Decision-Making in Retail Marketing:
The End of Gut Feel?
RETAIL THOUGHT LEADERSHIP PAPER
Decision-Making in Retail Marketing:
The End of Gut Feel?
Overview
This paper is the result of a strategic collaboration between Deakin University’s Graduate School of Business and Pitney
Bowes Software (PBS). In the first of a series of co-authored thought leadership papers, key strategic and tactical marketing
issues in retail will be addressed with recommendations and solutions proposed.
About the Authors
Deakin University
Pitney Bowes Software
Deakin University’s Graduate School of Business offers a
wide range of postgraduate qualifications for retail industry
professionals including retail, commerce, marketing,
management and leadership. Steve Ogden-Barnes is
a Retail Industry Fellow at Deakin Graduate School of
Business.
Pitney Bowes Software helps organisations and government
agencies to acquire, serve and grow customer/citizen
relationships. PBS products and services create business
value for customers by making it easier for them to
engage with and provide services to their customers more
effectively and efficiently.
Steve Ogden-Barnes
Retail Industry Fellow
Deakin University, Graduate School of Business
Melbourne, Victoria, 3125, Australia
Pitney Bowes Software
Level 7, 1 Elizabeth Plaza
North Sydney, NSW, 2060, Australia
Tel +61 3 9244 5021
Email s.ogdenbarnes@deakin.edu.au
Website www.deakin.edu.au/buslaw/gsb
2
Tel +61 2 9437 6255
Email pbsoftware.australia@pb.com
Website www.pitneybowes.com.au/software
Table of Contents
About the Authors.......................................................................................................................................... 2
Executive Summary....................................................................................................................................... 4
Introduction.................................................................................................................................................... 5
Changing Retail Landscapes......................................................................................................................... 5
The Omni/Multi-Channel Imperative............................................................................................................ 5
Limitations in Current Practices................................................................................................................... 5
Demography - The Changing Face of the Nation.......................................................................................... 5
The Marketing Challenge: The End of Gut Feel?.......................................................................................... 6
Research Findings: How Marketing Decisions Are Made ............................................................................ 6
Decision Processes ....................................................................................................................................... 7
Marketing Futures: The Central Data Sources............................................................................................. 8
Knowledge is Power...................................................................................................................................... 8
Interpreting Data: Turning Questions into Answers .................................................................................... 8
Data for Marketing Decision-Making............................................................................................................ 9
Case Study 1: Book R-etailer - Understanding the Customer Journey..................................................... 13
Case Study 2: Financial Services - Cost-Effective Direct Marketing ......................................................... 14
Conclusion .................................................................................................................................................. 15
Future Papers ............................................................................................................................................. 15
References................................................................................................................................................... 16
Appendix 1.................................................................................................................................................... 17
3
Decision-Making in Retail Marketing:
The End of Gut Feel?
Executive Summary
The dynamic retail environment is facing the most challenging times in its history. The retail sector is susceptible to
international competition, online retail, legislative changes and a slowdown consumer spending, as well as having to compete
with other industries for customer expenditure. A major challenge is managing the blend of physical and digital channels, as
a large portion of online sales originate from bricks-and-mortar outlets. Despite retail firms acknowledging the importance
of integrating new technologies into marketing practice, it remains limited. Customer segmentation techniques, for example,
are under-utilised leading to inefficient ‘one-size-fits-all’ marketing campaigns that risk undermining brand value.
The changing demographics of Australia present both opportunities and challenges for retailers. The key is to respond to
these evolving conditions with appropriate marketing solutions. Understanding the dynamic customer base can best be
achieved through relevant data interpretation, facilitated by intelligence tools. The current practice of placing instinct before
information could limit tangible strategies for marketing. Other common pitfalls include managers choosing convenient but
unsuitable models, being unaware of the techniques they can use and focusing on short-term financial measures over the
longer term impact of multi-channel marketing strategies.
