Analytics in the Retail and Consumer Packaged Goods (CPG) markets is more complex than the average corporate data ecosystem because of the variety of analytic approaches needed to support these organizations. Every business has operational management capabilities for core human resources and financial management, but retail adds the complexities of hybrid workforce management, scheduling, and operational analytics as well as the front-end data associated with consumer marketing, e-commerce, and transactional behavior across every channel.
In contrast, when retail organizations look at middle-office and front-office analytics, they are trying to support a variety of timeframes ranging from intraday decisions associated with staffing and customer foot traffic to the year-long cycles that may be necessary to fulfill large wholesale orders for highly coveted goods in the consumer market. Over the past three years, operational consistency has become especially challenging to achieve as COVID, labor skill gaps, logistical bottlenecks, commodity shortages, and geopolitical battles have all made supply chain a massive dynamic risk factor that must be consistently monitored across both macro and microeconomic business aspects.
The lack of alignment and connection between the front office, middle-office, and administrative analytic outputs can potentially lead to three separate silos of activity in the retail world— connected only by some basic metrics, such as receipts and inventory turnover, that are interpreted in three different ways. Like the parable of the blind men and an elephant where each person feels one part of the elephant and imagines a different creature, the disparate parts of retail organizations must figure out how to come together, as the average net margin for general retail companies is about 2% and that margin only gets lower for groceries and for online stores.
Analytic opportunities to increase business value exist across the balance sheet and income statement. Even though consumer sentiment, foot traffic, and online behavior are still key drivers for retail success, analytic and data-driven guidance can provide value across infrastructure, risk, and real-time operations. Amalgam Insights suggests that each of these areas requires a core analytic focus that is different and reflects the nature of the data, the decisions being made, and the stakeholders involved.
Facing Core Retail Business Challenges
First, retail and CPG organizations face core infrastructure, logistics, and data management challenges that typically require building out historic analysis and quantitative visibility capabilities often associated with what is called descriptive or historical analytics. When looking at infrastructure factors such as real estate, warehousing, and order fulfillment issues, organizations must have access to past trends, costs, transactions, and the breadth of relevant variables that go into real estate costs or complex order fulfillment associated with tracking perfect order index.
This pool of data ideally combines public data, industry data, and operational business data that includes, but is not limited to, sales trends, receipts, purchase orders, employee data, loyalty information, customer information, coupon redemption, and other relevant transactional data. This set of data needs to be available as analytic and queryable data that is accessible to all relevant stakeholders to provide business value. In practice, this accessibility typically requires some infrastructure investment either by a company or a technology vendor willing to bear the cost of maintaining a governed and industry-compliant analytic data store. By doing so, retail organizations have the opportunity to improve personalization and promotional optimization.
A second challenge that retail analytics can help with is associated with the risk and compliance issues that retail and CPG organizations face, including organized theft, supplier risk, and balancing risk and reward tradeoffs. A 2022 National Retail Federation (NRF) survey showed that organized retail crime had increased over 26% year over year, driving the need to identify and counter organized theft efforts and tactics more quickly. Retailer risk for specific goods and brands also needs to be quantified to identify potential delays and challenges or to determine whether direct store delivery and other direct-to-market tactics may end up being a profitable approach for key SKUs. Risk also matters from a profitability analysis perspective as retail organizations seek to make tradeoffs between the low-margin nature of retail business and the consumer demand for availability, personalization, automation, brand expansion, and alternative channel delivery that may provide exponential benefits to profits. From a practical perspective, this risk analysis requires investment in a combination of predictive analytics and the ability to translate the variance and marginal cost associated with new investments with projected returns.
A third challenge for retail analytics is to support real-time operational decisions. This use case requires access to streaming and log data associated with massive volumes of rapid transactions, frequently updated time-series data, and contextualized scenarios based on multi-data-sourced outcomes. From a retail outcome perspective, the practical challenge is to make near-real-time decisions, such as same-day or in-shift decisions to support stocking, scheduling, product orders, pricing and discounting decisions, placement decisions, and promotion. In addition, these decisions must be made in the context of broader strategic and operational concerns, such as brand promise, environmental concerns, social issues, and regulatory governance and compliance associated with environmental, social, and governance (ESG) concerns.
As an example, supply chain shortages often come from unexpected sources. An unexpected geopolitical example occurred in the United States when the government’s use of containers as a temporary barrier to block illegal immigration checkpoints on the US-Mexico border led to shortages at US ports for delivery. This delay in accessing containers was not predictable based solely on standard retail metrics and behavior and demonstrates one example of how unexpected political issues can affect a hyperconnected logistical supply chain.
Recommendations for Upgrading Retail Analytics in the 2020s
To solve these analytic problems, retail and CPG organizations need to allow line-of-business, logistics, and sourcing managers to act quickly with self-service and on-demand insights based on all relevant data. This ultimately means that to take an analytic approach to retail, Amalgam Insights recommends the following three best practices in creating a more data-driven business environment.
- Create and implement an integrated finance, operational, and workforce management environment. Finance, inventory, and labor must be managed together in an integrated business data store and business planning environment or the retail organization falls apart. Whether companies choose to do this by knitting together multiple applications with data management and integration tools or by choosing a single best-in-breed suite, retail businesses have too many moving parts to split up core operational data across a variety of functional silos and business roles that do not work together. In the 2020s, this is a massive operational disadvantage.
- Adopt prescriptive analytics, decision intelligence, and machine learning capabilities above and beyond basic dashboards. When retail organizations look at analytics and data outputs, it is not enough to gain historical visibility. In today’s AI-enabled world, companies must have predictive analytics, statistical analysis, detect anomalies quickly, and have the ability to translate business data into machine learning and language models for the next generation of analytics and decision intelligence. Retail can be more proactive and prescriptive with AI and ML models trained to their enterprise data to support more personalized and contextualized purchasing experiences.
- Implement real-time alerts with relevant and actionable retail information. Finally, timely and contextual alerts are also now part of the analytic process. As retail organizations have moved from seasonal purchases and monthly budgeting to daily or even hourly decisions, regional and branch managers need to be able to move quickly if there are signs of business danger coordinated revenue leakage, brand damage across any of the products held within the store, unexpected weather phenomena, labor issues, or other incipient macro or microeconomic threats.