5 ways AI helps marketers make data-driven decisions
AI is already fully capable of making the lives of data-driven marketers a whole lot easier. From data crunching to decision-making to personalization, AI is a worthwhile companion to have with you every step of the way.
Where do you fall on the AI (Artificial Intelligence) spectrum?
Are you losing sleep about losing your job to AI? Are you panicking reading this wondering if this was written by an AI bot who knows just a little too much about you? (Full disclosure; this article was written by a real human, although that’s probably what you would expect an AI bot to say if you were already skeptical).
Or are you ready to cede all sense of everyday autonomy and hand over your life to the robots?
Chances are, you probably fall somewhere in the middle but leaning more towards the former. In fact, a recent survey indicated that most respondents were, at best, cautiously optimistic.
While AI can’t do our dishes or laundry (yet), and there are real concerns when it comes to policing AI, it can make the lives of marketers way easier. That’s why it’s so important for marketers to understand all the ways AI can supplement your marketing team, not replace it.
Because as useful as AI can be, it’s only as effective as the humans who utilize it.
Allowing AI to do all the heavy lifting doesn’t mean much if you lack a clear strategic objective. Otherwise, AI is basically just lifting things up and putting them down.
If you’re still skeptical, here are 5 benefits AI provides that any marketer can start using right now to make data-driven decisions:
- Instantly analyzes large data sets
- Accurately forecasts market trends
- Uncovers ways to improve customer relationships
- Increases productivity and efficiency
- Personalizes web experiences
Instantly analyze large data sets
If you’re not using AI to crunch numbers and analyze data sets, you might as well be using pen and paper to do any kind of complex math instead of a calculator (remember to always show your work!)
When you’re working with multiple disparate data management systems, it’s important to have a workflow in place that allows you to analyze as quickly as possible.
What’s more, you’ll want to be able to automate this process so that you can replicate what works and eliminate what doesn’t.
AI is your supercharged assistant that digs deep into multiple sources of information, from spreadsheets to social media trends, aggregates that data, and distills it into something that’s easy to understand in just seconds.
Most importantly, it allows you to make the final decisions.
Here’s how it works:
All humans have cognitive biases, which is why AI is instrumental in deciphering non-linear relationships that are otherwise distorted by our own perception.
The framework above relies on AI to analyze large data sets, but the goal is to filter those insights through the purview of human judgment.
These insights typically fall into one of three categories:
- Decision making: These insights are typically helpful in allowing decision makers to act accordingly with all the information they need. Comprehensive, real-time data that is harvested by AI allows decision makers to invest in the right ideas as early as possible.
- Operational efficiency: Many insights lead directly to understanding bottlenecks and obstacles in workflows. For example, AI might uncover that the introduction of a particular piece of software embedded in a workflow doubles the amount of time needed to complete a project. This is a clear example of an insight that would lead to strategic intervention to improve efficiency.
- Hidden potential: Finally, AI’s capabilities continue to evolve and machine learning (ML) algorithms become smarter over time. You can use these features and capabilities to understand macroeconomic trends and position your brand for future success by uncovering hidden potential in your organization.
Most importantly, if you’re relying on recurring reports (our guess is you probably see spreadsheets in your sleep at this point), you can fully automate this process as part of your workflow.
So, stop permanently damaging your eyes and let AI analyze all those numbers and cells for you already.
Accurately forecast market trends
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.
Don’t discount the value of human intuition
The aim of AI-driven decision-making frameworks isn’t to automate the entire business process.
To get the most out of your models and analysis, you need to combine the intelligence you gain from AI and data with the expertise of your human resources.
The diagram below demonstrates what a human and AI data-driven decision-making model may look like.
Constantly reconciling business decisions with the framework and extracting the best insights from both human resources and AI gives your business the best chance at success. By remaining curious and constantly experimenting with different strategies, you can drive engagement that outperforms your competitors.
Conclusion
AI is stoking a lot of justified fear due to the unknown nature of its limitations, or lack thereof, despite it being at the forefront of so many media outlets. It has limitless power and its capacity to evolve is still indetermined.
But also...
...we still have a way to go.
One thing is certain; AI is already fully capable of making the lives of data-driven marketers a whole lot easier. From data crunching to decision-making to personalization, AI is a worthwhile companion to have with you every step of the way.
Just don’t ask it to create an ad promoting audiobooks.
