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AI: Overhyped and Misunderstood? A Data Scientist Answers the Questions You’re Afraid to Ask

In this five-part series from Dstillery, their AI, Machine Learning and Data Science experts will help marketers and market researchers demystify these emerging trends and offer ideas for taking action.

In 2018, it’s not enough to be a data-driven company. Forrester recently identified a new kind of business growing at more than 30% each year. Insights-driven businesses go beyond making isolated decisions with data by integrating insights into every process. This begs the question: How can your organization do the same?

In our opinion, Artificial Intelligence will become the primary tool to drive competitive advantage. This buzzword has hit peak saturation, so we decided to break it down in order to help you break through the noise and focus on what matters.

Before diving into the more advanced ideas, there are a few core concepts to understand: AI, Machine Learning, and Data Science. Melinda Han Williams, VP of Data Science and Analytics, will explain how these foundational concepts work together and how they will impact the market research industry.

What Is AI?

AI is a field of computer science that aims to develop machines that act “intelligently,” that is, machines that can make highly informed decisions autonomously. This concept has been around since the 1950s in academic circles. These days, we often hear about AI designed to replicate human behavior, like self-driving cars or computers that play chess. Pop culture regularly reinforces this characterization. Science fiction, for instance, has imagined AI in the form of humanoid robots that become self aware and eventually turn on their creators, like those depicted in Westworld or Terminator.

It’s easy to conclude that the only goal of AI is to give computers human intelligence, but reality is not what Hollywood imagines. As a consumer, you interact with AI-fueled products all the time. AI enhances the consumer journey so seamlessly that the processes behind the results are often overlooked. Take, for instance, Amazon’s ability to predict and recommend products you might like to buy. AI easily sifts through the purchase behavior data from hundreds of millions of customers in order to predict what you’re most likely to want next – an incredibly valuable capability for marketers that is only feasible with AI.

So while Westworld-type robots remain science fiction, computers capable of performing advanced cognitive tasks are now very much a reality. Marketers all over the world are using AI to gain insight into their customers behavior and ultimately predict who their next customers will be. They use it to identify the optimal time, place and message to target those prospects, as well as conduct market research based on observed behavior. Knowing which consumers are most likely to become profitable customers can transform your company into a true insights-driven business, something we’ll explore throughout this series.

What Is Machine Learning?

AI allows computers to act intelligently, but there are different ways to achieve that autonomous decision-making. Machine learning is an approach to AI where the computer learns from data. This makes it nicely suited to solve the dilemma marketers find themselves in – deriving actionable insights from massive volumes of data. Free from our cognitive bias, a machine learning algorithm can spot meaningful connections that are impossible for humans to find on our own.

Let’s say you’re a marketer for a furniture brand and you want to find consumers currently in-market for a new sofa. With machine learning, a computer can chew through browsing data from thousands of devices to identify behaviors that suggest near-term interest in a sofa. Some behaviors, such as browsing furniture sites, indicate an obvious intent to purchase. Other behaviors, like comparing paint swatches or pricing out moving vans, aren’t so obvious. A machine learning algorithm can analyze and combine all of these trends, from the obvious to the indirect, allowing you to identify and nurture those consumers at the earliest stage of the buying process.   

This is precisely why AI, machine learning in particular, is so valuable for market research. Companies have data on customer interactions with their own brand, and can also access massive amounts of data that consumers generate as they go about their digital lives. Machine learning uncovers insights-driven answers from data that would otherwise be hidden from human view.

What Is Data Science?

It’s not enough to understand this technology if you don’t know how to power it. That’s where humans come in. Data science is the human interface between the business problem, the data, and the technology.

It’s the data scientist’s role to:

  • Frame and translate the marketer’s business need from a qualitative problem into a quantitative question that an algorithm can answer.
  • Select and compile the data inputs that best speak to the question at hand. Some have called this “janitor work” but we don’t agree with that assessment. This data curation step is absolutely essential to making machine learning work, and it requires skill, experience and intuition. This can mean the difference between a successful or unsuccessful application of machine learning.
  • Choose the algorithm, tune the setup and parameters, evaluate whether or not the model and the overall approach is working, and iterate as necessary.
  • Assess the findings and remain vigilant to any problems that can affect data quality. Bad data means bad results.  

How Do AI, Machine Learning and Data Science Come Together?

In order to realize the full potential of AI to create business advantages, you need to have reliable and high quality data sources and the infrastructure to power it. The good news is that computing power is now widely available and affordable. A data scientist can set up a new type of model on her laptop with a few lines of code, then deploy that code to an entire data center where it can be used right away to extract insights from the whole universe of data that a company has access to.

AI can be used to answer one-off questions, but that’s like using your smartphone just to make phone calls. Ideally, AI is an “always on” process. In order to gain accurate and truly effective insights, a data scientist must optimize the system to continually ingest new data and deliver updated insights. With consumers, preferences and behaviors change over time. Taking full advantage of AI means that your data and insights evolve with your customers, allowing you to find unique stories and build strategies that makes your brand stand out.

What’s The Good News?

AI isn’t magic. It is the workhorse that enables companies with tons of data to transform into insights-driven businesses. And it provides better decisioning support, letting marketers and analysts understand and act on insights derived from consumer behavior. Here’s the good news: universities are churning out top quality data scientists, and companies that analysts and marketers typically partner with are adding them to their ranks. This means deploying AI within your organization is easier than you think.

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