventory....................................................................................................................28
Velocity: Weeks on Hand, Stock to Sales and Sell-Through...........................................................29
Weeks on Hand.............................................................................................................................29
Sell-Through................................................................................................................................30
Stock to Sales..............................................................................................................................31
Visualizing Velocity......................................................................................................................32
Store Traffic and Conversion........................................................................................................33
Conversion...................................................................................................................................34
Expanding Store Traffic and Conversion.......................................................................................37
Calendar Year, Fiscal Year, Seasons and Comparative.................................................................41

Visualizing Comparative Calendars.............................................................................................42
Budget and Forecast....................................................................................................................42
Visualizing Budget and Forecast..................................................................................................44
Promotions...................................................................................................................................45
Visualizing Promotions.................................................................................................................45
Clearance.....................................................................................................................................46
Single Tier Clearance...................................................................................................................46
Multi-Tier Clearance.....................................................................................................................46
Visualizing Clearance...................................................................................................................48
Social Media................................................................................................................................48
Visualizing Social Media Data......................................................................................................49
Cracking the Egg.........................................................................................................................50
Get Executive Sponsorship...........................................................................................................51
Ensure Data Integrity...................................................................................................................51
Color............................................................................................................................................54
Size..............................................................................................................................................55
Shapes and KPI Indicators...........................................................................................................56
Understand Data Depth and Data Width......................................................................................57
Resist the Lure of the Pretty.........................................................................................................60
Look at Others to Learn More........................................................................................................62
Teach Users to Tell Stories............................................................................................................62
Empower Your Users.....................................................................................................................63

About The Author
Ron Cruz is a Project Management Professional (PMP) and a business
intelligence evangelist who specializes in the retail industry. His writing and
innovative technology work has been recognized with industry awards. Ron's
work on KPI Cloud Analytics for NetSuite has been honored as an Innovation
Award finalist by the Business Intelligence Group.
Ron comes from an artistic background. Having majored in Classical
Guitar Performance and Pedagogy at Brigham Young University, he
studied, performed, and taught classes across various disciplines. This
nontraditional background for a technologist gives him a unique style and
approach to problem solving.
Specialties and Interests: Music, cooking, writing, soccer (football), and
spirited discussions about the intersection between technology and
business processes.

About KPI Partners
The Leader In Cloud Applications, Big Data, Business Intelligence,
and Data Discovery
KPI Partners provides strategic guidance and technology systems
for clients wishing to solve their most complex and interesting
business challenges involving cloud applications, big data,
business intelligence, and data discovery.
KPI works with both corporate technology departments and
corporate business units to develop value-added decision support
solutions, not just new technology deployments.
info@kpipartners.com
www.kpipartners.com
1-888-988-4KPI

P A R T N E R S
6Copyright © 2015 All rights reserved worldwide. | www.kpipartners.com
S h a r e t h i s e B o o k !
Optimizing Business Intelligence for the Retail Industry
A Tale of Three Stores
Companies within the same industry all face the same challenges, opportunities
and pains. The differences in how they approach each of these is where inefficient
companies are separated from the good, and the good from the great. As a consul-
tant, it’s my job to note these differences and move companies towards the good
differences and away from the bad ones. When I started working with retail clients, I
contacted my friends that worked in retail management and asked them for a “ride-
along.” I’d go spend a few hours with them and see what their day was like—specifi-
cally how business intelligence and reporting played into their daily activities.
On my first ride-along, I sat in on a merchant meeting. Sheets of reports in small
print on A3 paper were plopped on tables as reading glasses emerged from pock-
ets. For the next hour, the team plodded their way through the reports. If there was
insight to be had, I couldn’t glean it through the drone. After the meeting was over,
I asked the team how the meeting added value. The answer was simple: After a few
years with the company, one learned what to look for.
On my second ride-along, I mentioned that I was worried about some esoteric pro-
cess that happened on sheets of A3 paper. My friend chuckled and assured me that
would not be the case. This company had “thoroughly modernized” and left the
dinosaurs out to pasture. Throughout the day I found that, yes, they had modernized.
Instead of pulling out sheets of A3 paper, their reports were on iPads. The reading
glasses still made an appearance, as did the sifting through data for needles in hay-
stacks. The company may have put their reports into a high tech format, but the
esoteric and tedious process was still the same. It was the same wine in a new bottle.

