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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 READ MORE TRIAL VERSION READ MORE 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. READ MORE 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 NORWAY USA INDIA JAPAN AUSTRALIA Nedre Vollgate 8, One Woodbridge Center 14 & 15, Krishna Reddy Shibuya 3-chome Square Bldg 2F PO Box 97 N-0158 Suite 319, Woodbridge Colony, Domlur Layout 3-5-16 Shibuya Shibuya-ku St Peters Oslo NJ 07095 Bangalore - 560 071 Tokyo, 150-0002 NSW, 2044 Tel: (+47) 223 963 00 Tel: (+1) 732 726 9200 Tel: (+91) 80 4125 4242 Tel: (+81) 3 6868 7669 Tel: (+61) 4 0888 2007 Fax: (+47) 223 963 22 Fax: (+1) 973 556 1229 Fax: (+91) 80 4125 4181 Fax: (+81) 3 6730 9539