How ∆Q metrics helped BT to save £millions

∆Q is not only a breakthrough in terms of the science of network performance. It also is transformational to capital and operational costs. Here is one case study from BT Operate (from 2012) to illustrate. If you want to make similar large savings, learn how by coming to my ∆Q workshop in San Francisco next week.

The BT network with cost issues was 20CN, a legacy mass-market system facing revenue decline, and that had already been heavily optimised. The situation that BT faced was an increasing demand for capital spending that did not have offsetting income gains. Furthermore, they were at risk of being blamed for issues that were not under their control (e.g. in-building WiFi or packet arrival patterns).

My colleagues at Predictable Network Solutions were able to identify new optimisations and safely modify the capacity planning rules to run the network ‘hot’. They did this by putting on the ‘magic customer view glasses’ of ∆Q to get high-fidelity QoE metrics.

As you opened up the network load throttle, when did the QoE cliff bite, where and why? This could be done because ∆Q is the ‘ideal’ network metric and a strong universal proxy to QoE (and nothing else is).

The resulting £2.3m per quarter cost saving represented a 30% increase in asset utilisation.

∆Q-based insight also permitted a “sea change” in their performance design. It moved them away from a resource-centric “avoid the finger of blame resting on me”, to positive evidence that they were doing no harm to the overall end-to-end user experience. This shifted the nature of their customer relationship from a possibly adversarial one to a more collaborative and trusting basis.

To read the full case study on SlideShare, click here.

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  1. […] optimised the 20CN network planning rules and found 30% capex saving. (How? ΔQ measures gave visibility of true QoE and how “hot” the network could […]

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