The market

It is not a secret that the retail market has stepped into the ‘data science world’. Today, one of the main goals of retailers is to constantly find innovative ways to get useful insights of their customers through their goldmine of unstructured and structured data (Marr, 2015). One of the implications of these insights are personalized products. According to Mobasher, Cooley & Srivastava (2000), personalization is (any) action that tailors experience to a particular individual. In other words, the data is used to match the products as good as possible to the needs of every individual customer. The process of personalization goes as follows: first, retailers collect data about their customers and try to learn about their customers’ preferences and tastes. Second, they try to develop products or services that match these preferences and tastes. Finally, they evaluate the effectiveness of these personalized offerings, in order to optimize the personalization process (Tsekouras, 2019).

The business model and its opportunities

Since it becomes more generic for retailers to use data that they have about their customers, they have to differentiate themselves from their competitors in some other way than solely using data to obtain useful insights. Here, a new opportunity arises because of emerging technologies. In the present, retailers want to create superior customer experience (Verhoef, Lemon, Parasuraman, Roggeveen, Tsiros & Schlesinger, 2009). A superior customer experience could be delivered through accurate, personalized recommendations because customers like recommendations for the reason that it eases their selection process (Tsekouras, 2019). Next to personalized recommendations, a good in-store experience increases the customer experience (Phibbs, 2019). If you combine these two ways to deliver a superior customer experience, a new addition to a retailers’ business model arises, which could be turned into a competitive advantage. We’re talking about in-store artificial intelligence robots (AI robots).

Opportunities that come along with AI robots

The first opportunity is that the ‘real’ personnel of a retailer is able to do more high-level tasks. This is possible since the robots can replace them in general, repetitive interactions with customers (Ankeny, 2017).

Besides this, retailers find it hard to convert their goldmine of data into useful insights. Actually, the conversion rate from data to useful insights lies between 2- and 3% overall (Donaldson, 2018). By implementing AI robots, all the data is automatically processed through algorithms. This means that the robots are able to make the customer experience far more intimate, targeted and effective, while they are simultaneously improving over time (Donaldson, 2018).

Next to the data being processed better, the robots are able to collect more data, since all the ‘conversations’ with customers are stored into a database. Traditionally, conversations between employees and robots are not ‘recorded’ or anything. Justine Santa Cruz is the VP of partnerships at Satisfi labs, which is a company that makes AI robots. She states that by adding the robots in-store, retailers can learn about what their customers are actually thinking during the whole decision-making process.

One final opportunity is the fact that customers are curious and willing to use new sorts of technologies. Santa Cruz (2019) says that there are no generational challenges that have to be addressed, regarding the willingness to interact with the new robots. Apparently, as is observed in real-life situations, everybody wants to communicate with the robot (Nittle, 2017).

To strengthen these opportunities, a real-life example of the current applications of these kinds of robots is addressed in the next paragraph. This way, the business model becomes more tangible and the efficiency and value of this addition to a retailer’s business model will become clear.

Current applications of the business model and its value

Mall of America has adjusted its business model in a way that they implemented AI robots in their physical stores, called Pepper. Pepper is a robot that can guide customers to specific locations through the mall, talk to the customers about (personalized) promotions and deals and is able to connect customers to other employees. The process of people that interact with Pepper is described by Brandli (2018). But to address this process clearly, I created a figure that illustrates this process, which is presented below.

One stage that might need clarification is the ‘Pepper is unable to help with the customer’s query’. It might be that the AI-robot will not be able to respond to certain specific questions that the robot has not seen before. When this is the case, Pepper is able to connect the customer with a real person that works in the mall. This way, the customer is still being helped at his expectations (Brandli, 2018).

The implementation of Pepper in Mall of America could have resulted in negative feedback, but this has not been the case since its trial (Kumar, 2018). The Ave, a retailer who implemented the robots as well, published their tangible results. They give credits to Pepper for driving a 98% increase in the interactions with customers, a 20% increase in foot traffic and a 3x revenue jump (Ankeny, 2017). In addition, Nestlé implemented Pepper in its Nescafé stores. This resulted in their sales increasing double digits. Concluding, the results up until today are real (Ankeny, 2017).

Potential drawbacks

First, I would like to mention the additional costs for retailers that have to be incurred if you want to implement the AI-robots. If you want to add Pepper to your stores, you will have to pay up $1700 up front, $134/month for maintenance and $89/month for insurance (both for a minimum of 36 months). This means that your costs for Pepper in 3 years will be at least $9.728. This could be a large investment for retailers since they still need to staff other employees (Faw, 2016).

Next to the additional costs, Pepper is a robot that will cost some peoples’ jobs according to some. McCormick (2016) argues that cognitive technology, including artificial intelligence, will replace 7% of U.S. jobs by 2025. However, the tasks are replaced, not the jobs. The robots are not meant to replace employees, but rather assist them in simple tasks. So actually, the robots are a blessing for the employees, since they are able to work on more meaningful tasks that are not repetitive in nature (Donaldson, 2018).

Reference list

Ankeny, J. (2017). How robots in stores could revolutionize the customer experience. Retrieved from https://www.retaildive.com/news/how-robots-in-stores-could-revolutionize-the-customer-experience/434056/

Brandli, A. (2018). CX trendsetters: Is the future of malls a robot named Pepper?. Retrieved from https://blog.centriam.com/is-the-future-of-malls-a-robot-named-pepper-cx-trendsetters

Donaldson, S. (2018). How AI Is Changing the Retail Industry. Retrieved from https://www.business.com/articles/artificial-intelligence-changing-retail/

Faw, L. (2016). Pepper The Robot Will Be Your Companion (For A Price). Retrieved from https://www.mediapost.com/publications/article/276387/pepper-the-robot-will-be-your-companion-for-a-pri.html

Kumar, K. (2018). Mall of America tests robots, chatbot as it looks to improve visitor experience. Retrieved from http://www.startribune.com/mall-of-america-tests-robots-chatbot-as-it-looks-to-improve-experience/474997543/

Marr, B. (2015). Big Data: A Game Changer In The Retail Sector. Retrieved from https://www.forbes.com/sites/bernardmarr/2015/11/10/big-data-a-game-changer-in-the-retail-sector/#5e47c5209f37

McCormick, J. (2016). Predictions 2017: Artificial Intelligence Will Drive The Insights Revolution. Forrester. Retrieved from https://go.forrester.com/wp-content/uploads/Forrester_Predictions_2017_-Artificial_Intelligence_Will_Drive_The_Insights_Revolution.pdf

Mobasher, B., Cooley, R. and Srivastava, J., 2000. Automatic personalization based on web usage mining. Communications of the ACM, 43(8), pp.142-151.

Nittle, N. (2017). Mall of America Gets High-Tech With Chatbot and Humanoid Robots. Retrieved from https://www.racked.com/2017/12/18/16781234/mall-of-america-chatbot-humanoid-robots-pepper

Phibbs, B. (2019). 4 Ways To Improve Your Retail Customer Experience and Sales. Retrieved from https://www.retaildoc.com/blog/improve-retail-customer-experience-sales-follow

Santa Cruz, J. (2019). VIRTUAL RETAIL – THE FUTURE IS NOW. Retrieved from https://mr-mag.com/virtual-retail-the-future-is-now/

Tsekouras, D., 2019. Customer Centric Digital Commerce, Lecture 2, slide 20.

Verhoef, P. C., Lemon, K. N., Parasuraman, A., Roggeveen, A., Tsiros, M., & Schlesinger, L. A. (2009). Customer experience creation: Determinants, dynamics and management strategies. Journal of retailing, 85(1), 31-41.