The Art of Persuasion

Science Rockstars – Pioneers in Persuasion Profiling – White Paper – V1.0 Contact: [email protected]

PersuasionAPI “Pioneers in Persuasion Profiling”

Release Date 01-07-2013. By the Science Rockstar Team.

“Welcome to the Brave new World of Persuasion

Profiling” was the heading of a Wired article in august 2011. Since then, Persuasion Profiling has become a buzzword for interactive marketers. After over 20 years of e-‐commerce, the conversion

rates of online stores are magnitudes lower than those of offline stores. While the conversion rates of online stores have greatly increased by deploying efficient recommender systems and improving usability, they are still lacking. The key to this difference is personalized persuasion: The offline sales guy actively persuades individual customers to buy their products. Online we are also trying to use persuasion but we forget that what is truly powerful: personalized persuasion. This is exactly what persuasion profiles allow you to do. Initial evaluations of the use of persuasion profiles

in e-‐commerce have shown large conversion rates improvement of online stores. In this white paper, we will detail exactly what persuasion profiles are by answering 5 simple questions. Enjoy.

Question 1: Does Persuasion help? In 2001, Professor Cialdini, showed that if you want

people to do something, whatever that might be, it is not just the request that matters but also the way in which that request is framed [1, 2]. Based on a thorough observation of sales professionals, Professor Cialdini describes six “weapons of influences”: six ways to persuade people.

Reciprocity People are inclined to pay back a favor. Whenever

you do something for your customer, they will be inclined to do something for you. This is why companies give away free-‐trials, gifts, and products. The strategy is so powerful that even when you are given something you have never asked for, you will feel obliged to reciprocate [3].

Scarcity People value things that are scarce. The same

product, either sold with the message “abundantly available” or with the message “limitedly available” sells better in the latter case [4]. This is why we promote product saying “only three items left” and why we try to make things special and unique.

Authority People are inclined to listen to authorities. Over 50

years ago professor Milgram showed how strong this

effect is by showing the you and I, people randomly recruited from the street, will go as far a to kill another human being if an authority figure urges us to do so [5]. Now, we find celebrity endorsements and expert recommendations, which both utilize this strategy.

Consensus People do as other people do. As in the little line

segment experiment above, people are inclined to do what others do [6]. This is why we show customer evaluations and messages like “This weight-‐loss program was successful for 80% of the subscribers: you could be next!”

Commitment People do as they say they would. If someone

commits to something – for example by writing it down on paper – it is more likely that he or she will actually do it. This is why we have “wish lists” online: you commit to buying a product making it more likely that you subsequently will [7].

Liking People are inclined to listen to people they like. This

is why the traditional offline sales guy tries to establish rapport: “Really, that’s a coincidence, my wife also loves knitting!” Liking increases the likelihood that a request will be followed up. For each of these six persuasion strategies an

abundance of scientific studies showing their positive effects on compliance and eventually product sales exist. Thus, marketers are trying to use these strategies in the Online Stores, mailings, and other interactive marketing campaigns. However, with six strategies, and multiple possible implementations of each strategy, it is unclear how much persuasion we should use…

Question 2: Should we use all the strategies we can think off? So, we have covered persuasion and the six weapons

of influence. A natural next question to ask is whether or not you should try as much persuasion as possible, or rather you should make sure to select the “best” working persuasion strategy. You can see that persuasion strategies are already used on the web, and many companies choose to offer their products to customers with a special discount (scarcity), promoted by an expert (authority) and with ratings by other customers (consensus). Obviously you will not always


have the screen-‐space to present all of the strategies, and they might not all be beneficial together. Let look at some of the science. In 2010 Kaptein & Duplisnky [8] published a study

comparing the use of one persuasion strategy (consensus OR authority) with the use of multiple persuasion strategies (consensus AND authority). In that initial study, they showed overwhelmingly that the use of one strategy led to more compliance than the use of multiple strategies. The initial studies into this topic however were done

“in the lab”. More interesting, and more convincing, was their follow up. Kaptein & Duplinsky [9] created half a dozen of Google Ad campaigns: some implementing one persuasion strategy, and some implementing multiple. They measured the click-‐through rates on each of the ads and showed (see Figure 2) that the ads that used a single strategy always had higher conversion rates than those using multiple strategies. Piling up persuasion strategies might

not be very beneficial for several reasons. One, if you use multiple arguments this might lead your customers to scrutinize your product proposal more, and perhaps decrease trust. Second, there might just be strategies that do not work for some customers or for some product: piling up those with the ones that do work is bound to decrease the effect. Thus, while the six weapons of influence are super

powerful, you need to use them consciously. You cannot just go ahead and try “every trick in the book”.

