When does algorithmic personalisation improve consumer response?

Relevance, trust, psychological matching and the limits of persuasion measures

This retrospective review considers source versions available by 5 November 2025. The cutoff defines the evidence included; preparation and source checking took place on 21 October 2026.

Keywords: personalisation; consumer response; trust; retargeting; psychological matching

1. What personalisation changes

Personalisation can change who receives a message, what the message says or the context in which it appears. These operations are often combined, although their effects need separate assessment. A campaign can target a selected audience while showing everyone the same advertisement. It can also adapt content to a profile without changing the audience. A result from one arrangement cannot identify the contribution of another by implication.

This review asks when algorithmic personalisation improves consumer response and what additional evidence is needed to infer commercial benefit. It examines research on relevance, information specificity, trust, perceived control and psychologically matched messages. The aim is to describe the tested effects and their limits, rather than prescribe a universal degree of personalisation.

Generative models add a production capability to this process. They can create variations of a message from supplied profile information. That capability does not establish that the profile is correct, that the recipient welcomes its use or that the variation changes purchasing. Those are distinct empirical questions, each with a different comparison and outcome.

2. Method and evidence boundaries

Targeted searches on 21 October 2026 used personalised advertising, retargeting, privacy controls, psychological targeting, relevance, trust and generative persuasion. Twelve primary studies or research syntheses were selected from publisher pages, author manuscripts and university records. Full text was inspected for the generative-persuasion study and the accepted manuscript on the personalisation paradox. Several other sources were assessed through abstracts and publication records.

The extraction distinguishes targeting, message adaptation, outcome and comparator. Clicks, self-reported persuasiveness, intended behaviour and purchases are retained as separate endpoints. No pooled personalisation effect is estimated. The studies differ in medium, audience, data collection and treatment design, and no comprehensive database search or independent duplicate screening was performed.

The source-access register records the material inspected. Findings from historical display advertising are not presented as tests of current conversational agents. The review also does not provide legal advice or infer permission to use personal data from an observed marketing effect. Behavioural effectiveness and the conditions under which a practice is authorised are separate questions.

3. Targeting, relevance and response

3.1 An individualised message is not a single treatment

The information used for adaptation can concern a recent product view, a stated goal, a broad demographic group or a psychological trait. The resulting message may vary in product specificity, language, imagery or offer. Describing all of these as personalised removes details that can explain why results differ. Evaluation needs the actual input and the element that changed.

Tam and Ho develop and test a model connecting web personalisation with information processing and decision outcomes, including content relevance, self-reference and goal specificity [8]. Their laboratory and field work concerns the relationship between the presented material and the user's task. It does not establish that every available personal attribute improves response when inserted into a message.

3.2 The comparator defines the estimate

A matched message can be compared with a generic message or with one deliberately mismatched to the recipient. These comparisons answer different questions. A gain over a poor mismatch does not necessarily establish a gain over competent generic communication. If the generic version also differs in length, quality or product information, the treatment may include more than the intended adaptation.

The same issue arises when a platform chooses both recipient and content. A campaign's high conversion rate can reflect that it reached people already likely to purchase. To infer the effect of personalisation, the comparison must address what those people would have done under the alternative message or allocation. Observing successful purchases among selected recipients does not supply that counterfactual.

4. Information specificity, timing and placement

Lambrecht and Tucker studied an online travel firm's retargeting experiment. Their abstract reports that dynamically showing previously viewed products was less effective on average than generic brand advertising, while the pattern changed when browsing suggested more specific preferences [7]. The finding concerns a field comparison in that category. It does not establish that detailed product reminders always underperform or that a single browsing event reveals readiness to buy.

Bleier and Eisenbeiss combine two field and two laboratory experiments to examine content personalisation with timing and placement. Their abstract reports that effectiveness depended on those interactions, with highly personalised banners losing their initial advantage over time and placement affecting some outcomes [1]. This prevents interpreting degree of personalisation as an isolated universal lever. The entire exposure arrangement matters.

Timing can refer to time since a product view, stage in the decision process or frequency of repeated exposure. These variables should not be merged into one recency label. A recent visit can involve exploratory browsing or a near-complete decision. The observed trace is information about behaviour, not a direct readout of the consumer's complete preference state.

Placement similarly changes what can be inferred. An advertisement shown in a relevant content environment may supply useful information at a suitable moment. The same advertisement in another environment can interrupt a different task. A click is one response to that encounter, but it leaves comprehension, later consideration and purchase unobserved unless the study measures them separately.

