All posts by dimitrakaiafa

Kérastase Hair Coach: the smart hairbrush

When it comes to hair, it not a secret that especially women spend big time. In U.K women for example spend around £350 per year on hair salons- excluding other hair treatments and hair products( Healthy shiny hair is the dream of every woman and they will try every product and treatment to achieve it. It appears Kerastase heard all these wishes, and recently introduced the Kérastase Hair Coach Powered by Withings.

How it works

The smart hairbrush incorporates numerous fancy technologies that analyze and monitor the hair’s health. The first step is the assessment of the hair condition. The hairbrush is able to provide a hair quality score. Essentially, the hairbrush is equipped with a microphone and conductivity sensors that make all the evaluation. On the one hand the embedded tiny microphone detects the sound of the brush tines working through the hair. Then these auditory data are transmitted to a mobile app, where frizziness, dryness, split ends and breakage is detected with the help of a smart algorithm. On the other hand, the conductivity sensors recognize whether hair is wet or dry (because sound waves of wet hair imitate the sound of smooth and less damaged hair). Then combined with the data accumulated by the microphone the app is able to offer a more accurate reading.

The second step is the estimation of  the brushing quality. With the help of elegant tools (accelerometer, a gyroscope, and 3 axis load cells) the brush detects your brushing patterns. Basically, it evaluates the force & rhytme when combing the hair, analyzes the gestures and counts the strokes. The info is once again sent to the application that in turn make specific recommendation. In particular, the app is able to provide insight on how to avoid damaging hair, how to improve brushing habits and how brush use impacts the quality of hair.

The final step is for users to receive personalized advices and product recommendations. The app even takes into account external factors (humidity, temperature, UV and wind) before combining it with the rest of the accumulated data. Hence, thorough insight and more accurate personal consulation is given to the consumers. Based on the users’ hair profile the app then proceeds to recommend the appropriate Kerastase products and routines.

Value co-creation

Users brush their way into co-creating value by providing the necessary data for the smart algorithm to make the diagnosis and to subsequently provide personalized recommendations. User’s brushing habits, hair quality and geo location data (for external factors such as weather) are gathered and drive the whole process. Successful diagnosis leads to better customized advice and more accurate product recommendations.

Kerastase benefits by potentially increasing its products sales and by better targeting consumers. The first source of profit is the hairbrush itself. Subsequently through the hairbrush and the app the company can satisfy the needs of its customers by offering personalized advice and recommending the right hair products to the right consumer (instead of depending on hairdresser to recommend and promote its products).

Efficiency criteria

Even though it is a smart brush, users will still use it the same way they would use any other brush. Hence it is an easy to use product and no extra amount of effort is required. The hairbrush however is pricy (at $200), which means that users should spend a respectful amount of money to acquire it. Nevertheless, on the long run users could potentially save money and time.Improving their hair’s health means less visits to hair salons and less expenses on hairdressers, hair treatments and on wrong products.

For the company the hairbrush might be a new source of income, provided that users embrace it and are willing to actually spend $200 on a hairbrush. If indeed users are lured to try it the company can subsequently drive further its hair products’ sales and its revenue. The generated revenue will then have to cover the R&D costs, which should be quite high since it is an innovative technological product. Last but not least, by exploiting the collected data the company can increase its insight and use them for future developments.

Overall, time will tell if users indeed will be impressed by this innovative technology, invest on it and engage with it. Compared to conventional brushes the Kerastase smart hairbrush is without a doubt substantially more expensive. However, consumers and predominantly women already spend around $80 for other styling tools (e.g. hair straighteners), hence compared to them the hairbrush does not seem that expensive. There are two key factors that would determine its success. Firstly, whether the company will succeed in convincing the consumers to spend more on the short run and save more on the long run. Secondly, it has to ensure that users will actually buy the products that the app recommends (users could easily turn to another brand to e.g.  buy a shampoo for damaged hair). Since Kerastase hair products fall in the luxurious category and are thus pricy, the app could perhaps include more economical products as well. These products should however belong to L’Oréal which is Kerastase’s parent company(something that might lead to product cannibalism however it could also increase the sales of the L’Oréal company products over those of its competitors).


Ideal Flatmate: how to avoid roomates from hell


Ideal Flatmate

“Imagine…after months of searching for the perfect apartment, you finally did it!!you found the perfect house, close to work, recently renovated and within the budget! It even has a roof terrace!!but oh wait…it also comes with a flat mate! who is sloppy… and forgets to pay the bills…and he ate your sandwich! your SANDWICH!!!”