Question 3: Should we use the same strategies for everyone? Now that we know that if we want to properly use

the power of persuasion we need to make a choice between strategies, its obvious to wonder whether we should use the same strategies for all of our customers. In a recent paper published in the Journal of

International Marketing Kaptein & Eckles [10] examine exactly this question. They try different persuasion strategies to sell books to different customers. However, by trying each of the strategies multiple times they can estimate the effect of a specific strategy on an individual customer. One thing they find is not very surprising: For some

people some strategies work better than others. What is surprising however is the size of the differences between people. In their trial the consensus strategy increased average conversion the most, but still that strategy was detrimental for over 30% of their customers (Figure 3). They were able to identify these customers based on their behavior, and they showed that the responses to persuasion strategies of individual customers are stable over time. This last study, and several others, show that it pays

of to select a specific strategy for each of your customers. You should know which strategy “works” for your current customer and make sure to use the right weapon. Using the same strategy for all of your

customers might be better than not using persuasion at all, but you miss a big opportunity.

Question 4: How do you know which strategy to use? We have shown that the use of persuasive strategies

will increase your conversion and drive your online

Figure 1: The estimated click-‐through rates on a series of google Ads. The gray density shows the estimated click through on add implementing multiple strategies. The click-‐through on adds

implementing a single strategy (black density) are almost twice as high.

0.000 0.002 0.004 0.006 0.008 0.010

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Figure 2: The influence of different strategies on individual customers: While the strategies are effective on average, some persuasion strategies have negative effects for a large proportion of customers ( > 30%). Thus, not using the strategy would

have improved performance.

34

Estimate S.E t pIntercept 4.25 0.33 12.91 0.0002

Authority 0.37 0.15 2.51 0.0064Consensus 0.44 0.14 3.11 0.0020

Scarcity 0.06 0.14 0.43 0.6484

Table 3.3: Estimates of fixed effects in the preferred model. Using the control mes-sages as the reference, each of the point estimates of the average effect ofthe influence strategies on book evaluations is positive. Empirical p-valuescomputed with draws from the posterior using MCMC.

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Scarcity

Figure 3.1: Comparison of heterogeneity in the effects of influence strategies withthe average effects of those strategies. The solid black vertical lines arethe estimated average effects of each strategy, as compared with the con-trol message. The black curves are the estimated normal distribution ofstrategy effects for the population, while the gray curves are the densityof the estimates of the strategy effects for this sample. Estimates are fromModel C.

positive. The analysis shows that for 41.3% (95% CI [35.8,45.3] 2) of theparticipants the estimated effect of consensus is negative.

Qualitatively, one can compare the different standard deviations presentedin Table 3.4: The estimated standard deviation of participants’ responses tobooks not accompanied by influence strategies (the intercept varying by per-son, �̂2

I ) is of similar magnitude as the standard deviation of the residuals�̂2

err. The same is true for the estimated standard deviation of participants’responses to books accompanied by each of the influence strategies. Thus, in

295% confidence intervals in brackets were computed using the Bayesian pigeonhole boot-strap with R = 1000 (Owen, 2007)

34

Estimate S.E t pIntercept 4.25 0.33 12.91 0.0002

Authority 0.37 0.15 2.51 0.0064Consensus 0.44 0.14 3.11 0.0020

Scarcity 0.06 0.14 0.43 0.6484

Table 3.3: Estimates of fixed effects in the preferred model. Using the control mes-sages as the reference, each of the point estimates of the average effect ofthe influence strategies on book evaluations is positive. Empirical p-valuescomputed with draws from the posterior using MCMC.