5. Personalisation and perceived vulnerability

Aguirre and colleagues examine how information collection influences responses to personalised advertisements. Their research includes field observations and experiments, with the reported pattern distinguishing overt from covert collection. Greater personalisation was received differently when consumers experienced vulnerability from the collection process; trust-building cues could offset some negative responses [2]. The treatment concerns both information use and the consumer's understanding of it.

White and colleagues study reactance to highly personalised email solicitations and the role of justification and perceived utility [5]. This supplies evidence that relevance alone is an incomplete description of response. A recipient can find a message related to personal characteristics while disliking the apparent use of that information. The result belongs to its tested email context rather than every form of digital assistance.

Goldfarb and Tucker examine targeting and obtrusiveness in online display advertising [6]. Their findings distinguish the effects of these features and show why combining them cannot be assumed to add their separate benefits. For interpretation, an adaptation that increases perceived relevance can also make information collection more salient. The net outcome is an empirical result, not a consequence that follows from the word personalised.

Acquisti, Brandimarte and Loewenstein's review organises privacy behaviour around uncertainty, context and malleability [9]. It provides context for why a stated general preference about privacy need not predict every response in an advertising experiment. The review is not a contemporary legal standard. Nor does a consumer's click establish informed understanding of every data use behind the message.

6. Trust and control are distinct conditions

Tucker's field study compares personalised ad text with generic text around a platform change that increased perceived control over personal information without changing advertiser data use. The abstract reports stronger response to personalised ads after that change [3]. The result concerns a specific intervention in perceived control and a click outcome. It does not establish that all privacy settings or disclosures have an identical effect.

Bleier and Eisenbeiss's separate trust study examines how trust in the retailer moderates responses to personalised banners, using a field investigation and laboratory work [10]. The evidence treats the recipient's relationship with the sender as part of the context. The same message depth can therefore have different consequences in different relationships. Trust should be measured rather than inferred solely from whether a brand is well known.

Hayes and colleagues manipulate consumer–brand relationship strength, overt or covert collection and platform in an online experiment. Their abstract reports differences in the balance of perceived benefits and risks by relationship strength [11]. This design separates several contextual factors that broad claims about consumers often combine. It does not establish that a strong relationship removes every concern about data use.

Reported trust, perceived control and actual control are also separate variables. A person may feel informed without being able to change a setting, or possess a setting without understanding its consequence. An experiment measuring perceived control can establish a response to that perception. It cannot silently become evidence that the underlying data practice changed or that every participant understood it.

7. Psychological matching and observed behaviour

Matz and colleagues' 2017 research reports three field experiments using psychologically tailored advertisements, with clicks and purchases as outcomes [4]. It demonstrates that matching message content to psychological characteristics can affect behaviour in the studied campaigns. The widely cited maximum uplifts describe particular comparisons, not an average effect guaranteed for any product, trait or audience.

The distinction between measurement and inference of a profile is important. A study can use questionnaire-based traits or estimate characteristics from other information. Each introduces uncertainty. A message may be well designed for an accurately measured profile while performing differently when that profile is inferred poorly. Evaluating only the writing step leaves the accuracy of the targeting input unresolved.

A profile also does not identify the whole person. A broad trait can inform one adaptation while leaving immediate goals, constraints and prior experience unknown. The reviewed results do not establish that a psychological label is a complete consumer model. The appropriate conclusion names the trait, message comparison and observed behaviour, rather than presenting personalisation as an unrestricted capacity to determine choices.

8. What generative persuasion experiments add

Matz and colleagues' 2024 study examines generated personalised messages across four studies comprising seven sub-studies. The paper reports stronger influence from personalised than non-personalised messages across its tested domains and profiles [12]. It extends the evidence to automated message production while retaining an experimental assessment of recipient response. It does not measure the performance of every current model release.

In Study 1a, the final sample comprised 120 participants. GPT-3 text-davinci-003 generated paired consumer advertisements tailored to trait descriptions, and participants used bipolar scales to compare their effectiveness. Personality was measured separately. Later studies used other response measures [12]. This design makes the message-matching operation observable, but the initial outcome is a rating of relative persuasiveness rather than a recorded purchase.

A system that can generate profile-matched text can reduce a production constraint without resolving the other stages of a campaign. It still needs an appropriate audience definition, accurate input information and an evaluation of the intended outcome. The paper's evidence of persuasive differences should therefore be interpreted at its measured endpoint. Scaling the number of messages is a different outcome from scaling incremental commercial benefit.

