Tag Archives: recommendation

Business Case

EVERY OENOPHILE’S DREAM: VIVINO

Leave a comment

“Life is too short to drink bad wine.” – Johann Wolfgang von Goethe

You enjoy an occasional glass of red wine, or you just want to pair the day’s love infused dinner with an exquisite bottle of white. You go to the nearest wine shop, liquor store or supermarket only to be left alone staring at the abundance of options. Of course, you can ask the salesperson at the shop, but can he or she incorporate the knowledge of millions of wines into his recommendation? No, I didn’t think so either. It is your lucky day though, Vivino is here to help.

Finding the perfect bottle

Vivino boasts over 9 million different wines in its database covering over 3000 different wine regions for its community of 29 million wine lovers. Founded in the capital of Denmark, Copenhagen, by Heini Zachariassen and Theis Søndergaard in 2010, it is world’s largest online wine marketplace. The company is spread over three continents with their offices in Copenhagen, San Fransico, Ukraine and India, and so far has secured $56.3 million in funding including a staggering $25 million from SCP Neptune, the family office investment vehicle of the Moët Hennesy CEO, Christophe Navarre. A “community-powered e-commerce platform for personalized recommendations” as Zachariassen puts it, allows users to scan the labels of the whichever bottle they are about to buy and the app recognizes the key pieces of information such as the price, producer, year and the region of production. The app also gives tasting notes and recommends food pairings to go with your precious bottle.

Here’s a 60-second video explaining how:

Wine lovers unite!

The community dimension of Vivino is what makes it a truly customer-centric platform. It allows users to rate the wines, read the comments of other users and even follow their fellow oenophiles, possibly consisting of family and friends whose reviews will be highlighted in their feeds. Since the launch of the app some seven years ago, half a billion labels have been scanned and 88 million ratings have been submitted. With such a wealth of data, the company launched Vivino Market in 2017 offering wine lovers customized recommendations depending on their past behavior on the platform. The more labels they scan and more ratings and reviews they leave on the platform, the better recommendations the users get. Vivino seems to be the perfect conjunction of social media, big data, and machine learning assisting wine lovers to never be disappointed ever again with their choice of wine.

Screen Shot 2018-03-05 at 20.45.23
Vivino by the numbers

A new era for selling wines

Vivino’s value proposition does not only concern wine lovers in the pursuit of a good wine. It also benefits retail partners and sommeliers alike. Guest-to-sommelier interaction is usually an awkward one: guest trying to explain what kind of wine he or she likes and sommelier trying to pinpoint “the one” with not much to go on other than “dry”. Vivino successfully steps in at this point. The users of the platform can simply show the sommeliers wines that they previously enjoyed, making everyone’s lives a little easier. And Scott Zocolillo, Managing Partner, and Sommelier at Nectar in suburban Philadelphia’s Berwyn agrees: “Vivino, to me, shows trends and preferences. I love when a guest has their app out, [as] it helps move the conversation along and helps me do my job and get them the best wine for their experience.” It doesn’t end there, though. Through its marketplace, Vivino charges a flat commission for retailers on the orders that are bought on its website and app. With over $40 million worth of wine sold through Vivino, it provides a disruptive opportunity for wine producers to reach a vast community of users who are appreciative of wine. A win-win situation for all parties involved!

Powered by a solid community of users with its current data capabilities, the company plans to expand to emerging markets such as Hong Kong, Brazil, and Mexico through its increased partnerships. The goal is to sell $1 billion in wine by 2020 and with 2 years to go, that doesn’t seem to be an easy target. However, Zachariassen seems to believe in the potential of the online market for wines. “Wine is a $300 billion industry and if you look at the online part of wine, e-commerce, it’s still very, very small,” says Zachariassen, pointing towards a plethora of opportunities in the online wine retail business in the years to come. For now, what we can do as wine lovers is to sit back, relax and crack open that bottle of red which is guaranteed to be a pleasure.

Here is another article written about Vivino from 2014: https://consumervaluecreation.com/2014/05/18/viva-il-vino-exploring-wine-with-vivino/

References

Crunchbase. (2018). Vivino | Crunchbase. [online] Available at: https://www.crunchbase.com/organization/vivino#section-locked-marketplace [Accessed 5 Mar. 2018].

Freedman, B. (2017). The Launch Of Vivino Market Could Herald A New Era In Wine Buying. [online] Forbes.com. Available at: https://www.forbes.com/sites/brianfreedman/2017/03/30/the-launch-of-vivino-market-could-herald-a-new-era-in-wine-buying/#35e56f975ed1 [Accessed 5 Mar. 2018].

Page, H. (2018). Investors Pour $20M More Into Wine Curation And Delivery App Vivino – Crunchbase News. [online] Crunchbase News. Available at: https://news.crunchbase.com/news/investors-pour-20m-wine-curation-delivery-app-vivino/ [Accessed 5 Mar. 2018].

Scott, K. (2017). Vivino: This app is designed to turn anyone into a wine expert. [online] CNNMoney. Available at: http://money.cnn.com/2017/08/01/smallbusiness/vivino-wine-app/index.html [Accessed 5 Mar. 2018].

Vivino.com. (2018). About Vivino. [online] Available at: https://www.vivino.com/about [Accessed 5 Mar. 2018].

Yeung, K. (2016). Vivino raises $25M round, led by Moet Hennessey’s CEO, for its wine discovery app. [online] VentureBeat. Available at: https://venturebeat.com/2016/01/12/vivino-raises-25m-round-led-by-moet-hennesseys-ceo-for-its-wine-discovery-app/ [Accessed 5 Mar. 2018].