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It is this kind of situations that Ideal Flatmate aims to eliminate.

Ideal Flatmate is a UK startup which takes house hunting to another level. In similar housing platforms, users would search among all the available properties or rooms trying to pinpoint the most appropriate one. On the other hand, landlords and flat sharers would upload a listing of their property (along with rules and preferences), hoping to increase the chances of renting it. Indeed, renting an apartment or room became easier with these platforms. However, what they could not guarantee was the success of cohabitation. The launch of Ideal Flatmate might mitigate this tricky situation. The platform came to shake things up, by adding a little of “matchmaking” in the mix. Users are asked to complete a 20-question survey (devised by 2 Cambridge professor) with questions focused lifestyle, living habits and personality traits. Then though a dating style algorithm, Ideal Flatmate pairs users with compatible flat mates. The idea behind it is not completely novel. Some roommate matching startups already exist in the US; although, they are targeted to students only. The platform is the first to enter the UK market and for the time being it is available for London flat sharers and landlords.

How it works


The perfect roommate is 5 steps way:

  1. Register
    • Users can either advertise their available room or search for one among the listings.
  2. Ideal Matching
    • Users fill in the on-site survey. It takes 20 questions for the personality matchmaking algorithm to find and suggest the most suitable potential flat mates.
  3. Get to know each other
    • Users can chat and discuss with their matches through the message board. They can also create groups to find people to fill larger properties.
  4. Meet up
    • Users are now ready to take that “relationship” further and arrange to meet face to face!
  5. Secure your place
    • Users can move in and start enjoying the shared living experience.

Efficiency Criteria

Users primarily save time. Finding the right place to leave is like searching for a needle in a haystack. That applies especially to the London rental market where supply is currently on the rise(,2017). Browsing through a dozen of listings and subsequently meeting with numerous applicants,is time consuming. The tailormade suggestions of the platform mitigate this issue. The invested effort is also low, the platform is easy to use and even the survey is simple and quick to fill in. Furthermore, users are relieved from the stress and discomfort of ending up with unsuitable flat mates. In general, the platform comes at a low cost for the majority of users since browsing properties and flat mates is free of charge. However, in order to contact potential flat mates, users have to pay a subscription (either £4.99 for a week or £8,99 for a month). The subscription fee is a little bit pricy, albeit is more economical than the fees of competitive platforms (e.g Rival Spareroom charges £10.99/week and £23.99/month for complete access to the listings).Last but not least, specifically for landlords, placing a listing is also cost free.

Letting agents apart from being advertised, they also enjoy a grace period, since they also pay no fee.

To this point, Ideal Flatmate has not reached its full potential when it comes to maximizing its pay offs. The only source of revenue comes from the users that opt for the full functionality subscription. The platform is also available only on the London market, so the pool of potential users(and subscriptions) is also limited. However, the platform is backed up by government seed funding,which has probably covered at least the development costs.

Value Co – Creation

  • Users co-create by providing all the necessary information to facilitate the house/flat mate hunting process. By completing the survey, both flat sharers and flat seekers, provide the data (property/personality/preference data) for the matchmaking algorithm to work. Provided that the algorithm is accurate in pairing the right people, the chances of finding the perfect property with the most suitable flat mate increase. On the other hand, it becomes possible for landlords and letting agents to pinpoint the appropriate tenants (and essentially avoid short term contracts or early terminated contracts).
  • Ideal Flatmate benefits either way. The company does not own any property. It is essentially the intermediary that facilitates the whole process, by connecting and matching landlords with tenants and flat seekers with flat mates. Moreover, an amount of revenue is guaranteed since they have a subscription model (the company will have an income even if users do not find what they wish for)


Regarding the institutional environment the company adhere to EU regulation (privacy,cookies,age limit and use of personal data).Since the company does not own any property it also not liable for any damage that was not foreseeable or caused by other user(Terms and Conditions).Overall,even though the idea might not be entirely unique,it is quite promising since it could solve a long-standing problem.To reach its full potential the company ought to start charging a fee for landlords’ listings and letting agents’ advertisements (company owners have stated that they will soon begin to do so). Currently, the platform is limited to the London market although within the year it is likely to be launched across the UK. If it proves successful in accurately matching people and Londoners indeed embrace it, the platform could potentially expand to other European capitals facing the same problem…hello Rotterdam!!


