Colleagues, today on our operating table, we have a vacancy from a true titan – NielsenIQ.
For those who’ve spent the last 50 years in a cryo-chamber, let me explain. NielsenIQ is a global leader in consumer behavior research. They recently merged with GfK, and now this behemoth, by their own admission, operates with data covering over 90% of the world’s population. This isn’t just a company; it’s an entire data industry. They know what you’ll buy for dinner next Wednesday even before you’ve thought about it yourself.
And so, this data giant is looking for a Quantitative Research Project Manager in Belgrade. A person. With at least 7 years of experience. Their task is to prepare and conduct research projects, develop questionnaires, analyze results, and present them to the client. A classic of the genre. The company is trying to address a clear pain point: they need an experienced fighter who can take a client brief, transform it into a coherent research project, refine it, and beautifully package the findings. Seven years of experience are needed for someone to have gained enough bumps and bruises and intuitively understand which tool to apply in which situation. Essentially, they are looking for a well-trained, but very slow and expensive neural network on legs.
But hold on, it’s 2024! A company that proudly states in its job description that it uses AI for candidate selection somehow stops halfway. They automate HR routine, but the very essence – data processing and interpretation – they still want to entrust to a single person. It’s like buying a sports car to haul potatoes from your garden.
Let’s fantasize and assemble NielsenIQ’s new employee. Not a person, but a system. Let’s call it, say, “AI Project Director.”
Imagine a dialogue in a meeting room. On one side – Ivan, a seasoned skeptical manager. On the other – Anna, a proponent of new approaches.
Ivan: Anna, what AI? Human contact is needed here! To look the client in the eye, to understand their real pain, not just what they wrote in the brief.
Anna: Ivan, let’s go step by step. What does your ideal manager do? First – they receive a brief. Our “AI Director,” trained on thousands of past successful NielsenIQ projects, analyzes the client’s brief in 30 seconds. It immediately proposes 3 methodology options, from a classic survey to complex conjoint analysis, complete with preliminary cost and timeline estimates. Your seven years of experience are a database in your head. But the machine has a database for the entire company throughout its history.
Ivan: Granted. But the questionnaire! Crafting the right questions, without double meanings, without leading prompts – that’s an art!
Anna: It’s not art, it’s science. And it’s perfectly algorithmizable. We give the system the research objective and target audience description. It generates a draft questionnaire, using best practices. Moreover, it runs the questions through an answer simulator to identify potentially weak or biased formulations. Tools like SurveyMonkey are already implementing similar AI features. And we can create our own, tailored to our tasks. The human manager only reviews and approves. Not 3 days of work, but 3 hours.
Ivan: Alright, what about data analysis? These mountains of numbers… Intuition is needed here to find that very insight!
Anna: Intuition is pattern recognition. And machines handle that better than we do. Our “AI Director” connects to raw data and runs hundreds of hypotheses overnight. It builds correlations a human wouldn’t even consider. It segments the audience not by three attributes, but by thirty-three. It finds those “non-obvious insights” and immediately visualizes them as graphs. Instead of spending hours digging through SPSS, the manager receives a ready-made dashboard in the morning with key findings and anomalies to pay attention to.
Ivan: And what, this thing will even draw up the client presentation itself? With all those pretty arrows and conclusions on the slides?
Anna: Exactly. Integration with a hypothetical PowerPoint or Google Slides via API is a matter of technique. The system takes key graphs, adds generated executive summaries and recommendations based on best practices for the given industry. This results in 80% of a ready presentation. The human only needs to add the final touches, emphasize key points, and prepare for the presentation. They stop being a craftsman and become a strategist who works with a nearly finished product.
To reduce distrust, we’re not firing everyone tomorrow. We implement the system as an assistant. Initially, it helps with routine tasks – draft questionnaires, preliminary analysis. The team sees that it saves them hours. Gradually, trust grows, and the system takes on increasingly complex tasks. The human role shifts from “executor” to “controller” and “interpreter.”
And how do we check that our “AI Director” isn’t spouting nonsense? It’s elementary.
We take 10 completed projects from last year. We give the system the raw data and briefs. We compare the conclusions and recommendations generated by the AI with those prepared by the human team. How much do they align? Where did the AI find something new? Where did it make a mistake? This is the process of validation and retraining.
We launch a pilot project in parallel. One team works the old-fashioned way, the other – with an AI assistant. We compare speed, cost, depth of analysis, and most importantly, client satisfaction. The numbers will speak for themselves.
Ultimately, instead of searching for one expensive specialist with 7 years of experience in the Belgrade market, the company could invest in creating a system that encapsulates the experience of hundreds of such specialists. Such a system won’t go on maternity leave, won’t ask for a raise, and won’t burn out. It will simply do its job – turning data into money. And that, it seems, is exactly what giants like NielsenIQ want. They just, perhaps, haven’t fully realized it yet.
Источник: https://www.linkedin.com/jobs/view/4403666142/
