All posts by annahaffmans

Finding your way in a review rollecoaster: review analysis

You know that feeling, trying to find that one, perfect coffee machine on Amazon by scrolling through tons of reviews to find the one best suited to your needs?  Think about it. If we as individual consumers are having difficulties browsing through the reviews to find those that add value, imagine the difficulty companies must have in analysing those large amounts of reviews.

The above explained difficulty occurs mainly due to what we call the ‘4 V’s of Data’: volume, variety, velocity and veracity (Salehan and Kim, 2015). That’s where the paper ‘predicting the performance of online consumer reviews: a sentiment mining approach to big data analytics’ by Salehan and Kim (2015) comes in. The paper looks at the predictors of both readership and helpfulness of online consumer reviews (OCR). Using different techniques the paper aims to create an approach that can be adopted by companies to develop automated systems for sorting and classifying large amounts of OCR. Sounds exciting, doesn’t it?! Let’s have a look at how this works.

What this paper is about

Whereas previous literature focusses at the factors that determine the perceived helpfulness of a review, this paper takes a step back. It starts by considering the factors that determine the likelihood of a consumer paying attention to a review in the first place, since without reading a review you cannot determine its helpfulness. Hence the research questions are as follows:

Research Question 1: Which factors determine the likelihood of a consumer paying attention to a review?
Research Question 2: Which factors determine the perceived helpfulness of a review?

In order to answer the  research questions, the paper looks at a sample of 2616 Amazon reviews and considers several factors they believe may impact review readership, helpfulness, or both. Readership is measured as the total number of votes (helpful and not helpful), whereas helpfulness is measured as the proportion of helpful votes out of total votes. Since I see no reason to bore you with detailed methodologies, I made a quick and easy to follow overview of the different factors the paper considers using a random Amazon review as an example:



  • Longevity is measured as the number of days since the review was created. It has a positive effect on readership, meaning older reviews are more likely to be read. Whereas this may sound counterintuitive, this could simply occur due to the way in which Amazon sorts the reviews, since by default users view reviews with most helpful votes first, unless they change the setting to viewing the most recent review first.
  • Review – & title sentiment is measured by conducting sentiment analysis on the review content, which scores a review depending on how emotional the content is (either positive or negative). Both have a small, negative effect on helpfulness, which indicates that consumers perceive emotional content to be less rational and therefore less useful. These findings are somewhat different from previous research, which showed that reviews carrying a strong negative sentiment have a stronger impact on buyer behaviour than positive or neutral reviews.
  • Title length has a small, negative effect on readership meaning that a reviews with longer titles are less likely to be read.
  • Review length has a large, positive effect on both readership and helpfulness, meaning that longer reviews are read more and receive more helpfulness votes on average.

All above outlined findings are statistically significant. Whereas previous research focussed mainly on numerical rating and length of the review, this paper looks at the textual information the review contained. This means that the practical implementations are high. For example, the paper suggests that companies may use sentiment data to analyse large amounts of OCR which are constantly produced on the Internet. The paper also showed the importance of the title: make it short and not too emotional. This is something e-commerce companies can guide their customers in when writing a review.


In my opinion, a large limitation of this paper is that they use the number of ‘total votes’ as the number of times a review was read. I don’t know about you, but I certainly don’t hit the vote button every time I read a review. Hence I think using a different methodology might be better. For example you, could track customers as they move over a page, note how long they spend at the review and count the review to be ‘read’ if the this time was anywhere between e.g. 20 and 50 seconds (since you don’t want to count people that simply left the page open).

How about in practice?

This sounds great, but are there actually companies out there using similar approaches to make the life of their customers easier? A company that does this very well is Coolblue. Their aim is to be the most customer centric company of the Netherlands (Coolblue, 2018) and hence they go even further than described in the paper. Their product page contains an overview of the pros and cons for the product to allow for an easy overview. Whether these pros and cons come from frequently placed customer reviews isn’t clear. Moreover, they ask customers to fill in the pros and cons, so that customers looking to buy don’t need to read through long, unstructured sentences. Lastly, they use the review helpfulness to rank reviews according to relevance.


Salehan, M., & Kim, D. J. (2016). Predicting the performance of online consumer reviews: A sentiment mining approach to big data analytics. Decision Support Systems, 81, 30-40.

