Delivering the Goods Technology Investments that Boost Consumer Packaged Goods' (CPG) Performance

Delivering the Goods Technology Investments that Boost Consumer Packaged Goods` (CPG) Performance 1 Having weathered the downturn by streamlining operations and reducing costs, most consumer goods companies are only too happy to shift their focus to growth strategies. This re-orientation includes a fresh look at how technology can enable sales and marketing capabilities that contribute to top-line growth. More specifically, companies are evaluating investments in consumer-facing and enterprise technology capabilities that can help them connect with, understand and provide what consumers need when and where they need it. This Accenture Point of View explores three specific technology investments that can help consumer goods companies satisfy today’s discerning, demanding and always-connected consumer: I. Building on advanced analytics to address all dimensions of data (quality, security, semantics, integration, etc.) so that data is viewed as a platform to improve market performance and increase enterprise operating efficiency. II.Deepening digital marketing capabilities that provide faster, more cost effective ways for companies to provide differentiated, more personalized experiences to engage, acquire and retain customers than traditional marketing channels. III. Adopting more open, flexible enterprise architecture to more easily integrate innovations in consumer technology (e.g., mobile, social media) and enable more efficient multi-channel commerce to improve organization responsiveness and market impact. These investments may require a new kind of collaboration among business executives, marketing and technology, a partnership forged from a shared understanding that the right technology investment can yield strategic advantage and help companies achieve a higher level of performance. 2 The Connection between Analytics and High Performance Accenture’s research of high-performance businesses found that high performers—those companies that consistently outperform their competitors on a variety of dimensions across business cycles—are five times more likely to make extensive use of analytics than their lower-performing competitors. Not surprisingly, the importance of business analytics has spawned a healthy, data-centric industry all of its own, with analytic software posting with a compound annual growth rate topping 20 percent for the last four years.1 I. Build on advanced data analytics and address all dimensions of data to improve market performance and enterprise operating efficiency It is a universal truth that data can be a consumer goods company’s most strategic asset. Indeed, the past decade saw many companies invest in data quality and analytic efforts focused on evaluating trade promotions, segmenting customers more narrowly, or dissecting aspects of functions such as inventory management or supply chains in order to identify the source of problems. Yet, even the best ex post facto analysis by definition generates limited value for the enterprise. Accenture believes that companies need to extend these initial efforts to execute enterprise data management strategies as well as deepen analytic capability. A coherent data strategy that includes 3 cross-functional analytics will provide managers with one, integrated, secure, credible version of the truth, rather than fragmented scenarios. With that vision in hand, sophisticated modeling and extrapolations can help companies evolve from relying on descriptive analytics to figure out what happened and why, to more predictive analytics that generate insights that can both guide future growth strategies and avoid costly product or process experiments supported by "gut" instinct rather than data. Indeed, Accenture’s research and client experience identified several benefits of moving up the analytic curve, the most salient of which is that companies that rely on data analysis outperform those who do not (see sidebar, The Analytics-High Performance Connection). How? Because they are better able to: • Drive growth by identifying untapped opportunities in markets or consumer segments, and transforming processes such as new product development and marketing. • Enhance cost and cash advantage by increasing balance sheet efficiency, better management of working capital, and higher returns on investment. • Know what really works to improve operations through reengineering key processes to be more effective. • Restructure the business at scale by higher impact M&A, divestitures, alliances and value chain restructuring. • Manage risk by using more precise metrics and models to monitor changing business. Accenture has helped diverse consumer-oriented companies capture the benefits of customer and marketing analytics. • For a health and beauty manufacturer, a portfolio optimization analysis identified correlations between purchasing behavior and product attributes related to 150,000 SKUs. Through simulations and modeling, the company was able to rationalize products and eliminate more than 30,000 items, resulting in incremental sales gains as well as a far more simplified supply chain. • A consumer electronics and entertainment chain used transaction analytics to study what customers bought after making a major product purchase in order to improve product assortment and in-store placement, create unique bundles and increase replenishment and in-stock levels. The result: customers were offered more of what they are likely to need upfront, decreasing returns. • A world renowned theater group used customer segmentation and marketing analytics to devise a growth strategy focused on both deepening "core" customer attendance and broadening its overall reach. The results informed more targeted mailings and initiatives, ultimately increasing the company’s core audience base by 30 percent, and a critical customer segment by more than 50 percent. Achieving growth is a primary concern, yet so too are operational efficiency, process excellence and cost control. In these areas also industrial-strength analytic capability combined with a coherent enterprise data strategy proves a smart investment. For example, analytics conducted across multiple legacy data warehouses helped beverage leader Diageo identify and extract the most relevant data and expedite its use by executives, enabling faster, more informed decisions. Similarly, Unilever used analytics to identify the behaviors, methods and tools used by its best sales team members, then designed a capability development program around them—one that boosted sales within three months and improved morale among sales representatives. Both these examples show the power of analytics to improve processes, whether in the hard-wired area of IT or less technical functions such as staff capability-building. Yet, despite top-line and operational benefits of using analytics, many companies face hurdles in deploying analytic capabilities. A fundamental challenge: many companies’ analytics capabilities are stretched to the breaking point trying to handle masses of non-transactional data that actually feed enterprise insight. The "data explosion" ushered in with the YouTube and social media era has greatly expanded both the types and quantity of relevant data; one estimate is that business-related data doubles every 1.2 years. Thus, achieving enterprise-wide or cross-functional data analysis is just the beginning; companies will need to integrate that internal data with all types of external data—unstructured, visual, voice, geospatial—in order to gain the insights that will lead to competitive advantage. Such value creating data integration implies a data strategy that addresses data quality, security, semantics and governance issues. A solid analytics capability must take all these sources into account (see Figure 1), and allow companies to distill insights with enough granularity to make improvements at the function or process level. Figure 1. Analytics framework and potential allocation of responsibilities Business Functions (Sales, Marketing, Innovation, SupplyChain, Finance, RQT) Data Analysis Consumer Customer Operational Competition Services Reporting and Analytics Services Business Intelligence and Predictive Analytics Tools Enterprise Consumer and Demand Data Repository Business Warehouse Transactional Data Data Integration Services Consumer Data Loyalty Data Segmentation Syndicated Data IRI Nielsen Retail Data POS Transaction Log Shipment Data Linedepletion Invoice Operational Data ERP WMS Legacy ExternalData Integration InternalData Integration Accenture Manufacturer Third Party TBD Source: Accenture 4 ... - tailieumienphi.vn