Preliminary dashboard KPI research

1. Preliminary sample research. Not for public distribution 1
R E S E A R C H

2. Preliminary sample research. Not for public distribution 2
ANALYZE

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Branding Observations:
• Minimalist color palette:
Red, gray, white, black, airy
• Use red strategically for callouts and to draw
attention to specific points of interest. Note:
consider using a limited red palette for
negative data colors.
• Brand is clean, artsy, young,
fresh, trendy, cool, simple
Design elements
Primary ‘red’ color palette
Secondary ‘gray’ palette

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• Executive users need to focus on trends
over a long term timeframe. Generally need
to see at least three years worth of data.
• These users are busy, so it’s important to
make sure this information is presented
clearly and it’s quick to consume.
• Provide data context and give them
filters/tools to explore data within that
context if they need more detail. May not be
a power user, so be mindful of features.
• Executives want/need their information
actionable. Look for ways to incorporate
tools and features.
• Determine and verify what KPI’s should be
given prominence hierarchy. How might this
look different for the CEO, CIO, CFO, etc?
Other Personas to explore:
• Data Analyst: Analytical users often need
to make decisions in the medium term
(i.e. comparing this year to last), so they
will want to go back at least 12 months
and ask more questions of the data.
• Line of business user: needs daily,
detailed operational metrics. It is not
uncommon for them to drill down to
transactional level details.
Persona: CEO / Executive User
Non-dashboard information sources
• Broad knowledge of multiple external arenas
(i.e. emerging tech, new products, competitor
strategy, politics, market sector /stock
performance, etc.)
• Industry case studies, 3rd party partnerships,
qualitative reporting and perspective from
divisions throughout the company.
Background
Brian Cornell is board chairman and CEO of
Target Corp. He is responsible for Target’s global
business, including the company’s nearly 1,900
U.S. stores, digital properties and more than
350,000 team members.
Cornell joined Target in August 2014 after more
than 30 years in escalating leadership positions at
leading retail and global consumer product
companies, including three CEO roles and more
than two decades doing business in North
America, Asia, Europe and Latin America. His past
experience includes time as both a vendor partner
and a competitor to Target, and he brings insights
from those roles to the company today.
Obtained a Bachelor’s degree from UCLA in 1981
and attended its Anderson School of Management.
Analytical challenges / opportunities
• Sharing real time data and insights with
senior staff/leadership.
• Building analytical stories for Board of
Directors reporting; ability to annotate
for context.
• Using dashboard analytics to develop
rationale for online/brick and mortar strategy.
• Measure progress against benchmarks.
CEO
Board Chairman:
Brian Cornell
Years: 2014-Present
Operator Innovator
Introvert Extrovert
Analytical Intuitive

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Ideas for data visualization
Source: https://https://corporate.target.com/annual-
reports/2019/financials/financial-highlights
Data could be drilled down for might benefit
from greater context: Could be viewed by:
• Percentage growth
• Subset categories
• Dominant and best performing brands
• Declining and worst performing brands
Consider indicators and tools for highest
revenue generating subsets / products

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Ideas for data visualization
Sales per capita source: https://https://corporate.target.com/annual
reports/2019/financials/financial-highlights
How might we consider other datasets that
would be useful to view through a geographical
data visualization?
• Highest performing stores
• Regional management performance
• Heat map of online order shipping time
• Performance of remodeled stores
Since 2017, Target has focused on redesigning the interior
layout of it’s U.S. stores with a focus on enhancing the
customer experience. As a result, the sales per square foot
KPI may be be relevant, as they continue to test store layout
and experiment with smaller retail stores, especially in urban
centers, and in comparison to online

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Ideas for data visualization
Source: https://https://corporate.target.com/annual-
reports/2019/financials/financial-highlights
Determine / verify which datasets would be of
greatest interest to executive users on a
frequent basis
• Understand the client’s rationale
• Likely to be the KPI’s that investors / board
members are most concerned with

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VISUALIZE

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White boarding / wire-framing.
$72,618
‘14
$74,494
‘15
$70,271
‘16
$73,714
‘17
$75,356
‘18
$78,112
‘19
35%
20%
30%
15%
On tap breaks down quarters %
Year Revenue Q1 Q2 Q3 Q4
2019 $78,112M 35% 20% 30% 15%
2018 $75,356M % % % %
2017 $72,714M % % % %
2016 $70,271M % % % %
2015 $74,494M % % % %
2014 $72,618M % % % %
$72,618
‘14
$74,494
‘15
$70,271
‘16
$73,714
‘17
$75,356
‘18
$78,112
‘19
How do we show
quarterly breakdown
to look for trends in
highest grossing
quarters?
Could a dynamic
table serve as a
control for a
corresponding
breakdown of
total sale
categories?
How do we provide the
user a way to drill down on
KPI datasets for a fuller
story?
Note: Branding with Target’s
primary color palette presents
usability issues. It may work
for the user login, but a larger
palette may be necessary for
the dashboard.

10. Preliminary sample research. Not for public distribution 10
White boarding / wire-framing.
Category: Beauty & Household Essentials
Year Subcategory Revenue Q1 Q2 Q3 Q4
2019 Total $78,112M 35% 20% 30% 15%
Sub-cat 1 $--- % % % %
Sub-cat 2 $--- % % % %
Sub-cat 3 $--- % % % %
Sub-cat 4 $--- % % % %
Sub-cat 5 $--- % % % %
Sub-cat 6 $--- % % % %
2018 Total $72,714M % % % %
Sub-cat 1 $--- % % % %
Sub-cat 2 $--- % % % %
Sub-cat 3 $--- % % % %
Sub-cat 4 $--- % % % %
Sub-cat 5 $--- % % % %
Sub-cat 6 $--- % % % %
2017 Total $73,714 % % % %
$72,618
‘14
$74,494
‘15
$70,271
‘16
$73,714
‘17
$75,356
‘18
$78,112
‘19
27% 10% 35% 40% 36% 29%
If user selects a subcategory, like Beauty and
Household, they might see beauty and household
breakdowns by year (as in the donut charts
below floated above), and by subcategory (as in a
dynamic table to the right)