P A R T N E R S
By the time I went on my third ride-along, I was fairly pessimistic about being able to understand retail reporting
without years of reading glasses and sifting through mountains of data looking for the one data point that would
lead me to the decision that would put us ahead. As my friend walked me through the halls and introduced me to
the people in the company, someone greeted me with just the proper amount of courtesy, and then excitedly told
my friend that the recent promotion they had run increased sell-through nearly five percent higher than expected
in the targeted departments. The rest of the day was filled with moments like this. This was a wholly different ap-
proach to retail reporting. It was an approach devoid of A3 paper, spreadsheets and droning. Rather, it was filled
with people talking about insight derived from data.
I thought long and hard about the differences between the three companies and especially focused on the third
one. What was it that made the third company’s approach to retail reporting so different? Some would say it was
the technology that set it apart. But I don’t necessarily buy that. Technology cannot solve problems; it can only
automate the process. In the case of these three companies, their reporting systems were all created with the same
building blocks. There are only so many ways that demand, sales, returns, promotions, conversions and other retail
elements can be put together. Sure, the first company simply printed up their reports, while the second viewed it in
tablets. But there was no real difference in the process. Further, if technology was the only factor, companies would
have only to buy the same software and the problem would be solved. This was certainly not the case.
Others might say that it was the people or the company culture that made the difference. I can see the argument,
to an extent. However, I feel that answer is too nebulous. No company sets out to fail or have ineffective business
process or negative company culture. Further, it’s not a very quantifiable recommendation. Simply telling a compa-
ny to hire the right people and have the right culture is no more actionable or helpful than telling a company to fail
less.
In my opinion, what separated the third company from the first two was that it gave the users an “egg to crack,” so
to speak. I often use a cracking the egg parable when talking to clients across different industries and addressing
problems. Indeed, the egg to crack tends to separate great reporting implementations from just good ones.
What do I mean by an egg to crack? Let me take a moment to explain. Of course, like so many things in life, it all
goes back to cake mix.

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In the 1930s after the Great Depression, companies began selling cake mix. Just
adding water in order to make a cake would have seemed like an easy hit. However,
sales disappointed until the 1950s. What was the main change that caused sales to
take off? As with everything, there are a lot of factors. But the two main ones were
the perception of giving cooks the ability to create elaborate cuisine with less time
and the egg. Most cake mixes before this period had powdered eggs in the mix. The
thinking was that giving the cook an egg to crack elevated the act of simply mixing
up a cake to actually baking a cake. It gave the consumer a sense of ownership.
While the thought of giving the consumer a sense of ownership was all that was
needed to elevate cake mixes to the staples they are today, sadly it’s not entirely
true. The fact is, fresh eggs simply made better cakes.
Nevertheless, the lesson here should not be discounted. True, it wasn’t just the fact
that customers now had to take an action that put cake mixes over the top. But it
was the action the customer did that made the product better and enabled it to be-
come a staple. This is what I mean when I tell you to give the users an egg to crack.
Require the users to interact and that interaction will make the product better than it
would have been without it.
Think of the first two companies I visited. They were bound by the tyranny of their
reports. Their interaction with their reports was limited to looking for the hidden
insight somewhere in the pages of A3 papers or swipes of the tablet. At the third
company however, the users were enabled to answer their own questions, and create
their own journey with their data. This is what created a sense of excitement for the
users and elevated their reporting systems from something they had to use to some-
thing they got to use.
The Meaning of the Egg

P A R T N E R S
This book will address ways to accomplish giving users an
egg to crack for retail reporting and will be split into two
sections. The first section will address the ingredients of
retail reporting. You can’t make a good cake without know-
ing what should be in it. This section, while certainly not
exhaustive, will touch on the main components of retail
reporting. Further, at the end of each major element, there
will be some basic examples of how to visualize the element
using charts and graphs.
The second section will be the egg to crack and will discuss
how to implement a reporting system that enables users
to quickly understand their data and allows them to create
their own data journey.

P A R T N E R S
When talking about retail reporting, most people start on gross sales and net sales.
However, I always start with cost. Sales are easy. You sold something. How much
did you sell it for? Easy. Cost of goods sold (COGS)? Not nearly as simple. When
considering costs and cost of goods sold, it is important to delineate the difference
between the two. A cost is simply what an item cost you from purchase order to the
receipt. While cost of goods sold takes the costs determined earlier and then uses a
model to assign a cost to a day or to transactions.
But before we discuss which costing model to use to assign cost as cost of goods
sold, first let’s spend a second discussing how to determine costs. This should be
pretty rudimentary for most readers so this will be a basic recap.
Costs = First Costs + Landing Costs.
First Costs = initial cost for the goods
Landing Costs = additional costs incurred in attaining goods, e.g., agent commission,
freight, etc.First costs is pretty standard across companies. It is simply what was paid
out for the item, while landing costs (sometimes abbreviated as ELC when estimated
and ALC when actualized) can be comprised of different elements depending on the
realities of the business.
{ }
Cost Flow Models and
Costs of Goods Sold (COGS)

P A R T N E R S
When it comes to cost of goods sold, there are several different ways that compa-
nies assign it and each of the most common are outlined in the section following. The
main thing to keep in mind is that whatever method is used, it is of utmost impor-
tance that the organization understands the method being applied and how it works.
True Costs (Unit-by-Unit Costing)
Unit-by-unit costing can be a good way to handle costs, as long as it is realistically
implemented. In this approach, a unique ID is created upon receipt for each item and
that ID is tied to its relative cost.