This paper identifies the extensive sources and types of market data and tools that can help retailers make more informed
decisions. The volume and diversity of available data sources may seem daunting to managers, however solutions are
available to guide them in using the data effectively and efficiently. Data interpretation techniques, such as location
intelligence and predictive modelling, reduce campaign costs and increase campaign effectiveness by targeting customers
with personalised and relevant marketing treatment. The paper highlights the business benefits achievable using analytically
driven marketing techniques, how to overcome barriers to adopting them, and encourages change in current thinking.
This paper is the first in a series of retail thought leadership papers produced by Deakin University and Pitney Bowes
Software. Future papers will focus on areas such as location intelligence and campaign optimisation.
4
Introduction
Limitations in Current Practices
In this era of rapid change, marketing decision-making has
never been more worthy of scrutiny. This paper aims to
develop understanding of how managers make decisions,
with particular reference to current challenges faced
by retailers. By drawing on recent academic research
conducted at Deakin University and the industry experience
of Pitney Bowes Software, we endeavour to offer actionable
insights for retailers to solve marketing problems.
Despite businesses embracing a wide range of channels
for marketing, cohesive strategies have not always been
implemented. The practice of customer segmentation, for
example, remains under-utilised when planning an omnichannel marketing strategy. Customers can be lost in the
early engagement stages (on-boarding) due to inconsistent
communication across different channels of marketing.
Use of known customer channel preference information is
limited when it comes to increasing retention and reducing
potential ‘opt-outs’. Mass targeting and unstructured
follow-up processes when handling customer enquiries
demonstrate a lack of strategic thinking in the field.
Changing Retail Landscapes
The retail environment is changing at an exponential pace,
with the industry facing the greatest trading challenges
in living memory. The recent Australian Government
Productivity Commission report illustrates the complex
agenda Australian retailers have to contend with in terms of
international competition, online retail, planning, zoning and
workplace legislation. In addition, a prolonged slowdown in
consumer spending and a diminishing confidence level has
contributed to the toughest trading conditions in over 20
years. Retail also commands less of the discretionary spend
than it did in the past: 30% today compared to 35% in the
1980s [1]. It now competes with services, health, education
and the travel industry for the elusive shopper dollar. These
increasingly significant sectors have also raised their game
in areas of customer targeting, acquisition and retention,
often outperforming retailers in brand development and
marketing sophistication.
The Omni/Multi-Channel Imperative
With online retail in Australia currently accounting for 6-8%
of total retail sales, online sales have matured and continue
to grow year-on-year. The key challenge for retailers is
managing a cohesive mix of physical and digital marketing
channels. The synergy between these channels calls for
recognition, as one finding reveals that 80% of online retail
sales come from a geographic area where their bricks-andmortar retail store already exists. Simultaneous support of
revenue goals for both the physical store and the website
is therefore a priority in managing the omni/multi-channel
customer experience. As the online vs. in-store shopping
experience boundaries continue to blur, retailers need to
use every opportunity possible to reinforce positive brand
experiences at every point of customer interaction.
Perhaps the foremost challenge for retailers is deciding
how to respond to ‘tech-savvy’ consumers, who are wellequipped with new technologies to optimise their shopping
experiences. Shoppers are now choosing to ‘seek and
search’ on the move, exercising ever greater control over
the advertisements they are exposed to. Concurrently, the
search process raises global product and price awareness
of shoppers. This new breed of channel agnostic, cordless
(mobile) consumers are also threatening to turn traditional
bricks-and-mortar retailers into costly showrooms. The
real-time blend of being in and amongst the bricks-andmortar outlets whilst simultaneously using mobile devices
will complement the shopper’s experience.
Customers can now view products in store, make realtime deal comparisons and ultimately buy from a national
or international competitor that offers the best price. The
Australia Bureau of Statistics (ABS) reports that there are
now over 11 million mobile internet handset subscribers in
Australia, with wireless broadband connections accounting
for 47% of all internet connections [2], proving that the
mobile guerrilla shopper is here to stay.
Demography - The Changing Face
of the Nation
Australian consumers are changing in a multitude of ways.
The population of Australia is aging, living longer, choosing
different lifestyles and becoming more multi-cultural.
