{"id":32249,"date":"2018-11-13T16:42:54","date_gmt":"2018-11-13T21:42:54","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/craft-beer-by-the-people-for-the-people\/"},"modified":"2018-11-13T16:42:54","modified_gmt":"2018-11-13T21:42:54","slug":"craft-beer-by-the-people-for-the-people","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/craft-beer-by-the-people-for-the-people\/","title":{"rendered":"Craft beer by the people, for the people"},"content":{"rendered":"

Would you ever buy a craft beer whose taste is constantly changing?<\/em><\/p>\n

In the last few years, applications of Machine Learning and Artificial Intelligence have flooded the Consumer Goods Industry. From targeted advertising to hyper-personalized promotions, the main focus has been to predict and influence the consumption patterns of customers [1]. Nevertheless, few well-known consumer products have radically changed based on the feedback of the customer. For example, other than overall bitterness, recipes of the most iconic beers in the US have not changed much during the last 40 years [2].<\/p>\n

On the other side of the spectrum, Intelligentx, a UK startup founded in 2016, is challenging the way companies approach product development by leveraging Machine Learning algorithms to improve its beer recipes based on drinkers\u2019 feedback. You\u2019re reading this right, they are changing the taste of their beer and no two batches will ever taste the same.<\/p>\n

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Intelligentx website<\/em><\/figcaption><\/figure>\n

Intelligentx takes customer feedback through an online system and the data is integrated with a machine learning algorithm to alter the recipe and enable the system to immediately brew the next batch of beer [3]. Every bottle’s label has a code that connects you with a Facebook Messenger bot that complements the process by asking different sets of questions related to the aroma, taste, and looks of the beer. Levels of carbonation, bitterness and alcohol content all change based on how people are responding. The algorithm produces new recipes every month incorporating the feedback [4]. In addition to modifying the mix of the typical 4 beer ingredients (hops, water, yeast, and grain), approximately 1,000 wildcard ingredients could potentially be inserted into the recipe to keep the beer changing and to teach the algorithm [5].<\/p>\n

Intelligentx\u2019s strategy<\/strong><\/p>\n

In the short-term, the management team of Intelligentx will maintain its product development process of optimizing its recipes with a focus on the craft beer segment. In addition, they are expecting to expand their current production of 1,000-liter batch per month [6]. Finally, it could be speculated that management will continue to expand their beer offering, based on their decision of migrating from one to four beers after just one year of operation.<\/p>\n

In the medium-term, the plans for Intelligentx are extremely ambitious. First, the company has the aspiration to improve its data collection process. Their plan consists of collecting data from pubs and bars to discover exactly what drinkers want. \u201cYou could have a smart pub, where the glasses are connected [to the AI engine], where we know how much you\u2019ve drunk, at what temperature, how quickly, how much you left in the bottom of the glass\u201d, mentioned the co-founder [7]. Second, the company is not only focusing on beer. They have the vision of \u201cbuilding a future where all the things you buy, not just beer, are made just for you\u201d [8]. Therefore, the founders are looking for other products that can be improved in a similar fashion, such as coffee, chocolate, or perfumes [9].<\/p>\n

Opportunities and recommendations<\/strong><\/p>\n

In addition to Intelligentx current strategy for the short and medium term, I consider that there are two main opportunities on prioritization of future products and customer retention.<\/p>\n

On the prioritization of future products, I would advise the co-founders to favor products that have a lower development lead time, are less capital intensive, and are quickly consumed. In their product development process, the product\u2019s quality is linked to the amount of data that the algorithm can process; therefore, minimizing the lead time (time to market) and being easily consumable (e.g. a perishable good), would increase the number of data points necessary to feed the program. Additionally, choosing a less capital intensive product (as opposed to beer) would provide the possibility to increase customization, without a substantial increase in the cost-per-unit of the product.<\/p>\n

Customer retention will be one of the key challenges for the company going forward. Rob McInerney, co-founder of Intelligentx said that \u201cby making mistakes, [algorithms] learn to better interpret people in the future\u201d [10]. But how likely will a person that unluckily got a \u201cbad batch\u201d of beer repeat a purchase and provide the valuable feedback necessary for the product to improve? I would recommend Intelligentx to create a scheme that compensates customers when the algorithm identifies that they received a \u201cbad batch.\u201d For example, providing customers with a personalized thank you message for providing feedback and a free beer coupon.<\/p>\n

Open questions<\/strong><\/p>\n