In the Consumer Goods (CG) industry, competition is primarily driven by brand recognition, product innovation, and price. Other aspects that are also critical for success include the ability to meet consumer preferences, product quality, and promotions. To add to this complexity, the consumer is also evolving. In addition to a growing embrace of health and wellness lifestyle trends, there is a consumer shift toward greater eco-consciousness for improved product transparency and sustainability.
An immediate impact of the COVID-19 pandemic is that it has greatly accelerated this consumer evolution. In addition, the uncertainty regarding what the ‘new normal’ would look like has simultaneously added a few more facets to it, such as focus on meeting essential needs while cutting back on non-essential consumption, focus on value, waste reduction, socially conscious consumption, etc.
To thrive in this rapid consumer evolution sweeping the industry, CG companies (CGs) need to adapt their product innovation approach to not just meet but also influence and shape it.
Considerations for product innovation
Even prior to the pandemic, the consumer had distinctly pivoted toward experiential buying. The ongoing pandemic has added another level of complexity to this by impacting how consumers view, assess, and value products.
Hence, CGs need to evolve beyond focusing on just the functional product design. They also need to address emotional and experiential engagement in order to drive a more differentiated, contextual, and memorable engagement of consumers at key touchpoints along their path to purchase.
The uncertainty regarding the post-pandemic ‘new normal’ as well as the consumer evolution that has been further accelerated by the pandemic have implications on product innovation -- how to ensure that new products meet consumers’ needs as well as their evolving preferences. This can be addressed by adopting two tactics that would work in tandem to inform and guide the product innovation process:
Employ Design Thinking to drive product innovation: Design Thinking is an innovation framework that pragmatically and creatively resolves issues to deliver an improved product by using empathy to understand consumers’ unarticulated and/or unmet needs. This human-centered approach helps define and frame the correct problem statement, ask the right questions, choose promising ideas for further prototyping and testing, and, subsequently, introduce the most desirable product to the market.
An evolution of Design Thinking -- Experience Thinking -- helps innovate the consumers’ experience with the product in its entirety. Experience Thinking combines Design Thinking tools with Customer Experience Design tools (e.g. Customer Journey Map) to innovate every touchpoint in the total customer journey with the product. The outcome is an ability to provide a greatly differentiated and memorable brand experience to the consumer that delivers a much wider and more compelling value proposition (e.g. lifestyle improvement) instead of merely functional product innovation.
This holistic perspective would allow CGs to also address how to meaningfully engage consumers who may still be reluctant to visit stores in the post-pandemic environment. In addition to focusing on the functional aspects of the product, it would facilitate a fundamental redesign of both instore and online engagement of consumers to meet their evolving preferences while designing a consistent and seamless brand experience across both physical and digital channels. This would enable the CGs to effectively address the physical-digital divide, capture the consumers’ increasing shift to digital sales channels, and enable them to gradually reduce their dependence on instore retail for a majority of their sales.
In tandem, harness under-used and unused product data to inform and shape the ongoing product innovation process: Along with the desirability of product innovations to the consumer, CGs need to simultaneously focus on their feasibility and viability. As the Design Thinking process progresses through the Empathy-Define stages, CGs have to ensure that any design decisions taken while iterating through the Ideate-Prototype-Test stages are also aligned with the principles of Design for Manufacturing/Assembly/Automation, etc. This would result in product innovations that can be produced efficiently with minimal waste and high product reliability.
To enable this, CGs need to harness the vast amounts of potentially untapped product data that have significant consumer relevance and typically reside in Product Lifecycle Management (PLM) as well as other corporate IT systems such as quality/QMS systems, manufacturing/MES systems, ERP, etc. This data has the potential to yield crucial insights that can inform and enrich design decisions and lead to product innovations that can be mass-produced without issues and commercialized without production delays.
For instance, quality/QMS systems hold data that enables predictive analyses such as what types of quality issues typically occur based on similar products developed in the past, etc. This would lead to prescriptive analyses such as types of design features/ingredients to avoid, machine settings to improve yield, etc. Manufacturing/MES systems hold data that enables predictive analyses such as what types of issues are likely to be faced during production, how you can avoid them, etc. This would lead to prescriptive analyses such as what types of adjustments to make on the production line to improve OEE, etc. PLM systems hold data that would lead to prescriptive analyses such as which specific raw materials to avoid because of frequent quality or production issues in the past, geography-related regulatory implications based on product design and raw materials used, etc. ERP systems hold sourcing data that enables predictive analyses on vendor performance and would lead to prescriptive analyses such as which vendors to avoid and which vendors to source from for higher-quality raw materials as well as OTIF delivery, etc.
This is where Product Lifecycle Intelligence (PLI) comes into play. PLI applies machine learning techniques to integrated product-related data spanning PLM, QMS, MES, ERP, consumer panel input, etc., and enables the predictive and prescriptive analyses described above. In turn, these analyses can inform and refine product innovation.
Thus, insights derived from actual business scenarios, issues experienced and associated root causes, and their impact on product performance can help CGs inform and shape product innovation very early in the design cycle. PLI’s advanced analytical capabilities allow it to link the product innovation process with potential business outcomes related to product design, product costs, manufacturing, quality, regulatory compliance, logistics, etc.
Simply put, PLI strategically complements Design Thinking and Experience Thinking by harnessing insights from corporate-wide untapped product-related data to guide product innovation and make the likelihood of its commercial success more predictable.