Amazon frequently receives credit for successfully employing machine learning to engage consumers and drive sales with its well-known recommendation engine, which generates 35% of the company’s revenue, according to McKinsey .
copyright by www.hospitalitynet.org
Amazon frequently receives credit for successfully employing machine learning to engage consumers and drive sales with its well-known recommendation engine, which generates 35% of the company’s revenue, according to McKinsey . However, competitor Walmart has a surprising amount of machine learning activity going on behind the scenes. For instance, Walmart created a facial recognition system that allowed the company to pinpoint customers who were unhappy about waiting in line. The system alerted sales associates that new lanes needed to be opened, which increased customer satisfaction and helped the retailer to manage employee workflow more efficiently. While hotels are, in some ways, worlds away from retailers in terms of the scope of operations and product, the hospitality industry can learn from the experience of retailers when it comes to machine learning, positive customer service, and merchandising.
Machine learning automates the analysis and modeling of large volumes of disparate data, on a scale and scope that is impossible to do manually in a timely and cost-effective way. For those in customer service, machine learning can provide customized recommendations and promotions and, as Shopify notes, optimize pricing strategies in real-time with models that “can take key pricing variables into account, including supply, seasonality, and demand and offer insights on how to adjust… prices accordingly.” For revenue managers in hotels or hotel groups, machine learning can provide real-time pricing, offer assortment, and recommendation decisions for bookings and upselling that maximize profit without requiring teams of analysts, and can do this at any point in the guest journey, from booking to pre-arrival to check-in to in-stay.
Historically a front desk supervisor provided a rate table to agents with static room rates for upgradable rooms. Take, for example, a King to a King Suite upgrade offer – the static rate table showed the upgrade could be offered at $100. But with machine learning, an intelligent system would present an offer to the agent based on a high probability of conversion; that $100 per night upgrade should actually be priced at $130, because the model has predicted it’s the appropriate price for that guest for that stay. Consider that extra $30 across even just ten two-night reservations each week and a property has brought in an additional $30K+ that never would have been realized using a static rate table. Then consider that the same system can be used to calculate pricing for everything from breakfast add-ons to late check-outs. This heretofore unrealized incremental revenue directly impacts the bottom line.
The same holds true for hotels. An upgrade isn’t just a revenue line item; upgrades create a better overall experience for the guest. Who doesn’t have a better experience in a more spacious room where room service can be enjoyed not on the bed but on a table, perhaps with a view?[…]
read more – copyright by www.hospitalitynet.org
Amazon frequently receives credit for successfully employing machine learning to engage consumers and drive sales with its well-known recommendation engine, which generates 35% of the company’s revenue, according to McKinsey .
copyright by www.hospitalitynet.org
Machine learning automates the analysis and modeling of large volumes of disparate data, on a scale and scope that is impossible to do manually in a timely and cost-effective way. For those in customer service, machine learning can provide customized recommendations and promotions and, as Shopify notes, optimize pricing strategies in real-time with models that “can take key pricing variables into account, including supply, seasonality, and demand and offer insights on how to adjust… prices accordingly.” For revenue managers in hotels or hotel groups, machine learning can provide real-time pricing, offer assortment, and recommendation decisions for bookings and upselling that maximize profit without requiring teams of analysts, and can do this at any point in the guest journey, from booking to pre-arrival to check-in to in-stay.
Historically a front desk supervisor provided a rate table to agents with static room rates for upgradable rooms. Take, for example, a King to a King Suite upgrade offer – the static rate table showed the upgrade could be offered at $100. But with machine learning, an intelligent system would present an offer to the agent based on a high probability of conversion; that $100 per night upgrade should actually be priced at $130, because the model has predicted it’s the appropriate price for that guest for that stay. Consider that extra $30 across even just ten two-night reservations each week and a property has brought in an additional $30K+ that never would have been realized using a static rate table. Then consider that the same system can be used to calculate pricing for everything from breakfast add-ons to late check-outs. This heretofore unrealized incremental revenue directly impacts the bottom line.
The same holds true for hotels. An upgrade isn’t just a revenue line item; upgrades create a better overall experience for the guest. Who doesn’t have a better experience in a more spacious room where room service can be enjoyed not on the bed but on a table, perhaps with a view?[…]
read more – copyright by www.hospitalitynet.org
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