Most schools of thought on capital expenditure concentrate on the “what” of the purchasing decision, with the occasional undercurrent of “how much” from the finance department. But while these are important questions, deciding “where” the resulting capital asset is deployed could be the real key to whether the decision proves effective.
Here are just a few examples of sectors where location data can help to make capital expenditure decisions.
The real key to making smarter decisions for retail locations is recognizing the importance of trends rather than relying solely on static figures. It’s understandable that decision-makers want to know whether a particular location offers suitable footfall right now, but to justify the capital expenditure of a new outlet they must have some idea of what that footfall will be in years to come. That means combining information about known future events (new housing being built nearby; development of a crowd-attracting facility such as a sports arena) with historical and current location data that reveals trends.
It’s also invaluable to be able to combine multiple sources of data and then drill deep. For example, if the local population is growing but average income is falling, the busy location is not the goldmine it might seem for a luxury goods retailer. Contrastingly, a trend towards younger adult residents in a neighborhood could mean building a babywear outlet will be a wiser capital investment in a few years than it might appear on today’s statistics alone.
Location data is equally vital for a telco’s capital expenditure plans. While technology and branding are both important for winning customers and boosting revenue, efficient operations with minimal wasted expenditure are also vital to the bottom line. That means getting equipment and infrastructure in the right place to keep adequate service levels without excessive unused capacity.
Take even seemingly fundamental decisions such as where to locate a new cellphone tower. Again, it’s not merely a case of knowing where potential customers are now, but how that population pattern will change during the lifespan of the tower. And again, it’s not just about the numbers: A region that comes to be dominated by students and graduates may have very different levels of demand for mobile data than one increasingly popular with retirees.
It’s no secret that location is key for even the most primitive capex decision processes, with factory sites chosen with supply chains and expansion opportunities in mind. Not every company makes the most of location data, however.
For example, detailed analysis about local populations and the potential labor supply could sway decisions. It’s not just a matter of knowing the demographics of the local workforce, but also the transport situation. If local transport between the factory site and population centers is limited or expensive, wages might have to be increased to recruit staff who face more costly commutes.
Meanwhile, location data can affect capex decisions on a smaller scale. For example, access to major roads is naturally a key part of siting a factory or warehouse in the first place, but deeper analysis of local traffic might inform vehicle purchasing. A company that initially assumed a full-size truck was the most efficient purchase might analyze minute-by-minute records of its fleet’s location and discover that it made more sense to buy two smaller vans. The loss of efficiency might be outweighed by being able to access quieter roads without vehicle size restrictions, speeding up deliveries during peak traffic hours.
That in turn could make it worthwhile to track the movement of staff and packages within the factory to figure out if switching to smaller consignments had changed the most efficient way to route parts and goods from production line to dispatch.
Beyond these specific examples, using location data to inform capex decisions has some broader, universal benefits. A decision-maker who can point to location data, particularly if it challenges conventional wisdom or appears counterintuitive, will have a far simpler time justifying capex decisions to other departments and executives in the organization.
Location data also has the benefit of covering a field that is inherently changing rapidly – in some cases, by the minute. That makes it much more likely that an insight based on location data will show trends and allow realistic forecasts than one derived from other sources such as financial figures that cover weekly, quarterly, or even annual results.
The real key to remember is that raw location data is not always enough to make smart decisions in itself. It needs to be transformed from data to information – hence the term “informed decision.” To do that, data needs to be gathered and processed in a way that allows comparisons, analysis, and cross-referencing. Only then can it reveal the patterns, trends, and context that combine to produce genuine actionable insight.
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