Is Advanced Analytics the Answer to Boosting Predictive 1-to-1 Marketing?

predictive 1-to-1 marketing

Communications service providers (CSPs) provide lots of value to their customers, yet revenue and subscriber growth are still declining!

Is there a way for CSPs to use their existing infrastructure as a basis for transforming business processes and launching new services?

According to a 2015 GSMA report, the increasing levels of maturity in developed markets combined with the recent growth in developing markets means that there will inevitably be a slowdown in subscriber growth. From 2008 to 2014, the rate of unique subscribers grew at a CAGR of 7.6%.

However, this rate is expected to decrease to 4.0% by 2020. Revenue growth is also expected to decrease from a CAGR of 4.0% to 3.1% by 2020. CSPs, therefore, have an urgent need to monetize the explosive growth in data traffic with value added services.

Technological advancements, lower barriers to entry, and increased competition are having a major impact on customer loyalty and retention. Personalized services, with the ability to provide contextual offers, are becoming necessities for CSPs to differentiate their services.

Predictive 1-to-1 marketing is becoming more relevant as part of this to better serve customers and drive growth. Predictive 1-to-1 marketing has 4 key steps: (1.) identifying customers, (2.) differentiating / segmenting customers, (3.) interacting with customers, and (4.) customizing services to fit each individual customer’s needs.

Let’s see how advanced analytics plays a role in the above mentioned 4 steps. We are working with O2, a leading CSP in the United Kingdom, to provide predictive 1-to-1 marketing offers for its roaming customers.

Identifying Customers

To launch a predictive 1-to-1 marketing initiative, you must be able to locate and contact customers directly. It is critical to know customers in as much detail as possible – not just their names and addressable characteristics (e.g., account information), but also their behavioral, contextual, and situational information. O2 uses advanced analytics to determine in real-time when customers are about to roam off the UK network when traveling abroad from UK airports or when using international train services, such as the Eurostar.

Differentiating Customers

O2 identifies its customers so they can focus their efforts on the most appropriate customers and provide differentiated services. O2 tailors its behavior to each customer in order to reflect that customer’s needs and location.

The roaming detection uses sophisticated real-time analytics to determine when customers cross and remain in geo-fenced zones in and around airports of interest. Geospatial tracking of customers is used along the route of the Eurostar correlated with the train timetable.

Figure 1: Geospatial tracking of customers correlated w/ train schedules

Figure 1: Geospatial tracking of customers correlated w/ train schedules

Interacting with Customers

Interacting with customers is a critical component of a predictive 1-to-1 marketing program.

Cost-efficiency improves by directing customer interactions toward more automated and therefore less costly channels. Effectiveness and revenue increase by generating timely, relevant information, providing better insight into a customer’s needs. For example, having established that a customer is about to roam, O2 then sends the customer an automated roaming-related offer, such as a low-cost data roaming plan.

Customizing Behavior

Ultimately, any enterprise must adapt its behavior to meet a customer’s individual needs by tailoring some aspect of its services.

The service delivery has to be able to treat a particular customer differently based on behavior, context, and the situation. The result of O2’s real-time detection is a roaming offer that is relevant to the customer and therefore one which is much more likely to result in an opt-in.

The explosion of data in all forms in telecoms requires a more robust and broader lens in order to enable smarter timely actions and better outcomes. All the types of analytics must be unified into a single advanced analytics platform to ensure scalability and real-time performance. This includes historical analytics (descriptive and diagnostic analytics), real-time streaming analytics, predictive analytics, and prescriptive analytics.

The approach to advanced analytics mentioned above is a good first step for CSPs. However, it is the ability to execute the advanced analytics in real-time across the analytics value chain (streaming, historical, predictive, and prescriptive analytics) with relevant contextual and situational data that addresses the critical “last mile” for timely outcomes.

If this is then combined with the ability to take the next best action in any particular scenario, it creates the greatest value.

Figure 2: Faster analytics across the value chain

Figure 2: Faster analytics across the value chain


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