The maturation of AI technology has created profound opportunities for digital transformation for telecom operators. The sheer volume of data from networks has forced a massive shift to decision-making with AI-based automation models underpinning critical decisions across every facet of operations and business strategy.
To explore the role of data and AI in propelling new initiatives across telecom operators, Zeng Xiao, Chief Business Architect of SmartDecision at Huawei, a leading global ICT solutions provider, spoke to Alejandro Cadenas, Associate Vice President at International Data Corporation (IDC), a premier global market intelligence firm, on how AI is paving the way for digital transformation across telecoms.
Delivering marketing intelligence via advanced data models
The conversation, which took place at Sep’s DTW 2023 event in Copenhagen, saw both parties delving into the role of data in powering intelligent decision making for telecoms. One of the key areas to benefit from AI-based intelligence is marketing, which is at the core of business growth and sustainability. Demonstrating Huawei’s cutting-edge analytics platform, SmartDecision, Zeng explains how a robust data platform can tap into a telco’s data reservoir to deliver valuable campaign insights that can help improve marketing results.
SmartDecision is essentially a decision intelligence platform that uses operator data, for example, network and subscriber logs, to model various decision scenarios, allowing operators to understand customer behavior, predict take up rates and analyze churn issues and ultimately, shape customer engagement on new campaigns. The platform can predict, for example, the response rates for different timings and target customer segments for a new 5G campaign. Highlighting the platform’s advanced learning capabilities, Zeng reiterates how its analytical models can be further trained using operators’ own filtered data, allowing continuous adaptation. Adding to this, Alejandro elaborates on the importance of sufficient data and data efficiency in training these data models. Huawei has recorded major improvements in this aspect, with data collection tenures currently reduced to 6 months from 12 months previously, according to Zeng.
Contextual data vs historical data
Alejandro commends Huawei’s use of contextual data alongside historical data in modeling the outcomes of various marketing activities, for example, campaign conversion rates. The use of contextual data greatly amplifies campaign results, with Zeng sharing how Huawei’s customer product and channel mapping resulted in conversion rates going up to 50% while campaign processing tenures were slashed from 30 days to just 3 days. Alejandro adds that the adoption of contextual marketing aligns to the telco-to-techco shift among operators where IT-like capabilities such as automation and APIs are being adopted to drive better results.
Digital twins for real-life simulation
In the session, Zeng discusses how Huawei uses digital twins to conduct root cause analysis (RCA). Digital twin is a virtual representation of the segmented customer groups updated in real-time from data gathered from the network. SmartDecision leverages digital twins to deliver decision intelligence for marketing, allowing operators to tweak their strategies in accordance to the simulation results. The use of digital twins speeds up planning and decision-making while improving campaign effectiveness.
Beyond marketing
Responding to Alejandro’s comment on the extension of the data platform to areas such as network planning, for example, to analyze capacity issues, Zeng shares Huawei’s plans in the coming years. At present, Huawei’s approach is to model on each operator’s specific scenario. However, in the next two years, Huawei will be providing APIs that will enable operators to simply plug-in their datasets and execute AI-powered marketing, network planning & optimization and experience assurance.
The AI cultural shift
Alejandro also highlights how a cultural change at the organization level underpins the shift to becoming data-driven. To this, Zeng asserts the importance of a gradual shift, from digitizing to automating and finally, delivering the intelligence for decision-making. This layered approach builds a solid foundation for embracing intelligent decision-making and for fully utilizing the advancements in data and analytics.
Addressing the challenges that might stand in the way of such transformation, Zeng names data quality issues, in terms of data completeness and relevance. To address this, it is important that each department takes the ownership of their data, with this data then converged into a common pool. This also calls for operators to distinguish between data knowledge, business knowledge and modeling knowledge and ensure all three aspects are addressed. Alejandro concurs that ultimately, data quality determines how well raw data is translated into actual business outcomes.
AI-talent readiness
Addressing Alejandro’s question on the adequacy of talent in this area, Zeng elaborates on different stages of operator readiness. Citing China Mobile as an example, Zeng believes some operators are higher on the scale in terms of AI and data expertise, compared to others. Regardless of their level of readiness and location, operators can tap into Huawei’s expertise via on-project training and other facilitation, including the deployment of the Digital Talent Maturity Model, which won Huawei and four other operators the DTW23 TM Forum Catalyst Best New Catalyst award. Huawei also spearheads continuous development in intelligence-driven decisions with various partnership efforts across the globe, including collaboration with universities.
In a nutshell
Wrapping up, Alejandro recaps the importance of data-driven intelligence in pushing digital transformation among operators, and how a platform such as Huawei’s SmartDecision can leapfrog operators’ initiatives in this space. Decision intelligence and data intelligence are the inevitable path of digital transformation and with the rapid increase in business complexity and the increasing demand for agile response to requirements, data-driven decision-making will become the trend. Decision intelligence enables operators to make decisions based on data rather than relying solely on experience, and data intelligence provides accurate and converged data models through in-depth data analysis by combining data knowledge, business knowledge and modeling knowledge and ensure all three aspects are addressed. This is akin to adding “Smart” to “Digital Twins”, allowing operators to accurately identify market opportunities, optimize resource allocation, predict user demands, and make wiser decisions, thereby improving marketing and network operation efficiency and effectiveness and business outcomes.
