Does revenue today mean lost potential?

Data analytics deployments by their definition are long-term projects but they need to be rolled out in a climate in which short-term ROI is required to gain investment. Jonny Evans explores how business cases be constructed that deliver the short-term gains necessary to receive investment approval.

Potential short term gains may seem attractive but when planning data analytics deployments, communications service providers (CSPs) should keep the long and medium term potential of the technology in mind, or risk losing a far larger pot of gold further down the line.

Mounir Ladki: Budgetary environment remains challenging for most CSPs
Mounir Ladki: Budgetary environment remains challenging for most CSPs

Caught between the competitive race to lower prices and soaring consumer expectations, there’s a demand to deliver a near immediate return on investment. “The budgetary environment remains challenging for most CSPs, and investment proposals need to be fully justified to receive management and procurement approval,” says Mounir Ladki, the president and CTO of Mycom OSI.

When creating business cases for successful deployments, advocates should push the message that these are not the same as old-fashioned business intelligence rollouts; these are root and branch technology solutions that enable and inform new business opportunities.

The challenge may seem huge: “Companies might be discouraged by the fact that their data is in a combination of legacy and new systems housed in various locations. They may think that a lengthy installation process or custom tool development is necessary to answer this question,” explains Rupert Naylor, the UK vice president at Applied Predictive Technologies. “Organisations can use the large amount of data they now have available to run tests to determine the impact of each new business idea. [This] helps organisations determine cause-and-effect relationships between business actions and key metrics, and identifies opportunities to tailor and target those initiatives to improve ROI.”

Matthew Roberts: The positive impact of a data analytics deployment should be seen fairly immediately
Matthew Roberts:
The positive impact of
a data analytics
deployment should be
seen fairly immediately

There is potential for fast return. “The positive impact of data analytics deployment should be seen fairly immediately, whether that’s improving marketing performance, customer care or network optimisation,” explains Matthew Roberts, the director of marketing for big data analytics and strategic innovations at Amdocs.

Gartner recently predicted that an average CSP could potentially generate US$300 million a year in additional margins, including cost savings and revenue uplift, by using data analytics.

CSPs can use data analytics technologies in numerous ways to address challenges such as customer churn, or to precisely address identified problems in capacity provision, enabling significant cost savings and improved service agility.

“It’s not about how many petabytes or exabytes of data a CSP can store. It is about how much value can
be extracted from these data records,” adds Ladki.

For Chris Purdy, the CTO of CENX: “Data analytics can be used very effectively within CSP operations to
drive revenue due to improved level of service with customer self-service portals for dynamic provisioning and real-time monitoring of data connectivity services.”

Data will “typically include geo-location, demographic data, statistical models, as well as items calculated from a combination of external and internal sources, such as proximity of customers to the organisation’s network assets,” says Steve Farr, a product marketing manager at TIBCO Software.

Neil Lilley: CSPs can enable new revenue streams from valuable insights extracted from user data
Neil Lilley:
CSPs can enable
new revenue streams
from valuable
insights extracted
from user data

In the long term CSPs will eventually be able to create user profiles for customers in order to develop tailored services; even further down the line. “CSPs can enable new revenue streams from partners and third parties, such as advertisers, content publishers and social media by providing access to the valuable insights extracted from user data,” says Neil Lilley, Ericsson’s OSS product marketing director.

From JIC to JIT

Part of CSPs’ strategies should address requirements to reduce opex and capex and position themselves for long-term gain. “The long term possibilities of data analytics are that the CSP can move from a Just-in- Case (JIC) approach for managing capacity in their networks to a Just-in-Time (JIT) upgrade capability to significantly reduce capex and opex while still delivering quality user experiences,” adds Purdy.

Additional opportunities may include:

  • Providing CSPs with a deeper understanding of traffic trends to enable predictive deployment of virtual services
  • Helping CSPs to understand app and content provider relationships as they become OTT service partners
  • Enabling CSPs to offer appropriate analytics identified services to the right users at the right time, such as providing high quality video to travellers.

To bring such opportunities in on time and on budget, while making good deployment decisions, CSPs should focus on what they want out of the analytics system, keeping costs down and the value proposition defined. “Combining this with cloud engagement will help keep the costs justifiable but also enable the transition between technical choices in the longer term,” says Fergus Wills, the director of product management at Openwave Mobility.

However, this is not a one-size-fits-all scenario. “There is no single solution that can address all the use cases, so interoperability is extremely important,” says Purdy.

Ladki urges caution. “A rushed decision made today may mean that a CSP is unable to use real-time
analytics for customer experience management or to drive network planning and operations or to best
support the smart world tomorrow,” Ladki warns.

Rupert Naylor: CSPs might be discouraged by data being in a combination of old and new systems housed in various locations
Rupert Naylor: CSPs might be discouraged by data being in a
combination of old
and new systems
housed in various locations

Purdy points out that the best analytics solutions make optimal use of open source software, which keeps them interoperable and faster to deploy. Among many other things, good data analytics systems could enable efficient and reliable services, paying a loyalty dividend that, arguably, in this age of customer churn, may prove even more valuable in the long-term.

“It’s really important for CSPs to realise that data analytics isn’t about generating revenue, that’s only a small proportion of it. It’s about improved customer care and marketing, and more efficient networks and operations,” Roberts observes.

“It is all about the business case, and there is a strong business case here,” Ladki points out. Nevertheless, decisions must be made carefully with interoperability and extendibility at their heart. “If the wrong technology is used then there is a high risk of rising costs and declining responsiveness – which is a negative spiral for any project,” warns Wills.

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