Christmas, and the holiday season, in general, have been an opportune time for retailers to step up sales and improve customer engagement. However, this period of intense retail activity also puts an enormous strain on supply chains already adversely affected by the Coronavirus disease (COVID-19).
The lingering impact of the pandemic (on supply chain) aside, retailers will have to contend with growing customer expectations and revenge buying. While enterprises will have to satiate the customer appetite for a wide range of products, they cannot lose sight of the increasing importance of personalization. Historically, retailers have been inclined toward maintaining a large portfolio (of products) to respond quickly and effectively to these customer needs – incurring increased costs in the process.
To manage this difficult trading environment, several retailers are increasingly adopting sophisticated data analytics to improve both supply chain management and marketing capabilities. Data analytics can make a decisive difference by enabling better demand planning for retailers.
Optimizing distribution centers
Not long ago, McKinsey estimated the value of excess inventory from spring 2020 collections somewhere between $160-185 billion worldwide. In their endeavor to adapt to shifting market demands, retailers often encounter the problem of excess inventory and spiraling costs. The lack of visibility and quality insights into inventory and supply chain can translate into poor control and revenue losses.
Data analytics can play a key role in driving optimized management of inventory and distribution centers. To cite a relevant example, a consumables manufacturer (that operated at very low margins) leveraged advanced analytics-led clustering and stock optimization to overcome a highly fragmented distribution center network. This resulted in the company reducing logistics cost to about 5% of the total revenue, without sacrificing customer experience.
Tracking disruptive, emerging competitors
Alongside difficulties relating to supply chain and customer preferences, companies have to account for agile and emerging brands that are rapidly disrupting markets. Thus, many major consumer packaged goods (CPG) brands are using Big Data-enabled tracking tools for early identification of emerging brands and trends. Typically, these tools leverage artificial intelligence (AI) and machine learning (ML) to unearth brands in any marketplace. A blend of supervised and unsupervised ML algorithms is deployed to identify themes and sub-themes of consumer conversation at the individual brand level as well as across universal trends.
Consequently, companies benefit from the knowledge of early demand indicators and are able to identify the next big brand by scanning through multiple indicators. The insights from the tools feed into a wide range of strategic processes such as brand re-positioning, product innovation, new ventures and mergers and acquisitions (M&A).
These “always on” platforms track and flag trends and brands with the highest potential across multiple markets around the world, enabling businesses to understand the untapped consumer needs that warrant new product development or product extension. For instance, a leading CPG collaboratively created a solution that could continuously monitor emerging brands. The company leveraged this solution to improve decision-making on acquisition/negotiation of license agreements with new brands, combat potential threats and grow revenues.
This solution includes taxonomy for identifying themes in unstructured data, including social media and online content. This methodology has formed the basis for the development of algorithms that help spot 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 across key themes. Data is automatically refreshed on a cyclical basis with minimal manual intervention.
Driving hyper-personalized marketing campaigns
Creating hyper-personalized experiences is extremely crucial in a competitive marketplace. As Deloitte observes, 80% of customers are more likely to purchase from a company that offers personalized experiences. AI- and ML-led hyper-personalization goes beyond segmentation, enabling enterprises to drill down to the minutest of details and design marketing efforts tailored to an individual customer level.
When a global hotel chain realized that its existing campaigns were not exactly delivering the desired results, a quick analysis showed traditional segmentation-based targeting (without personalization) to be the underlying problem.
It developed ML-based predictive models to create customized offers. An analytics-based hyper-personalization engine was used to reach more than 20 million customers with targeted offers. Concurrently, Big Data computing significantly reduced the campaign go-to-market time. The efficacy of the marketing campaigns was then measured with scientifically designed controls.
This analytics-driven personalized marketing led to 360,000 new members enrolling for the program along with a $440 million incremental revenue increase. It also helped the client capture total addressable problems (TAP) opportunities.
With the holiday season fast approaching, time to act is now
Considering the holiday season is upon us, a full-fledged data analytics-led supply chain and marketing transformation may seem improbable. However, it’s never too late for driving limited but effective interventions. In the current challenging landscape, even a small (but focused) improvement in sales can trigger a significant increase in revenues.