Artificial intelligence

By Shruti Bhat, Senior Director of Cloud and Emerging Technologies, Oracle

Companies intent on using artificial intelligence to evolve from merely data-driven reactors to truly adaptive enterprises need their chief marketing officers to step up to that urgent challenge.

Broadly speaking, AI is an advanced software engine that ingests customer and other data to sense, analyze, reason, and propose actions. As such, AI-imbued applications are becoming indispensable in helping CMOs and their organizations read customers’ digital body language and anticipate their needs, helping companies adapt their business models accordingly.

AI tools are particularly critical to CMOs in three key areas:

Delighting each customer, one at a time. AI finally lets CMOs deliver on the long-time promise of “extreme personalization,” tailoring products and offers to each individual rather than broad groups.

Traditionally, marketers have segmented customers into such groups based on similar interests, needs, or locations. The expectation is they will have common requirements and respond similarly to marketing offers, but this approach makes the inherently flawed assumption that groups of people behave alike.

I’ve previously experimented with “one-to-one marketing” at a software-as-a-service startup, where I created 10 segments—each consisting of just one company. Each was a specific account that we really needed to land, and this approach allowed us to be extremely targeted and focused.

However, it wasn’t a scalable approach, so we took it a step further, creating our own AI-based marketing bot that would “personally” interact with thousands of potential customers. The bot then combined insights from those interactions with signals from other data sets to deliver a highly customized onboarding experience, followed by constant relevant engagement to continuously grow usage of our product. By combining historical purchase patterns with rich consumer behavior data, AI helps CMOs anticipate exactly what each individual customer needs.

Powering “growth hacking.” Growth hacking is the process of using insights gleaned from data to develop a hypothesis on how to rapidly grow the business—and then quickly testing it out using live experiments across marketing and product development.

Airbnb is the poster child. The room-sharing service once struggled with a classic chicken-or-egg growth challenge: It realized that having more apartment and home listings would attract more renters, but it needed more renters to attract more listings. Airbnb experimented with autopostings on Craigslist to reach a broader base and consequently realized exponential growth.

During the last five years most startups have embraced growth hacking, but large enterprises still struggle with rapid experimentation, mainly because of the vast amounts of data they need to grapple with. With AI, the explosion of enterprise data becomes an inherent strength rather than a roadblock for large companies, because it enables more experimentation and faster results. For CMOs, this means the ability to help adapt not just the marketing strategy, but the product and distribution model as well, constantly optimizing on all fronts for customer acquisition, retention, and viral growth.

Increasing customer loyalty—the right way. It’s standard practice to measure customer lifetime value—how much a customer spends with your company from the time he or she first buys one of your products. But most companies don’t quantify customer lifetime happiness, or how satisfied the customer is at different touch points.

While it’s common for companies to measure social media buzz and conduct broad surveys to gather customer feedback, that data says nothing about how annoyed a particular customer felt because of a late delivery and how a simple service tweak or discount in the next month might increase his or her loyalty over the years. Getting to that level of service requires companies to connect intelligence across their entire value chains.