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Scarcity

Figure 3.1: Comparison of heterogeneity in the effects of influence strategies withthe average effects of those strategies. The solid black vertical lines arethe estimated average effects of each strategy, as compared with the con-trol message. The black curves are the estimated normal distribution ofstrategy effects for the population, while the gray curves are the densityof the estimates of the strategy effects for this sample. Estimates are fromModel C.

positive. The analysis shows that for 41.3% (95% CI [35.8,45.3] 2) of theparticipants the estimated effect of consensus is negative.

Qualitatively, one can compare the different standard deviations presentedin Table 3.4: The estimated standard deviation of participants’ responses tobooks not accompanied by influence strategies (the intercept varying by per-son, �̂2

I ) is of similar magnitude as the standard deviation of the residuals�̂2

err. The same is true for the estimated standard deviation of participants’responses to books accompanied by each of the influence strategies. Thus, in

295% confidence intervals in brackets were computed using the Bayesian pigeonhole boot-strap with R = 1000 (Owen, 2007)


sales. However, you should not use just any trick in the book, and it pays off to get to know your customers and select the right strategy. But, how do you do this?

Off course there are questionnaires to measure which strategy might work [11]. However, these are cumbersome to administer to all of your customers. And, probably customers might themselves not really know which strategies work for them and which wont, which makes it hard to tell you by filling out a questionnaire. There is another method though: You can measure

how your customers respond to different persuasion strategies. As long as you can identify your customer, represent different persuasion strategies to support your products, and measure whether or not the strategy was a success, you can start selecting strategies based on your customers past behavior. Obviously, its not super easy to make the right

choices. What if you have never seen that customer before, which strategies do you show? If the first try is not successful, do you show another strategy? Or do you try again? Basically: how do you select the strategies in such a way that you optimize conversion. Luckily advances in BigData storage, Bayesian

Statistics and Multi Level Modelling1 allow you to optimize the selection of strategies in real-‐time. Thus while your customer browses your e-‐commerce site, you continuously select the exact right strategy for that customer! Obviously, this begs trying out. This is exactly what

has been done a few times now [12, 11]. So lets look at the results. Figure 3 shows the conversion rate of an affiliate online store over 3 months time. The solid line (the bottom one) shows the conversion of the original store. The dashed line (the top one) shows the conversion of the e-‐commerce store while dynamically selecting persuasion strategies based on the behavior

1 Sorry, we will discuss those in our next white paper.

of its customers. Conversion rates upped from about 10%, to over 13%. That’s quite impressive isn’t it?2

Question 5: What are Persuasion Profiles So, if you monitor the behavior of your customers,

and log their responses to persuasive strategies, you can greatly improve your conversion. You can, in real-‐time, and perhaps with some help of people that know the math’s behind it, optimize the selection of influence strategies for each of your customers. This might sound a bit like selecting the right

product for your customer – like you do with behavioral targeting, or recommender systems. However, it is not. It is bigger. You can create a profile for each of your customers

that describes how he or she responds to different persuasion strategies. This profile – the Persuasion Profile – is exactly what PersuasionAPI creates, updates, and manages. A distinct Persuasion Profile, indicating the likelihood that a strategy works and how certain you are about this, is kept for each individual customer (Figure 4). The Persuasion Profile is useful not just in your

online store: It can tell you how to approach your customers in your direct mails, or how to talk to them in your call-‐center. The Persuasion Profile details which persuasive strategies appeal most to your customers – information you can use across many

products and channels.

The Marketing Currency of the Future PersuasionAPI provides you with an API that allows

you to log the responses of your customers to persuasion principles, used in multiple channels for multiple product. Next, PersuasionAPI will advice, in real time, which persuasion strategy to use next. PersuasionAPI selects persuasion strategies for each

2 Some people say that is an increase of 30%. Some

erroneously say its 130%. We don’t.

Figure 3: Comparison of a static e-‐commerce website with one using adaptive persuasive strategies. The e-‐commerce site powered by PersuasionAPI clearly outperforms the static

(holdout) version of the website.

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No strategy - holdoutPersuasion API - test

Figure 8.8: Comparison of estimated conversion rates for consumers in the holdoutcondition — those visiting the no strategy version of the website — andconsumers in the test condition — those looking at the adaptive persua-sive system.

