Going Beyond the Telecom Industry with Hyper-Personalization

How does your telco organization stand out from the competition? In today’s telco market, organizations are often forced to compete on price or perceived quality to maintain market share. Creating a lower price point may no longer be an option in most cases, leading an increasing number of organizations to improve their campaign management process by making it completely data-driven, omni-channel and hyper-personalized. What does this mean for your organization?

What is Hyper-Personalization?

Hyper-personalization is a trend that originated in marketing. Instead of communicating with one large target audience, hyper-personalization creates smaller and more specific segments, ultimately communicating with segments of one. These smaller segments allow marketers to create content and journeys that are more engaging and provide customers with a better experience.

Hyper-Personalization in the Telco Market

The need for customer engagement through hyper-personalization is reinforced by a number of trends in the telco market.

Customer Expectations

Customers have come to expect that their service providers will proactively upgrade, or at least suggest upgrades, based on their usage. In an era of commoditization, the average customer is no longer hunting for the best deal, but rather making decisions based on brand image and product or service appeal. By proactively upgrading hardware and improving service, operators can improve their brand image, increase customer intimacy and reduce churn.

Shorter Communication Cycles

Messages are communicated to customers in ever-shortening cycles. When faced with faster time-to-market and the need to control overhead costs when rolling out new offers, measuring and improving the effectiveness of campaigns for increasingly smaller segments is the only way for operators to stay ahead.

Targeted Products and Services

Hyper-personalization empowers telco organizations to better understand their customers’ situations, enabling them to identify and act upon changes. For example, a streamlined process to update an existing contract when a customer moves house could prevent the customer from churning. However, data and timing are key. If the message arrives too late, the customer might already have signed up with a competitor.

Challenges in Implementing Hyper-Personalization

However, there are challenges in using hyper-personalization that need to be mastered for the best outcomes.

Data

Accessible and good quality data is essential for hyper-personalization. However, accessing data from legacy systems is often complicated and expensive. In response, most telco organizations have established data pipelines to gather data from different sources into a data lake, which analysts use to generate insights. How the data is handled from here is important as modelling raw data from different sources in the data lake can cause the model to become increasingly complex and unstable as new sources are added.

While it is essential for telco organizations to focus on delivering the solution, data integration should not be deprioritized. Without seamless data integration, reports, insights and market segmentation, hyper-personalization becomes more expensive or can even be hindered.

Data governance can help untangle data issues by discovering the introduction of new platforms or changes to data flows on existing data streams and the target data architecture. It also helps to limit the number of data marts by advertising to the rest of the organization what has been built and fed with properly linked data.

Matching Offers to Segments

After establishing a source of accessible and good quality data, the second challenge is selecting the messages to be sent to each hyper-personalized segment. Monitoring and testing is important here, as too many or wrong messages will lead customers to opt-out of newsletters and direct mails. It is recommended to use a small group for testing before scaling up.

When promoting different offers to customers, machine learning models can predict the right offer to use for each segment. These offers can consist of hardware, services, discounts, or a combination of any of these. Machine learning models can also group customers to ensure each segment receives the offer that maximizes the economic return, as defined by the conversion rate multiplied by the increase in the customer’s lifetime value. 

How to Make Hyper-Personalization Work

Cognizant has been working with telco organizations on an iterative process to start infusing extra data and further personalize actions to bring hyper-personalization to life. This involves four steps:

1.     Execute Personalized Actions

Ideally, actions can be executed via different channels, for example a message being delivered by email or by a call center agent, in response to a change in the customer’s life, such as the release of a premium smartphone or the customer moving homes. The first iteration should be tested on a limited audience before AI optimizes the action for a wider audience.

2.     Collect Feedback

How did your audience react to the action? This feedback is the cornerstone of becoming data-driven as it shows which campaign strategies are working and which customer segments are not responding as expected. Based on the feedback, uplift models can then predict the incremental effect of a message on a segment.

3.     Calculate the Delta in Customer Lifetime Value

How does each transaction contribute to the overall customer lifetime value? Analyzing the data per-customer helps to refine the criteria on which segments to address and the types of offer they are likely to accept.

4.     Recalibrate Models

Incorporating the previous steps in the machine learning models will optimize the outcomes and best following actions for upcoming communications with existing and newly discovered segments.

Last Thoughts on Hyper-Personalization

To conclude, implementing hyper-personalization comes down to identifying customers’ triggers to better cater for their needs. Machine learning models use the feedback from different variations of the same journey to predict which variation will perform best for each customer and increase the customer lifetime value. However, to achieve the best outcomes, the systems involved needs to be seamlessly integrated and easy to use, which becomes increasingly challenging as both data sources and number of iterations increase. The good news is that a lot of the foundation needed for personalized marketing is in place, with solutions such as Salesforce Marketing Cloud or Adobe Experience Cloud. With machine learning models and the right data of the right quality, these platforms can be reused to start your hyper-personalization journey. Learn more about how AI can play an important role in staying relevant and seizing business opportunities in the future.