We’ve already shown how AI helps you figure out what’s in front of you, but what next?
Through unfiltered analysis and repetitive insights gained through automation (i.e. doing the same thing over and over), AI can help predict future trends to help you stay ahead of competitors.
In fact, AI is already better at predicting stock forecasting than we are. A recent paper published by the University of Chicago showed that AI-generated models outperformed consensus forecasts. And all the AI tool had to go on was numerical financial statement data. No information was collected about industry context or company overviews.
When your AI solution is entrenched within your martech stack, you can feed it enough information to make predictions on market trends and consumer behavior over time.
Uncover ways to improve customer relationships
We’ve already determined that AI is instrumental in analyzing data sets, distilling that information in an intelligible fashion, and using that information to predict where the market is going.
But there’s no value in all that prescience if it’s not being transferred onto your customers and helping build more meaningful relationships with them.
For example, let’s say you’re an online footwear apparel company and you notice that one of your products gets returned more frequently than others.
You might be inclined to assume there’s a defect in that sneaker, but you also notice that specific model is one of your best sellers.
So, you make a second assumption: Most customers are buying multiple pairs of the same sneakers just in case their typical isn’t accurate.
Without AI, this is where the story would end. You would attribute this to usual buyer habits.
However, your AI engine notices that in this case, 87% of customers returned the smaller shoe size, indicating that the shoe runs small.
Your AI engine can then crawl review sites across the web or social media to note key queries like “runs small”. AI engines are sophisticated enough to analyze complex emotions engrained in text to understand what customers are feeling and thinking.
And thanks to AI, designers can then make the decision to either adjust the size of the shoe or tailor the messaging to reflect that the sneaker runs on the smaller side.
Simply by looking at the data, your AI engine was able to improve the customer experience by uncovering a design flaw that would result in less sneakers being returned.
Increase productivity and efficiency
If you haven’t already received the message loud and clear, AI does the things you don’t want to do so that you have more time to do the things you want to do.
That means less data dumps for you to try and comb through like you’re on Storage Wars and you just bought an abandoned container.
Except your spreadsheet probably doesn’t have an entire My Little Pony collection hidden in one of its tabs (maybe use VLOOKUP, just to be sure, though).
As a marketer, you don’t want to sift through thousands of data points in excel sheets, try to uncover trends or insights, validate those insights with market research, and then further analyze to see if it makes sense for your broader marketing strategy.
What you want is for an AI engine to do all of that behind the scenes and give you a breakdown of what it found out.
AI automates boring data analysis tasks, freeing up researchers to focus on big-picture thinking and new ideas, making them more productive and innovative.
Collect only data that informs your business decisions
More data doesn’t necessarily mean smarter business decisions. Anyone who’s been in marketing for 5 minutes knows there’s often a somewhat inverse relationship between the amount of data in front of you and a clear objective for what to do with the data. If you have gaps in your current datasets, only gather additional data that will add value to your business goals.
Organizations often make the mistake of thinking that bringing in more data will naturally start providing greater insight.
By setting out your business goals and connecting only the data that helps you answer your strategic questions, you can reduce the workload.
Remove disruptive hurdles and get things done faster
Every transaction, customer interaction, social engagement or microeconomic indicator you can think of is available to use in your analytical framework. While this increases the reaction speed with which you can make decisions, it also removes any cognitive biases from the process. Modern business intelligence (BI) frameworks now regularly deploy AI and data-driven algorithms as part of the analytics process to classify, segment and contextualize data into actionable information.
Personalize web experiences
Users aren’t just hoping for a personalized web experience; they’re expecting it.
Visitors to your site are at the point where they know they’re going to be tracked in some capacity, so it’s crucial to create as relevant and personal an experience as possible.
Because the only thing more annoying than being tracked is being tracked incorrectly. AI lets you personalize at scale with product and content recommendations that resonate with visitors.
If you have enough inventory (whether its actual inventory like merchandise or consumer goods, or web inventory like content), AI can automate personalized recommendations for similar products or content in real-time.
And the more accurate the recommendations, the happier your visitors will be.
When you have the right tools to collect first-party data, your AI engine will be able to surface exactly what the customer is looking for to improve ROI, create a stronger brand affinity, or, at the very least, leave the customer with a positive impression of your site.