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The Door to Data-Driven Outcomes Isn’t Locked

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Marketing tech stacks grow larger each year as new tech, trends, and tools emerge to help us be more efficient, faster. I know my fellow marketers will agree that it can (at times) be hard to keep up. Now that we are adding artificial intelligence to the mix, it can understandably be intimidating, confusing, and daunting, but it’s actually much more approachable than you think.

Of course, there are many different types of AI tools for all different skill levels and needs, from chatbots to recommenders to automated analytics tools. But, let’s move past the hype and buzzwords that surround AI and actually breakdown what it’s like to approach it for marketers and what that actually means — from common obstacles to use cases.

Current State of AI in Marketing

AI adoption is starting to look like the new gold rush for business and if you just look at headlines, you’d think you’re already far behind. The reality is that while adoption is high, the rate of failure is high as well. Rushing headlong into a new initiative without thinking through how you will implement and act on the insights is a recipe for disaster. This is why context is key to understanding the full solution you are looking to vet and how it will fit into your enterprise ecosystem.

But, we all have to start somewhere, so let’s take a look at some need-to-know stats for marketers.

  • Enterprise adoption of AI technologies has grown 270 percent over in the past four years as new AI tools, such as sentiment analysis, have evolved to help marketers more accurately capture consumer preferences.
  • As a Gartner study notes, businesses are worried about lacking AI skills among staff (56 percent). The research firm also cites uncertainty around key benefits and use cases (42 percent). For marketers, this underscores the need for turnkey AI solutions capable of integrating with current tools and delivering insight without the need for technical expertise.
  • Forbes cites that content personalization and predictive analytics from customer insights are the two areas CMOs most prioritize AI spending today.

Applicable AI for Marketers

When applied effectively, AI improves marketing success through data-first strategies and insights. What that means is that AI tools are now allowing marketers and marketing teams to turn their data into actionable insights, fast.

AI solutions are rapidly evolving, and as a result, the scope of applicable marketing strategies informed by AI is increasing.

  • Reduced Customer Churn: Using AI, marketers can better identify key points in the purchase or service cycles where customers are likely to leave, and identify ways to proactively keep them. With the combination of historic business data and customer data from multiple sources, AI tools create connections and patterns that are not immediately obvious from simple analysis, surveys, or data in isolation.
  •  Improved Segmentation: Here, the advantage of using AI comes from intelligent tools that are able to quickly analyze large data sets and identify trends, and then filter out information that isn’t immediately relevant.
  • Predictive Lead Scoring: AI tools can take over a lot of the heavy lifting to help identify target markets, prioritize high-level leads, and give back valuable time to your sales and marketing teams.

Addressing AI Obstacles

While AI offers many benefits for marketers, to really take advantage your team needs to be prepared. That means, for example, creating a proper data management strategy within your organization, having C-Suite/management buy-in, knowing what tools you should and can use, and more.

Data Management

Creating a proper flow of access and governance is key to a clean approach to AI. Data governance and management has historically run through IT teams and as a result is siloed and difficult to access. It’s possible this may be obvious or it may be a more challenging task because of multiple data sources. Consider the quality, quantity, and accessibility for your data and make sure it aligns with your AI strategy and targets.

C-Suite/Management Buy-in

Gartner reported that 85 percent of AI projects will fail through 2022 due to bias in data, algorithms, or failure of the teams managing the data. Companies need to address the gap between business executives and their understanding of AI projects and analytics tools. Analytics projects can take time and resources, which is why having that C-Suite/Management buy-in allows these projects to start on the right foot and be trusted from the top down. This is a true start on the journey to becoming a data-guided company.

AI Tools

Ready to begin your search for the perfect analytics toolset?

With a new AI tool coming out every day, it’s important to know what it is you need and are looking for. Maybe all you need is the right AI chatbot for your website, a tool to help look at online reviews and complete sentiment analysis, or a full-on automated analytics platform to overhaul your strategies. Whether they are at the enterprise, department, or user level, start by drilling down and understanding what your needs are.

AI has matured to the point that it is accessible to businesses of all sizes. So starting on the right foot, or even going back and re-evaluating your AI approach will save you many headaches in the end. This is how you will be able to not only approach but also apply AI marketing at scale to improve your marketing success. The door to data-driven outcomes is not locked; just make sure you are prepared with the right key.

Keyence’s goal is to create and provide added value from our over 40 years of experience as a data-guided company. If you are interested in learning more about opening the door to data-driven outcomes, download The Marketers Guide to Approaching AI.

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