Articles

Personalized Online Adverstising Effectiveness: The Interplay of What, When, and Wher

Leave a comment

If you go to any website, or online store specifically, your behaviour is tracked. Landing page, time spent, clicks, exit page: you name it, it is tracked. But even when you leave a page, a company does not really leave you: they saw what you clicked on, and based on your browsing behaviour, they retarget you: they show you a (sometimes personalized) advertisement on another channel, hoping you will come back and purchase the product you viewed.

Retargeting can either be done during or after a website visit, and is done based on a customer’s visit. When showing personalized recommendations, for example, it is important to take into account the quality of the recommendation, the level of personalization and the timing. This is what Bleier & Eisenbeiss (2015) looked at: what should they show, when should they show it, and where should they show it.

As with any academic article, past literature is analysed and hypothesis are developed. In order to test the what, when and where of personalized online advertising effectiveness, Bleier & Eisenbeiss conduct two large-scale field experiments and two lab-experiments. The first field experiment looked at the interplay of degree of content personalization(DCP), state, and the time that has passed since the last online store visit, at a large fashion and sports goods retailer, who carries over 30000 products. The second field experiment, conducted at the same retailer, looked at the interplay of placement and personalization. Based on the results from these two field experiments, two lab experiments were designed: one focussing on web browsing in an experiential model, the other focussing on goal-direct web browsing.
Within this paper, thus, many things are studied and confirmed. The papers shows the importance of how to determine the effectiveness of online personalization’s, and which one works best when. When a customer sees a personalized ad right after his/her website visit, the ad becomes more effective. This is mainly because preferences are not constant: they can over time. Thus if you liked a shirt 5 minutes ago, you will mostly still like it now. Thus if a company is able to directly respond to a consumer’s behaviour, the CTR is expected to be higher.

While the effectiveness of recommendations decreases over time, the level of personalization plays a moderating role. This means that high-level personalization in later stages of the decision making process have lower effectiveness, because of changes in customers tastes’ and preferences. The personalized ad is therefore not applicable anymore. Thus, the more personalized an ad, the sooner after a website visit it should be sent. Moderate personalized ads are thus more effective over time, as they take into account these changes in preferences. As visual recommendations are often highly personalized, these type of recommendations are more relevant shortly after a visit. Cross-sell recommendation, which is a more moderate recommendation type, performs better later in time

So what does this all mean? When retargeting customers and showing them personalized ads, it is important to keep in mind how long ago they visited a website. Given that this research was performed at a large fashion/sports retailer, it would be interesting to see whether the same conclusions hold for other settings. What do you think? And when do you consider (personalized) ads target to you most effective?

Bleier, A., & Eisenbeiss, M. (2015). Personalized Online Adverstising Effectiveness: The Interplay of What, When, and Where. Marketing Science, 669-688.

Articles

Technology Usage and Online Sales: An Empirical Study (De, Hu and Rahmad, 2010)

Leave a comment

Many internet retailers offer their customers advanced technology features to enhance the shopping experience, such as search functions and recommendation systems. However, how do these technologies influence consumers’ shopping behavior? Does the way these consumers use these technologies influence sales or their purchasing patterns?

Information systems, such as search and recommendation technologies are used to enhance the customer experience, by reducing the steps required to come to a preferred product. Furthermore this systems help the consumer to discover products that they would not have sought out otherwise. In the consumer journey, the consumer passes the “information search” stage before the stage of “alternative evaluation” and “purchase”. And during this information search, consumers first try to activate prior knowledge, before acquiring external sources. There are two types of products: products that are displayed in a company’s advertisement, also called promoted products; or products which are not displayed in any advertisement, called non-promoted products. Some consumers search for a specific product, with an exact name. This is called “direct search”. Some consumers do not know where they are looking for and type just a word, like “dress”, this is called “non-direct search”. Consumers who look for a product with a direct search often used their prior knowledge.

tabel-blog

Interesting to see, is that consumers who use direct search influence online sales, however, they only affect promoted products. This indicates that consumers who are encountered with promoted products through advertisements, use that prior knowledge to directly search for the products to purchase it. Furthermore, direct search is negatively related with non-promoted products. Furthermore, recommendation systems positively influence online sales, for both types of products. However, the recommendation system works stronger in categories with many products, than in categories with a few products. An explanation might be, it is likely that consumers lack prior knowledge of a large proportion of the product assortment and therefore find provided recommendations more beneficial. An unexpected finding is that non-direct search has no influence on consumers’ purchase behavior.

These findings are interesting for internet retailers, but it proves that it is beneficial to invest in advanced technology features, such as search and recommendation systems because this will lead to higher levels of sales. Furthermore, if online retailers want to increase sales via search systems it is suggested to also promote products because otherwise the tool only enhances the customer experience.

An example that successfully adopted these technology systems is Amazon. Amazon is one of the largest online retailers in the world that sells almost everything. Another advanced technology feature that Amazon uses is collaborative filtering systems, where the consumers get information about what other consumers bought after buying a particular product: “Consumers who bought this product also bought this…”. Unfortunately, this study has not included this technology feature. However, no worries, other research has proven that these systems also increases the diversity and amount of products purchased by a consumer (Lee and Hosanagar, 2015).

However, findings from this paper still show the importance of investments in information technologies, as it influences consumers purchase behavior. Furthermore internet companies are continuing to develop more sophisticated search and recommendation systems, which is a good trend.

De, P., Hu, Y.J. and Mohammed, R.S. (2010) ‘Technology Usage and Online Sales: An Emperical Study’, Management Science, 56, 11: pp. 1930-1945.

Lee, D. and Hosanagar, K. (2015) ‘People Who Liked This Study Also Liked: An Emperical Investigation of the Impact of Recommender Systems on Sales Diversity’, available online from: https://papers.ssrn.com/sol3/papers2.cfm?abstract_id=2603361 [14 February 2017].