RETAIL ANALYTICS TRENDS DAN MITCHELL, RETAIL INDUSTRY GLOBAL PRACTICE

Transcription

1 RETAIL ANALYTICS TRENDS DAN MITCHELL, RETAIL INDUSTRY GLOBAL PRACTICE C o p y r i g ht , S A S I n st i t u t e I n c. A l l r i g h t s r e se rv ed.

2 SAS FOR RETAIL MARKET TRENDS PERSONALIZATION FRICTIONLESS REAL-TIME C o p y r i g ht , S A S I n st i t u t e I n c. A l l r i g h t s r e se rv ed.

3 SAS IN RETAIL BUSINESS ISSUES INCREASING TOP LINE REVENUE REDUCE COSTS & IMPROVE PROFITABILITY INCREASE CUSTOMER ENGAGEMENT PROFIT Acquisition Retention COSTS C o p y r i g ht , S A S I n st i t u t e I n c. A l l r i g h t s r e se rv ed.

4 SAS IN RETAIL HOW TO CRACK THE CODE What products do I sell Merchandise attributes Where is it in my supply chain What is my competition doing? Product Information Customer Information Transaction Information When Where Payment method Who are my shoppers Where do they shop What do they want When do they want it $$$ Business Results $$$ C o p y r i g ht , S A S I n st i t u t e I n c. A l l r i g h t s r e se rv ed.

5 Analytics are Crucial in Digital Business Big Data + Advanced Analytics + Embedded (real time) Decision Making = Monetization Data sources relatively small and structured, from internal systems Majority of analytical activity was descriptive analytics, or reporting Creating analytical models was a time-consuming batch process Few organizations competed on analytics analytics were marginal to strategy Decisions were made based on experience and intuition Complex, large, unstructured data sources New analytical and computational capabilities Data Scientists emerge Online firms create data-based products and services Analytics integral to running the business; strategic asset Rapid and agile insight delivery Analytical tools available at point of decision Cultural evolution embeds analytics into decision and operational processes Businesses can create databased products and services Source: International Institute for Analytics, 2014 C o p y r i g ht , S A S I n st i t u t e I n c. A l l r i g h t s r e se rv ed.

6 INTERNET OF THINGS ANALYTICS AS A KEY ENABLER IoT creates a big data problem that analytics must solve transform huge volumes of data into a small (readily synthesized by a human mind) quantity of usable/actionable information. - Gartner 2013 C o p y r i g ht , S A S I n st i t u t e I n c. A l l r i g h t s r e se rv ed.

7 C o p y r i g ht , S A S I n st i t u t e I n c. A l l r i g h t s r e se rv ed. DATA OVERLOAD & CHANNEL ATTRIBUTION

8 CORE ENABLERS FOUNDATIONAL AND INTEGRATED Data Management Big Data Platform Analytics & Visualization Operations Excellence Expanded client data Introduce data variety Customer analytical environments Orchestrated decision-making Balance of privacy vs. Extended data partnerships Self serve analytics Metrics realignment to customer personalization Real-time and contextually aware Better visualization of data & insights management drivers Data Governance insights Real-time enablement C o p y r i g ht , S A S I n st i t u t e I n c. A l l r i g h t s r e se rv ed.

9 Disruptive Technology Unrivaled Processing Power Infinite Volume and Variety of Data New Problem-solving Mindset C o p y r i g ht , S A S I n st i t u t e I n c. A l l r i g h t s r e se rv ed.

10 DATA & ANALYTICS INSIGHTS FOR CUSTOMER JOURNEY C o p y r i g ht , S A S I n st i t u t e I n c. A l l r i g h t s r e se rv ed.

11 C o p y r i g ht , S A S I n st i t u t e I n c. A l l r i g h t s r e se rv ed.

12 C o p y r i g ht , S A S I n st i t u t e I n c. A l l r i g h t s r e se rv ed.

13 SAS Retail Localization, Personalization.and Relevance Customer Analytics Merchandise Intelligence Customer Intelligence KNOW PLAN, OPTIMIZE, ACT ENGAGE/ INFLUENCE

14 SAS Retail Customer Analytics KNOW Who are my customers? How are my customers different from one another? What similarities do my customers share? What/when/where do my customers buy? How do customers engage with me? How do customers feel about my brand/product? Customer Segmentation & Customer Profiling Market Basket & Behavior Analysis Social Media and Sentiment Analysis Online/Web Analytics

15 Proven Capabilities Customer Analytics KNOW Target Zappos.com Dick s Sporting goods Land s End Coke Best Buy Lowe s Macy s Williams-Sonoma Pão de Açúcar Sobeys Cabela s Catalina Marketing 10 to 20 percent lift for customers browsing in new merchandise categories who had not purchased in those categories 100 percent lift (for the first three deciles) for women who shopped at the men's site but had not yet purchased 20 percent increase in new member conversion rates. This is a measure of customers who join but haven't purchased

16 SAS Retail COMMUNICATE/ INFLUENCE Customer Intelligence Define customer engagement strategy that balances company objectives with customer preferences. Personalize customer interaction with relevant content at the right time with. Maximize the value of each customer touch point through the optimal allocation of company assets. Campaign Management Marketing Optimization Targeted Promotions Real-Time Offers Marketing Mix Analysis Marketing Resource Management Customer experience Mobile Analytics

17 Proven Capabilities Customer Intelligence COMMUNICATE/ INFLUENCE Colruyt The Sports Authority Leroy Merlin Chico s Drogerie Markt Harry & David Bed Bath & Beyond SM Marketing Convergence Staples Ahold Circle K Sunkus A larger number of households now use our promotional coupons. This increased use of coupons has also resulted in a larger average amount of money spent in our stores by cardholders. Equally important, it indicates a clear increase in customer loyalty. Bart Van Roost Head of Colruyt s Analytics Department

18 THANK YOU Company Confidential - For Internal Use Only Copyright 2014, SAS Institute Inc. All rights reserved.