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Today’s shopper journey is becoming increasingly more digital – and though consumer product (CP) firms have made big investments in data and analytics, many have struggled to reap the rewards.
According to Statista, 42 percent of CP firms believe they are not using their existing technology to its fullest potential. Further, CP firms report that less than one percent of data collected is actually analyzed – leaving troves of potentially valuable insights on the table.
For CP firms, data transformation is about making better decisions that directly support critical business problems. This means establishing a process to capture, organize and analyze data across multiple sources to inform decision-making across the entire organization.
The shopper journey has become inherently digital, connected across online and offline channels that let consumers create unique paths to purchase based on their preferences.
Because first-party data comes from direct interaction with consumers, it provides the deepest line of insight for CP firms across the entire shopper journey, filling in critical gaps that second or third-party providers may not have, or may not be able to deliver accurately in real-time.
CP firms can collect valuable first-party data through owned channels that cross every touchpoint along the shopper journey:
With expanded reach of insight and united data across multiple sources, CP firms can develop an intelligence-led strategy, with new opportunities for value creation across three opportunity areas:
Deeper personalization through highly targeted messaging and promotion speak directly to consumer needs and shopping habits. A more personal touch eliminates wasted advertising spend on mass messaging and allows investment into targeted customer experiences that deepen and extend engagement.
Intelligence-led CP companies have a deep understanding of consumers, anticipate their needs, act to meet them, and measure how effectively they do this to continuously improve. They accomplish this through data-rich consumer profiles and advanced analytics and machine learning.
Knowing customer product and service preferences inform innovation in product design (like new offerings or better packaging driven by consumer preference, for example) and SKU management to cycle out products that may not be yielding the best return on investment. Artificial intelligence or machine learning is layered on to process purchasing data, opening opportunity for dynamic pricing that maximizes ability to move inventory while meeting shifts in consumer demand – shortening paths to purchase and improving return on investment.
Intelligence-led CP companies integrate data and analytics into their core product development and sales processes. They analyze data on consumer actions and experiences to create better products, while having data-led approaches to how products are priced, product assortment portfolios are developed, and where they focus sales resources.
Integrating data and advanced analytics into key decision processes lets CPs firms operate more efficiently across the entire organization through optimization and automation, resulting in more effective resource management and lower operational cost. A solid data strategy enables CP firms to plan more effectively, with better intelligence that delivers a competitive edge.
Intelligence-led CP companies run their businesses better by integrating data and advanced analytics into key decision processes. They operate more efficiently by optimizing and automating processes while planning more effectively with better intelligence.
Though data transformation can help CP firms achieve multiple business outcomes, data and analytics are only as valuable as the strategic decisions they support and the ability to action the insights they create.
Your organization’s data strategy should be underpinned by four foundational strategy questions:
When answering these questions, Publicis Sapient has found that CP firms generally fall into one of four categories of digital maturity – each with their own level of readiness and needs:
Data transformation is an ongoing process – one that will be shaped by continuous innovation. Establishing a strategy grounded by technology will give CP firms the foundation they need to better understand insights they’re collecting and build on processes that provide more value to both the business and the consumer.