Future Foresight believes that deep insights can only come from the world's most advanced telecoms model.
Model the entire market
When a model ignores the total addressable market in any geography, it invariably delivers a result of more than the available market share. This results in high valuations that are never achievable. Furthermore, such models assume subscriber growth for a purely internal view. There is a complex dynamic where market share is actually built from a share of gross adds available in the market in any month, which comes from new entrants into the market combined with churn off all operators. Every operator in the market grows by attracting a share of this number. By failing to model all operators in the market, one ends up with a completely unrealistic model, and this is seen when all operators in a market are summated and invariably get to over 200% total available market.
Focus on revenue credibility
Operational and capital expenditure costs display very little flex in the real world, large swings in assumptions make very little difference to the NPV. However, minor changes in revenue drivers (churn, SGA, MOU, tariffs) make significant differences to the NPV. The unfortunate reality is that even in the most developed markets, these numbers are horribly manipulated by the operators (‘robust’ active subscriber definitions, inflated ARPUs, etc). Our model triangulates such that any overstatement in one number will be self-corrected through the redress in another. Later, output metrics are used to understand where this may be a little imbalanced. Again and again we have defended extremely high value debates between parties where one cherry-picks parameters to adjust NPV to their self-interest, and our model has proven its worth.
Key to telecoms strategy is an understanding that ARPU (the tariff/MOU balance) is driven by segment mix. Having a lot of low-value customers is less interesting than a few high-value ones – the low value customers churn frequently which has a substantial effect on acquisition costs. This is worse than ever with telemetry and IoT devices. Most models we have seen completely miss the strategic insight that emerges when one correctly models how segments shift over time. This requires that each segment has its own churn, SGA, and APRU. Frequently we have shown operators that their marketing strategy is going to drive their mix to a point of diminishing returns that they did not understand. Often the pain is hidden in service costs (call centres must reduce service levels as lower segments build up) or even in interconnection (an adverse mix can swing the operator from net receiver to payer of traffic).
Traffic balanced across whole market
The bulk of our model manages the balancing of the total market traffic. No operator exists in isolation and there are overt and subtle interactions between operators in a market. Many telecoms models we see fail in that they over-state the individual operator’s revenue position, as they fail to cross-check that the total market minute volumes are within national ranges. The additional advantages of this approach are correct interconnect calculations (and the slew of strategic insights one can derive once this is properly modelled such as LRIC), as well as the ability to model any operator be they the first mover through to a new entrant. The model thus easily accepts scenarios that include a new license (or even an operator’s demise). In the past, we have successfully predicated the demise of MVNO’s based on missing interconnect revenues in the Super-SP model that reigns in SA. However, for this IMT bid, we have found that this approach was not important for interconnect (no longer a big issues n SA), but revealed a load about MOCN, MORAN, and Carrier agreement parameters.
Our model works for, and has been used across, a wide range of business models:
Traditional MNO, be it incumbent or new entrant
MVNO and Super SP entries to the market
MVNO hosting dynamics
MOCN and MORAN roaming dynamics (the Achilles heel of late entrants)
IMT (spectrum) auction, and very sensitive business case requiring deep strategy
WOAN (carrier of carriers) auction, a surprisingly lucrative opportunity
Extra info on model
National operator inclusion and balancing
Model uses the total country market as its starting point, as opposed to a build-up which could otherwise result in more than the available market being taken up in the forecasts
Detailed traffic balancing, including fixed line minutes. This is essential for calculating interconnect payment directions which have a significant impact on the MNO (and indeed MVNO) valuations
Financial information derived from published sources is triangulated into the model to deliver proxies for the total market
Triangulation on inputs helps eliminate the contradictions in operator-published results (eg over-stated subscribers yields a lower ARPU, which then contradicts MOU and tariffs forcing one to rectify the over-statement and thus keep the valuation honest)
Comprehensive value-based market segmentation within the major pre-paid and post-paid segments
Cannibalisation of segments between operators including churn dynamics and the stickiness of two year contracts. Buy-back options also exist.
Segment development enables robust tracking of the development of a market – generally top down penetration from early adopters to late mass
Segment capability allows different sub-sections of the market to be modelled accurately as distinct business opportunities through to gross margin levels
Segmented approach results in highly defendable blended APRU swings during radical repositioning of the business which is normally unmanageable otherwise
Closed User Group pricing, with cognisance of the value destructive regulatory impact on interconnect
In 2019 & 2020, our model updates focused intensely on the swing from voice into data, and how to project usage given extreme elasticity and exponential growth curves
Insights into large-scale shifts in the market
Whether it is a new entrant, new technology or a regulatory drive, operators face a changing landscape.
Our model approach balances developments in ARPU with tariffs and traffic volumes to spot hidden CAPEX or OPEX requirements from obvious market developments.
As an example, the recent drive by the South African government to reduce data costs has results in a concomitant increase in traffic. This requires additional CAPEX spend or new technology to be implemented in an operator’s network to deal with the additional volumes to be switched.
Strong OPEX cost drivers linked to operational statistics for customer numbers, usage and capital projects
Financial reconciliation including all statutory financial statements
Detailed output provides in-depth analysis of revenues, costs, capital projects and operational metrics
Proprietary scenario manager allows the management and evaluation of an unlimited number of strategic options, with model versioning updating throughout all scenarios