3 Steps to Intelligent Marketing with AI
The age of artificial intelligence is upon us. Advanced AI capabilities are already bringing transformational benefits to organizations, and one significant area where AI is making a difference is in the marketing realm.
In fact, according to a new study, 64% of early adopters with advanced AI capabilities are applying it to marketing use cases. They’re turning to AI to help find and engage new customers, and to analyze the growing volume and velocity of data critical to targeting their audiences. AI solutions help speed up discovery and synthesis of information and augment decision making to reach, engage and maintain valuable customer relationships.
There’s also a blend of roles that influence AI technology requirements and purchases. Marketing goals like improving customer acquisition might have traditionally been the domain of sales and marketing teams, but organizations are approaching AI from various vantage points – the working teams can now include IT and business leaders, data professionals such as analysts and scientists, and even users.
1. Start with a targeted use case
Applying AI to marketing initiatives can be a natural leap for many companies that already use analytics to understand customer behavior or for social or sentiment analysis. AI-infused solutions can range from virtual assistants to sophisticated recommendation systems and lead-generation platforms. In any case, the key to getting started is selecting a use case that’s essential to the business and can scale out with feedback and iteration.
Integrating AI into existing proprietary or pilot solutions can easily be done through composable APIs that allow for a building block approach. Adoption can be organic and evolve as an organization starts seeing benefits from initial efforts and experimentation. But the key is to connect a pilot project to the business’s nervous system to be fully functional and to yield optimal results. For example, a lead-generation application could connect to customer relationship management systems, marketing dashboards, procurement and supply chain management processes.
2. Focus on the business value of data
One of AI’s strengths is its ability to find value in new or underutilized data. But that doesn’t mean that organizations have to mine unstructured data to find value. True value can come from re-examining existing data – whether it’s structured or unstructured – through an AI lens.
Structured data ranges from proprietary organizational data sitting in databases and spreadsheets to licensed 3rd-party data. Unstructured data – which accounts for an estimated 80% of all data created – can include anything from scraping information from websites to pulling in and analyzing data from social channels including Twitter, YouTube, Instagram, be it text, audio or video.
3. Consider the business-to-business advantage
Some companies use AI capabilities like natural language processing, pattern recognition or machine learning to mine insights for use in their own organizations, but a growing number of companies are embedding AI into broader solution sets that they can offer to their own customers – intelligent insights about the market or customer behavior, preferences and buying patterns directly at their fingertips without having to deal with the underlying cognition processes.
AI is quickly finding a place at all levels of business, with the promise to provide new ways to help find and engage new customers. In addition to impacting the bottom line with leads and revenue, stepping up marketing efforts through AI capabilities can change the strategic conversation because of the visibility and intelligence that marketing and sales teams can have about their prospects and customers.
Main image via Tej3478
This post was originally published on Social Media Today