5 AI And Analytics Trends Marketers And Brands Should Be Investing In

Clearly there are some big AI and analytics trends for 2020. The big elephant in the room: now what? With so many new opportunities in analytics, how do organizations tap into not just trends, but the value those technologies can create for their companies and customers? As we head into the next decade, I believe the greatest value of analytics will come in the form of clear, fast decision-making, predictive trends, and ROI (which, again, will lead to better decisions). The following are a few trends that can help grow and improve your company this year.


Recognizing Baseline Data is Not Enough

An Excel document—flat numbers on a page—won’t cut it in 2020, no matter how great your Excel skills are. Because of the vast amounts of data, and the ability to make complex connections from it, marketing teams need to learn to augment their data with machine learning and AI. The flat data—baseline data—is a starting point. But the value comes in making active observations, finding patterns, and developing new queries based on the connections found. Oh, and those connections need to be made quickly. Like—right now.

analytics trends

If you’re looking at your budget and freaking out, don’t worry. While demand for real-time analytics has increased, the cost of in-memory processing has also gone down, making it more accessible for more companies. Also converged databases, common data models and cloud applications are making it more achievable to streamline data and leverage analytics from your favorite analytics tools such as SAP, Tableau (Salesforce), Microsoft Power BI, Oracle or SAS.

My recommendation moving forward is to not just focus on gathering more data. But also, focus on gathering the tech that will help you crunch it most meaningfully.

Know the Great Value Comes in Prediction, Not Description

This isn’t exactly a new trend, but moving forward, predictive analytics will be a basic requirement of any successful marketing team—not a nice-to-have for the rich and famous. More and more, it’s essential that teams focus not just on “where we are” but “where are we going?” What do consumers want to buy? When? What do they need that no one has yet given them? Finding those niche opportunities are the way to take the lead in your market sector. And with advancements in AI and machine learning, noted above, those predictions will only become more accurate and more powerful.

Invest in Data Visualization and Graph Analytics

Not everyone is a numbers person. With data paving the way for much of the decision-making happening across the enterprise, it’s essential that employees find quick, easy ways to make meaningful connections with the data they’re receiving. One of the ways to do that: graph analytic.  Data visualization/Graph Analytics will better help your team understand complex connections between people, customers, places, times, and things—without overwhelming them with numbers. These visualizations can also be especially helpful with things like scenario planning and risk management—big issues with lots of moving parts and lots to lose or gain.

There are many powerful tools for data visualization from Microsoft Power BI to Zoho Reports to Tableau Desktop. Many people have had some experience with these types of analytics with Google Analytics. In short, it’s often easier when we can visualize what the data is saying. As tools become more advanced that data can be enriched and predictive models can be developed.

Use Analytics for Lifecycle Management

Lifecycle management is key in terms of product development, and the smartest marketing teams will be using AI and machine learning to optimize their processes at scale, from app development and testing, to launch, support, and recovery.


On the flip side, no algorithm was meant to last forever, no matter how well it worked for your team. As we head into the next decade, we’ll see better rules of engagement in terms of lifecycle management of analytics from development to testing to back up to recovery. Which works? When do they need to be reworked? How do we make sure that algorithms and coding don’t go the way of the data swamp? Dirty data costs the United States $3.1 trillion a year! How much does bad AI cost?

Natural Language Processing

Again, not all of us are not numbers people. Luckily, the tech powers that be understand that. In the future, we’ll see an increasing ability to run queries by voice command, which will make things especially easy for those marketing teams who know what they want to find out—but don’t know which metrics to use to get there.

For many people, natural language processing has become common place because of devices like Amazon Echo and Siri or Bixby on your premium smartphone device. But the use of NLP is expanding rapidly as we are finding speech recognition capabilities being baked into more software like Zoom or Cisco Webex for meeting translation or Microsoft’s new Office 365 Unified App where people will be writing documents on their mobile device with speech to text. Companies from Oracle to Salesforce to Microsoft Dynamics are all touting AI and NLP as part of their current and future plans for enabling marketers to more quickly extract analytics from data.

Remember: this is no magic bullet in building brand and market awareness. Analytics are a great tool, but even the best numbers are meaningless if you don’t have a plan for executing on your discoveries or keeping your data up to date. Moving ahead, the amount of data flying at you from all angles is only going to increase as data grows exponentially and the proliferations of tech trends such as 5G and Internet of Things gains steam. The main point to remember is that being a data-driven organization is no longer an option—it’s a necessity.

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