By Patrick Leonard
When organizations think about AI-driven marketing initiatives, they quickly imagine vast mountains of data only made actionable by a small army of marketers and data scientists. That sounds expensive – and it is! Using AI to more effectively meet organizational objectives has been a huge win for large corporations but only they can afford it right? Perhaps not!
The cost may have been a barrier in the past, but the reality is that machine learning is leveraged by small businesses and organizations every day through a variety of accessible and affordable tools. The widespread accessibility of AI means that any non-profit or small business can take advantage of them – but should you?
What if machine learning could help you cost effectively save time, streamline your operations, or even increase donations? I don’t know of many non-profits that wouldn’t benefit from a few more people pressing the donate button. For non-profit organizations ready to embrace new technology and improve marketing performance, here’s a list of three ways your non-profit can start using AI today.
1. Ad Platforms
You’re likely already well aware of the Google grants program offering eligible non-profits up to $10,000USD every month in ads. What you might not be aware of is the ability to use Google’s machine learning capabilities to optimize for key conversions like donations by using smart bid strategies such as “maximize conversions.”
You can do this by first importing your analytics goals into the ads platform or adding conversion scripts to your goal-completion landing pages. Google will then use the available data and the power of machine learning to show your ads to people more likely to convert. A smart bid strategy is able to leverage behavioral insights and a variety of data points that your smartest ads experts simply can’t duplicate with a manual approach.
In some cases your organization might be hard-pressed to actually spend the full budget and efficiency may be of less importance. But it’s a big win for non-profits that need to get the most out of their grant dollars.
Facebook ads is another example of a major marketing channel that delivers machine learning potential to anyone that wants to improve marketing results. For example, this platform can be perfect for non-profits that want to retarget potential donors or sign-ups that got away. A series of targeted messages can help turn “on-the-fence” visitors into engaged action-takers by building trust in your mission. Again, by tracking conversions within the platform, Facebook can transform insights into performance and increase efficiency.
2. Live Chat Tools
Finding and keeping skilled volunteers is a never-ending challenge for non-profit leaders. Maybe you just can’t afford to have someone available to answer questions on your website all day. That’s where AI-based communication solutions like chatbots come into play.
A live chat solution like the one from Drift can employ chatbots that learn how to interact with your visitors and answer questions. This effectively frees up time for your staff and can even help you boost desired outcomes. There was certainly a time where allowing robots to talk to your visitors on your behalf was a questionable solution. But the sophistication and machine-learning capabilities built into new chat platforms make this a realistic and often savvy option for site owners. This is particularly true if you have enough traffic and interactions for the AI to quickly learn the patterns and solutions to efficiently handle common queries.
3. Customer Relationship Management
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Platforms like Salesforce have started implementing AI that enables the organization to analyze feedback from their communication channels and automatically adjust marketing activities. This kind of analysis could help non-profits identity users most likely to donate and adjust the messaging accordingly. AI-infused
These 3 suggestions ultimately just scratch the surface of what a non-profit could potentially do with new AI-based technologies. Small organizations are already using machine learning to do things like SEO, accounting, and human resource tasks. With endless possibilities, how will your organization look to implement AI in the coming year to boost efficiency and outcomes?