9. Evaluating the complete personalisation system

9.1 Validate the input

An evaluation should identify whether the adaptation uses an observed action, a declared preference or an inferred attribute. Missing or stale information can change the assignment. A shopper's earlier product view does not necessarily describe the current need. If a profile is inferred, performance on held-out data is relevant to the input claim, while the message experiment addresses a separate part of the system.

A practical distinction is whether errors in the profile are included in the evaluation. Testing messages against known questionnaire scores estimates performance with relatively controlled inputs. Deploying against inferred scores introduces additional error. A production claim should make clear which of those conditions is represented. Otherwise, the writing model's demonstrated capability can be mistaken for the capability of the entire targeting process.

9.2 Isolate message adaptation

To estimate the effect of adaptation, the reference condition should preserve other consequential features where possible. Differences in price, offer, format and audience can produce a package effect. Such a comparison can be useful, but it answers whether the package performed better. It cannot identify the independent contribution of a changed adjective, visual treatment or psychological frame without further variation.

Multiple message variants introduce selection opportunities. Choosing the strongest variant after observing the same test outcomes can overstate likely performance on a new audience. A separate validation sample or later prospective test can assess the selected version. The number of candidates and the selection procedure belong in the method, particularly when low-cost generation makes many candidates available.

9.3 Preserve the outcome scale

A click-through intention differs from an observed click, and an observed click differs from an incremental purchase. A positive change in one can be relevant without establishing the others. The reviewed studies use several endpoints, so no common personalisation effectiveness percentage is calculated. The label should identify the behaviour or judgement actually recorded.

An attributed purchase also differs from a purchase caused by the intervention. People selected for retargeting have already interacted with the seller and may be unusually likely to return. Randomised message comparisons can estimate relative effects within that group, while a no-ad or alternative-allocation comparison asks a broader incremental question. The unit and baseline must remain visible in any reported return.

10. Interpreting variation across consumers

A campaign average can combine positive and negative responses. The same adaptation may provide useful product information to one group and seem intrusive to another. Research on relationship strength, perceived utility and collection context provides examples of this variation [2], [5], [11]. It does not establish a universal rule for segmenting every new audience. Those relationships require testing in the intended use.

Subgroup analysis should also distinguish planned questions from patterns discovered after many comparisons. A small sample can generate apparently large subgroup differences by chance. If a personalisation system assigns different treatments based on those patterns, it needs evidence that they persist in fresh observations. More finely divided segments do not automatically provide more accurate decisions.

The cost of adaptation includes more than producing text. Profiling, delivery, quality assurance and monitoring can change the economic result. A system may improve a response metric while increasing costs elsewhere. The reviewed behavioural findings do not provide a universal business case. Economic evaluation requires the relevant outcome, counterfactual and resources for the implemented system.

11. Limits of transfer to conversational marketing

Most historical sources concern banners, emails or websites. A conversational assistant can elicit information over several turns and change its message in response. That interaction differs from showing a preselected advertisement. Evidence from static messages can identify questions worth testing, but cannot establish the effect of an adaptive conversation without observing that conversation.

A conversation also makes attribution more complex. The assistant may help clarify preferences, supply factual information and frame an option within the same exchange. A changed choice can reflect any combination of those operations. A controlled comparison can assess the whole interaction, while component-level claims need designs that isolate the relevant difference. The presence of personal language is not evidence that psychological targeting caused the outcome.

12. Limitations and conclusion

The selected literature spans historical digital advertising and a generative-message experiment. Several sources were inspected only through abstracts or summaries. The studies differ in medium, data collection and outcome, and no universal effect size is estimated. The review does not infer commercial benefit from self-reported persuasiveness or establish authorisation for data use from behavioural response.

Personalisation can improve response in specified comparisons, while its effect depends on information specificity, timing, placement, trust and the collection context. Generative models can automate the production of matched messages, but this capability leaves profiling accuracy and incremental outcomes to be tested. The defensible conclusion describes the full assistance arrangement and the behaviour measured. More individualised content is a production property; better consumer or commercial outcomes remain empirical findings that a suitable comparison must establish.

Source access and appraisal register

Sources were consulted on 21 October 2026. Access categories describe the material inspected for this narrative review. An abstract supports only its reported summary; it does not establish a check of the complete analysis or supplementary material. No independent replication or formal risk-of-bias scoring was undertaken.

References