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Pedro Amorim highlights how customers typically order multiple things from different sources online.

Image courtesy of interviewee. February 5, 2024

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This video video is a transcription of a discussion about a study on consumer preferences in e-commerce, particularly regarding delivery methods. The speaker highlights the challenges retailers face in balancing speed of delivery and customer satisfaction. Many retailers prioritize rapid delivery, but the study investigates whether customers actually value consolidated deliveries—receiving multiple items together—even if it takes longer.

The research employed a natural experiment involving an online fashion marketplace that occasionally delivered orders either consolidated or split. Using granular data collected over two years, the study focused on the impact of these delivery methods on the likelihood of product returns. The findings revealed that customers preferred receiving their orders consolidated, resulting in lower return rates, which is significant for retailers facing high costs associated with returns.

This video video preference is explained through psychological mechanisms, particularly the "need for closure," where receiving items together reduces mental tension and frustration associated with staggered deliveries. The study emphasizes the importance for retailers to reevaluate assumptions about customer preferences and to better orchestrate their supply chain to increase consolidated deliveries without incurring excessive costs.

Key managerial implications include the need for data-driven approaches to understand customer preferences and to orchestrate supply chains effectively to enhance delivery methods that align with these insights.

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Here are some similar articles that delve into the topics of consumer preferences in e-commerce delivery methods, the psychology behind those preferences, and the implications for retailers:

1. **"Consumer Preferences for Delivery Methods: A Psychological Perspective"**
- This article discusses how various psychological factors, including the need for closure and cognitive load, influence consumer preferences for delivery methods in e-commerce. It highlights research findings that suggest customers prefer consolidated deliveries when shopping online.

2. **"The Impact of Delivery Speed on Customer Satisfaction and Return Rates"**
- This study examines the trade-offs between delivery speed and overall customer satisfaction, similar to the referenced discussion. It analyzes a variety of e-commerce delivery strategies and their effects on product returns and customer loyalty.

3. **"E-commerce Delivery Strategies: Balancing Speed and Efficiency"**
- This article explores different delivery strategies that e-commerce retailers can adopt to balance promptness and customer preferences. It emphasizes the importance of understanding customer feedback and data analysis in shaping delivery policies.

4. **"Consolidated vs. Staggered Deliveries: An E-commerce Dilemma"**
- This research article investigates the consumer behavior associated with receiving consolidated shipments versus staggered deliveries. It provides insights into how delivery preferences relate to overall shopping experiences and return rates.

5. **"Supply Chain Management in E-commerce: Aligning Operations with Customer Preferences"**
- This article discusses the importance of aligning supply chain operations with customer delivery preferences as gleaned from consumer data. It covers strategies for implementing more efficient delivery methods without increasing costs substantially.

6. **"The Role of Delivery Experience in Online Shopping: Customer Preferences Explored"**
- This article reviews the role of delivery in the overall online shopping experience and identifies key factors that influence how consumers perceive delivery effectiveness and satisfaction.

7. **"Data-Driven Strategies for Enhancing Online Retail Deliveries"**
- This piece emphasizes the necessity for e-commerce retailers to adopt data-driven methods to tailor their delivery strategies. Insights from consumer behavior studies are highlighted to optimize delivery processes.

Each of these articles builds on the themes presented in your original video: consumer preferences, delivery methods, psychological factors, and the implications for retailers in the e-commerce landscape.

X-TIMESTAMP-MAP:LOCAL=00:00:00.000,MPEGTS=900000

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The context from the study actually started a bit

00:03.005 --> 00:04.765
before this, this study.

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Um, so I've been interested in this, landscape of,

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e-commerce, retail

00:09.905 --> 00:12.405
and, in particular less mile distribution

00:12.405 --> 00:14.085
because as we know, a lot

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of retailers are actually struggling to, you know,

00:19.085 --> 00:20.485
square their profit

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and loss statements, across different, sectors,

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you know, grocery fashion and, and other, and other, and,

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and electronics and consumer goods, whatever.

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Uh, and one thing that we have, you know,

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understood while we were talking with these retailers

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and trying to understand how we can help them to actually,

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you know, have a better business, we understood

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that a lot of assumptions about what customers actually

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want were driving their decisions.

