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The consumer industry has never been more complex or moved so fast.  The pace of change is accelerating to the point that many consumer product companies and retailers are paralyzed by the speed at which they need to move, because they don’t always know what direction to take. As consumers take advantage of new channels and interact more through social media and mobile devices, the need to respond immediately is more pressing than ever before.

Consumer loyalty is declining as more people are choosing to buy less-expensive store brands. Commodity prices are volatile, and food and product safety threats are far too common. Consumer products companies must fully utilize all of the data available from internal and external sources to address these challenges and take advantage of market opportunities.  Loyal consumers must be protected, and for acquiring new customers, precise and accurate segments must be identified and targeted for growth. In today’s economy, consumer products companies must meet customer demands with new and innovative products, while containing costs throughout the product life cycle. A rapidly expanding set of new data sources must be employed coupled with re-engineering the enterprise to make and anticipate better decisions about offers and other actions targeted at consumers. In other words, sales and marketing, brand management, product innovation etc will not be recognized as we know them today. The lines of business will be realigned structurally by how information is generated from consumer demand.

Meanwhile complex supply chains, procurement and product production must be aligned with the other operational units to deliver the right products in the right sizes or packaging to the right locations through the right channels. Consumer products companies that want to succeed in this environment must employ predictive analytics horizontally and across the enterprise to anticipate the right decisions and for making decisions in real time on consumer demand for achieving a differentiated approach to their competition.

·        What products and features are desired by customers now and what will they want in the future?

·        How can we improve on-shelf availability and reduce stocks-outs?

·        Which customers are most likely to respond to which products on promotion?

·        What are the most effective promotions and trade spend by location?

·        Which areas of our business are performing well and where do we need to make improvements?

·        How can we reduce supplier and raw materials costs?

·        What processes can be implemented to improve supply chain and operational efficiency, while reducing costs?

When considering the value of predictive analytics, consumer product executives should ask four key questions.

Are We Organized to Support and Execute Predictive Analytics?

Consumer product companies must leverage a mix of internal and external data resources to initiate their intra-collaborative efforts between operations and departments.  Predictive analytics is an enabler. It’s a way of thinking about your business in a multi layered context and applying a solution to a specific line of business to make it “task oriented” with actionable information. Therefore, a critical first step is assessing your entire business to determine where and how new data sources coupled with real-time access can be applied. It is these new data sources that will necessitate the realignment of organizational change to address consumer demand more precisely.

Do We have the Right Policies and Security Standards in Place?

It’s mandatory to establish policies that address how (and which) employees interact with existing and new data and social media platforms. Guidelines should cover the tone (authentic), style (informal), and nature of the information being shared and used in real-time to address the concerns of the consumer.

How Are We Employing Predictive Insights Across Lines of Business, Such as Sale and Marketing, Brand Management, Procurement, Product Innovation, Customer Service and Supply Chain?

Structured and unstructured data are critical components to the metrics and dimensions required to assess today’s consumer in real-time and anticipate demand. Social media insights aren’t useful unless they’re actionable. Make sure processes are in place to distribute information to business units that are best equipped to act on them and align to the strategic direction of the company.

What Metrics Are Most Appropriate for Anticipating Consumer Demand?

Part of the “trial and error” approach lies in determining which metrics are worth tracking for all enterprise operations. It will be necessary to measure how sales, consumer engagement, promotional impact, supply chain and distribution costs, fulfillment,  etc. translates into business and financial value. If marketing “owns” the social media strategy, it should be working closely with finance to develop metrics that align with business key performance indicators (KPIs) that align with other operations to benefit from having predictive capabilities to take action.

Having the capacity to employ predictive analytics is about understanding the enterprise operational performance to address potential issues or create differentiated approaches to retain and acquire customers for long term growth. If there’s one critical lesson to be learned : start small, align to strategic roadmap for identifying key initiatives, define respective kpi’s  for predictive analytics proliferation to multiple lines of business and adjust as necessary. It’s the value of the insight for the one unique solution to address the new opportunity—along with the metrics to evaluate it—that truly matters and is potentially the differentiator in today’s intensely competitive consumer product/ retail ecosystem.

Predictive analytics is no longer for just the data scientist.  It requires extending predictive capabilities throughout the enterprise to shorten the trading lifecycle by anticipating consumer demand.

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Russ Hill has more than 30 years of experience in the retail, wholesale distribution, and data warehousing industry. He also has a wide variety of retail expertise in procurement, marketing, sales, and distribution, and effectively led a cross-functional team that developed a retail program of analytical and operational applications at Teradata and now SAP. Russ focuses on business analytics for the retail and wholesale distribution industries, has written numerous articles, and interacts with many multinational retailers.

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