Our 22+ million population is growing at around 1.4% per
annum [3]. Migration patterns show that new arrivals to
these shores accounted for 46% of population growth last
year, indicating that the cultural mix within our towns, cities
and communities is rapidly evolving. However, population
growth is slowing in our cities and it is the outer suburbs
that are showing the fastest growth [4]. New communities
are springing up in green field sites which, for many, are
a popular and necessary alternative to the higher priced
inner suburbs. This creates entirely new multi-cultural
communities of consumers waiting to be served. Our
5
Decision-Making in Retail Marketing:
The End of Gut Feel?
median age has risen from 31 to 36 in the last 20 years,
and the proportion of population aged 65+ has increased to
account for 13.6% of our population. Australia has one of the
highest life expectancies in the world, with the proportion of
population aged 85+ doubling over the same period [5].
The 2011 Census reveals a lot more about the Australian
population. This presents new opportunities for retail
businesses to identify new consumer segments, enabling
more accurate and effective targeting with relevant
products, services, channels and locations.
The Marketing Challenge: The End
of Gut Feel?
In order to meet growth targets in a consumer landscape
which is changing culturally, socially and technologically,
retailers need to make informed and effective decisions in
relation to their future. In the current retail climate, it is the
marketers who face the biggest challenge in understanding
the ‘how, where, when, why’ of our new consumers.
Simultaneously, they have to develop focused strategies for
customer attraction, engagement and retention across the
diverse channels available.
The role of the CMO (Chief
Marketing Officer) has evolved
dramatically over the last 10 years.
The average CMO is on the job for
just 23.6 months. The CEO and board
of directors are demanding a new
level of accountability from senior
marketing leaders which requires
tangible justification for marketing
decisions. Marketers can no longer
afford to make decisions or measure
results based on gut feel. For CMOs,
this leads to one undeniable truism;
job security is directly correlated
with data-driven marketing [6].
6
Information and data have always been essential for
management decision-making. Decision-makers now have
a greater volume and complexity of data at their fingertips
than ever before. However, those responsible for marketing
decision-making often put instinct before information, gut
feel before logic and preference before performance. With
current pressures upon retailers to optimise all aspects of
their business through knowledge-based action, there is no
better time for marketers to make the paradigm shift from
heart to head decision-making. In other words, marketers
should follow the lead of their supply chain, production
and IT colleagues. This is not to say that marketing
decisions should not continue to be intrinsically creative,
entrepreneurial and ambitious. However, the holistic
management and interpretation of data should become a
discipline in decision-making, not an exception. For those
with decision-making responsibilities involving branding,
marketing, location planning, segmentation, campaign
management, promotional strategy, and customer
retention, the interpretation and use of data is the name of
the game.
As with any evolutionary process, it is essential to recognise
the limitations of current practice and highlight tangible
strategies for improvement. Recent research into retail
marketing decision-making conducted by the Graduate
School of Business at Deakin University provides the
foundation for this discussion.
Research Findings: How Marketing
Decisions Are Made
Marketing continues to evolve and develop as new
paradigms, functions, relationships and technologies
interact to change the dynamics of the discipline.
Advertising, for example, has in the last century moved
from being a largely print-based communication tool into
a more complex multi-channel, multi-media discipline.
This will likely continue to evolve with advances in both
internet functionality and the new generation of mobile
commerce, or ‘m-commerce’ opportunities. However, in
defining marketing strategy, marketers may often rely on
familiar techniques and technologies to approach unfamiliar
territory. As one strategic marketing commentator notes:
“Managers are likely to choose ideas, models
and techniques which they understand, find
easy to use, and which resonate in their
business context. Whether this chosen
‘technology’ is also the best or most
appropriate is, of course, an entirely different
Matter [7].”
The Deakin University PhD-level research set out to
establish within a sample of retail organisations how
strategic marketing decisions were made, to uncover
the decision processes and influences that determine
marketing strategy. The research revealed strong criticism
of the methods that decision makers employ and the
ineffective use of information and resources during the
decision-making process. One leading commentator, for
instance, criticised the lack of rigour in practical marketing
decision-making, stating:
Some marketers probably only know about half of what they
should about the concepts they use [8].