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Let me give you a very, straightforward example.

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Most of these retailers were betting on speed all the time.

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We need a fashion retailer.

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And they wanted to deliver, you know,

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like Farfetch is delivering Gucci bags in 20 minutes.

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Farfetch is like the one of the world luxury,

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largest luxury e-commerce players.

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And then when we look to food retailers like gorillas in

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Europe, they were delivering in 10 minutes food.

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So a lot of them were banging on speed.

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This is of course very expensive.

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And our observation is that they didn't really have,

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let's say, the, the, we didn't have the knowledge

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to understand that, do customers just value speed?

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Are there other things that they were valuing?

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And while, you know, investigating this question, we came

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to understand that, for example,

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we were, you know, giving priority

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to speed over consolidating goods when you have multiple

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packages arriving to your place.

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So that is one angle in which we saw, again

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and again speed, but I can give you others.

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We were, you know, again, putting

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priority on speed over precision, for example,

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or the precision of the slot in which you deliver.

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And we wanted to get a little bit deeper in really

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understanding is really speed the sink

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or could it be that actually re customers, sorry,

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could be actually be interested in something else,

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rather than speed.

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In this particular study, as I was saying,

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we were interested in investigating this difference between,

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or, or how, what was the priority that customers were giving

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between actually speed and consolidation?

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And just to be clear about what consolidation is,

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very often when you buy in websites like Amazon

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and things like that, what happens is that you actually,

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when you order more than one product,

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you end up receiving packages in different points in time.

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Uh, again, because retailers are prioritized speed.

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So what we wanted to do here was actually to have a setting

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a retailer that would actually allow us to study this.

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Uh, this is tough

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because it'll be hard to tell a retailer, well, from now on

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for this given amount of customers,

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you'll always send it consolidated even if later.

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And for another set of of customers you would send as fast

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as you can, but not consolidate it.

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You know, that's something that would be hard to enforce,

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kind of giving a real world experiment of how these things,

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would, would be, you know, would we have

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what we have to have to actually, you know,

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measure the effect that we wanted?

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What was, the impact of, consolidating versus speed?

03:19.025 --> 03:22.245
But we were lucky enough to actually find one retailer that

03:23.125 --> 03:26.645
randomly sometimes consolidated orders to customers.

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And sometimes they actually didn't consolidate these orders

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to customers and they were millions, you know, like

03:33.765 --> 03:37.045
thousands of, of orders that we could learn from

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and that we could actually kind of, we already had kind

03:40.005 --> 03:43.005
of a natural experiment that we, we were able

03:43.025 --> 03:45.485
to leverage into, our study.

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So basically we used two year granular data, okay?

03:49.245 --> 03:51.725
This was actually an online fashion marketplace

03:52.095 --> 03:56.045
where we were able to really have a causal inference.

03:56.105 --> 03:59.125
So we were able really to understand if consolidating

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is actually impacting the customer behavior.

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And the way we measure that customer behavior were actually

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what was the likelihood of return?

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So if we give the products consolidated

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or if we give them splits,

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but faster, was the customer more likely to return or not?

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So that was really the research question

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that we tapped into using this, this natural experiment,

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which was actually we were lucky enough to have a player

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that allowed us to have this quasi random sample of orders

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that we were able to, to actually exploit,

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in our approach basically.

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And so just to give a very visual, idea of

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what we did, we were able to match two others,

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which were exactly the same.

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Like if you have a evil twin

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and you were, you know, living on the same place

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as your evil twin, but you always got it, you know,

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consolidated, but you know, maybe, a little bit later

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and your twin was always able to receive it faster,

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but split order.

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So you are when received the trousers

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and the T-shirt together

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and your twin was receiving, its the separate,

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and we were not only having one twin of Sam,

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we were having the twin of Sam, the twin

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of Pedro, the twin of Jessica.

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So we were having a lot of twins that we were then, able

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to measure what was the impact?

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Was the even twin returning more

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or was the good twin re returning less?

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And that was actually what we measured in this,

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better together stu study.

05:29.865 --> 05:32.685
As you said, we came without, to be honest,

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to the study without not a lot of expectation.

