Smarter Analytics for Retailers
Adapt to the Information Empowered Customer
The relationship between retailer and customer is changing as customers are becoming more knowledgeable and empowered by information provided through the web, mobile technologies and social media. Read this paper to learn how retailers are challenged to adapt to these changes and adjust their business model to stay connected to their customers. Retailers are turning to business analytics to get forward looking insight into their customers’ needs to improve customer satisfaction, strengthen the brand experience and maintain customer loyalty.
Analytics can greatly improve retailers’ customer satisfaction and loyalty when part of a Smarter Analytics solution.
- Align the organization and IT infrastructure around real-time customer intelligence
- Integrate predictive analytics into operational processes to identify future behavior and trends
- Organizations that utilize predictive analytics in addition to business intelligence achieve an average return on investment of 250 percent (source: IDC: The business value of predictive analytics, June 2011).
Senior Executives; Marketing, Operations and Merchandizing Managers; IT Managers; Business Intelligence Managers; Business Analysts
Insight in the High-Velocity Retail Environment
Retailers today are facing more technology-savvy, demanding customers and more sophisticated competitors, which are forcing changes in retail business models driven by three imperatives:
1) Delivering a smarter shopping experience
2) Developing smarter merchandising and supply networks
3) Building smarter retail operations
The types of insight that retailers can use to serve their customers better, and ultimately drive business success
- Pricing insight
- Customer insight
- Sales insight
- Marketing insight
- Merchandising insight
- Business success
Smarter Analytics for Smarter Retail
- Align the organization and the IT infrastructure around information to effectively gather, manage, and analyze the growing volume, variety and velocity of data, which drives the need for a scalable integration platform to meet current and emerging data warehousing needs.
- Anticipate and accelerate actionable insights with systems and storage optimized for analysis and information delivery to understand consumer behavior and build strategy, shifting the analytics process from a purely passive, after-the-fact model, to an active, during-the-fact model.
- Act with confidence in real time with pervasive and embedded analytics supported by an infrastructure foundation capable of swiftly handling critical actions to drive action.
Performance and Organizational Benefits of Smarter Analytics
- Retailers applying a Smarter Analytics approach enable themselves to harness the full power of analytics on structured and unstructured data, with superior IT economics.
- The approach allows the retailer to get a holistic view of what is happening with customers, suppliers, partners, and the market, beyond the surface indications of purchasing activity.
Retailers Using a Smarter Analytics Approach to Gain Competitive Advantage
- GS Retail Propels Growth with Customer Insight
- Intersport is Future-Proofing with an Analytics Advantage
Deeper Insight, Better Responsiveness, and Business Success
- Retailers striving to deliver a smarter shopping experience want to engage their customers on a personal basis, serving them whenever and wherever the customers want, and matching inventory and brand experience across channels.
- Developing smarter merchandising and supply networks involves gathering customer information continuously at every touch point to manage and deliver assortments based on customer insight.
- Building smarter retail operations involves inserting intelligence into customer data management and processes to understand and predict sales trends, while improving management across production, product development, and assets to drive operational excellence and lower costs.
Retail CIOs and line-of-business managers should consider adopting a Smarter Analytics approach if:
- The organization typically relies on information that is weeks or days old
- More management time is spent looking back at historic data than at real-time findings or predicting probable outcomes
- Analysis is limited to looking at lists of data output, rather than looking at exceptions, proactive alerts, and graphic visualizations of findings