Decision Processes
Research into the marketing decision process reveals key
common stages, illustrated in Figure 1, which ultimately
defines strategic outcomes.
However, this process is always organisation-specific and
is influenced by a number of factors: business culture,
organisational structure, competitive environment,
stakeholder involvement, traditional decision-making
methods, use and interpretation of data sources. These
influences indicate that, without an objective knowledge
base against which decisions are made and options are
modelled, subjective compromises can often be made.
This in turn impacts the optimisation of marketing strategy
and affects return on marketing investment (ROMI). In
addition, unless comprehensive evaluation criteria relating
to marketing impact is utilised, short-term single focus
financial measures tend to be employed over what should
be long-term multi-channel impact measures. These longterm measures include pipeline analysis, lead quality and
conversion, target market recall, geographic penetration,
CLV (Customer Lifetime Value), engagement and retention.
Therefore, knowing which data applies to most effectively
define and evaluate marketing strategy is central to
informed decision-making. This is especially critical in
areas of:
• Business expansion: physical locations
• Market expansion: targeting new customers
• Portfolio preview: defining product and service mix
• Building customer loyalty: retaining and engaging
customers
• Shopper demography and behaviour: understanding
what makes your customers tick
• Profit optimisation: conversion, cross-sell, up-sell
• Omni-channel integration: developing a seamless
experience
Figure 1: Marketing Decision-Making Process
7
Decision-Making in Retail Marketing:
The End of Gut Feel?
• Marketing and advertising: what message and which
channel
• Sales promotion: creating value-adding promotions
which have impact and appeal.
Making effective decisions in these diverse areas is
increasingly becoming a science, rather than an art; and as
with any scientific discipline high quality, relevant data is
essential. The challenge for marketers is to know what data
exists, and how to access and interpret it.
Marketing Futures: The Central
Data Sources
As highlighted, knowing what data to use and where to
source it from, is the cornerstone of data-driven marketing.
Retailers need to be aware of the full range of macro
and micro data sources to help guide more effective and
objective decision-making. Appendix 1 entitled “Key Retail
Related Marketing Data Sources” provides example data
sources for the retail trade.
Knowledge is Power
Skilful interpretation of this complex information and
knowing how objective data is utilised remain at the
centre of the marketing decision-making debate. This will
only become more critical with the advent of new media
technologies being identified as a ‘chaotic’ force in decisionmaking [11]. This adds greater complexity to an already
challenging arena. Internal and external factors such as
market orientation, organisational structure and culture,
stakeholder networks, politics, decision-maker expertise
and the dynamics of the competitive environment were all
acknowledged as influencing marketing decision-making.
However, organisations surveyed reveal a tendency in firms
to approach annual strategic marketing in a procedural way,
rather than a progressive way. Judgement, prior experience
and historical reference are often taking precedent over the
intelligent use of comprehensive marketplace data via tools
of interpretation.
This highlights the potential limitations of current decisionmaking approaches including: an over reliance on intuition,
historical anchoring, and the ‘status quo bias’ (a lack of
evaluation), the ‘budget +10%’ approach and ‘group think’
outcomes resulting from consensus-based decision making.
Where marketing evaluation was discussed, assessment of
short-term marketing effectiveness - based largely on sales
data - was more strongly emphasised over long term multifactor marketing impact assessment.
8
One marketing brand manager seemed to capture the
decision-making mind-set concisely.
“I’ve just built our marketing plan
for next year. What was the easiest
way to do that? It was to copy and
paste last year’s marketing plan and
then tweak it. Do I personally agree
with that? Not necessarily. If it was
my own business I may not do it that
way. However we’ve got history of
sales spikes at certain times, and we
have to kind of mitigate that through
using, I guess, the tried and tested
approach [12].”
The research therefore reveals that despite the need for
retailers to be evolutionary in their marketing approach
to the emergent retail landscape, traditional thinking may
still compromise the quality of decisions. This is perhaps
nowhere more evident than in the lack of using data and
data interpretation technologies to determine strategy.