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It was not so clear to us, how this would turn out

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because again, a lot of people were betting on speed.

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Were we really after shaping, I think, you know, so

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that was, how we actually started,

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but we ended up in a very different situation.

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We actually understood that customers prefer

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to receive it later,

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but consolidated that rather than faster,

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but split, we actually understood

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that you can lower about one percentage points

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of the return rate, which is very important.

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As you know, most of these retailers are struggling

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with billions of euros of returns skills, right?

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So you, this is,

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this can actually make a mark not only on the economic side

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but also on sustainability side of this business.

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But lemme focus again on the results.

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We were able to see that actually customers return less.

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That actually has an impact on the bottom line

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of these retailers

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because returning less means that you keep more of the sales

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that you actually made.

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We saw that an improvement of one point 23 percentage points

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when actually if you were to actually send the goods

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consolidated instead of split,

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it was puzzling for us, right?

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Because then, okay, why, why could it be the case?

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So entering in the mechanisms, as you said, and, and,

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and, and we have actually,

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so we are operations management professors.

06:56.835 --> 06:59.645
Basically the ones that were co-author these,

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these two papers, what we then went to, okay,

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but maybe we need a more more

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multidisciplinary approach here.

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So we went actually to dig into the psychology literature

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and to understand why, why could it,

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what could explain some pre, you know, the reversal of, of,

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the, the customer's preferences, right?

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Because they bought something

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and then they are, returning it.

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But that depends on the fact that they see it split

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or consolidate, okay?

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And we saw various different mechanisms

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that could actually help to explain.

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For example, we know from psychology literature

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that if you receive things staggered, they seem longer.

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So this could be actually an interesting aspect

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because then okay, in the end it's

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because you are perceiving it as being, you know,

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as taking more time.

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So there were a lot of mechanisms that we understood.

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And the great thing is that with our data, we were able

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to test this mechanism, okay?

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We were able to see if, for example,

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and focusing on this case of multiple orders, we could see

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that, you know, then if this mechanism was in place,

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having four splits would be worse than having two splits.

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And we didn't really get into that, conclusion.

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What we don't find was that found, sorry, was

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that there was another mechanism

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that could really explain that.

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And that mechanism is

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what we commonly call the need for closure.

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Let me give you an example. You have your to-do list, right?

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And we all feel some pressure while we are having

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these two go lists, right?

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While we don't really close some of the deals

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that we have there, there's always a mental, mental tension

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that we keep going on, with us with along the time.

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So that is actually on, the literature,

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the psychology literature.

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We call that the zagar effect.

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So that's an effect of having dismantle folder while you

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still have an active test that you didn't really conclude.

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Let's get back to our retail setting.

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You know, you ordered something at one time, your trousers

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and a blouse or you know, a shirt or whatever.

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One thing is that when you get it consolidated, you know,

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you, you scratch out of your to-do list right away.

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Whereas if you get it, your trousers today

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and your sweater in three days, you see,

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you still carry some mental tension along the way.

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And guess what? It turns out that

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that mental tension leads into some frustration

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and you just,

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return more than you will return if you get it,

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altogether consolidated, as I said in the beginning.

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So that was really the mechanism that we found

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that was driving the results.

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And that was very interesting

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because we were even to get one step, you know,

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in deeper and understanding

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that the more the goods are apart, so

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two situations you receive on day one

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and you receive on day three, or you receive in day one

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and on day five, if you do receive it one day, one

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and day five, you are more likely

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to return than if you would receive, you know, still split

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but closer together

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because you carry that tension more along the way.

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So I think the first managerial implication is that

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we are urging retailers to stop assuming things about

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how customers actually, what do you actually want?

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And you better, you know, find ways to actually measure

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that either like we did in our,

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I would call it more data-driven approach,

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but maybe even asking to be something as it starts to see

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where they, they actually are.

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Because what happens is that retailers are committing a lot

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of resources, spending a lot of money in things

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that actually may turn out not to be what customers want.

10:32.625 --> 10:35.625
So I would say that is a very broad first managerial insight

10:35.625 --> 10:37.865
that, you know, starts broadly important, right?

