Top 5 things to know before introducing AI to your marketing

Top 5 things to know before introducing AI to your marketing

Top 5 things to know before introducing AI to your marketing

The marketing industry as a whole has been at the forefront of the AI revolution, incorporating into business processes, underlying technology, and customer interactions.

The same, however, cannot be said for all marketers. While some have introduced AI-driven automation and transformation projects to great success, others are simply following their peers, failing to adopt AI capabilities in a way that actually contributes to their unique goals.

So what should marketers keep in mind as they look to make AI an integral part of their customer engagement efforts? I will highlight those key considerations in this blog.

1. Data is everything.

It is definitely easy to get your Google Home or Alexa to recommend songs or inform you of any upcoming events. When a marketer tries to target the right audience with an ad using the same tools, things are a bit more complicated.

They cannot just rush in and get started. AI and machine learning can deliver nothing without ingesting data. And not just any data.

You need the right volume and variety of data, from all the sources that are relevant. This means identifying gaps in your existing data sources. What do you have right now? What sources need to be added? Are they in one place? Then you need to determine what—technology, processes, and so on—can help tear down any silos between these sources and bring them into a single pool.

Only then can your AI algorithms continuously learn from the data to offer increasingly better recommendations, enable better targeting, and optimize a variety of other marketing activities.

2. AI is an organization-wide effort.

The marketing department, to a large extent, probably does not own or control the data at its organization. In other words, to succeed in extracting the most value out of AI, marketers will have to ensure all the necessary data can flow from other departments. For instance, transactional data, which can be used to track conversions, may have to be acquired from sales and finance teams. The implications of AI are wide-ranging, and its success hinges on the collective commitment of the entire organization towards transformation.

3. Guide the AI project with a clear ROI.

With the data and organizational aspects of the project sorted, you need to decide how its success will be measured. For the marketers, this entails, at least in part, spelling out the project’s expected ROI and continuously monitoring it after AI implementation.

4. Establish a robust marketing automation foundation.

AI must be embedded into the marketing business process. Given the scale, complexity, and speed of digital marketing efforts today, marketers have to juggle a variety of channels, analytics, communications, and customer journeys. As part of an omnichannel marketing automation solution, AI can distill valuable insights into your customers (e.g., propensities), optimize how they are engaged in real-time, and facilitate seamless experiences across channels.

5. Use AI for good.

For all the talk of AI’s convenience and power, marketers cannot miss the crucial ethical concerns surrounding it. They need to implement and utilize it in a way that delivers unbiased outcomes, protects privacy, and avoids endangering the welfare of society.

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