Kate Sinnwell

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Thank you so much for this blogpost! I was not aware of AirDNA and it seems to be a very interesting business. Especially how the Matching Algorithm is able to figure out double listings on different websites. I am wondering if it would be possible to expand the search easily over more websites, or if AirDNA is limited here. Another point I would worry about is how closely the company is connected to access to data. Regulations in many countries might get in the way of their analysis. Furthermore, they seem to be really focused on analyzing some companies closely, like AirBNB, so their success is bound to the success of other companies?

On November 29, 2022, Kate Sinnwell commented on IKEA’s Leap Forward with Data and AI :

Thank you so much for this blogpost! It is impressive how IKEA was able to increase relevant recommendations by so much and how they make use of AI. I am wondering how far IKEA could take their strategy with using AI. I have some reservations, because I believe that many customers go to IKEA, because they love to walk around the store, only need one thing, and buy some candles and a hotdog on the way out. For IKEA, I think it would be necessary to focus on staying offline as well and focus on the warehouses as that is what at least in my head IKEA still stands for. On the other hand, I can see that using AI can improve the customer experience in other areas, for example if they are looking for anything specific.

On November 29, 2022, Kate Sinnwell commented on ASOS: Optimizing customer preference using Machine Learning :

Thank you so much for this blog post! It is impressive how ASOS is using AI and machine learning to improve the customer experience. I am a little shocked how openly they discuss their AI system and approach. It makes me wonder how far the competition is with implementing something similar. Is this unique to ASOS and is it a competitive advantage or are other companies at the same level wir AI and ML? I am also wondering if they could use these models even more to personalize the user experience. For example – if a user always returns items due to the wrong size, they could offer a service of making sure that what the user buys actually fits. It might be possible to understand what could fit by analyzing what customers return and do not return. This would also help to decrease the number of returns.

Thank you so much for the post!
I think it is shocking to see that the AI is only showing men for this statement, even though the statement does not involve the word „men“. There is not much that it could have picked up on – which means „leaders“ is what it must have based the human component on. On top of the points mentioned in the post, I am surprised that the word leaders is only connected to male characters, because there are also female stories that made history.

On November 13, 2022, Kate Sinnwell commented on Is Dall-E really improving? :

Thank you so much for this post!
It is interesting to see how the AI defines these inputs. It seems like the AI does not have picked up diversity and variety. This might be based on the training set – but I wonder if this can still be changed now or if it is already too late. In that sense the question comes up at what point it is too late to change a bias in an AI or if one has to start from the beginning again.

Thank you so much for this post!
The insight into this cross-cultural experience is really interesting! The AI seems to have captured the problems of having a cross-cultural dinner. It seems like it just mixed both cultures without a clear line for how much of which culture or why mixing it. Overall, the AI did not understand the meaning of cross-culture in this sense, it just gave meal options from both cultures.

On November 1, 2022, Kate Sinnwell commented on Goodreads: A Platform for Readers and Authors :

Hi Michelle,
Thank you so much for this blogpost! I did not know Goodreads existed before reading this post.
I am wondering if they are reluctant to change the UI/UX experience, because so many of their loyal customers are already used to it and might not want to get used to a new interface. It is interesting to see that the users they have seem to stay with them even if they do not try to improve the user experience. It seems to me that the switching costs for users are quite high, as the recommendations would get better over time. On top of that if users use the platform to keep track of what books they read, it would be a pain to type in this information on a new platform. In terms of scalability I would therefore see an opportunity to build a platform with better UI/UX in a new geographical market which speaks a different language for example.

For Goodreads, I am wondering if focusing on existing users is a disadvantage long term. If they attracted the same kind of readers for example, they would miss out on capturing value on new categories of books and readers that were invented/developed. This would also mean that they become less diversified over time, which could influence the quality of their recommendations, or at least only make it usable for a specific customer group in my opinion.

On November 1, 2022, Kate Sinnwell commented on Too Good To Go – the social enterprise tackling global food waste :

Hi Irina,
Thank you so much for this blogpost! I know TGTG from living in Switzerland – where food prices in comparison to Germany are a lot higher. An interesting development I found is that TGTG in Switzerland seems to be well known by international students, and other young people not used to the swiss cost-level for food, especially meat products. I am wondering if this phenomenon is the same for other countries, or if it only works this way for Switzerland and this is a one-way effect (cheaper country to more expensive country —> people look for alternative options to buy food). It would be very interesting to see in which regions and countries TGTG attracts the most consumers, because if it really is a one-way effect I would also be worried about scalability.

