Potential Customers

The QoSIS.net market contains two types of customers:

1. customers in a Business-to-Business market segment, containing MoSPs and WNPs/MNOs/MVNOs acting as MoSPs; and

2. customers in a Business-to-Customer market segment containing mobile service end-users (i.e., customers of MoSPs).

Recalling that in our scenario, Sophie is a MobiHealth.com customer and a user of its health telemonitoring service. MobiHealth.com is an example MoSP (van Halteren, Bults, Wac, Konstantas et al. 2004). Maria is a MNO-3UK customer and SeeMeTV mobile service user; her MNO acts as a MoSP (3UK 2008). Sophie and Maria have role of QoSIS.net service users. Eric is a Facebook.com (MoSP (Facebook 2007)) customer and user, and a customer and user of QoSIS.net.

Business-to-Business Market Segment

MoSPs like MobiHealth.com or MNOs like 3UK are potential business customers of QoSIS.net. They are particular mobile service providers in e.g.

mobile information, healthcare or entertainment domains. Services provided by these types of MoSP require frequent exchange of application data between a mobile device and server on the Internet and hence frequent use of WNPs. Mobile user’s satisfaction depends on the QoS provided by the WNP used. For MoSP, QoS prediction can be used in a WNP selection process at a given geographical location and time. For a MNO acting as a MoSP, QoS prediction can be used in a selection process of a wireless access network technology (WLAN/GPRS/UMTS/HSxPA) at a given geographical location and time. The goal of MoSPs is to best fulfil users’ QoS requirements. QoSIS.net can play an instrumental role in fulfilling this goal.

The QoSIS.net fulfils the MoSP’s need of knowledge of QoS provided by different WNPs at a given geographical location and time of a mobile service user. For a MNO, it adds to its existing coarse-grained knowledge of its network; a MNO can analyze its provided QoS to mobile service users at

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given geographical locations and time intervals and be willing to improve its provided QoS in order to stay competitive amongst the WNPs. For a MNO, the information about its provided QoS can facilitate its network planning, dimensioning and management, as well as better design of own mobile services provided to mobile users.

Moreover, once encouraged, a WNP can even provide QoSIS.net with some additional information regarding its network configuration, which would facilitate more accurate QoS prediction service. Without the existence of the QoSIS.net, MoSPs and MNOs try to assure QoS to its users by using coarse-level QoS estimations.

The advantage for QoSIS.net of entering in a business relation with a MNO would be that they can be an equal co-partner in designing the QoS prediction and QoS measurement functions (e.g. mobile device configuration), have integrated billing for both services, co-marketing and co-branding strategies, bringing even more customers to the MNO.

When using QoS prediction, a MoSP better manages user’s resources involved in its services delivery e.g. lower service-data delays, improved data-rates or optimizing user’s device battery life. The biggest incentive for a MoSP to use QoSIS.net would be the fact that via QoSIS.net it gets indirect access to QoS-information collected by many other mobile users, which further enable providing accurate QoS prediction to its own users.

By using QoSIS.net, A MoSP can increase its customers’ satisfaction and hence its revenues.

Although it sounds contradictory to what we have said earlier, we claim that MoSPs and particularly MNOs can use QoSIS.net services as a way to additionally ‘lock-in’ mobile service users to own services and networks. It is because a customer satisfaction is a strong predictor of customer loyalty (Eshghi, Haughton et al. 2007), and QoSIS.net facilitates fulfilling QoS requirements and hence improving this satisfaction. Namely, if users are satisfied from the services provided to them, and they experience them at least at the required quality level, they are more likely to use even more services from this MoSP. Additionally, the cost of switching to other MoSP for this user can be high: new MoSP may not use QoSIS.net, and even if they do, the user’s historical QoS-information (i.e., measurement data) might not be portable to this new MoSP. In this situation it will take time before QoSIS.net collects enough QoS-information for this user, such that accurate QoS prediction can be provided to this user and used by his new MoSP.

Business-to-Consumer Market Segment

Mobile service users like Eric are potential customers of QoSIS.net, but only if they have a mobile device with indoors and outdoors location determination technology, e.g., WiFi triangulation and integrated GPS.

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Because their perceived service quality level relates to the QoS provided by the WNP used, they are interested in the use of QoSIS.net’s value-added service in order to facilitate (proactive and automatic) choice of the WNP that fits best to their requirements at a given geographical location and time. The user grants responsibility to the QoSIS.net on how to use QoS prediction, i.e., when to demand new QoS prediction and when to choose another WNP.

QoSIS.net for users like Eric needs to have a low intellectual barrier of entry. Namely it should be easy to download to any mobile phone, easy to configure and disable, if needed.

A QoSIS.net user saves resources involved in e.g. Facebook services delivery e.g. money while choosing a WNP with cheaper tariffs, or mobile device’s battery life. The incentive for a mobile user to use QoSIS.net is the improvement of his service perception when using his mobile services.

Without the existence of QoSIS.net, Eric would be using an arbitrary WNP as available in his location and time, probably perceiving service at quality lower than he expects while paying more than he is willing to.

QoSIS.net can equally target mobile users living a village or city suburbs with limited WNP choice, as well as big number of users living in the city centre with plentiful of available WNPs.

QoSIS.net’s Critical Success Factors

We identify that a critical success factor for the QoSIS.net service is to reach a critical mass of QoS prediction service users. This is because QoSIS.net business thrives on historical QoS-information (i.e., QoS measurement data) for its QoS prediction service. We envision that the primary market segment is B2B, and the secondary is the B2C. The B2B market entry strategy is to convince MoSPs and MNOs that QoSIS.net adds an accurate fine-grained geographical location and time based QoS prediction service to their QoS management and facilitates the improvement of perceived quality level of their customers, hence increases their revenues. This way the QoSIS.net user-base will be equal to MoSPs/MNOs customer base. However, the initial hurdle of getting enough QoS measurement data for accurate QoS prediction remains. QoSIS.net needs to agree with its customers on service launch-time duration, after which it will provide QoS prediction service with a given accuracy. We envision that the service launch-time will be a function of mobility patterns of mobile service users (Gonzalez, Hidalgo et al. 2008). Once the QoSIS.net B2B market is substantial enough (i.e., sufficient historical QoS-information is available for QoS prediction), the B2C market can be targeted.

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