10:37.865 --> 10:41.385
Let's start that way, but going more specific into our,

10:41.415 --> 10:44.545
into our results, I think what we are really calling out is

10:44.545 --> 10:48.265
that retailers, they need to orchestrate their supply chain.

10:48.575 --> 10:51.665
It's not enough to really, you know, get an order

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and, you know, sense to the disease

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or to the suppliers that are feeling that orders,

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but there's a need of some orchestration

10:59.125 --> 11:01.465
and that orchestration needs to be such

11:01.815 --> 11:04.225
that you are increasing the likelihood of getting

11:05.585 --> 11:07.425
consolidated shipping to your customer.

11:07.775 --> 11:09.705
Okay? Where you are doing that consolidation,

11:09.985 --> 11:13.225
I think it'll depend a lot on the network structure

11:13.405 --> 11:15.225
of the retailer that we are talking with.

11:15.285 --> 11:17.825
So in our case, we were partnering with a marketplace,

11:17.925 --> 11:20.465
so basically they were connecting suppliers

11:20.465 --> 11:22.305
and customers with direct shipment.

11:22.815 --> 11:26.985
That means that maybe instead of sending right away the,

11:27.125 --> 11:29.905
the, the orders to be fulfilled with different suppliers,

11:30.315 --> 11:33.905
maybe they need to wait a bit in one hand or leave the,

11:33.965 --> 11:37.865
or, you know, or expedite the other to make sure that

11:38.485 --> 11:42.065
the likelihood of having a consolidated delivery at the end

11:42.155 --> 11:43.265
would, would increase.

11:44.205 --> 11:48.505
Of course, you can also enforce that, for example, talking

11:48.525 --> 11:50.465
to your third party logistics provider

11:50.485 --> 11:52.785
and say, Hey, everything that

11:52.785 --> 11:55.565
Goes to this customer, you hold it in your hub,

11:55.665 --> 11:57.605
you do whatever you want, but you consolidate.

11:57.625 --> 12:01.125
And by the way, some three pls are already doing that, like,

12:01.125 --> 12:03.405
for example, DHL of offer these services.

12:03.985 --> 12:05.365
But of course there's the cost to that.

12:05.425 --> 12:07.485
So I guess there's also the need

12:07.545 --> 12:10.005
and not to rush into, you know, doing things

12:10.005 --> 12:13.125
that then they don't pay off to balance the fact

12:13.125 --> 12:15.525
that you have actually low re lower returns

12:15.745 --> 12:17.725
to the adding costs that some

12:17.725 --> 12:20.405
of the solutions may actually have.

12:20.785 --> 12:23.645
So being it, so in a nutshell, you can, I think you,

12:23.825 --> 12:26.165
we are saying that you can do it in a more soft approach,

12:26.845 --> 12:28.485
orchestrating more the supply chain

12:28.505 --> 12:30.005
to increase the likelihood

12:30.085 --> 12:31.485
of getting a consolidated delivery

12:31.905 --> 12:34.725
or you can enforce it, orchestrating comes for free.

12:34.915 --> 12:37.605
It's more like putting some sort into your process

12:37.705 --> 12:39.125
and make sure that that happens.

12:39.625 --> 12:41.885
If you are going for like a full enforcement,

12:42.165 --> 12:44.885
I guess there's some additional calculations

12:44.885 --> 12:45.845
that you actually need to do.

00:00 - The context from the study actually started a bit before this, this study. so I've been interested in this, landscape of, e-commerce, retail and, in particular less mile distribution because as we know, a lot

00:14 - of retailers are actually struggling to, you know, square their profit and loss statements, across different, sectors, you know, grocery fashion and, and other, and other, and, and electronics and consumer goods, whatever. and one thing that we have, you know, understood while we were talking with these retailers and trying to understand how we can help them to actually, you know, have a better business, we understood that a lot of assumptions about what customers actually

00:46 - want were driving their decisions. Let me give you a very, straightforward example. Most of these retailers were betting on speed all the time. We need a fashion retailer. And they wanted to deliver, you know, like Farfetch is delivering Gucci bags in 20 minutes. Farfetch is like the one of the world luxury, largest luxury e-commerce players. And then when we look to food retailers like gorillas in Europe, they were delivering in 10 minutes food.