8.5 ConclusionsThis Case Study chapter presented the design and evaluation of three adaptivepersuasive systems. For each of the systems different means of identification,representation, and effect measurement were used to enable creation and us-age of dynamic persuasion profiles. Without exception in each system userswith distinct responses to distinct social influence strategies could be iden-tified, and these differences were attended to by the systems. For the lattertwo of the three case studies attending to these differences led to a signifi-cant increase in compliance — either docking the activity monitor or orderingproducts online. These case studies thus are (a) exemplars of implementationsof adaptive persuasive systems that use dynamic persuasion profiles and (b)strengthen the results brought forward in the previous chapters that personal-ized persuasion indeed increases compliance.

The three designs presented in this case study chapter, contrary to thestudies presented in CS I, used operative measures of susceptibility to per-suasion to dynamically derive persuasion profiles. The two methods of pro-filing presented in the two CS chapters are however easily combined: Meta-judgmental measures of susceptibility as obtained using the STPS can be usedas a starting point for a dynamic profile, instead of using the average responseto a distinct strategy as done in the designs presented in the current chapter.Meta-judgmental measures can thus be used to (partly)overcome the cold-startproblem that many learning algorithms face (Lam et al., 2008), while dynamicadaptation can overcome changes in users responses to social influence strate-

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No strategy - holdoutPersuasion API - test

Figure 8.8: Comparison of estimated conversion rates for consumers in the holdoutcondition — those visiting the no strategy version of the website — andconsumers in the test condition — those looking at the adaptive persua-sive system.

8.5 ConclusionsThis Case Study chapter presented the design and evaluation of three adaptivepersuasive systems. For each of the systems different means of identification,representation, and effect measurement were used to enable creation and us-age of dynamic persuasion profiles. Without exception in each system userswith distinct responses to distinct social influence strategies could be iden-tified, and these differences were attended to by the systems. For the lattertwo of the three case studies attending to these differences led to a signifi-cant increase in compliance — either docking the activity monitor or orderingproducts online. These case studies thus are (a) exemplars of implementationsof adaptive persuasive systems that use dynamic persuasion profiles and (b)strengthen the results brought forward in the previous chapters that personal-ized persuasion indeed increases compliance.

The three designs presented in this case study chapter, contrary to thestudies presented in CS I, used operative measures of susceptibility to per-suasion to dynamically derive persuasion profiles. The two methods of pro-filing presented in the two CS chapters are however easily combined: Meta-judgmental measures of susceptibility as obtained using the STPS can be usedas a starting point for a dynamic profile, instead of using the average responseto a distinct strategy as done in the designs presented in the current chapter.Meta-judgmental measures can thus be used to (partly)overcome the cold-startproblem that many learning algorithms face (Lam et al., 2008), while dynamicadaptation can overcome changes in users responses to social influence strate-

Figure 4: Graphical representation of the Persuasion Profile used by PersuasionAPI.

89

Estimated effect

AuthorityCommitmentConsensus

LikingReciprocityScarcity

-0.5 0.0 0.5

Figure 6.1: Example of a persuasion profile

of the effect of this strategy are relatively uncertain. A persuasion profile en-sures that designers can attend to individual differences and can choose socialinfluence strategies. Persuasion profiles can be based on peoples self-reportedresponses to social influence strategies or constructs relating to social influ-ence strategies (meta-judgmental measures) or peoples actual behavioral re-sponses to social influence strategies (operative measures) (Bassili, 1996b).Chapter IG III explored different meta-judgmental means of creating a per-suasion profile.

The STPS presented in IG III presents a validated 26-item scale to de-termine people susceptibility to different social influence strategies a prioriusing meta-judgmental measures. The scores on the STPS directly indicatepeoples susceptibility to each of the six social influence strategies identifiedby (Cialdini, 2001) and as such can be directly used by designers of persua-sive systems to attend to individual differences and choose social influencestrategies. In the following Case Study chapter (Chapter 7) the applied valueof profiles based on measurements obtained using the STPS for health relatedinterventions is assessed.

Next to using meta-judgmental measures to build a persuasion profile, theprofile can also be build, or updated, by observing behavioral responses ofusers to different social influence strategies and thus obtaining operationalmeasures. This approach allows designers to create dynamic adaptive per-suasive systems. Persuasive Technologies likely benefit from an approach inwhich both sources of information about users are combined to obtain accu-rate conditional estimates.