P A R T N E R S
While ideal, this approach is typically only possible for smaller retailers whose entire
inventory consists of only a few high priced items. Otherwise, there will be so much
time spent tracking costs that it will quickly become prohibitive. If you are in the
small group of companies where such a model can be considered, then this is a good
way to assign costs of goods sold. However, by far, most will find the effort to do this
unsustainable.
Given this, let’s look into costing models that do not rely on such a huge amount of
effort.
Both of these models rely on the order of the receipt and the amount sold; they just
apply it in different directions.
In FIFO (first in, first out) costing, the user assumes that all costs from the first re-
ceipt from a purchase order must be completely sold before moving the next cost
from the next receipt.
FIFO (First
In, First Out)
and LIFO (Last
In, First Out)
Costing

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Average Cost (AVCO)
With LIFO (last in, first out) costing, the reverse is held true. All items from the latest receipt must be sold first before as-
signing costs from earlier receipts.
The issue with both FIFO and LIFO costing is that it can be confusing to track how many units were decremented from
which receipt when something new comes in. For example, let’s say that an item took receipt of 100 units on the first of Jan-
uary at a cost of $10.00 per unit and began selling shortly thereafter. By the fourteenth of January, the item had sold eighty
of those units at cost of $10.00 per unit. Now on the fifteenth, a new receipt for fifty more units at a cost of $8.00 per unit
comes in and the item sells another sixty-five units. In FIFO, care would have to be taken to ensure that the first twenty units
were costed at $10.00 and the remainder at $8.00. In LIFO, it’s even more complicated. The first fifty units would have to be
assigned a cost of $8.00 and the last fifteen units assigned a cost of $10.00.
Because of this, unless a company has a specific reason to use a FIFO or LIFO cost flow (for example, a fashion retailer will
likely use FIFO as they’ll want to move their inventory out when it’s pertinent), many companies go with average costing, or
AVCO.
Average costing addresses the complexities of FIFO and LIFO costing by applying a weighted average across the remaining
inventory and its relative costs and thereby eliminates the issues that FIFO and LIFO can create. It is important to note that
AVCO is a periodic model. This means that it is applied at a specific time and then applied to transactions within that period.
Most companies run their AVCO calculations at the end of each business day and that becomes the COGS for the day. Other
companies run AVCO once a week and others only once a month. Generally, daily is best; however, if receipts or fluctuations
in price do not happen often in your company, then a less frequent calculation schedule might make more sense.
The AVCO calculation is drawn out simply enough:
COGS = Total Cost of Inventory/Total Units in Inventory
However, it can be confusing for some to grasp based on just the formula. It’s best to see it in action. To illustrate how AVCO
works, let’s run through the same example we used in FIFO and LIFO.

P A R T N E R S
The order of events is this:
1/1/2014 Initial Receipt of 100 units at
a cost of $10.00 per unit
1/14/2014 Sale of 80 Units
1/15/2014 Receipt of 50 additional units at a cost of $8.00
1/16/2014 Sale of 65 units
Using AVCO, the Cost of Goods Sold will work out thusly:
Date Cost of Goods Sold Per Unit
1/1/2014 - 1/14/2014 $10.00
1/15/2014 $8.57
1/16/2014 $8.00
And in an effort to appease the spirits of my high school teachers, here’s where I
show my math:
Date Total Inventory Total Cost Cost of Goods Sold
1/1/2014 -1/14/2014 100 $1,000 $10.00
1/14/2014 20 $200 $10.00
1/15/2014 70 $600 $8.57
1/16/2014 5 $40 $8.00
An Important Note on COGS
and Purchasing Models
The example given above was
a very simple, single location
calculation. However, pur-
chasing models need to be
taken into account. For exam-
ple, running AVCO or FIFO/
LIFO on a single store when
purchasing is done from a
centralized location will not
show true costs. Instead, it is
important to ensure that the
calculations are run using the
right combination of loca-
tions. Running per store or
distribution centers tends to
be more of a manufacturing
or distribution model, while
retailers tend to run at a mar-
ket or subsidiary level.

P A R T N E R S
When it comes to costs, tracking by SKU over time is an obvious use:
Visualizing Costs
Another good use of visualizing costs is to track an average cost by the highest level
in the item hierarchy (usually department) as a percentage of the whole in order to
tell where your largest spend is.

P A R T N E R S
When visualizing costs of goods sold, the focus is on the sale of the goods, so it’s
generally most helpful to be able to show the cost along with the sales. However,
before we start visualizing this, we should talk about the margin measures and define
them.

P A R T N E R S
MMU stands for maintained markup. It also goes by other names such as markup or
margin. As such, there are many names I could call it and any name I use will alienate
a set of users that have never called it that, so I’ll stick with MMU as it’s what I’ve seen
most often.
The main thing to keep in mind is that MMU takes your sales and tells you how they
compare with the cost of goods sold. There are two main metrics associated with
MMU:
MMU Cash = Sales − Cost of Goods Sold
MMU % = MMU Cash/Sales
It is important to note that there can be different versions of MMU. For example,
instead of comparing costs to sales, maybe you want to compare it to demand.
Thereby, you would swap out sales for demand and create a demand MMU and or
swap out sales for gross sales and so on.
MMU is the most used in this family of metrics simply because it shows the reality of
what happened: what the item actually sold for against the cost.
MMU, IMU and CMU
{ }
MMU

Optimizing Business Intelligence for the Retail Industry

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