01:11 - So a lot of them were banging on speed. This is of course very expensive. And our observation is that they didn't really have, let's say, the, the, we didn't have the knowledge to understand that, do customers just value speed? Are there other things that they were valuing? And while, you know, investigating this question, we came to understand that, for example, we were, you know, giving priority to speed over consolidating goods when you have multiple

01:42 - packages arriving to your place. So that is one angle in which we saw, again and again speed, but I can give you others. We were, you know, again, putting priority on speed over precision, for example, or the precision of the slot in which you deliver. And we wanted to get a little bit deeper in really understanding is really speed the sink or could it be that actually re customers, sorry, could be actually be interested in something else,

02:08 - rather than speed. In this particular study, as I was saying, we were interested in investigating this difference between, or, or how, what was the priority that customers were giving between actually speed and consolidation? And just to be clear about what consolidation is, very often when you buy in websites like Amazon and things like that, what happens is that you actually, when you order more than one product, you end up receiving packages in different points in time.

02:35 - again, because retailers are prioritized speed. So what we wanted to do here was actually to have a setting a retailer that would actually allow us to study this. this is tough because it'll be hard to tell a retailer, well, from now on for this given amount of customers, you'll always send it consolidated even if later. And for another set of of customers you would send as fast as you can, but not consolidate it. You know, that's something that would be hard to enforce,

03:04 - kind of giving a real world experiment of how these things, would, would be, you know, would we have what we have to have to actually, you know, measure the effect that we wanted? What was, the impact of, consolidating versus speed? But we were lucky enough to actually find one retailer that randomly sometimes consolidated orders to customers. And sometimes they actually didn't consolidate these orders to customers and they were millions, you know, like thousands of, of orders that we could learn from

03:37 - and that we could actually kind of, we already had kind of a natural experiment that we, we were able to leverage into, our study. So basically we used two year granular data, okay? This was actually an online fashion marketplace where we were able to really have a causal inference. So we were able really to understand if consolidating is actually impacting the customer behavior. And the way we measure that customer behavior were actually what was the likelihood of return?

04:07 - So if we give the products consolidated or if we give them splits, but faster, was the customer more likely to return or not? So that was really the research question that we tapped into using this, this natural experiment, which was actually we were lucky enough to have a player that allowed us to have this quasi random sample of orders that we were able to, to actually exploit, in our approach basically. And so just to give a very visual, idea of

04:38 - what we did, we were able to match two others, which were exactly the same. Like if you have a evil twin and you were, you know, living on the same place as your evil twin, but you always got it, you know, consolidated, but you know, maybe, a little bit later and your twin was always able to receive it faster, but split order. So you are when received the trousers and the T-shirt together

05:04 - and your twin was receiving, its the separate, and we were not only having one twin of Sam, we were having the twin of Sam, the twin of Pedro, the twin of Jessica. So we were having a lot of twins that we were then, able to measure what was the impact? Was the even twin returning more or was the good twin re returning less? And that was actually what we measured in this, better together stu study.

05:29 - As you said, we came without, to be honest, to the study without not a lot of expectation. It was not so clear to us, how this would turn out because again, a lot of people were betting on speed. Were we really after shaping, I think, you know, so that was, how we actually started, but we ended up in a very different situation. We actually understood that customers prefer to receive it later, but consolidated that rather than faster,

05:57 - but split, we actually understood that you can lower about one percentage points of the return rate, which is very important. As you know, most of these retailers are struggling with billions of euros of returns skills, right? So you, this is, this can actually make a mark not only on the economic side but also on sustainability side of this business. But lemme focus again on the results. We were able to see that actually customers return less.

06:24 - That actually has an impact on the bottom line of these retailers because returning less means that you keep more of the sales that you actually made. We saw that an improvement of one point 23 percentage points when actually if you were to actually send the goods consolidated instead of split, it was puzzling for us, right? Because then, okay, why, why could it be the case? So entering in the mechanisms, as you said, and, and,

06:53 - and, and we have actually, so we are operations management professors. Basically the ones that were co-author these, these two papers, what we then went to, okay, but maybe we need a more more multidisciplinary approach here. So we went actually to dig into the psychology literature and to understand why, why could it, what could explain some pre, you know, the reversal of, of, the, the customer's preferences, right?

