Earlier categorized under the standard umbrella of cosmetics, the sector has now branched into health, beauty, personal care, skin care, wellness, and more. What makes it a definite opportunity for retailers is the fact that the industry is about to hit the USD $580 billion mark by 2027. However, the opportunity doesn’t come sans challenges.
As many as 42% of consumers across the major global economies enjoy trying new brands and are increasingly shopping across price points. The emergence of online webpages for skincare guidance and buying imported products, live streams, influencer-led brand hopping, and a dynamic shift in consumers’ purchasing habits, have put retailers under pressure. Taming the spikes and lows induced by seasonality, forecasting demands in a highly volatile landscape, and planning for stockouts and markdowns are becoming increasingly challenging. Enter the diminishing boundaries between two buckets, such as beauty and wellness, where the combined sector is accounting for as much as USD 2 trillion globally for brands, retailers, and investors, and the problem snowballs.
Rising above such disruptions requires a careful and strategic supply chain and retail planning, and hinges on granular analysis. Let’s find out how granular forecasting can be a game-changer for retailers in highly competitive health, beauty, and wellness segments.
Overcoming Disruptions in Health Beauty and Wellness Retail with Granular Forecasting
1 – Demand Forecasting Solution at the SKU Level
One of the major drawbacks of traditional retail forecasting and replenishment strategies is the siloed approach. The sales data, customer data, and data from multiple stores, channels, and categories are processed individually, which leads to multiple blindspots, eventually leading to partially optimized forecasts.
On the other hand, retail-tuned AI and ML-powered solutions can easily process highly unstructured data sets to find hidden trends and patterns and generate actionable insights. So, retailers can move beyond category-level forecasting and get granular SKU-level forecasts. They can analyze data from multiple sources, like historical sales, market trends, consumer behavior, and more to arrive at highly accurate and consolidated forecasts that predict demand for specific products and categories at specific locations. This minimizes the risk of stockouts and overstocking.
2 – Uncover Hidden Trends to Account for Seasonality
The health, beauty, and wellness retail market experiences rapid shifts in terms of consumer purchases, product categories, and micro-trends. As the unit costs are also high, stocking new items comes with double cost bleeds, and looming markdowns. All these disruptions are in addition to seasonal fluctuations, leaving retailers clueless about the “just-approaching” or “can-affect” scenarios such as weather changes, holidays, festivals and special days, sales events, and promotions. This renders the traditional one-size-fits-all forecast methods ineffective.
Smart solutions built on powerful AI algorithms and ML models trained for detecting and predicting analytical variations can easily identify emerging trends, and disruptions. Advanced analytical capabilities like anchor-level predictors and sub-category-level variables for factoring in seasonality-level variables and granular-level demand distribution (for SKUs) empower retailers to manage complex retail use cases.
3 – Cross-Channel Dynamic Inventory Optimization
According to the 2023 US Beauty Consumer Survey, more than half, 54.2% of online beauty buyers say that trying on a new product during in-store shopping helps them discover new brands. Hence, having accurate inventory across all channels is critical for customer satisfaction, sales revenue, and customer acquisition.
For this, retailers have to identify and create highly targeted constraints for optimizing their inventory to overcome the planning challenges for nuanced use cases. However, planning manually or with static retail planning software cannot help retailers optimize their inventory across categories, channels, and stores in one go. They cannot manage supply fluctuations such as lead time variations and fill rate variations as well, leading to forecast errors.
On the other hand, algorithmically-driven solutions crafted purely for retail can easily integrate and analyze data from multiple unrelated sources, like sales, competitors and external markets, weather, and macroeconomic variables to arrive at holistic and granular forecasts. These solutions are crafted to manage and map the supply fluctuations, volatility, and constraints. This helps optimize inventory planning and offer truly seamless shopping experiences to the customers irrespective of the channel, store, or category they are shopping for.
4 – Forecast Model Customization
Every retailer has unique retail challenges when it comes to forecasting and replenishment, especially in highly competitive segments, such as health, beauty, and wellness. So, a standard set of constraints for forecasting is no longer relevant to all of them. Manual modeling considers a standard range of variation and fails to manage the promotion mechanics variables, leading to costly consequences, like markdowns, cannibalization, stockouts, etc., ultimately amounting to the loss of free cash flow.
ML algorithms powered by AI and advanced analysis enable retailers to factor in complex variables such as time as a function of demand across different stores, categories, and channels. Retailers can set highly specific and custom constraints based on their sales history, products, locations, etc. This ability to customize forecast models based on multi-variable factors, such as weather patterns, microtrends, and promotions, offers event-centric predictive insights. Businesses can understand the outliers, promo patterns, and price elasticity to evolve alongside cyclicality/seasonality/micro trends in health and beauty segments. Retailers can also create multiple scenarios to evaluate the sensitivities of different forecast models and unlock greater accuracy and granular planning.
5 – Real-Time Demand Sensing and Sourcing
Real-time demand prediction modeling helps retailers factor in seasonality, promotions, and other external factors that impact demand. Coupling it with ML-powered intelligent algorithms facilitates accurate demand prediction and responsiveness to interstore transfers, promotions, and supply chain disruptions.
Further, suppliers are key supply chain stakeholders in the health, beauty, and wellness retail industry, especially when it comes to international vendors. Without granular forecasting and strategic sourcing intelligence, retailers cannot understand the impact of sourcing disruptions, such as delays and lead times.
Forecasting and demand planning solutions with tailored AI algorithms can overcome these challenges by solving inventory variability and velocity, supplier constraints, demand & supply volatility, etc. This can help retailers optimize their sourcing strategies, minimize lead times, and unlock greater cost savings.
As retail moves toward more integrated shopping experiences and the digital divide vanishes further, intelligent solutions with algorithmic superiority pave the way for strategic business planning. Such solutions can help retailers save costs via 90% accurate forecasts, tailored data engineering, and quick modeling for different scenarios, making them ideal for future-proofing the business.
Also read: Navigating the Beauty Maze: AI’s Role in Retail and Supply Chain Planning
