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It’s no secret that businesses are suffocating under the weight of data. While some struggle to get a handle on different data points, others don’t know how to turn them into marketing insights. How can we make data-driven decisions when we don’t know which points will help us make decisions? Data provides an opportunity to improve customer experience if we know how to use it.
Today, we’re investigating the role of AI and why it is the solution we all need to filter data.
Overloaded with Data
If you deal with data on a daily basis, you’ve undoubtedly noticed its exponential growth in recent years. Not only has it grown in volume, but it’s also changed and become more complex. Because of this, AI is the key to effective data analysis.
Overused Resources
While we normally focus on the benefits of choosing AI, we often ignore the human impact of NOT using it. Humans could work through the night with no sleep and still not be able to compete with AI’s data processing, pattern recognition, and actionable insights. Why put employees under this pressure when it’s impossible to compete with companies already using AI?
Even if you had the quickest and most efficient team in the world handling all your data, you would still be draining resources unnecessarily and wasting time on analysis instead of implementing what the data suggests. AI has the potential to improve customer experience and optimize targeting and creatives; whereas with human analysis, by the time you’re ready to act the data is outdated and the competition has already moved forward.
Data Integration
A huge challenge for marketers is lacking the ability to bring all data under one roof. The marketing team gathers data from a variety of platforms, yet they are uncentralized. Because of this, businesses should seek to gain a comprehensive view of customers and keep everything in one place. These steps will make AI marketing tool analytics more reliable and durable.
Data Interpretation
Data becomes useless as soon as it’s interpreted incorrectly. With the wrong interpretation, you could have the most relevant data possible—it won’t make a difference because the outcomes will be wrong anyway. When attempting to make data-driven decisions, you can’t afford to misinterpret the facts.
Data Silos
Finally, we’ve talked about the importance of having a centralized system for all data. When data isn’t shared between teams or campaigns, we call this a ‘silo.’ A common silo comes from individual reporting email marketing campaigns and PPCs. Why report them individually when you can improve the marketing strategy as a whole and report them together?
Thankfully, we no longer need to rely on limited human capabilities because we have technology that will filter data on our behalf; it will organize and analyze data at a faster rate than we could.
We can integrate artificial intelligence at all stages of the marketing analytics process. We can bring together data that was once kept separate, organize it effectively, and automate insights. You get answers to important existing questions, and answers to questions you hadn’t even thought of.
Don’t rely on our word alone, Gartner has predicted that three in ten businesses will leverage AI in at least one sales process before the end of 2020.
Simplify Analytics (Natural Language Friendly)
Asking simple questions is one thing, but building a model to accommodate these questions is another. Analytics platforms powered by natural language allow us to deal with complex questions. Wacul AI, a new tool on the market, turns complex and difficult Google Analytics results into understandable phrases.
Merge Data Sources
As long as there’s historical data for AI to draw upon, it will quickly uncover consumer behavior. By learning everything there is to know, artificial intelligence will determine the likelihood of “churning” (which means losing customers). The more the AI learns, the easier it is to discover how to keep customers and market to them in the right way.
Take Domo as an example. Upon implementation, Domo will merge all of your marketing data so it is in one place, predict results and identify patterns.
Automated Data-Driven Decisions
Historically, human-driven analytics has hindered marketers because of a lack of scalability and the excessive time it takes to get started. AI has fixed both of these issues. Artificial intelligence builds models to improve customer experience and overall market effectiveness.
Social Media Analytics
AI is especially valuable to social media marketers, especially when it comes to Natural Language processing and computer vision. Artificial intelligence provides them with real-time marketing outcomes and curated insights.
One of our top picks in this niche is Unmetric. After setting up, Unmetric will assess the social media activity of competitors to predict which social media strategies might yield the best results.
Improve Customer Experience
Why do we do anything in business? The answer normally comes down to one of a few things: to improve sales, increase profit, boost reputation, or make the customer experience smoother. As marketers, our goal is to understand our customers and learn what they like, don’t like, what they resonate with, how they shop, etc. You probably don’t need us to tell you at this point, but AI will do exactly this.
We now have the ability to sift through mountains of text and discover patterns in customer reviews. Gavagai, for example, will search through thousands of reviews and come up with a summary of frequently used phrases and potentially important patterns. Will this tool, we learn what makes customers tick and can use these insights for marketing, product development, and content creation.
If you couldn’t see the value of AI in filtering and organizing data before, you should now. In this guide, you’ve learned the role that artificial intelligence is beginning to play in marketing analytics and its application.
It’s natural to feel overwhelmed by the sheer amount of data available these days, but now you know there are solutions. AI organizes and analyzes data far better than human capability alone. It offers real-time insights and suggestions which you can implement immediately. When you assign AI to your data analysis tasks, your team will have more time to focus on larger aspects of your business.
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