07:19 - Because they bought something and then they are, returning it. But that depends on the fact that they see it split or consolidate, okay? And we saw various different mechanisms that could actually help to explain. For example, we know from psychology literature that if you receive things staggered, they seem longer. So this could be actually an interesting aspect because then okay, in the end it's

07:43 - because you are perceiving it as being, you know, as taking more time. So there were a lot of mechanisms that we understood. And the great thing is that with our data, we were able to test this mechanism, okay? We were able to see if, for example, and focusing on this case of multiple orders, we could see that, you know, then if this mechanism was in place, having four splits would be worse than having two splits. And we didn't really get into that, conclusion.

08:10 - What we don't find was that found, sorry, was that there was another mechanism that could really explain that. And that mechanism is what we commonly call the need for closure. Let me give you an example. You have your to-do list, right? And we all feel some pressure while we are having these two go lists, right? While we don't really close some of the deals that we have there, there's always a mental, mental tension

08:35 - that we keep going on, with us with along the time. So that is actually on, the literature, the psychology literature. We call that the zagar effect. So that's an effect of having dismantle folder while you still have an active test that you didn't really conclude. Let's get back to our retail setting. You know, you ordered something at one time, your trousers and a blouse or you know, a shirt or whatever. One thing is that when you get it consolidated, you know,

09:03 - you, you scratch out of your to-do list right away. Whereas if you get it, your trousers today and your sweater in three days, you see, you still carry some mental tension along the way. And guess what? It turns out that that mental tension leads into some frustration and you just, return more than you will return if you get it, altogether consolidated, as I said in the beginning. So that was really the mechanism that we found

09:31 - that was driving the results. And that was very interesting because we were even to get one step, you know, in deeper and understanding that the more the goods are apart, so two situations you receive on day one and you receive on day three, or you receive in day one and on day five, if you do receive it one day, one and day five, you are more likely to return than if you would receive, you know, still split

09:57 - but closer together because you carry that tension more along the way. So I think the first managerial implication is that we are urging retailers to stop assuming things about how customers actually, what do you actually want? And you better, you know, find ways to actually measure that either like we did in our, I would call it more data-driven approach, but maybe even asking to be something as it starts to see where they, they actually are.

10:23 - Because what happens is that retailers are committing a lot of resources, spending a lot of money in things that actually may turn out not to be what customers want. So I would say that is a very broad first managerial insight that, you know, starts broadly important, right? Let's start that way, but going more specific into our, into our results, I think what we are really calling out is that retailers, they need to orchestrate their supply chain. It's not enough to really, you know, get an order and, you know, sense to the disease

10:54 - or to the suppliers that are feeling that orders, but there's a need of some orchestration and that orchestration needs to be such that you are increasing the likelihood of getting consolidated shipping to your customer. Okay? Where you are doing that consolidation, I think it'll depend a lot on the network structure of the retailer that we are talking with. So in our case, we were partnering with a marketplace, so basically they were connecting suppliers

11:20 - and customers with direct shipment. That means that maybe instead of sending right away the, the, the orders to be fulfilled with different suppliers, maybe they need to wait a bit in one hand or leave the, or, you know, or expedite the other to make sure that the likelihood of having a consolidated delivery at the end would, would increase. Of course, you can also enforce that, for example, talking to your third party logistics provider and say, Hey, everything that

11:52 - Goes to this customer, you hold it in your hub, you do whatever you want, but you consolidate. And by the way, some three pls are already doing that, like, for example, DHL of offer these services. But of course there's the cost to that. So I guess there's also the need and not to rush into, you know, doing things that then they don't pay off to balance the fact that you have actually low re lower returns to the adding costs that some

12:17 - of the solutions may actually have. So being it, so in a nutshell, you can, I think you, we are saying that you can do it in a more soft approach, orchestrating more the supply chain to increase the likelihood of getting a consolidated delivery or you can enforce it, orchestrating comes for free. It's more like putting some sort into your process and make sure that that happens. If you are going for like a full enforcement,

12:42 - I guess there's some additional calculations that you actually need to do.

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