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Artificial intelligence algorithms are designed to make decisions based on constantly updated data in real time. Passive machines, on the other hand, are confined to mechanical or predetermined responses.

Artificial intelligence gathers information from a variety of sources, analyzes it in real time, and acts on the results. Artificial intelligence is, in fact, purposely constructed by humans and makes decisions based on its fast processing.

Artificial intelligence has a bright future ahead of it. Aside from automating repetitive tasks and the near-future of self-driving vehicles, the TechTalks blog mentions a number of firms that have already benefited from AI implementation. Listed below are a handful of them:

Education

Healthcare

Human resources

Marketing

Supply chain management

Customer service and experience

Logistics

Cybersecurity

According to an introduction from an MIT artificial intelligence course, “making computer models of human behavior” is at the core of artificial intelligence. Intelligent behavior models must be artificial intelligence because we believe humans are intelligent. Artificial intelligence algorithms, on average, make less errors than humans.

Therein lies a substantial impediment to the future successful deployment of artificial intelligence. There aren’t enough people who can convince other corporations to view the planet via the lens of machine-powered improvement. To put it another way, there aren’t enough people who know how to program machines that can think and learn for themselves. Worse, fewer people are capable of understanding artificial intelligence output and applying the findings in practical ways. Worse, developing a good artificial intelligence system is currently much too difficult.

So, who is in the greatest position to improve the utility of artificial intelligence? To act as a bridge between algorithms and their outcomes?

The answer may be found in the emerging field of data or analytics translators. According to Google, “a data translator is a conduit between data scientists and executive decision-makers.” They are very good at understanding a company’s business needs and are data savvy enough to be able to speak tech and explain it to others in the organization. In a Forbes article, Bernard Marr argues, “Forget Data Scientists, Hire A Data Translator Instead.”

Marketing research experts are uniquely prepared to meet this demand. The new data/analytics translation, like the merging of qualitative and quantitative research, is a mixture of traditional marketing research skills with continual bandwidth expansion. Algorithms for artificial intelligence are developing at a dizzying speed. As a consequence, data from customers, machine learning, and social media floods the system. There are a lot of tools and a lot of data. Customers and C-suite executives are ready to hear the story, one message resonates loud and clear.

Artificial Intelligence, Big Data, and the Problem with Insights

Predictive analytics and marketing research are relatives. The former aims to employ sophisticated analytics for investing, commercial, and security purposes, such as text mining, image recognition, process optimization, cross-selling, biometrics, medicine efficacy, credit scoring, sector timing, and fraud detection.

Predictive analytics and marketing research are two distinct fields. Artificial intelligence is a subset of predictive analytics by definition. Both companies, on the other hand, have data scientists on staff. To come up with insights, marketing research companies analyze firm information on a regular basis. I’ve done voter targeting for Burger King, Pfizer, Ohio State University Medical Center, Cheesecake Factory, REI Adventures (a large adventure travel company), and a presidential campaign.

The project roadmap for these activities is detailed below.

The table’s Data and Information section demonstrates how artificial intelligence experts calculate results to improve model efficiency. They are unable to provide comprehensive yet concise reports.

Because of their reporting skills and high analytical horsepower, marketing research professionals are well-positioned to analyze that avalanche of data for the C-suite. This also takes into account the project’s right side, which is Knowledge and Information. It also enables a researcher to pursue a career as a strategic consultant, sometimes known as an analytics or data translator.

Open-Source Power: The R-Project

The R Foundation for Statistical Computation supports R, a free programming language for statistical computing and graphics. R is a free-of-charge programming language for statistical computing and graphics supported by the R Foundation for Statistical Computing. For readers unfamiliar with open-source statistical software, R is a free-of-charge programming language for statistical computing and graphics supported by the R Foundation for Statistical Computing. Statisticians and data miners often use the R programming language to create statistical applications and do data analysis. While R has a steep learning curve, marketing research professionals may quickly master it.

In the past, expensive SAS licenses or many SPSS modules were required to access the firepower that is now available for free on the internet. Hundreds of open-source modules are available in the R-Project, which are mentioned below. Some of the most well-known and commonly used artificial intelligence algorithms are included in my list:

Bayesian Inference

CHAID Trees

Feature Selection Regression

General Linear Models

Logistic Regression

Machine Decision List Functions

Neural Networks

There are a host of additional possibilities.

Any marketing research business may now collaborate with a data scientist to provide consumers with not just a research report, but also the ability to mine corporate databases with artificial intelligence-like complexity. Each of the algorithms outlined above may be used to marketing research in the real world. These extra abilities may also be learned via open-source training. Hundreds of free, brief online courses on Coursera, for example, may educate any experienced marketing researcher how to appraise and compress analytic data.

Bringing Analytics Translation into the Mainstream

So far, we’ve shown that marketing researchers have access to the tools and bandwidth they need to do artificial intelligence or database mining today, and that this will continue to improve over time.

So, how can the marketing research sector make the most of this new opportunity

Marketing researchers are specialists in constructing surveys and summarizing data. Artificial intelligence individuals, on the other hand, are not. While marketing researchers can surely calculate the most economical marketing mix or product placement, they win hands down when it comes to summarizing results and presenting them to a CMO.

Why? Because marketing researchers are skilled in inductive reasoning, the Knowledge and Wisdom side of the graphic—is an important part of a project report. This is the deliverable for the C-Suite. We have a natural ability to translate data.

Summing Up

We have the ability to do any work. We take the data, analyze it using high-performance open-source software, summarize the results, and provide strategic thinking to our clients. We blend advanced artificial intelligence capabilities with marketing research story-telling talent to produce clear and compelling findings. We are data translators, the “must-have” profession of the future.

Despite its increasing sophistication, artificial intelligence will always need human eyes.