The other thing that came up for me is the quality of the TGTG products. From experience, users develop a word-of-mouth network of which restaurants give out the biggest portions (price value) or the best quality food (important for Sushi TGTG) in their area. I am wondering how TGTG could tackle these community questions: Would they want to help users communicate on the platform and help certain restaurants become „Star TGTG sellers“ or would this create higher barriers to entry for new entrants. I think this comes down to the question of network effects and which side to focus on: building a great community and focusing on users to spark the business growth, or focusing on restaurants.

On November 1, 2022, Kate Sinnwell commented on mPharma – is a platform model always successful? :

Hi Yifei,

Thank you so much for this blogpost! I found it really interesting to read about mPharma. One of the points that stood out to me were the problems they had with the cold start. I wonder if they could have solved this problem by focusing on one customer group first, for example finding pharmacies that are able to give discounts in exchange for geographic and demographic knowledge. Or maybe loose money at the beginning by substituting the drug costs and prescription costs to spark network effects. Or maybe bulk orders could have been a solution if one town would need many of the same antibiotics for example. Another solution could have been to sell to local pharmacies in these regions instead of the customers directly maybe. I think it is interesting how the company pivoted to a data focus, which in my opinion has little to do with their original mission. I think what I am learning from reading this blogpost is how important the right kind of data has become in our society.

On October 4, 2022, Kate Sinnwell commented on Marriott: Data-driven Customer Experience for Decades at Scale :

Thank you so much for this blog post! It is really interesting to read about data in the hotel industry.
What was a focus for me in this post is how data should be used to measure customer experience, not only revenues. I wonder if this focus will result in a competitive advantage for Marriott, as smaller hotel chains may not have the same ability to collect and measure data.

It is interesting to see how they developed and changed strategy since the 70s. Especially the Dynamic Pricing Model seems to allow to maximize profit and distribute staff optimally. I wonder if there is also a downside, for example that new customer groups are more likely to be addressed because old customer groups are not willing to pay a different price for the same service.

One aspect that I find somewhat lacking at Marriott is transparency of data usage towards the customers. Why is the price being changed? How will my data be used? Especially the hacker attacks and data leaks intensify the urge for transparency and data security.

On October 4, 2022, Kate Sinnwell commented on Penguin Random House: Can it beat Amazon at its own game? :

Thanks for the blog post! I find this industry very exciting because it wasn’t built on data from the beginning, but is now making the change from data-less to data-driven.

It seems to me like Random House focuses on the book sales area with their data once the books hit the market. I wonder if it wouldn’t be exciting to write some of the books specifically for the market. To turn the process around, so to speak.
That would have the advantage of bringing users into the process as well. For example, do a survey with loyal customers on which topics they would like to see more in the future. These customers would then have even more incentive to purchase these books and Random House would have a more direct customer relationship. I would identify that as a competitive advantage over Amazon.

In addition, I wonder if it would not be possible to use the individual customer data to create customer profiles. This would allow Random House to make individual recommendations to each customer. Customers would then not have to search for books on Amazon, but would only have to click on buy in their personal mail recommendation. I imagine the lower the search costs for the customer, the higher the NPS would be.
This would give Random House an additional opportunity to understand their entire customer base. This would allow Random House to focus on an open-minded customer group and apply the process described above.

On October 4, 2022, Kate Sinnwell commented on Flo Health: Big Data in “FemTech” :

Thanks for the blog post! I find the topic of FemTech very exciting.
Learning that FloHealth shared data without consent makes me skeptical about data security in the FemTech space.
I wonder how users can be protected here. Users are forced to enter their data if they want to use the app. I wonder if it would also be possible to use the app for general information and tips – without having to enter specific data.
Here I see the next problem, because the app is confronted with very similar but very individual data. Since each body functions individually, I wonder how accurate the recommendations are – what part is based on general data, what part is based on my data to tailor recommendations.

Another issue I identify from the user perspective is a lock in effect. Once you start using FloHealth and data is collected you start perfecting your recommendations. The more data, the better they understand you, the better your recommendations. This makes the user vulnerable as they have high switching costs.

One more area which I think could be interesting for FloHealth to look into are urinary tract infections. Especially acute or chronic cystitis have big market potential and I wonder if any research has been done here.