How convincing was GPT-4 when it accessed basic personal information in brief online discussions? We describe the robust finding of the 900-participant experiment and its limitations that cannot be directly generalized to marketing.
ACADEMIC WORK REVIEWED
On the conversational persuasiveness of GPT-4
ResearchersFrancesco Salvi, Manoel Horta Ribeiro, Riccardo Gallotti and Robert West.
Nature Human Behavior · 19 May 2025 · 9:1645–1653 · Peer-reviewed article · Source language: English
English review: 9 September 2026 · The source date appears in the citation above.
Published review
Source and editorial checks are complete. DijitalPi did not conduct a customer experiment for this paper.
LET'S READ THE RESEARCH TOGETHER
We first explain the researchers' question, method and findings. We then discuss how to interpret the results, clearly separating DijitalPi's commentary from the source.
01 / WHAT DID THE RESEARCHERS WANT TO UNDERSTAND?
Is GPT-4 given personal information more persuasive than humans in short discussions?
Salvi and his colleagues investigated whether large language models not only produced persuasive text but also adapted their arguments more effectively when they received basic sociodemographic information about the other person.
Explaining the context · DijitalPi commentary
Personalization is a familiar idea in marketing: showing a message that fits a person's need or context, rather than the same message for everyone. Because generative AI can do this job quickly for large numbers of people, even a small persuasion difference can become significant when scaled up.
A change in opinion after a personalized message is not the same as a product purchase. This study concerns short discussions about political and social issues. The marketing connection is our interpretation; the researchers did not measure ad conversion or sales revenue.
02 / HOW WAS THE RESEARCH CONDUCTED?
How did they test the question?
In a preregistered experiment, 900 participants had a brief, multi-round online discussion with a human or GPT-4. In some conditions, the opponent received basic information such as age, gender, education, employment status, ethnicity and political affiliation. The design created 12 conditions based on whether the opponent was human or AI, had access to personal information and how strongly the participant initially held the view.
Understanding the method · DijitalPi commentary
The participation level of the participants before and after the discussion was compared. Thus, not only whether they liked the text but also how close they came to the defended view was measured. However, short-term attitude measurement does not imply permanent behavior change.
The resulting value of 64.4% does not mean that AI won in 64.4% of all conversations. In matchups where AI and humans were not equally convincing, personalized GPT-4 stood out as the more convincing side with a 64.4% probability. The paper also reports this as an 81.2% relative increase in the likelihood of higher post-discussion participation.
03 / RESEARCH FINDINGS
Accessing personal information, GPT-4 showed a distinct advantage in the comparison.
In comparisons where humans and AI were not equally persuasive, personalized GPT-4 had a 64.4% probability of being the more persuasive side. This corresponded to an 81.2% relative increase in the likelihood of higher agreement with the advocated view after discussion. The 95% confidence interval was +26.0% to +160.7%, and the sample included 900 people. GPT-4 without personal information did not show the same clear advantage over humans.
GPT-4 in unequal matches
64.4%Probability that personalized AI was more persuasive in unequal matches; not a success rate for all conversations.
Relative probability increase
81.2%Relative increase in the likelihood of higher post-discussion agreement with the advocated view; not an increase in sales.
This study suggests that personalization may be more powerful than just putting the person's name in the message. The model is able to reflect basic information in argument selection and expression. For business, the question is not to collect more data but to test what information actually makes the message more useful.
A wide confidence interval indicates that there is uncertainty about the size of the effect. We cannot use the central figure of 81.2% as a definitive rate that can be repeated in every environment. Different models, languages, products, message lengths and target audiences require a new experiment.
If personal data increases persuasive power, it also increases ethical and legal responsibility. Explicit consent, data minimization and avoiding unexpected inferences must be considered alongside performance. This section is DijitalPi's practical interpretation; the article did not test KVKK-compliant campaign design in Turkey.
05 / CONCLUSION AND OPEN QUESTIONS
What did we learn, and what do we still not know?
The study shows that GPT-4 can gain a persuasive advantage over human opponents when it receives basic personal information in short online discussions. The result is limited to political debates, specific model versions and short-term attitude change; it does not establish effects on ad conversion, purchases or long-term trust.
Would the same effect be seen in Turkish messages, different product categories and real customer behavior? To know this would require a separate field experiment with a control group without personal data, predetermined behavioral measures, and data use limits.
We created these scenarios to make the topic concrete. They are not cases from the paper or measured client results.
EXAMPLE 01
Is the more personal data the better the result?
The team wants to use age, location, profession and interests simultaneously to personalize ad copy.
Does the research support collecting all possible data?
Open the recommendation for example 01+
Start with minimal data.
Do not test data that is unnecessary for the business purpose. Keep the version without personal data as the baseline. Define explicit consent and a retention period; greater persuasive power does not justify unlimited data use.
EXAMPLE 02
Likes have changed, sales are not yet known
Those who saw the personalized message spoke more positively in the survey. No purchasing or application behavior was measured.
Can you announce the campaign as a sales success?
Open the recommendation for example 02+
Report the measured result under its own name.
Track attitude change, click, referral, and sale in separate columns. If the experiment measures vision only, limit the conclusion sentence to that. Do not infer the income effect without testing the subsequent behavior separately.
EXAMPLE 03
Customer feels watched
Because the message is so personal, the recipient questions how the brand knows this information.
Is a more personal message a better experience under all circumstances?
Open the recommendation for example 03+
Measure discomfort as well as perceived benefit.
Clearly explain the source and reason for personalization. Offer the option to opt out. Track not only conversion, but also complaints, unsubscribes and trust evaluations in the same experiment.
TRY IT WITH YOUR TEAM
Test a personalization decision with a data map.
Next to each data field you use, write three pieces of information: where did it come from, does the customer expect it, what measurable benefit does it serve? Leave the area with no answer out of the pilot.
The academic work belongs to the researchers named above. This page contains DijitalPi's explanatory review and original business examples; it is not a full translation of the paper.