Increasing Revenue and Customer Profitability

Transcription

1 Telco Customer Management Insights Increasing Revenue and Customer Profitability Leveraging Big Data with Advanced Customer Data Analytics By Glenda Wheeler & Carn Iverson Tharollo Consulting

2 2 Increasing Telco revenues and profitability: the challenges Consistent with the developed world, the South African telecommunications market has matured and become saturated. Consequently, it is no longer feasible to increase revenue and profitability through a focus on acquiring new entrants to the customer base. The most attractive customers already have long standing relationships with established operators and therefore the cost to acquire new customers has increased. Emphasis now needs to shift to identifying, acquiring and retaining profitable customers with a longer tenure. Generating sales and reinforcing margins has to be achieved by conversion and up-selling. Without this shift, average revenue per user and margins will continue to fall and customer churn rates will rise. These threats to performance are compounded by increasing levels of fraud and the impact of regulatory changes. Oversubscription of networks indicates strong user-demand but has caused service levels to fall. It has consequently created growing dissatisfaction among customers. To increase revenue and profitability it is clear that Telcos need to move from a focus on products and become more customer-centric. Increasing revenue and profitability by leveraging Big Data Since the conceptual advent of Big Data it has been obvious that Telcos could leverage their customer databases to become more customer-centric. This would allow Telcos to formulate strategies designed to select, acquire, retain and grow their most profitable customers. An accurate and highly detailed understanding of every customer would facilitate the creation of products and services to satisfy customers actual needs. Such an understanding would enable data-empowered Telcos to reduce churn by reinforcing loyalty. It would also mean that their infrastructure and marketing investments could be allocated to providing relevant services to customers that deliver the highest returns. At the same time, detailed analysis of the customer base would enable Telcos to make informed decisions on how to reduce the costs of serving persistently unprofitable customers. Achieving Big Data insights through Advanced Customer Data Analytics To address the need for generating commercially actionable insights into an entire Telco customer data set, Tharollo Consulting has developed an Advanced Customer Data Analytics (ACDA) solution. ACDA s revolutionary innovation is based on a set of integrated, mathematical models using self-organising maps and machine learning algorithms. The models apply internationally recognised research into Telco customer analysis methods and are based on mathematical principles.

3 3 Unequivocal customer insights provided by Tharollo Consulting enable a Telco to: Maximise revenue by developing products for customer clusters based on accurate ARPU segmentation Determine optimal revenue opportunities through, for example, effective bundling of different products, discount structures and device types using accurate bearer revenue segmentation Adjust service levels using accurate insights into individual customer value and profitability Improve the results of marketing campaigns and product design by targeting specific customer clusters Plan future positioning of products based on actual purchasing patterns Devise more effective cross-selling and up-selling strategies Adapt incentives and products according to changes in behaviour and usage in customers Life Stage Develop predictive early warning mechanisms to reduce churn and increase lifetime value Determine cost of retention Determine the lifetime value of each and every customer Using ACDA to create a customer-centric business To effect the transition to a truly customer centric-business, an organisation must acquire an accurate and comprehensive understanding of each element in a typical Customer Value Chain: Why are we doing this? Who are our customers? What do our customers want from us? Where and how do we touch our customers lives? What do we need to be able to do? How do we do it? The insights produced by ACDA provide accurate, actionable answers to the key questions that arise from a shift towards building a customer-centric organisation.

4 4 Four pillars of a customer-centric Telco: Select, Acquire, Retain, Grow ACDA enables a Telco to increase revenue and profitability through four key activities: Select the most attractive customers Retain the highest-value customers Acquire those customers Grow the value derived from existing customers

5 5 Profitable knowledge: monetising Big Data with Advanced Customer Data Analytics from Tharollo Consulting Across the entire customer data set, ACDA s five analytical models produce insights in the following areas: 1. Revenue Defines revenue, profitability and tenure characteristics for every individual within the customer base 2. Behaviour Reveals the mathematical clustering of customers according to their actual usage and behaviour 3. Lifecycle Reveals lifetime value and usage patterns based on the customer s life stage 4. Migration Reveals why, when and how customers migrate between products, devices and sales channels 5. Churn Provides advance warnings about customers who are likely to move to the competition in the near future. This enables appropriate corrective action to be taken to minimise this threat to revenue. The insights provided by these models empower Telcos to increase revenue and profitability by leveraging a reality-based understanding of all their customers. Advanced Customer Data Analytics enables a Telco to leverage their entire customer data set - plus data from external sources - across all business areas, bearers and customer types. When extrapolated, they provide an accurate understanding of the local telecommunications market as a whole.

6 7 Advanced Customer Data Analytics: the models, the insights and their applications 1. Revenue Percentile Through a mathematical segmentation of customers according to revenue and tenure characteristics, the model provides accurate knowledge of individual customer value and profitability. With a completion time of under three months, the Revenue Percentile model produces rapid, actionable insights. For example, the delivery of a call centre s services could be aligned with measures of profitability. Accurate ARPU segmentation highlights the appropriate products and mix that will maximise revenue for each segment Accurate bearer revenue segmentation determines optimal revenue opportunities through, for instance, effective bundling of different products, discount structures and device types

7 7 2. Customer Behaviour Using machine learning algorithms and self-organising maps, this model segments usage-patterns into customer clusters. Based on measures of similarity or dissimilarity, behavioural clustering reveals the mathematical (not assumptive) groupings of customer data. The insights produced by the Customer Behaviour model enable: Development of accurately customised marketing strategies to target specific customer clusters Improved effectiveness in designing products for specific customer clusters 3. Customer Lifecycle When used in conjunction with the Value and Behavioural models, the Lifecycle model reveals usage patterns in relation to life stage. The insights it produces can be used to guide decisions regarding the most effective way to: Improve customer retention strategies Devise product and marketing strategies based on device preferences Plan future positioning of products based on actual purchasing patterns Plan discounting and pricing structures 4. Customer Migration This model reveals why, when and how customers have migrated between products, devices and sales channels. Insights into migration can be applied to develop: More effective cross-selling and up-selling strategies Predictive early warning systems to enable prompt and effective responses to migration trends - such as those occurring in an economic or market downturn 5. Predictive Churn This predictive model identifies the determinants of churn and those customers with a propensity to churn. It reveals in advance those customers a Telco is likely to lose in the near future. To minimise this likelihood, insights from this model can be leveraged to: Develop effective strategies to reduce churn rate and increase lifetime value Prioritise customer segments to focus on specific retention strategies

8 8 Implementing ACDA: engineering the solution Tharollo Consulting s implementation of ACDA is based on an iterative application of structured project governance and a selection of solution engineering disciplines. This approach combines best practice and pragmatic experience in the areas of project and risk management, business and data analysis, business architecture and requirements engineering. Our approach ensures: Quick delivery of accurate business insights Opportunity for rapid business response Cost effectiveness Reduced project risk The Advanced Customer Data Analytics solution is proprietary to Tharollo Consulting. Our unique analytical models are based on proven international research and have been developed specifically for the Telco industry. We have already successfully completed our first Advanced Customer Data Analytics engagement for a major South African Telco. Our proven and referenced expertise lies in delivering R40m to R300m projects on time and within budget. Established in 1997, Tharollo s project-delivery capabilities are exemplified by a 100% success-rate in all its consulting engagements. Tharollo is a North Sotho word meaning, solution for a problem. Tharollo Consulting (Pty) Ltd Glenda Wheeler glenda.wheeler@tharollo.com Carn Iverson carn.iverson@tharollo.com