It seems like every marketer out there is trying to get ahead of the pack in the AI space. Most start by reading articles by industry luminaries, paying big bucks for AI training systems, and watching data science YouTube videos for inspiration. This is extremely lucrative for many industry experts, but also expensive, and potentially risky. So should I, and other marketers, get ahead of the game, and start building AI solutions for my business?
Ultimately, this is a question for your business. When should you hire a data scientist for your business operations? Well, you need to be clear on what you expect to achieve by employing data science and AI technology for your business. If you are aiming for better data management, storage, analytics, and backup, AI and data science could reduce a lot of your workload by automating repetitive tasks. So, businesses that are still in the process of establishing a data centre for complete data management, perhaps by obtaining aid from a consultation provider such as Walt Coulston, could adopt data science for improved and efficient operations. This technology can also help companies to attain a sustainable approach to running a business and contribute to the environmental cause. Additionally, you can consider a few factors listed below to decide if you should consider data science and AI for your company.
What does your business technology look like?
In my experience, most brands begin by creating content using traditional data sources, such as a blog, e-mail campaigns, and small collections of analytical dashboards. However, companies that are investing a significant amount of money in their tech-whether that’s search analytics or data analytics for a real-time marketing platform or other business operations-are more likely to start using AI in a big way. If you see AI as an opportunity to replace employees, consider this: artificial intelligence currently costs between $80,000 to $100,000 for a data scientist, depending on how much research and development is needed. When deciding on whether to go for an AI data model or hire a data scientist, you may also consider GDPR audit and compliance (read more from these guys here), especially if your business is established in Europe. Every company has to undergo a data audit to follow GDPR rules and laws set by government authorities to follow data protection principles. Your choice of data management and operating system might limit you from complying with all the set measures.
How much is your data or analytics team already costing you?
It might sound like a difficult question, but keep in mind that some companies spend $300,000 per person on their marketing technology. Cost effective solutions exist in the form of the likes of Grid Dynamic’s Digital Transformation Solutions. Otherwise if this is your team, that’s potentially $3 million per year, or around 20 percent of your business budget. In other words, your data team might already be costing you a substantial amount of money, so it might be worth breaking it up to pay for one person’s salary, rather than an entire team.
Can you afford to spend an additional $3 million per year on something you don’t understand?
Perhaps you’re a small company. In this case, you’ll have to try, test, and adjust your analytics methods until you get your AI to work. Start small, and do it in small batches. Start by using your AI to compare one product over another. Once you’ve proved your solution works, your AI should also be trained to make predictions for new products in the market. You can then start to train your machine with a large amount of data, using a small batch size. In the end, this process will be a learning process for your AI, and it will probably break down at some point. In this case, your team will need to keep training your AI over and over again, creating a path for the machine to learn, and determine which products are most profitable for your business.
Building AI isn’t as hard as you might think. But you need to start with simple analytics. It might be a good idea to reach out to firms like Adverity (adverity.com), which offer advanced data analytics methods for constructing great marketing strategies if you don’t have enough resources to hire your own team of data analysts. However, if you have a capable team to build an analytics mechanism, you’ll be able to reap the benefits and create more sophisticated machine learning. And with AI, the most important part is understanding what you’re building, and using it to reach your business goals.
So, if you’re already developing marketing automation platforms, and you’re spending lots of money to build advanced machine learning solutions, go ahead and take the plunge, and do it now. Just make sure you understand your business first and find the technology to support your specific goals.