New KPIs Impacting Sales Strategy When Using a Sales Engine with Attribute-Based Selling
Once you make the decision to exchange your online booking engine with a proactive sales engine (including novel sales & conversion techniques such as attribute-based selling), you also need to turn your attention to new KPIs to optimize performance.
CONTEXT IS EVERYTHING
When booking hotel experiences, the buying behavior of guests is largely influenced by the purpose of their trip, their accompanying travelers, length of stay and overall amount spent.
For example, the time spent booking a hotel and evaluating alternatives are significantly different when going for a routine trip travelling alone, going on a weekend trip with your spouse or looking for a summer holiday for the family.
To gain actionable insights for your selling strategy, the performance measurements should be distinguished through different booking flows relating to different buying behaviors and psychographics.
CONSIDER BOOKING FLOW PERFORMANCE
Sold room products are measured through the following three main booking flows:
- Lowest priced product
- Recommended products to the booker
- Self-selected preferences by the booker
Lowest priced product
The lowest priced product is a dynamic measure of the lowest available and offered room product to the booker for their desired stay period. In most cases, this is the standard or lead in room category of a hotel.
Recommended products
Those are room products directly offered to the booker which are higher priced than the lowest selling product as well as other products suggested and include specific room features and attributes. For example, a bedroom on a high floor, balcony and midday sun using the social proof concept to convert a higher priced product.
Self-selected preferences
Room products are shown here according to the features and attributes selected by the booker. If the booker wanted a queen bed, away from elevator etc., matching products are being offered for the respective product value.
3 Month Booking Flow performance dashboard – Example Resort Property:
Data source GauVendi of a midscale property in a leisure destination
- Match suggests that the booker selected their own preferences and is paying more for personalized options.
- Direct and most popular are recommended room products & attributes of higher room categories
- Lowest price is the lowest priced product with no room attribute upselling (Standard room etc.)
Key take-aways from this example:
- Roughly 1/3 of bookers chose their preferences themselves and yielded an AVR of 114 EURO (15% higher than the lowest priced products), while staying 6.8 nights on average and spending additional 9 EURO per person for other services[1] at time of booking.
- 57% of bookers went for the suggested products for an AVR of 122 EURO (23% higher than the lowest priced products), while staying on average 5.4 nights and spending additional 7.4 EURO per person for other services at time of booking.
- Only 13% booked the lowest available product price for an AVR of 99 EURO, while staying 6 nights on average and spending app 5 EURO per person for other services at time of booking. This is vast contrast to OTA bookings where the majority is the lowest priced product.
Selling adjustments executed for this hotel example:
- We adjusted the selling strategy and are pushing higher value products for shorter stays to find out at what price points are guests trading down to lower priced products – price elasticity testing.
- Guests booked through Lowest Price are the primarily targets for ongoing upselling activities prior to their arrival and receiving additional emails.
WHAT TO DO WITH THOSE NUMBERS?
We are not suggesting that those numbers are conclusive when using attribute-based selling. However new selling strategies require a sense and adapt approach. The KPIs described are best used to measure the trend and impact of regular adjustments of you selling strategy. In general, our findings confirm that it’s well worth for any type of property to start automated personalized selling and use tactics like attribute-based selling to maximize yields.
To learn more about a sales engine with feature-based intelligence contact us for a free consultation on gauvendi.com.
ABOUT THE AUTHOR
Markus Mueller is the co-founder of GauVendi with over 25 years’ experience in leadership roles in multi-country and culturally diverse hospitality organizations across the Caribbean, Europe, Middle East and Asia within the tourism industry, holding an MBA with Distinction from Warwick Business School.
[1]Services may include F&B services, amenities or other products such as bike rental. In this example only breakfast was an offered extra service.