Coolblue, 2018. Yearbook 2017, Accessed via

Peaks: investing your change

Do you feel demotivated seeing your interest fall to 0.05%? Would you like to invest your money but do you not know where to start? This is where Peaks comes in. Peaks believes that investing should be for everyone, not only for the happy few!

How does it work?
Peaks digitally collects your change by rounding up all your purchases to whole euros. Let’s assume you bought a coffee for €2,40, Peaks will then automatically transfer €0,60 to your investment account. If you don’t want to provide Peaks access to your account, you can also choose to transfer let’s say €1, – a day to your investment account. The Peaks App provides an easy overview of the amount saved and the current value of your investments. All you have to do is determine the amount of risk you would like to take choosing from four risk levels, where a higher risk corresponds to a higher expected return. The video below outlines the idea behind Peaks, although it’s in Dutch, I’m sure you’ll be able to follow.

Business model
The Peaks business model is based on joint profitability of both the investors and the company itself. The investors benefit since they are able to invest small amounts of money, which they would otherwise not have been able to invest due to the high one-off investment costs charged by funds (Peaks, 2017). Since many investors invest small amounts of money, Peaks can share investment costs among this large group.  Peaks itself on the other side, benefits from profits the investor makes in two ways. First, higher profit motivates investors to invest more money, resulting in an increase in fees being paid to Peaks. Secondly, higher profit motivates new customers to join the platform. With regard to switching costs, there are alternative investment platforms which accept small investments, such as ‘Semmie’. However, since Peaks is a startup financed by Rabobank, its integration with the Rabobank banking system differentiates it from competitors. According to Peaks, integration with other banks will follow soon. Hence, switching costs in terms of convenience are quite high for Rabobank customers yet slightly lower for customers of other banks.

In terms of institutional arrangements, power given to investors is limited. An interesting comparison is Dell computer. Research looked at the degree of user design customization that was optimal when selling a computer. Turns out that the majority of people actually had no idea about the technical specifications of a computer. Letting customers customize their computer using technical design parameters hence resulted in a ‘design defect’: a choice of design parameters that does not maximize user satisfaction (Randall T., 2005). Such design defects can be mitigated by using ‘needs based interfaces’: instead of asking would you like “512MB,DDR,333MHz 2 Dimms or a “512MB,DDR,333MHz 1 Dimm” memory they would ask “do you find the performance of program X important”. In a way, the same can be applied to investing in the stock market. Since the target group of Peaks is unfamiliar with investing money and has little knowledge about the financial market, they are given only a limited degree of freedom. The only question investors are asked is, ‘how much risk would you like to take’?  Based on this level of risk, four different funds are possible: mild, spicy, very spicy and hot. According to your risk preference, the ratio of stocks versus bonds is set automatically. Using this ‘needs based’ approach rather than a ‘parameter based’ approach limits the chance of a design defect occurring and hence increases user satisfaction (Randall T., 2005).

Looking at the institutional environment, the social norms dimension is particularly applicable. Peaks plays into the current movement of Corporate Social Responsibility by banning funds that invest in controversial businesses such as weapons, alcohol/tobacco production and pornography. In terms of polity and judiciary dimensions, Peaks has a permit and is being supervised by the Dutch Authority for Financial Markets. (Peaks, 2017)

Peaks 2

Sounds great! Why not invest?
Empowering everyone rather than just the happy few to get a return on their savings sounds like a great cause. Moreover, expected returns in terms of percentages sound decent. However, after deducting the yearly costs, returns of some of the portfolios become negative. Considering that people investing at Peaks usually have little affinity with investing money, having such low transparency is appalling. For example, if you would like to invest for a period of 1 year with an investment of €30 a month your returns would be as follows (Drabbe, 2018):

  • Mild portfolio: – €3,79
  • Spicy portfolio: – €2,02
  • Very Spicy portfolio: – €0,10
  • Hot portfolio: €1,82

In conclusion, if you want to make money using Peaks you’re going to have to invest more than €30,- a month, which contradicts the whole idea of investing your change. It’s a shame since I believe the idea has a lot of potential! What are your thoughts on the topic? Would you invest your money using Peaks?


Carson S. J., D. T. (1999). Understanding Institutional Designs Within Marketing Value Systems. Journal of Marketing Vol 63, 115-130.

Drabbe, M. (2018). Peaks: beleggen met je wisselgeld. Retrieved from Consumentenbond:

Peaks. (2017). De app. Retrieved from Peaks:

Randall T., T. C. (2005). Principles for User Design of Customized Products. California Management Review.