“Zombie” products and brands—those which create a resource drain for CPG businesses by underperforming over long periods—have been around for a while. In fact, as far back as 2013, it was found that the F&B industry was incurring annual losses of £600m on R&D, with “zombie” and “cannibal” products across industries cutting into profits and impairing business health. In more recent times, it has been seen that more than 75% of all new CPG product launches fail within a year.
While CPG product launches have only increased over the years—from approximately 20,000 in 1996 to 39,000 as per recent estimates—so have complex supply chains, production processes, and distribution requirements. Further, given the move towards more personalized products, DTC approaches, and adapting to shifting market conditions, the profitability of existing products and brands must be re-evaluated periodically, to ensure value add in the long run. With the pandemic making agility the need of the hour, and with demand and supply chains severely impacted, the need to simplify portfolios has become apparent to major CPG businesses:
• An American snack MNC announced its plans to withdraw 1/4th of its SKUs from production. According to its CEO, as “the consumer is driven more to our core offerings, it is an ideal moment to simplify our portfolio as well as our innovation pipeline to focus on our value-driving core.”
• A US-based food company reduced its SKUs in the early half of 2020 in response to reduced demand; while some categories are back in production, others will remain relegated to the backburner.
• North America’s third-largest F&B business reduced its SKU count by approximately 20%, according to its US zone president.
• A multinational consumer goods business decided to sell approximately half of its 180 brands. According to its CEO, “Although smaller-volume SKUs meeting special consumer needs will return, opportunities for continued rationalization remains.”
Identifying a zombie SKU should occur as part of a regular analytical exercise, with manufacturers periodically reviewing data on portfolio size to both stimulate high-performing products as well as identify low-performing ones.
When done wrong, SKU rationalization can create as many challenges as it can solve: for instance, increase customer churn by eliminating low-performing, but high affinity products, increase data and process complexity, entail slow turnaround times due to sign-offs by multiple stakeholders, and potentially limit the growth of promising products.
However, done right, SKU rationalization can reduce portfolio complexity, eliminate resource wastage and high inventory costs, streamline consumer choices, increase sales, improve flexibility, and make store-level assortments easier to manage.
What’s required here is a continuous method of SKU rationalization factored into a company’s annual plan, one that involves a cross-functional team for decision-making as well as a setup where low-performing SKUs are strategically identified, analyzed, and finally eliminated.
Step 1: Identifying low performers using a range of data-driven metrics, including price, placement, store locations, seasonality, product dormancy, basket size, affinity, product life cycle, revenue, market share, profitability, inventory costs, and regulations.
These can be mapped onto a single, integrated application using AI to obtain a comprehensive picture of product performance as well as the cross-product impacts. At this stage, a careful consideration of factors such as potential growth, seasonal spikes, product affinity, and so on must be undertaken to further narrow down on the list of poor-performance SKUs.
Step 2: Evaluating SKUs by measuring product performance over time, as well as forecasting factors such as customer requirements, switching, cannibalization, impact of the SKU on other products, and so on. Simultaneously, a plan for the product to be phased out must be put in place, and this must be testable: starting with a select geography, and after examining customer feedback and reactions, scaling gradually—AI can greatly speed up the planning process by providing sound business insights. Finally, an important factor deciding SKU rationalization is estimating demand transference: whether the demand for that particular SKU can be absorbed by another similar product or not.
Step 3: Studying the financial impact of product elimination, as a poor understanding of the impact of SKU rationalization on P&L remains a major barrier. For instance, in a typical CPG setting, 30% of the worst-performing SKUs may translate to 1% or less in profitability. Using forecasting tools, the impact of zombie product elimination on the company’s revenue, profitability, and so on, can be effectively studied, to inform final decisions.
Once zombie products and brands have been identified, there are certain crucial steps that can be aided by AI to ensure a seamless transition for any CPG business:
Re-positioning products: In cases where products/brands can be repurposed for certain demographics/markets or revamped through packing, design, and marketing to present a fresh offering, AI can offer insights into changes that will likely succeed in the market, including in terms of taste, texture, product design, features, and demographics.
Further, certain rationalization decisions may yield negative results for manufacturers. For instance, the decision to remove a low-performing product, in a controlled test setting, from shelves may lead to certain consumers choosing to shop elsewhere. Such granular impacts can be tracked and highlighted using AI & ML to restore certain low-performing yet high-impact products to portfolios.
Simplifying supply chains: Upon eliminating zombie products/brands, reorienting or divesting in production processes, distribution methods, warehousing, and transport, among others, can be a major challenge. Here, multiple courses of action can be simulated and examined using AI to ensure effective operations. Digital twin technology can help visualize minute changes to the supply chain, and their impact, before actual implementation, ensuring minimized costs and maximum speed and efficiency. For instance, a major beverages company discontinued a juice business and divested the transport network that also catered to other brands in the portfolio, choosing an alternative distribution route. Such changes can be planned in advance to ensure a seamless transition for all stakeholders involved.
Responding to volatile markets: While the focus has shifted toward innovative products that can quickly meet demand, the rate at which products/brands become obsolete has also increased exponentially. For instance, the aforementioned beverages company’s attempt to replace its original flavor with a new formula saw instant backlash, and the new offering was withdrawn within 80 days of release. Data-driven insights will be crucial to such quick decision-making and pivoting in response to markets, ensuring agility for CPG businesses.
Ensuring a strong customer focus: As decisions to pull a product/brand from the market might be accompanied by negative reactions from certain consumer bases, relevant and consistent messaging becomes key to customer retention. AI can help CPG businesses keep their ear to the ground and collate feedback from a variety of sources, including social media, videos, blogs, and customer reviews, to modify their messaging accordingly. AI-optimized content can go a long way in catering more closely to consumers’ needs and ensuring optimal communication strategies.
As nostalgia marketing has been a strong means of ROI for CPG brands, the opportunity to revive zombie products/brands for limited runs, and even full-fledged re-release, remains ripe.
For instance, older snacks and brands with strong nostalgia elements saw renewed popularity during the pandemic, with “the nostalgic strength of consumer goods brands – the only industry in our study where nostalgia is the dominant archetype –playing a large role in helping people cope during the COVID-19 crisis.” Further, such strategies are prevalent in the industry as a whole: a CPG giant, in 2018, brought back a snack discontinued in 2006, responding to consumers’ petitions and demand for the product’s re-release.
AI can help CPG businesses keep track of such demand in the market, examining the market, consumers’ feedback, and public reactions to give businesses quick and actionable insights on market trends as well as products/brands that can likely be revived in the near future. While zombie products/brands may be discontinued in view of overall financial health, their influence can continue on, making them a valuable resource in reinforcing as well as creating points of engagement for customers.
With 10 years of experience in creating analytical solutions for diverse business problems, William counts building end-to-end products, providing quality decision science support, and bootstrapping a successful startup among his key achievements. Outside of work, William can be found playing squash, dishing out guitar solos, and killing it in fantasy football.