The modern RM: Simplicity is not about having less, it's about finding the essence

Quick one to start with: I built a ChatGPT that can perform a monthly analysis for Oracle / Opera reports at segment and room type level. Fancy a test drive click here. An whilst we are at it:

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Now over to the modern RM:

In the ever-evolving world of revenue management, complexity is often seen as a necessary evil. But, what if we could simplify the process, making it more effective and less daunting?

Complexity is the enemy of execution

Core Point 1: The Nature of Data Sets

Data sets in revenue management are like the winds in sailing: they can be favorable (positive), opposing (negative), or neutral (business as usual). Understanding the nature of these data sets is crucial.

For instance, a positive data set might be an upward trend in room bookings due to a local event, whereas a negative one could be a sudden drop in demand due to external factors like economic downturns.

Real-Life Comparison:

In the realm of relationships, data sets can be likened to different phases in dating. A positive data set is like the honeymoon phase, where everything feels perfect and promising. A negative data set could be akin to a rough patch, where misunderstandings or disagreements arise. Neutral data sets are those typical, everyday moments that maintain the status quo in a relationship.

Core Point 2: The Dynamic Nature of Data Sets

Data sets are not static; they evolve over time. A positive data set can become neutral or even negative, and vice versa. This fluidity requires constant vigilance and adaptability from revenue managers.

Real-Life Comparison:

Just as in relationships, the dynamics can shift over time. A relationship in its honeymoon phase (positive data set) may eventually enter a more neutral phase as the initial excitement stabilizes. Conversely, a period of conflict (negative data set) can transform into a stronger, more understanding relationship over time.

Core Point 3: The Data Constellation

Dealing with multiple data sets simultaneously forms what can be termed a 'Data Constellation'. It’s the job of revenue managers to read the constellation, simplify this constellation, therefore making it understandable and actionable.

Further more, in revenue management, decisions like price changes are often made without documenting the underlying reasons. This practice can be compared to a chef adjusting a recipe based on experience and instinct, without necessarily noting each alteration. This makes it very tricky when we have to explain: Why did we drop the rate last week?

Common Data Sets in Hotels: Telling a Story

The Key: YOU! In the hotel industry, standard data sets such as pace data, our pricing, competitor pricing, forecasts, and positioning tell a story. They provide insights into market trends, guest behavior, and operational efficiency. By interpreting these data sets effectively, a revenue manager can make informed decisions that drive profitability and growth.

Conclusion: Simplicity is not about having less, it's about finding the essence and making things work efficiently.

Simplifying the approach to revenue management doesn't mean overlooking complexities; it means making them more manageable and actionable.

By understanding and effectively interpreting the various data sets, revenue managers can transform complexity into clarity, turning challenges into opportunities for growth and success.

BTW I am running a webinar on prompt engineering and ChetGTP. Let me know if you want to join: Click here.

Love,

Fabi