March 6, 2020

Boosting Customer Experience with Artificial Intelligence

AI-driven analytics in OSS/BSS enable a wider range of personalization in the customer journey moving forward.

The use of artificial intelligence (AI) in telecom networks and OSS has some obvious potential with automation. Using it in BSS and customer-facing systems makes us immediately think of chatbots and self-service tools. But what else is it capable of? Here, we look at three real-world examples of AI bringing about significant, measurable advances in the way we approach customer experience management.

Smart marketing communications

Content pushed to customers in emails, social media or apps should not be generic marketing material. The relationship that the service provider has with the customer means they are privy to vast amounts of personal data regarding that person’s preferences. Data points such as the amount of time customers spend using certain apps, the flavor of value-added services they use, the hours they are most active and their geographic movement can be used to form a highly dynamic interaction schedule by the service provider. Of course, the right BSS system must be in place to take advantage of this data.

AI-enabled analytics in the BSS allows more nuanced decision-making when it comes to selecting and building personalized, automated messaging. Doing this at great speed and scale in B2C telecoms is where AI analytics becomes the only option.

Customer behavior analysis

In the past, it had been necessary to analyze customer behavior by organizing people into groups based on certain defining metrics, but now, it is possible to refine this down to the individual user. This can be used as a powerful tool in positively influencing the customer experience. By comparing the qualitative data against the quantitative and using it in conjunction with a customer journey map, a CSP can see which persona bought what product, what interactions they had, when they had these interactions and where these interactions took place. In compiling the two sets of data and comparing them, a much richer version of a customer journey map can then be assembled.

AI-enabled analytics can provide deeper understanding into the context of certain customer behaviors, thereby making it possible to identify the data sources that will inform effective actions. As more and more information is introduced in this way, the AI can continuously refine the analytics data model to better inform future decision-making.

Predictive churn reduction

Churn is a much-studied subject across all industries that have a subscription model. The way CSPs use customer data to reduce churning is by identifying subtle signifiers of dissatisfaction and countering with value-added services or offers. However, the growing trend is to proactively influence the customer satisfaction trajectory much earlier through personalized loyalty programs.

AI-enabled analytics stand as the only way to process the volume of data that churn reduction analysis takes in, from internal data in the customer journey system to external sources such as social media sentiment analysis. The model built by processing all of this data needs to pick up on very nuanced human behavioral indicators, something that the first generation of big data analytics platforms were not entirely successful in processing. Only now, the majority of the human interaction can be taken out of running live analysis.

The right AI-enabled BSS components for customer experience excellence

BSS can vastly impact the experience of a customer base if it is endowed with the correct mix of interoperability, common data sharing models and AI-powered analytics. CSPs should be looking to add modules for churn and retention management, loyalty management, advanced customer information management and campaign management. In addition, they need to take an omnichannel approach to implementing these changes to instill consistency in their outbound messages.

As the business model of most operators is changing towards providing digital services alongside more traditional communications products and services, the scale and diversity of the data being processed will inevitably increase, underlining the need for establishing AI analytics best practices now.


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