Interpreting Data: Turning Questions
into Answers
As identified in Appendix1, marketers have access to more
data sources than ever before. Yet for many this poses
challenges rather than enabling guided decision-making
like:
• Is data usable in its raw format?
• How can we turn it into information that can be used to
facilitate decision-making?
• Can we have too much information? Is there still room
for intuition/gut feel?
• Should we be listening to the big data pundits who claim
that predictive customer analytics is now the answer to
everything?
• Are we setting ourselves up for ‘paralysis by analysis’?
Data for Marketing Decision-Making
• Can we or should we make decisions based on our past
experiences?
How can diverse and complex data be utilised to support
more effective decision-making to:
• Should all decisions rest with individuals or can effective
decisions be made in teams?
These tough questions can mean that the use of data to
support decision-making sometimes falls into the ‘too
difficult’ category. Yet appropriate data that is intelligently
used in decision-making can inform judgements and
identify critical points at which new strategies are required.
We will now turn our focus to some methods and solutions
available to support data-driven decision-making and
facilitate optimal marketing performance.
• Pinpoint opportunities and make more informed
decisions
• Create personalised communications and consistent
customer experiences
• Integrate accurate customer information into business
processes
• Evaluate trends and rapidly respond to changing
customer needs
Pitney Bowes Software (PBS) works on resolving these
challenges by equipping retailers with data, tools and
advice for optimising the customer lifecycle.
Figure 2 shows how an integrated and strategic approach
in using marketing data can enable lifetime customer
relationships by increasing value to the relationship
between organisations and customers every step of the way.
Figure 2: Enabling Lifetime Customer Relationships
9
Decision-Making in Retail Marketing:
The End of Gut Feel?
Retailers constantly need to find (new) ways to enhance the
customer lifecycle for successful on-going relationships.
This is evident in the following key lifecycle stages:
• Customer Acquisition. This process is enhanced by
enriching customer personal data with, for example,
location data. PBS attempts to capture the context of
the customer at the moment of interaction to maximise
the receptivity of the customer to marketing messages.
In addition, knowing the demography of specific regions
(catchments) can increase the success rate of acquiring
new customers by embarking on more accurate
marketing campaigns or in, best identifying new branch
locations.
• Customer Service and Growth. Serve and grow
customers with analytic insights, where the customer’s
behaviour can be understood and predicted through
modelling software with relevant data input.
• Customer Retention. Retain customers with timely
interactions and cross-channel communications support
for better customer fulfilment.
Figure 3 describes a typical customer journey and the
analytical insights that can be derived to strengthen a
company’s relationship with its customers.
• Customer On-boarding. Companies should leverage the
critical honeymoon (membership) period, for example in
the first 90 days, from when customers come onboard, to
build trust and increase profit. As mentioned, customers
can be lost here due to inconsistent messages
received across multiple communication channels,
mass targeting and lack of follow-up. Management
of communication and tools for co-ordinating crosschannel communication is therefore crucial in retaining
these customers.
Strengthen the Customer Relationship Through Analytical Insights
Figure 3: A Typical Retail Customer Journey
10
Figure 4 summarises a data and communications working
platform. Different sources of data are managed utilising
analytical intelligence tools. Solutions incorporate data
quality, location intelligence, predictive analytics and
communication management into critical customer
relationship management workflows and business
operating systems.
Figure 6 shows how customer data can be captured and
summarised on scorecards by an analytical system, such as
Pitney Bowes Software Portrait Explorer, to develop these
models.
Figure 5 shows a customer data modelling system, Pitney
Bowes Software Portrait Uplift, where customers are
divided into groups of four main behavioural characteristics.
It shows, for example, that there is a higher likelihood
of accepting a campaign offer by focusing on the
“Persuadables” group; and conversely the “Sleeping Dogs”
group will be negatively impacted by a campaign and
therefore should be dropped.
The following two case studies illustrate the use of data
and systems through the customer journey and the benefits
brought to the organisations and their customers.
Appendix 1 identifies example data sources utilised in the
customer lifecycle process.
