Efficient network planning requires accurate forecasting of customer behaviors in the near and long term. The goal is to anticipate the location and magnitude of capacity augments months, and even years, into the future using forecasted monthly bits per second demand (Mbps). This prevents outages, increases reliability and prepares the network to handle faster speeds. However, no longer does a blanket growth rate apply to every neighborhood. Network architecture is transitioning to run fiber deeper into the network and make Service Groups (SGs) smaller, decreasing households passed while increasing speeds. This adds further challenges as smaller SGs are more sensitive to individual household patterns and faster speeds create more usage spikes. As an example, single releases of popular games downloading to a console are enough to create network congestion as are increasingly frequent live sports streaming events.
In the existing analog node world, an office park could hang off the same SG as a housing complex. The difference between day versus night peak, and weekday versus weekend, are washed away due to the size of the SG and augment decisions could be made by the macro trend to consume more traffic at faster speeds. Now, in the digital node world, SGs are smaller. An office park may have a dedicated SG and a residential community may have its own dedicated SG. If we sum their traffic at the headend level, we will certainly see the macro trend, but now the individual SGs have their own driving forces. The office park is driven by weekday, daytime peaks with seasonal dips for vacations and holidays, not to mention pandemics. The residential component is driven by nighttime and weekend peaks that spike heavily in work-from-home situations or when popular games have new releases (and homework lay forgotten). In fact, popular gaming releases can more than double the number of SGs that peak on a single day. With the popularity of gaming and the increase of sporting events dedicated to streaming, these spikes are anticipated to become more frequent. As such, the trends of these SGs cannot be accurately predicted using a headend or national level CAGR (Compound Annual Growth Rate