89

Estimated effect

AuthorityCommitmentConsensus

LikingReciprocityScarcity

-0.5 0.0 0.5

Figure 6.1: Example of a persuasion profile

of the effect of this strategy are relatively uncertain. A persuasion profile en-sures that designers can attend to individual differences and can choose socialinfluence strategies. Persuasion profiles can be based on peoples self-reportedresponses to social influence strategies or constructs relating to social influ-ence strategies (meta-judgmental measures) or peoples actual behavioral re-sponses to social influence strategies (operative measures) (Bassili, 1996b).Chapter IG III explored different meta-judgmental means of creating a per-suasion profile.

The STPS presented in IG III presents a validated 26-item scale to de-termine people susceptibility to different social influence strategies a prioriusing meta-judgmental measures. The scores on the STPS directly indicatepeoples susceptibility to each of the six social influence strategies identifiedby (Cialdini, 2001) and as such can be directly used by designers of persua-sive systems to attend to individual differences and choose social influencestrategies. In the following Case Study chapter (Chapter 7) the applied valueof profiles based on measurements obtained using the STPS for health relatedinterventions is assessed.

Next to using meta-judgmental measures to build a persuasion profile, theprofile can also be build, or updated, by observing behavioral responses ofusers to different social influence strategies and thus obtaining operationalmeasures. This approach allows designers to create dynamic adaptive per-suasive systems. Persuasive Technologies likely benefit from an approach inwhich both sources of information about users are combined to obtain accu-rate conditional estimates.


of your individual customers to optimize your conversion. Persuasion Profiles are valuable information about

your customers. Building a detailed Persuasion Profile, and combining it with your other marketing efforts and intelligence, will help you beat your competition.

Further Reading [1] R. Cialdini, Influence, Science and Practice. Boston: Allyn & Bacon, 2001. [2] R. B. Cialdini and N. J. Goldstein, “Social influence: compliance and conformity.,” Annual Review of Psychology, vol. 55, no. 1974, pp. 591-621, 2004. [3] S. S. Komorita, J. A. Hilty, and C. D. Parks, “Reciprocity and Cooperation in Social Dilemmas,” Journal of Conflict Resolution, vol. 35, no. 3, pp. 494-‐518, 1991. [4] T. M. M. Verhallen and H. S. J. Robben, “Scarcity and preference: An experiment on unavailability and product evaluation,” Journal of Economic Psychology, vol. 15, no. 2, pp. 315-‐331, 1994. [5] S. Milgram, Obedience to Authority. London: Tavistock., 1974. [6] T. H. Freling and P. A. Dacin, “When consensus counts: Exploring the impact of consensus claims in advertising,” Journal of Consumer Psychology, vol. 20, no. 2, pp. 163-‐175, Apr. 2010. [7] R. E. Guadagno, T. Asher, L. J. Demaine, and R. B. Cialdini, “When Saying Yes Leads to Saying No: Preference for Consistency and the Reverse Foot-‐in-‐the-‐Door Effect,” Personality and Social Psychology Bulletin, vol. 27, no. 7, pp. 859-‐867, 2001. [8] M. C. Kaptein, S. Duplinsky, and P. Markopoulos, “Means based adaptive persuasive systems,” in Proceedings of the 2011 annual conference on Human factors in computing systems, 2011, pp. 335-‐344. [9] M. C. Kaptein, S. Duplinsky, and E. M. Go, “Simultaneous Usage of Multiple Influence Strategies in Online Marketing,” Submitted to: International Journal of Internet Marketing and Advertising, 2012. [10] M. C. Kaptein and D. Eckles, “Magnitude and Structure of Heterogeneity in Responses to Influence Strategies,” Journal of Interactive Marketing, vol. IN PRESS., 2012. [11] M. C. Kaptein, B. de Ruyter, P. Markopoulos, and E. Aarts, “Tailored Persuasive Text Messages to Reduce Snacking.,” Transactions on Interactive Intelligent Systems, vol. IN PRESS., 2011. [12] M. C. Kaptein, “Adaptive Persuasive Messages in an E-‐commerce Setting: The use of Persuasion Profiles,” in Proceedings of ECIS 2011, 2011.

Technology

The Art of Persuasion