Figure 4: Data and Communications Platform
11
Decision-Making in Retail Marketing:
The End of Gut Feel?
Figure 5: Data and Communications Platform
Figure 6: Customer Scorecard
12
Case Study 1
Book R-etailer - Understanding the Customer Journey
Customer Profile - Wendi
Wendi is busy homemaker who views her home as a
sanctuary and makes it the centre of all activities. As a
result, she frequently researches online for recipe books as
she loves cooking. She makes purchases both online and
in-store.
Figure 7 illustrates the customer’s preferences and buying
habits being captured by tools of intelligence and how the
flow of prompts caters to her needs.
Following Wendi’s Journey
Wendi has been a rewards member with the retailer for
several years and receives an email notification of the new
look program - she immediately clicks on the link to convert
and completes her profile.
Figure 7: Flow of Prompt by Intelligent Tools
13
Decision-Making in Retail Marketing:
The End of Gut Feel?
Case Study 2
Financial Services - Cost-Effective Direct Marketing
Background
Benefits
One of the largest commercial bank in the USA is using
direct mail to entire postal routes to acquire new demand
deposit account (DDA) customers, but low response rates
and lift versus control made even their best targeting
models unprofitable.
• Turned around campaign from loss to profit due to
targeting of locations that will have higher success rates
Business Challenge
Figure 8 shows how return on marketing investment (ROMI)
can be maximised by incoproating location-based attributes
into predictive customer analytics.
To find more cost-effective ways to acquire new DDA
customers using direct mail.
• 91% increase in incremental revenue
• Generate additional $300K per campaign
Solution
The Portrait Uplift predictive analytics solution from Pitney
Bowes Software delivered more effective targeting models
using existing data, and achieved campaign profitability by
enhancing the data with new location-based attributes such
as local product penetration rates and nearest branch drive
time.
Figure 8: Maximising ROI through Location Enhanced Uplift Analytics.
14
Conclusion
Future Papers
Our research identified that the lack of data-driven
decision-making in the retail sector is a key reason for the
slow take-up of an omni-channel marketing approach. The
challenge is in the will within retail organisations to engage
in an omni/multi-channel future. Another major barrier is
the skills gap in applying the technological integration to
marketing strategies. Some may resist the move, believing
that logistical difficulties will surface as more channels lead
to confusion and greater customer dissatisfaction.
This is the first in a series of research-based thought
leadership papers. Future papers will focus on more
specific areas in the new retail era, including location
analysis, sales promotion campaign management and
omni-channel strategy development. The next two related
papers will cover:
However, there are workable solutions. Adequate
training/mentoring can be provided to staff so they can
transform comfortably to data-based marketing decisionmaking company. A central marketing campaign system
can be created to ensure a consistent and integrated
communication approach with customers. Technology can
help retailers optimise these customer interactions by
utilising data to personalise communications. As result
this will maintain and enhance an on-going customer
relationship.
Omni-channel retailing is here to stay. Marketers, which
have historically focused on the intuitive roll-out of their
4P’s strategy, need to evolve in line with the marketing
channel evolution. The intelligent use of data will determine
the future of retail marketing. Having the competency and
solutions to access, interpret and synthesise diverse data
sources will be central to optimising marketing strategy.
This is particularly important as markets, consumers and
channels continue to evolve at an exponential rate.
New Location Perspectives: In the Zone
• The Census and the changing population influences on
physical location and cyberspace.
• Why is it important to get location right?
• What are the costs of getting it wrong?
• How are location decisions currently made?
• How can technology help with better physical location
planning?
• What is cyber location analysis and how can it be
leveraged?
Campaign Optimisation: Playing to Win
• Research on how businesses currently make sales
promotion campaign decisions
• Where the opportunities lie for effective decisionmaking.
15
Decision-Making in Retail Marketing:
The End of Gut Feel?
References
1. A
ustralian Government Productivity Commission, Economic Structure and Performance of the Australian Retail
Industry. 2011.
