Doron Youngerwood will explore whether the chatbot will be a game-changer for customer experience at TM Forum Live! Asia 2017 in December. Here’s a taste of his ideas.
While artificial intelligence (AI) makes headlines for its potential to transform our lives, an underlying and somewhat unsung trend powers it – big data.
Data is the source for developing insights into a business’ operations, customers, and prospects. However, how it enables AI to convert these insights into strategy is equally critical. A frequently drawn analogy is oil, which like data is costly and time-consuming to locate and extract, and significantly, must also be refined to realize its full value.
Harnessing the power of data
AI’s predictive capabilities will play a crucial part in its success. Predictive analysis depends on historical data to automate predicting consumers’ behavior, so that their experience can be improved by appropriate actions. For example, picking up when a customer is likely to leave or stop using a service to trigger a promotion to entice them to stay. You can also trigger preventative action to avoid things that could impair customers’ experience, such as network capacity problems.
A 360-degree view: Who are you selling to?
“Get closer than ever to your customers. So close that you tell them what they need well before they realize it themselves,” Steve Jobs.
Excellent customer experience is at the top of communications service providers’ (CSPs’) agendas, but the main challenge lies in determining exactly what the customer wants and delivering it at the right time, via their channel of choice.
CSPs already have access to so much data from their own systems. But, for an entirely contextual, 360-degree view of the customer, they first need to extract actionable insights from that data, and then combine it with actionable third-party data from social media and other sources. What they get from this is a more comprehensive, multi-dimensional view of the customer’s behavior– both online and offline. They’ll also be able to track the customer relationship for longer, and discover patterns of how their behavior changes over time. This can bring about a better service and improve cross- and up-selling opportunities.
Another crucial factor is data quality. Since insights are only as good as the information they are sourced from, data sources need to be:
- fresh – so providers can take immediate action;
- clean – prevent the ‘garbage-in, garbage-out’ phenomenon (the processing of nonsensical data); and
- complete – partial data leads to poor, uninformed decision making.
Ultimately, those CSPs with the best quality data will be able to leverage their AI capabilities to extract the maximum business value.
Use business context
Engaging with the wider business is critical. CSPs should not limit efforts to well-understood areas like IT, networking and finance. Areas like customer service, marketing, and sales are gold mines for valuable customer insights. Furthermore, working with third parties helps broaden the context of data and introduces customer experience insights that providers may not be able to find in their own data alone.
Evolve data architecture
CSPs’ investments in data capabilities are some of their most valuable assets. They should extend them to handle the scale, variability and speed that comes with new, unstructured data types emerging from things like sensors and the Internet of Things. This will likely mean developing new big data capabilities that can both ingest and store the data, and integrate multiple data sets. In many cases, placing new capabilities in the cloud or other managed services will allow the flexible scaling of technology without impacting any existing architecture.
Integration and unification
The foundation for intelligent customer interactions in marketing and customer care lies in how well providers can understand their customers’ intents, and seizing those moments in real time to provide relevant, personalized and proactive customer experiences across all channels. To obtain this 360-degree view, providers should fuse the widest variety of first-party data with third-party data.
A roadmap to the future
The past few years have seen service providers moving away from subjective-based decision making, and towards big data-driven strategies, and the advent of AI is bringing the industry ever-closer to an era driven primarily by intelligence and real-time insights. But, to realize the full potential of data-driven intelligence, each service provider needs to develop its own comprehensive data management roadmap. This is the only way to seamlessly and continuously process, extract and integrate the increasingly diverse and rapidly growing data sources to help make accurate predictions, automate decisions and manage conversations directly with customers.