Ideal Flatmate wants to be a matchmaking platform for UK flatshares


Research Framework, Strategies, And Applications Of Intelligent Agent Technologies (IATs) In Marketing

What is an agent?

Anything that perceives its environment through sensors and in return acts upon it(Russell and Norvig 1995).

What is an intelligent agent? An agent that displays machine learning abilities.

Does perhaps Amazon Alexa, Apple’s Siri, Google Assistant, Microsoft’s Cortana ring a bell?

In “Research framework, strategies, and applications of intelligent agent technologies (IATs) in marketing applied” the authors attempt to define how are these intelligent agent technologies used in the context of marketing and how can marketers understand and exploiting them. First step towards that direction was to try and establish a marketing centric definition. Hence, Intelligent Agent Technologies are according to authors:

Systems that operate in a complex dynamic environment and continuously perform marketing functions such as:

  • dynamically and continuously gathering any data that could influence marketing decisions
  • analyzing and learning from data to provide solutions/suggestions
  • implementing customer-focused strategies that create value (for customers and firms)

The second step was to classify all marketing applications of IATs in a way that would demonstrate relationships and differences among them. A useful and understandable tool for researchers and managers, the proposed marketing taxonomy is depicted below:



To answer all these research questions the authors reviewed the existing literature and then conducted 100 in depth interviews with managers form 50 randomly selected companies. Two independent researchers analyzed the interview data, that were then used to shape the taxonomy and the below framework.They also made some propositions that would help researchers and mainly managers to utilize IATs and ultimately drive company performance.


Overall, implementing the right IAT can assist the progress of numerous marketing functions permitting companies to achieve a sustainable competitive advantage. Both firms and customers can benefit from them. Companies are in a position to understand and put customers’ interest first (through collaborative filtering, personalization, recommendation systems) and in return gain customer loyalty and trust.On the other hand IATs offer consumers value, by providing them with convenience, better information, customized selection and less information overload (e.g price-comparison engines or agents that configure and customize their computer systems on the basis of their preferences).

Strengths and Weaknesses:

%ce%b4%ce%b9%ce%b1%cf%86%ce%ac%ce%bd%ce%b5%ce%b9%ce%b11Since there was no concrete research or a fully developed theory surrounding IATs in marketing and subsequently no certain phenomena or existing theoretical frameworks to test, the authors rightfully opted for the grounded theory approach.So in contrast to the traditional research method they tried to construct a theory by discerning which ideas and concepts are repeatedly used in the interview data. These patterns were then grouped into categories that formulated their theory and shaped both the taxonomy and the framework.


Although the authors reasonably based their analysis on grounded theory, whether they applied it correctly is another question. The fact that they reviewed the existing literature in order to formulate the interview questions somehow conflict with the grounded theory methodology. The goal of this approach is to discern natural patterns. However, the used questionnaires possibly inhibited this since they kind of predisposed the managers’ answers since the queries were literature related.

%ce%b4%ce%b9%ce%b1%cf%86%ce%ac%ce%bd%ce%b5%ce%b9%ce%b13Given further progress in recommender systems (or other means of reducing costs for the customer), a situation might arise in which a “ready-made” solution provided by the system delivers higher preference fit than a customer-designed product—which, on the other hand, delivers the advantage of enabling “I designed it myself” feelings.” (Franke, N., Schreier, M. and Kaiser, U, 2010). This poses a very serious question for companies. When it is preferable to let an agent customize, decide or recommend a product/website? How quickly and how frequently should the agents respond and adjust to user needs? Ultimately what is more beneficial for both parties, implementing agents or give consumers the freedom to tailor products and and websites to their needs. Perhaps, technological advancements and the machine learning capabilities of IATs could soon enable companies them to successfully distinct these two categories of consumers and accordingly present them the proper interface.



Franke, N., Schreier, M. and Kaiser, U. (2010). The “I Designed It Myself” Effect in Mass Customization. Management Science, 56(1), pp.125-140.

Kumar, V., Dixit, A., Javalgi, R. and Dass, M. (2015). Research framework, strategies, and applications of intelligent agent technologies (IATs) in marketing. Journal of the Academy of Marketing Science, 44(1), pp.24-45.

Russell, S. and Norvig, P. (1995). Artificial Intelligence: A Modern Approach. 1st ed. Prentice Hall, p.31.