How Data and Analytics Can Sharpen Your Branch Network Strategy Amid Rising Costs

Industrials companies commonly operate sales, service, distribution and manufacturing networks. As these networks become larger, they become more complicated to operate, giving rise to inefficiency. Network optimization is even more important today as companies seek to differentiate themselves in an inflationary environment.

Data and analytics can point decision-makers in the right direction. But you still have to know where to look. In this Executive Insights, we’ll highlight some of the analytical techniques and often-overlooked considerations for creating an optimal network strategy that can drive significant competitive advantage.

Key analytical techniques

Do I have the right locations to serve my business? Would changing them drive greater sales? How can I set my network up to minimize delivery time and cost? Which site characteristics are driving profitability? 

While these questions aren’t new, industrials companies haven’t always had access to the kind of data that yields detailed answers. Now they do, and advanced analytics can tease out the insights. Examples of what can be addressed through network optimization analysis include the following:

• Profitability optimization: Assessing drivers of site profitability in detail, then deciding how to adjust operations to maximize profitability of each site

• Footprint consolidation: Moving to a more cost-efficient branch network by closing or combining sites 

• Whitespace identification: Identifying which geographic areas to prioritize for new site locations, based on their demand dynamics and how well they fit with the current branch network 

• Route optimization: Finding the most cost-efficient route(s) for a business based on its current footprint, typically by changing the delivery schedules, order of drop-offs, type of vehicles, etc. 

By harnessing big data, distributors, industrial manufacturers, service providers and other network-based companies can quantify their profitability at the site level. They can further reveal the market and operational conditions that are correlated with high profitability (think proximity to other sites, labor and wage distribution, and cost of real estate).

To understand how analytical insights play into a successful network optimization strategy, let’s walk through a couple of examples.

Example #1: Focusing on local market share

Whatever the network — branch locations for a distribution business, sales force footprint in a manufacturing company or something else — higher local market share typically lines up with regions or locations that have higher revenue and/or profitability (see Figure 1).

The problem is that companies tend to focus on the larger national or regional picture, thus missing out on big opportunities for growing local market share. An industrials company could have high overall share on a national or regional level, but much weaker share compared to local competitors in specific markets.

With access to internal and external data, companies can pinpoint demand, sales and share for local market areas. We’ve used multivariate regressions to calculate extremely precise market shares by analyzing demographic, psychographic and behavioral variables using block groups of 600-3,000 people (see Figure 2).

Insights like these give companies a realistic view of how the overall market is currently served and how to best compete within local markets, so they can adjust accordingly if market expectations shift.

Example #2: Pinpointing high concentrations of customers

Locating closer to customers means faster deliveries, more walk-in traffic and stronger customer relationships. At the same time, a higher concentration and presence of customers allows companies to use assets in the region more efficiently. Salespeople could be more productive with their time, and stockrooms could include slower-turning but more profitable SKUs.

For companies with numerous locations or resources in a specific market, the challenge is finding those areas of concentrated demand. On the flip side, it’s not always obvious when the company lacks the right number of resources to serve a particular market. That’s especially so post-pandemic, with city dwellers flocking to the suburbs and homebuyers heading to lower-cost regions.

Today, companies can identify high concentrations of customer demand with rigorous geospatial analytical techniques to develop catchment insights. The customer demographic and preferences data for this type of analysis is readily available from sources like census and other large routine surveys, business registries, commercial real estate data, credit card data, and cellphone data. This makes it possible to locate demand concentrations right down to the block level.

What can companies do with this information? Suppose a distribution company analyzed external data to reveal the geographic distribution of product demand not only at the level of local markets, but also at a detailed level within local markets. Let’s further suppose the company created an algorithm that pairs minimized distance to customers with maximized distance from competing distribution sites. Now the company has the insights it needs to plot optimal locations for branch expansion (see Figure 3).

Getting data analytics right

Industrials companies looking to diagnose the performance of their networks now have a powerful set of tools at their disposal. Advanced analytics has made it more feasible than ever to describe customers and markets, understand competitors, and predict the most effective course of action to optimize the business. These same capabilities can be applied beyond network strategy to inform overall growth strategy, M&A development and other tough challenges where data and insights provide a strategic advantage.