Can artificial intelligence make your company’s marketing more “human”?

The answer is, Yes! But there are a few key things to consider before implementing AI to get it right. As part of an ongoing series, we will be defining what AI really is, what to consider before implementing AI, and how to use AI powered insights. Let’s jump in!

Part 2. Steps marketers can take to get their organizations AI ready.

In part 1, I defined the uses of AI for marketing and laid out the ways the models can be used effectively. AI engines are powerful machines, and like any machine, AI engines require a fuel source, enter data.

Before you can begin implementing AI, you should work with your AI partner or internal teams to decide a strategy and define the criteria for the data to power your models. Once you know what you need to fuel your models, you will need a consistent quality stream of data to ensure that the models produced through AI will be accurate and effective. Imagine trying to bake a cake with only half of the directions, or write a paper with the incomplete source material, that is what the results will be for your AI strategy if you don’t ensure the integrity of your data, incomplete, inaccurate, and ineffective. Data is the key!

Here are 3 steps you can take to insure your data is AI ready.

1. Start with cleaning house.

The first step in any project is the evaluate what you currently have available. Review the information your company collects on each customer and prospect at every stage of your process. The most logical places to start is with your CRM or marketing automation software, but don’t forget to include operations and customer service.

Focus on how data is collected, what is collected, and who controls the input. Is there a standard process at every point of data entry? Do you have a data policy and are your team members trained on data management?

Once you have evaluated what you are working with, you can begin to craft a plan to implement changes, if necessary, and work with your AI partners to formulate project plans.

2. Evaluate your external data sources

Most company’s use 3^rd party data sources to supplement internally sourced leads. It’s vitally important to understand what information is available, how it is collected, and how often the information is updated. It isn’t uncommon to hear reports that leads purchased through 3^rd party channel is out of date and inaccurate.

Don’t rely on bad 3^rd party data! If you haven’t found a good vendor keep looking, spend the money, or hire a company to provide data enrichment. It’s important to eliminate any information that can’t be verified for quality, regardless of whether it’s sourced internally or through an external partner.

Using corrupted data to fuel your AI engine is like putting sand in the gas for your car, it won’t work.

3. Create a data policy

Standardizing how data is collected and treated through out your organization to ensure consistency and reliability of your source material. This is easier to say than to completely implement, but it is vital to ensure your AI-powered predictive models are accurate.

Decide what key elements you need to collect, understand how that information works within your AI models, and train your staff on data entry. It’s important to create buy-in across your organization and sell them on the importance of the changes your making and how they can have a positive impact on your AI transition. Let your team know why data integrity matters.

Clean data can improve your AI sourced models, but there will always be variables that complicate the process. No two clients are exactly the same, nor are the people at those companies that make decisions. Maintaining data integrity and providing enough insights into the process can help mitigate the impact the variables will have on your AI models.

Now that you know what to do internally, we need to cover how to select an AI vendor that can help you meet desired objects. We will cover selecting AI vendors in Part 3.