Download discover hidden patterns in customer behavior for

INDUSTRY INFOSHEET:
Retail
DISCOVER HIDDEN PATTERNS IN CUSTOMER
BEHAVIOR FOR MORE EFFECTIVE RETAIL
STRATEGIES WITH WORLD-LEADING
DATA ANALYSIS
Increase average basket
size and drive sales
revenue
Improve customer
segmentation & marketing
strategies
Optimize demand
planning & merchandising
assortments
Bring data to life
02 CAMO
Retail
UNDERSTAND COMPLEX RELATIONSHIPS BETWEEN
CUSTOMERS, STORE & CHANNEL PERFORMANCE
Retail is a competitive and fast changing industry, with an ever-growing
range of product offerings and technological advances creating new
touch points for increasingly sophisticated consumers.
To remain competitive in this complex environment, retailers need to understand
not only their consumers’ behavior today, but also to anticipate and predict their
behavior tomorrow.
Today’s smart retailers are evolving from the old business model which treated
each store and channel as the same. Powerful data mining and predictive
analysis software enables them to capture value from the wealth of data
available.
Our advanced multivariate data analysis software gives retailers deeper insights
from their data to drive business improvements and build a competitive edge.
REAL BUSINESS BENEFITS
Multivariate data analysis can be used in retail from market segmentation to demand planning.
Market segmentation
Shopping basket
analysis
Demand planning
Use cluster analysis to more precisely segment
Identify customer purchasing preferences and
Understand sales fluctuations and variability
customers with similar or unique patterns to tailor
understand the relationships between basket
over time to develop more robust predictive
strategies at store and chain level
size across segments, stores, regions, seasons
models andforecasts
Better understand the spending patterns,
Improve cross-selling initiatives and optimize
Help improve supply chain efficiency and
communication and merchandising preferences
product bundles to increase the average
reduce over stocking with more accurate
of customers for more effective marketing
basket size and drive revenue
stock allocation across channels and stores
Increase customer loyalty by speaking
Improve assortment planning and validate the
Improve customer satisfaction by reducing the
appropriately to the defined segment and
effectiveness of marketing promotions
incidence of stock outs
optimizing the allocation of advertising resources
DON’T WASTE YOUR VALUABLE DATA!
Most advanced retailers collect an enormous amount of data, yet the majority does not exploit its
full potential due to the perceived difficulty and lack of statistical knowledge. However, today’s
generation of data mining and analytical tools are much simpler to use and even more powerful,
enabling industry leaders to get valuable insights from their data which are driving significant
business improvements.
Predictive analysis and data mining – the new competitive edge in retail!
> Bring data to life > camo.com
03 CAMO
Retail
EXAMPLE APPLICATIONS OF
MULTIVARIATE ANALYSIS
Example 1. Understanding the relationship between
customer behavior and store performance
A nationwide clothing retailer wants to better understand their different
customer segments and their specific buying behavior at store level, in
order to develop targeted, store specific sales promotions. Using basket
data, historic sales data by store, customer-specific data (credit cards &
store cards), marketing and sales data from previous promotional offers,
demographic and socionomic data based on store location, the retailer
would be able to:
Use cluster analysis to identify segments within each store and
determine which promotions are most effective for each. They would
also be able to carefully validate the uniqueness of these segments.
Use regression analysis to relate the characteristics of the products to
the gross margin or revenue for product groups and the
characteristics of the various consumer segments. The Unscrambler®
X is unique in providing the so-called ”L-model” for this purpose.
Example 3. Developing a new alcoholic beverage
An alcoholic beverage producer wanted to introduce a new product to
fill a gap in the market. The main challenge was to define the sensory
properties for the new product and how this related to the consumer
preference. This was made possible using sensory analysis and
consumer testing of existing brand(s), and from a model relating
sensory and consumer preference the producer could predict the
sensory profile of the planned new product. New recipes were made
and the consumer preference for candidate products was predicted,
from which the most promising recipes were chosen for consumer
testing. The actual preferences confirmed the predicted values and the
product is now on the market tailored towards a specific consumer
segment.
Use predictive analysis to model expected revenue for alternative
product promotions.
Example 2. Introducing an own-label food product
A major supermarket multiple was considering introducing a new range
of own-label food products. The main challenge was to identify attributes
which would allow the own-label products to compare with the branded
products. This was made possible using sensory analysis of existing
brand(s) and new recipes. The project involved the use of Design of
Experiments (DoE), analysis of the sensory profiles and regression analysis
between recipe and sensory profile. This information could also be
related to consumer preference and actual purchase behavior.
WHAT IS MULTIVARIATE DATA ANALYSIS?
In retail, everything interconnected: The effectiveness of marketing campaigns drives basket
size and store performance, which requires accurate demand planning. These variables
determine the profitability of individual channels, stores and ultimately, the entire chain.
Because these, and many other, variables are related, looking at each variable in isolation does
not show the full picture. This is why multivariate data analysis is becoming an
increasingly important tool in the retail sector.
Multivariate data analysis is the investigation of many variables, simultaneously, in order to
understand the relationships that exist between them.While traditional (univariate) statistical
approaches such as mean, median, standard deviation etc serve their purposes for
investigating and understanding simple systems, when the relationships between variables are
complex, as with the retail industry, a single variable cannot adequately describe the system.
Exploratory data analysis (data mining), clustering, regression and predictive analysis are typical multivariate tools which help retailers identify the
variables with the greatest impact on sales and business performance.
DOWNLOAD FREE GUIDE: What is Multivariate Data Analysis ?
> Bring data to life > camo.com
CAMO SOFTWARE
PRODUCTS & SERVICES
Get deeper insights from your retail data with our range of
powerful, yet easy to use and affordable data mining and
predictive analysis solutions.
The Unscrambler® X
Unscrambler® X Process Pulse
Leading multivariate analysis software used by
Real-time process monitoring software that lets
thousands of data analysts around the world
you predict, identify and correct deviations in a
every day. Includes powerful regression,
process before they become problems.
classification and exploratory data analysis tools.
Affordable, easy to set up and use.
TRIAL VERSION
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TRIAL VERSION
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Training
Consultancy and Data Analysis Services
Our experienced, professional trainers can help your team
Do you have a lot of data and information but
use multivariate analysis to get more value from your data.
don’t have resources in house or time to analyze it?
Classroom, online or tailored in-house training courses from
Our consultants offer world-leading data analysis
beginner to expert levels available.
combined with hands-on industry expertise.
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CONTACT US
READ MORE CONTACT US
Our partners
CAMO Software works with a wide range of leading systems vendors and data formats, with the flexibility to easily
add new formats when required. For more information please contact your regional CAMO Software office.
Find out more
For more information please
contact your regional CAMO office
or email [email protected]
www.camo.com
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