2. A
BS. Almost half of all internet connections are mobile wireless. 2012 [cited 2012 25/04];
Available from: http://www.abs.gov.au/AUSSTATS/abs@.nsf/mediareleasesbyReleaseDate/F2D32B785378BC9CCA25758D
002B6804?OpenDocument
3. A
BS. 3101.0 - Australian Demographic Statistics, Sep 2011. 2011 [cited 2012 24/04];
Available from: http://www.abs.gov.au/ausstats/abs@.nsf/mf/3101.0
4. ABS. 3218.0 - Regional Population Growth, Australia, 2010-11. 2012 [cited 2012 25/04];
Available from: http://www.abs.gov.au/ausstats/abs@.nsf/latestProducts/3218.0Media%20Release12010-11
5. A
BS. 3201.0 - Population by Age and Sex, Australian States and Territories, Jun 2010. 2010 [cited 2012 24/04];
Available from: 3201.0 - Population by Age and Sex, Australian States and Territories, Jun 2010.
6. Aberdeen Group. Data Driven Marketing 2009 [cited 24/04 2012];
Available from: http://www.aberdeen.com/aberdeen-library/6224/RA-data-driven-marketing.aspx
7. F
ranklin, P., Problems and Prospects for Practice and Theory in Strategic Marketing Management. Marketing Review, 2001.
1(3): p. 341.
8. Corkindale, D., Are marketers only half right? Marketing Review, 2009. 9(1): p. 19-29.
9. Roy Morgan Research.Roy Morgan Research Single Source. 2012 [cited 2012, 01/06];
Available from: http://www.roymorgan.com/products/single-source/single-source_home.cfm
10.The Nielsen Company. Shelf Space Health. Nielsen Hong Kong - Products & Services - Analytic Consulting - Shelf Space
Health. [cited 2012, 01/06]; Available from: http://hk.nielsen.com/products/ac_shelfspace_health.shtml
11.Samli, A.C., Developing counterchaos marketing strategies: the key to survival and success in modern chaotic markets.
Marketing Review, 2010. 10(2): p. 185-202.
12.Ogden-Barnes, S., Promotional competitions: management decision-making in the Australian consumer marketplace, in
Graduate School of Business. 2012, Deakin University: Melbourne
16
Appendix 1
Example Retail Marketing Data and Tools
TYPE
SOURCE
PROVIDER
APPLICATION
National Economic
Performance
• Key Economic Indicators
• Australia Bureau of
Statistics (ABS)
• Provides information on
national accounts, retail
consumption, production,
prices, labour, housing,
etc.
National Population and
Demographic Data
• CENSUS Data
(Every 5 years)
• ABS
• Provides national
demographics, e.g. age,
sex, ethnicity, income
levels, etc., at granular
geography levels
Taxation Statistics
• Income Taxation
Information
• Australian Taxation Office
• Provides aggregate
details of personal tax
returns by postcodes,
including income gains/
losses
• Tallies the number of
people who earned
dividend or interest
income and the total
amount of income earned
• Gives authoritative and
current information on
the state of household
finances
Retail Pricing
• Consumer Pricing Index
• ABS
• Provides information on
changes to the consumer
and retail pricing across
key products/services
• Pitney Bowes Software
(PBS)
• Understand consumer
spend potential based on
household expenditure in
over 200sub-categories
down to Census
Collection District
(CCD) level (around 220
households)
• Retail Trade Statistics
Consumer Spend Potential
Australia
• Cash flow analysis of the
National Accounts
• GDP figures gathered by
ABS
• Income tax data from the
Australian Taxation Office
• Demographic/CENSUS
data
• Household Expenditure
Survey by ABS Social
Media Tracking
17
Decision-Making in Retail Marketing:
The End of Gut Feel?
