Christmas and the holiday season in general have been a great time for retailers to step up sales and improve customer engagement. However, this period of intense business activity is also straining supply chains already affected by coronavirus disease (COVID-19).
Aside from the lingering impact of the pandemic (on the supply chain), retailers will face rising customer expectations and revenge shopping. While businesses will need to satisfy customers’ appetites for a wide range of products, they cannot lose sight of the growing importance of personalization. Historically, retailers have been inclined to maintain a large portfolio (of products) to respond quickly and effectively to the needs of these customers. – incur increased costs in the process.
To manage this challenging business environment, several retailers are increasingly adopting sophisticated data analytics to improve both supply chain management and marketing capabilities. Data analysis can make a decisive difference in enabling better demand planning for retailers.
Optimizing distribution centers
Not too long ago, McKinsey estimated the value of excess inventory from the Spring 2020 collections to be between $ 160 billion and $ 185 billion worldwide. In their efforts to adapt to changing market demands, retailers are often faced with the problem of excess inventory and skyrocketing costs. Lack of visibility and quality information about inventory and the supply chain can result in poor control and lost revenue.
Data analysis can play a key role in driving optimized inventory and distribution center management. To cite a relevant example, a consumables manufacturer (which operated at very low margins) leveraged clustering and inventory optimization based on advanced analytics to overcome a highly fragmented distribution center network. This allowed the company to reduce its logistics costs to around 5% of total revenue, without sacrificing the customer experience.
Monitoring of emerging and disruptive competitors
Along with supply chain challenges and customer preferences, companies need to consider agile and emerging brands that are rapidly disrupting markets. For example, many leading consumer packaged goods (CPG) brands are using big data-based tracking tools for early identification of emerging brands and trends. Typically, these tools take advantage of artificial intelligence (AI) and machine learning (ML) to find brands in any market. A mix of supervised and unsupervised ML algorithms are deployed to identify themes and sub-themes of consumer conversation at the individual brand level as well as across universal trends.
Therefore, companies benefit from knowing the first metrics of demand and are able to identify the next big brand by browsing through multiple metrics. Information from the tools feeds into a wide range of strategic processes such as brand repositioning, product innovation, start-ups, and mergers and acquisitions (M&A).
These ‘always-on’ platforms track and report the trends and brands with the highest potential in multiple markets around the world, enabling businesses to understand the untapped consumer needs that justify new product development or product expansion. . For example, a leading CPG collaboratively created a solution capable of continuously monitoring emerging brands. The company leveraged this solution to improve decision making on acquiring / negotiating licensing deals with new brands, tackling potential threats and increasing revenue.
This solution includes a taxonomy to identify themes in unstructured data, including social media and online content. This methodology served as the basis for the development of algorithms that help identify the potential of emerging brands and trends. These algorithms use past data and business inputs to set thresholds to identify top performing trends as well as brands on key themes. Data is automatically refreshed on a cyclical basis with minimal manual intervention.
Management of hyper-personalized marketing campaigns
Creating hyper-personalized experiences is extremely crucial in a competitive market. As Deloitte notes, 80% of customers are more likely to buy from a company that offers personalized experiences. AI and ML-based hyper-personalization goes beyond segmentation, allowing companies to explore the smallest details and design marketing efforts to suit each customer’s level.
When a global hotel chain realized that its existing campaigns weren’t delivering exactly the results they wanted, a quick scan showed that traditional segmentation-based targeting (without personalization) was the underlying problem.
She has developed ML-based predictive models to create personalized offers. An analytics-based hyper-personalization engine has been used to reach over 20 million customers with targeted offers. At the same time, Big Data computing has significantly reduced the time to market for the campaign. The effectiveness of the marketing campaigns was then measured with scientifically designed controls.
This analytics-based personalized marketing led to the enrollment of 360,000 new members in the program as well as an additional revenue increase of $ 440 million. It also helped the client seize Total Addressable Problem (TAP) opportunities.
As the holiday season approaches, now is the time to act
With the holiday season approaching, a full-fledged supply chain and marketing transformation driven by data analytics can seem unlikely. However, it is never too late to conduct limited but effective interventions. In today’s difficult landscape, even a modest (but targeted) improvement in sales can trigger a significant increase in revenue.