To put our money where our mouth is, we at Comptel have developed a new framework for digital and communications service providers to start challenging the status quo and building an appealing digital buying and service experience for Generation Cloud customers.

This framework represents Comptel’s view on Nexterday, and is explained here using the example of Comptel products and solutions. However, it can used as a blueprint and roadmap for any Operation Nexterday.



  1. Data Refinery works as a data super-facility whose role is to collect and process both past data and in-the-moment (realtime or fast) data, to provide a comprehensive situational awareness about each customer and the related context as it happens. This situational awareness can be summarised as the WHO (customer), WHERE (location), WHEN (time), WHAT (apps / services), HOW (device), WHY (behavioural pattern) and WITH WHOM (social context). Combine that with the known customer profile created in the past (customer VALUE, POTENTIAL and RISK), and you have a full picture of what the Data Refinery produces.
  2. Monetizer™ enables the creation and re-creation of dynamic, contextual offers in minutes instead of months. We are not at the point where every recommended offer can be built on the fly, but that should be the objective. This can only be achievable if we have tools efficient enough to masspersonalise and tailor rich offers for each context. We call these business policies, as they are built to cover policy for the technology end and policy for the customer-facing end, followed by charging policies. In yesterday´s systems, this was easy, as offers were more static, simple and slow. In Nexterday´s model, we do not have time; there is more to monetise, and complexity is mounting. Old offer design, policy and charging toolsets are not adequate anymore.
  3. Data Fastermind™ works inside Data Refinery as artificial brainpower. It processes the situational information instream to create anticipatory, accurate and more compelling business logic to serve and satisfy customers in-the-moment. It can make decisions about what actions will succeed in the right context, and it can create and simulate pre-designed logic based on customer patterns. In practice, these patterns and logic are then utilised in actions like, “When customer enters context A+B+C and has profile X, then initiate action D (e.g. service action).” When everything happens in real time, it revolutionises traditional static segmentation and average mass market approaches. We truly do not care about static customer segments or mass offers anymore. Instead, we can observe customers in their moments and act upon that reality.
  4. Both Data Refinery™ and Data Fastermind™ can fire automatically recommended actions and / or offer recommendations directly through a customer-facing medium. The distinction here is really the level of sophistication. Data Refinery is able to perform hard-coded logic like “Trigger X offer to the customer’s mobile client when customer enters Y situation.”
  5. Data Fastermind, on the other hand, can perform real-time automated actions that require more sophisticated logic like, “When X behavioral pattern is matched in Y context, trigger A “Next Best Product,” and if B anomaly occurs, trigger C service assistance message.”
  6. Once a customer receives the recommendation or experiences the service action, they respond by accepting, rejecting or ignoring it.
  7. Embedded intelligence is the most revolutionary part of the whole framework. We embed analytics everywhere and apply machine–learning capabilities based on real-life customer responses. This puts an end to manual business case calculations by the BI team, costly ABC testing and endless amounts of uncertainties. In other words: “Just do it.” Launch the service, and let customers teach you with their responses.
  8. FlowOne™ takes care of the convenient delivery. In the case of positive responses to recommendations, the service delivery flow (validation, provisioning and activation) is initiated automatically, without delays. In the case of more complex orders, like enterprise connectivity / storage / security bundles, quick and analytical order validations and check-ups are done, and orders are managed.
  9. Closing the loop – the well-designed digital buying experience framework provides nearly instantaneous insights and foresights for your business and product teams regarding the engine’s performance and functionality. Built-in machine learning can automatically tune action / recommendation algorithms and customer profiles, so recommended offers / service actions can be more accurate each and every time.
Ari Vänttinen, Comptel
Moving Telco companies in Europe and Asia from ground to cloud. Born 1969, M.Sc. (Economics). Joined Comptel in 2014, member of Executive Board since 2014. Has previously held various marketing executive and management roles at McAFee and Stonesoft Plc since 2010. Before that management consultant at Talent Partners 2007 – 2010 and marketing management and business development roles at Nokia Networks 2004 - 2007.
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