I know I talk a lot about “demand density” in my blogs, so much that you could think I’m saying it’s the essential underpinning of network evolution. Well, it kind of is. The future of networking is written in dollars, not in bits, and demand density is the fundamental dollar reality. I’ve been working through modeling just how it’s impacting us today, and how it will continue to impact us over the rest of this decade, and I want to share it here.
To start with, the “demand density” concept is one I developed over a decade ago in response to what seemed like a simple question: “Why do some countries have so much better broadband than others?” I took a lot of economic data on the major market areas and ran a series of models to try to find correlations. It was obvious that the potential ROI of network infrastructure in a given area (like a country) depends on the combination of the economic potential of the area and the extent to which public right of way is available.
The model showed that the former factor was significant if you related it to GDP per square mile of inhabited area, and the latter to the road and rail miles within the country. Demand density was the combination of these factors, and I’ve always expressed it in relation to the US demand density, set to 1.0. The easiest way to understand the importance of demand density is to take two extremes, the very low and the very high, and compare them.
When a country has a very high demand density, its economic potential is highly concentrated, meaning that the infrastructure investment needed to connect the country and realize that economic potential is relatively low. ROI on infrastructure is high, so countries with high demand density are likely not to have felt the profit-per-bit squeeze at this point. The high economic return of infrastructure makes it easier to invest in new services, and so service innovation is high. Overall, infrastructure planning is likely to be aimed at revenue generation more than at cost control.
Operations costs are also lower in these high-demand-density countries. On the average, the “process opex” costs associated with service and network operations in countries with a demand density of greater than 7 is 40% less than that of a country with a demand density of 1. That’s partly because human resources are more efficient when their range of activity is small; they spend less time moving around and a central pool of resources can support a greater number of users and devices. The other part is that it’s possible to oversupply resources where demand density is high, because of that higher ROI on infrastructure. More resources mean less resource management.
The low-demand-density country is in the opposite situation. Because demand is spread over a larger geography, connecting users is more expensive and infrastructure ROI is lower. That translates to quick compression of profit-per-bit, and in the extreme cases makes it difficult to sustain investment in infrastructure at all. Opex for countries with demand densities below 0.33 have opex costs that average 20% higher than those with a demand density of 1.0, because human resource usage is relatively inefficient.
I’ve used “countries” here because it’s generally easier to get economic data at a country level, and because most countries have been served by a national carrier. In the US, it’s fairly easy to get data by state, and there are multiple operators serving the US. A quick look at two, AT&T and Verizon, is another window into the importance of demand density.
AT&T’s demand density is 1.3, which means that it fits into the “relatively low” value range. Comparing it with country data, it ranks roughly the same as Chile. Verizon’s demand density is 11.5, which ranks slightly below that of Japan, in the “high” range. Verizon has been aggressive in deploying FTTH and AT&T has not, because the former’s demand density suggests it could profitably connect about 40% of its customers with fiber, and the latter’s data says they could connect only 22%. The company has recently said it was dropping new DSL deployments, and since its demand density is such that fiber support for these customers would not likely be profitable, and you don’t have to look further than the stock prices for the two companies to see the difference in how the financial markets see them.
AT&T has been perhaps the most aggressive large operator in the world on infrastructure transformation, particularly in the deployment of “white-box” technology. That’s obviously aimed at reducing capex, but the largest component of capex for an ISP is the access network. AT&T’s low demand density means its access network technology options are crippled. DSL has to reach to far, and fiber to the home is too expensive.
This is where 5G mobile and 5G millimeter wave come in. In areas where demand density is low, 5G technology could provide an alternative to copper-loop or FTTH but with a much lower “pass cost”, meaning the cost to bring service into an area so customers can then be connected. 5G in any form reduces the access cost component of capex, which can relieve pressure on the overall capital budget. More significantly, it lets an operator raise its per-user bandwidth at a lower cost, making it more competitive and opening the opportunity to deliver new services, like streaming video.
5G seems most valuable as a tool in improving service cost and profit where demand densities are below about 3, which would correspond to US states like Vermont and West Virginia or countries like Italy. Where demand density is higher, fiber becomes more practical and the impact of 5G on overall profit per bit is likely to be steadily less.
This is important in transformation planning, because it divides network transformation goals into “zones”. Where demand density is high (greater than 5), profit per bit is not under immediate pressure and neither significant network transformation nor significant 5G exploitation is likely to be needed in the near term (to 2023). Where it’s between 3 and 5, 5G is likely a competitive and service opportunity driver. Between 1 and 3 and 5G and general network cost effectiveness combine to create the transformation drivers, and between 0.2 and 1.0, transformation has to be pervasive in both access and core, in order to control profit compression.
Obviously, pure mobile operators are going to be transformed primarily through the evolution of the mobile backhaul and core networks, so 5G standards would likely dominate transformation planning goals. Where demand density comes in is in the area of cell-size planning. High demand densities mean efficient backhaul even if microcells are used, and so 5G networks would likely trend toward smaller cell sizes and larger numbers of cells. This would also favor the introduction of bandwidth-intensive applications of 5G because per-user bandwidth could be higher. In low-demand-density areas, cell sizes would likely be larger to contain overall deployment costs, which would also reduce per-user bandwidth available and limit the new services that 5G could support.
Millimeter-wave 5G/FTTN hybrids would seem most valuable where demand densities hover in the 1-4 range, too low for large-scale FTTH but high enough that the range limitations of 5G/FTTN wouldn’t be a killer and so that delivered bandwidth could remain at a competitive level. As demand density falls to the low end of that range, 5G mobile infrastructure to serve fixed locations would become increasingly economical, and as it rises to (and above) the high end, FTTH becomes competitive.
This last issue of transformation focus may be the most important near-term factor influenced by demand density. Is a mobile network mobile-first or network-first in planning? For vendors prospecting network operators, that’s a big question because it relates to both sales strategy and product planning focus. Obviously, operators whose planning is dominated by 5G issues aren’t as likely to respond to a generalized network transformation story; they want to hear 5G specifics. The opposite is also true. However, there are exceptions. Operators whose demand density favors microcells would be doing a lot of backhaul and aggregation, and thus would build a core that looked a lot like that of a wireline network. Those with large 5G mobile cells could be doing so much aggregation in the backhaul network that they’d almost be dropping their mobile traffic on the edge of their core.
One thing I think is clear in all of this is that demand density has always been an issue. Australia, who went to a not-for-profit NBN experiment, has a demand density of 0.2. AT&T, who just committed to an open-model network (see my blog HERE) has a demand density of 1.3. The general curve of profit compression that operators always draw will reach the critical point of inadequate ROI quicker for those whose demand density is lower, and measures to contain capex and opex will be taken there first, as they have been already.
Another thing that’s clear is that transformation strategies aren’t going to be uniform when demand density is not. It’s simplistic to believe that a salesforce could be armed with a single song to sing worldwide, and succeed in selling their products across the globe. There’s always been a need to tune messages, and with operator budget predictions down almost universally, this is probably a good time to pay special attention to that tuning. Factoring demand density into the picture can be a big help.
