How to Make AI Smarter
Machine learning and artificial intelligence (AI) can provide the differentiating edge for sales and retention in the hyper-competitive communications market. In this landscape, the step-by-step gains that AI can bring in offering customers the right product, in the right channel, at the most appropriate time in their journey, will separate the winners from the losers. And, in this active M&A market, AI can help determine which companies will be left standing.
Early adopters are implementing machine learning to improve customer service through technologies that aid sales in the store, through the call center or by chatbots available 24/7 to address needs and opportunities. According to recent research conducted by Forrester Research Inc. (disclosure: Amdocs sponsored this research), just about half (49%) of communications service provider (CSP) decision makers say they intend to increase AI budgets by at least 6% in the next 12 months. Plus, a large majority of them (87%) plan to expand their already sizable AI teams.
But there is a spoiler: The decisions around what should be done with AI offerings are being made without involving marketing. This is a mistake because marketing and intelligence/data teams must be closely aligned in data analysis to help slow churn and drive new sales.
The Forrester research showed that the decision makers are largely on the IT side (39%), with customer service executives lagging behind (26%) and few marketing programs driving AI (only 6%). Given that the primary goal of service providers is to boost sales and reduce churn, I found this to be incredibly surprising.
At the end of the day, it's not about the data in a given system, but about the data and intelligence flowing between the different divisions that allow providers to most effectively upsell to customers. Marketing usually has a series of point solutions, such as products for campaign management, market segmentation or channel stimulation. These solutions generally don't have a holistic, integrated view of data from many systems or of data in action as customers go through their journeys. Only getting the data and AI strategy right across the broader landscape of a service provider's business lines will enable effective cross-selling.
Here's a key example. We know that customers are materially less likely to churn if they are on a multi-play bundle vs. a single service. Let's say a customer is calling to ask about a premium channel package to add to his or her pay-TV service. There is an opportunity for a chatbot to upsell an unlimited wireless plan so the customer can watch content on the go as well. Crafting such an offer for a customer in real time across multiple lines of business requires strong coordination from the IT and marketing organizations, based on a well-executed data and AI strategy.
While we see positive strides in AI, mostly in the care and customer service channels, there is a compelling case to bring in a broad AI strategy for all marketing projects.
AI and machine learning are where cloud technology was ten years ago. Enterprises that started early in the lifecycle are today far ahead of the competition. During this process of launching and fine-tuning AI solutions, marketing should be front and center as an integral part of any AI team, developing with IT the strategy and goals for the business. Such coordination between the CMO and CIO will drive the best top-line results and establish the leaders of tomorrow.
— Gary Miles, Chief Marketing Officer, Amdocs