How do you use analytics to drive better collaboration and alignment between marketing and supply chain?
Centric Cleveland’s Hugh Walters and Rahul Pavanan explain.
Tell me what’s wrong with this statement: “Supply chain analytics should be the central analytic activity in any company with a supply chain.”
If you’re a supply chain professional like me, then your answer is probably “nothing.”
You know that the data produced by supply chain transactions can be manipulated and mined to populate everything, including reports, graphs, and dashboards, and also provide a sense of the operational tempo of an organization—the rate at which the company is making and spending money. For this reason alone, supply chain analytics should be the central analytic activity.
However, and more importantly, by co-mingling supply chain data with data from marketing, the value of the analytics can increase by an order of magnitude because it can highlight opportunities for revenue and profit growth.
Enriching Supply Chain Analytics for Deeper Insight
Typically, when people think of supply chain analytics, they think of facts and figures about orders, purchases, inventories, trucks, and warehouses. While this is all admittedly very valuable, it is one-sided and “siloed” because it is inwardly and operationally focused. But some of these transactions really represent something more: they represent a customer behavior and the task – the value-add – is to incorporate this behavior in a way that provides transferable insights among the customers. Fortunately, this value can be captured with just a bit of the right kind of data.
This new data describes particular attributes of the customer (for example, large vs. small, working within a particular market segment or across markets, serving a particular geography or national, intermediary, etc.). By connecting this information to operational data, a powerful linkage between what a customer is with how he behaves (order size, order frequency, market basket, rate of payment, etc.) can be created. In this way, marketing and operations can come together to better understand and group customers based on a set of similar attributes. These actionable insights can be leveraged to grow revenues and profits by identifying underserved customers and markets, as well as product and service gaps.
Deeper Insights from Segmentation
Developing these insights requires multiple steps. First, there is a need to group customers according to a logical set of criteria to facilitate the identification of group behaviors. This can be difficult as it often requires the development of subjective and relative definitions. For example, what is a large customer? What is a large order? What is a long lead time? A large customer in one company may be a small one in another – likewise for orders and lead times.
Further, what should the threshold be that separates a large customer, a large order, a long lead time from a medium one? Developing these definitions and then classifying the data is a combination of science and craft. The result enables a new perspective where patterns and behaviors can be compared within and across groups. The result also facilitates the development of generalizations and rules, identification of trends and outliers, and the formulation of plans, actions, and policies. The diagram below details elements of the process at a high level.
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The type of information needed to group customers is similar to the type of information needed for a segmentation study. Frequently, supply chain organizations are enlisted to conduct a segmentation study because of their association and familiarity with transactional data. However, they typically do not take the segmentation study far enough and frequently default to a “Walmart and Everyone Else” or a “Big Guys and Everyone Else” segmentation methodology because of the volumes associated with these groups. Such an approach severely limits the analysis and usually fails to yield any real insight into the particular markets and customers served.
Sharing Data to Drive Collaboration
Marketing organizations frequently possess the necessary data to perform a respectable segmentation. They typically rely on Customer Relationship Management (CRM) systems (or a home-grown version of one), websites that drive e-commerce, or purchase data from companies such as Nielsen, IRI, Hoovers, Dun and Bradstreet, and Bloomberg. Unfortunately, it is seldom in a format that promotes collaboration.
However, this can be overcome with a little effort and organization. For example, the “Walmart and Everyone Else” segmentation could be transformed into integrated, big box, full-service grocers; large footprint national grocers; large footprint regional grocers; small footprint regional grocers; small footprint local grocer. This is a customer grouping that a cross-functional team can really work with to analyze behaviors, develop insights and capitalize on opportunities.
To obtain an understanding of behaviors, a team should review:
- Products ordered together in a single transaction (market basket)
- Products ordered over time (time-phased market basket)
- Frequency and timing of orders (time between orders, requested lead time)
- The quantity of products ordered (product quantities and number of order lines)
- Invoices and Payments (timeliness, completeness)
To further drive collaboration and alignment between marketing and supply chain, the combined data should be analyzed regularly to ensure that plans, projects and policies are effective. Creating this sustainable process requires cross-functional expertise, not just in supply chain and marketing, but also in business intelligence (BI). The BI team can facilitate the automation of data extraction, translation, and manipulation, as well as the generation of reports and the visualization of information. This leaves the combined marketing and supply chain teams free to identify and prioritize opportunities.