TYPE
SOURCE
PROVIDER
APPLICATION
Demographic Estimates and
Projections (E&P)
• Population Projections
based on ABS data
• PBS
• Population and
households estimates
generated annually, with
projections for the next 5
and 10 years at Census
Collection District (CCD)
level
Geographical/Location Data
• Digitalised Maps
• PBS
• Geographical data which
can be used in developing
retail trade areas
• PBS
• Demographic analytics on
geographic attributes
Geography/Location Analysis • AnySite Software
Geo-demographic data/
maps with over 12,500
variables
• Identify location
opportunities or trade
area “hot spots” for
marketing activities
• MapInfo (Metadata)
Access to worldwide
geographic data
Marketing Evaluation
Consumer/Market
Segmentation
• C
alculate distances
and drive times based
on detailed maps and
boundaries
• Portrait Software
• PBS
• Company Data
• Internal
• Geo-Demographic
Segmentation data
• Third-party providers,
• e.g. CAMEO
• Tracks success rates,
measure campaign
effectiveness and ROI
• Define customer/prospect
segments with similar
attributes
• Develop target marketing
and geography based
applications
Media Consumption And
Usage
• Internet Traffic,
• e.g. search rankings,
site visits, search engine
results page (SERP),
click-data, offline, online,
1st party, 2nd party, 3rd
party data
• T
hird-party providers,
e.g. Nielsen, Hitwise
• Understand website
prompt sequence from
initial search through to
purchase
• Pay TV Membership
Social Media Tracking
• Facebook
• Twitter
• LinkedIn
• Blog
• YouTube
18
• Uncover information
relating to web/digital
data
• Optimise website traffic
• Social media providers,
e.g. Google analytics
• Third-party providers
• A platform for
relationship marketing
and building where realtime feedback can be
monitored and addressed
more efficiently
• Track public sentiment
towards a brand
TYPE
SOURCE
PROVIDER
APPLICATION
Mailing Lists
• Consumer Databases
• Third-party providers,
e.g. Veda Advantage, List
Factory, Acxiom, D&B
• Used for direct marketing
campaigns
• Commercial Databases
Market Share/Sales
Information
• Point-of-Sale (POS) Data
• Purchases/ Transactions
• Third-party providers, e.g. • Understand product and
Nielsen, Aztec, Polk, IMS
category level market
share and competitive
position
Volunteered Personal
Information (VPI)
• POS Surveys
• Internal
• Online Customer
Feedback
• Understand customer
preferences, e.g.
• Third-party providers, e.g.
preferred communication
PBS, Roy Morgan
and purchase channels
Customer Profiles
• Portrait Software
• PBS
• Company Data
• Internal
• Published Materials,
News and Statistics
• Public information
• Economic, Demographic
and Government Data
• Third-party providers, e.g. • Provides industry
IBIS World
analysis, including
financial ratios and
industry ratios
Competitor Performance
Industry-Based Research
• Third-party providers
• Profile and segment
customers to better target
campaigns
• Understand the impacts
of competitor activity on
the sales performance
• Industry performance by
product and market
Identity and Movement Data
• Identity-related
transactions, e.g. change
of address, passport
interviews
• Australia Post
• Captures identity-related
data and tracks address
changes
Psychographic Data
• Australian Lifestyle
Survey
• Third-party providers,
e.g. Australia Post, Roy
Morgan, Mintel
• In-depth analysis of
consumer behaviour
• Internal
• Explores profitable ways
to manage shelf space/
display through comparing
stock/shelf share figures
with competitors
• Focus groups and field
survey work on geodemographic statistics [9]
Store Product Shelf Space
Data
• Return on Inventory
Investment (ROII)
• Rate of Sale
• Stock Keeping Unit (SKU)
Rankings [10]
• Third-party providers,
e.g. Nielsen, Aztec
19
For more information please contact:
DEAKIN UNIVERSITY
PITNEY BOWES SOFTWARE
+61.3.9244.5021
s.ogdenbarnes@deakin.edu.au
www.deakin.edu.au/buslaw/gsb
+61.2.9437.6255
pbsoftware.australia@pb.com
www.pitneybowes.com.au/software
Pitney Bowes Software Inc. is a wholly-owned subsidiary of Pitney Bowes Inc. Pitney Bowes and the Corporate logo are [registered]
trademarks of Pitney Bowes Inc. or a subsidiary. All other trademarks are the property of their respective owners.
© 2012 Pitney Bowes Software Inc. All rights reserved.