During a recent convention, I had an interesting exchange with a small group of wholesaler owners based in USA and LatAm. It went more or less like this:
Owners: “We’re sick of X, Y and Z hotel chains! Even if we sold millions for each brand since forever, they treat us worse and worse every season. Contracting with them became a nightmare or just outright impossible.”
Me: “Yeah, tell me about it. But let me ask you this: do your current systems support direct integration to those hotels’ dynamic rates flow?”
Crickets noise, puzzled looks.
Me: “So you don’t propose dynamic rates at your selling points, yourselves?”
Owners: “But we are already getting dynamic rates through bed banks and the aggregators…”
Me: “Of course you do, but the dynamism lies only in the hotel side, and surely you are all getting the same prices from the same providers; what I meant was whether you apply the same mark-up all over your segments, your markets, for different dates…”
Crickets going crazy, expressions ranging between “What’s this moron talking about?” and “Hey, maybe I need to look into it”.
Mind, I certainly did not want to be lecturing a bunch of people with decades in the business and whose yearly turnovers amount to millions of dollars (at least): my remarks were delivered in the humblest tone humanly possible, but I am afraid I came out as smartass to some, nevertheless. Apologies, it still surprises me big deal to find out that multi-million worth companies haven’t implemented at least a data collection and analytics policy, not to mention a less than cutting-edge byproduct like revenue management. No doubt, all those companies operated efficiently so far, but the traditional business is quickly shifting towards online and mobile milieu, where technological means are critical to be competitive. And a mere “BI module” or add-on to their existing booking engines just won’t do.
Alas, at present, way too many buzzwords are confusing the bejesus out of these founders and managers, and I’m already seeing in action unscrupulous hustlers trying to lure them into all sorts of crap that can include in its presentation A.I., Blockchain, and so on. Thus, with so much clatter going on, Operators owners may miss a prosaic, mature approach to technology that doesn’t sound as sci-fi, but most certainly delivers. Enter Revenue Management, ladies and gentlemen.
What for, Why, How
If you are not aware what RM is at this point, here’s the shortest description of the concept:
RM implies selling the right product, to the right customer, for the right price, at the right time, through the right channel.
I’ve been advocating the RM strategy creed for years now, wrote several related articles as well as two specific ones (for outgoing operators >> as well as DMCs >>). Please refer to those texts for further details, as I won’t go over all the definitions, benefits and reasons to adopt RM now, but here’s a shortlist:
– To identify buying habits and fine-tuned segmentation by willingness to pay more or less is already a primordial necessity in any B2B commercial operation. Just like hunting for romantic partners at Tinder: see my segmentation approach here >>
– Operators are sitting in a small hill of data (wouldn’t call it a “mountain”) but they’re not converting it into actionable insights. That’s a pity, because among other things they would be able to generate demand forecasts, which in turn would help predict market size, detect new opportunities, pinpoint popular destinations (which shift by the minute these days), and so on.
– Demand in travel is always high, and by the looks of it, it will be higher in years to come, with customer segmentation playing a pivotal role. Therefore, pricing correctly is a must. See my guide to design and implement a dynamic pricing strategy >> For instance, sales peaks during events and holidays can and should be milked in a better, more lucrative manner.
– Once all of the above is known and acted upon, relationships with customers and providers can be positively managed with known cause, instead of just conflict-avoidance. My friends the large USA and LatAm wholesalers could even acquire pre-paid allotments from the formerly hated chains, just to mention a practical example…
The bottom line is
[ctt template=”5″ link=”6og0I” via=”no” ]RM is not just a matter of adjusting prices based on demand levels. It must be considered a broad company-wide strategy that involves all departments and requires the adoption of a data-driven mentality from all stakeholders.[/ctt]
Hence, in order to even start thinking about profiting from RM, you’d better start (properly) collecting data for analytical purposes and bracing for a change in reporting habits, at least. See my guide to assume a data-driven culture in an organization >>
Yes, it takes a considerable effort (proportional to the company’s size), but it’s not rocket science and the reaping absolutely worthwhile: at the very least, survival in this hyper-competitive market.
Now let’s see how RM can be implemented on a tour operator, be it outgoing, incoming, mixed model, whatever. It shouldn’t come as a surprise that all this is also perfectly valid for bed banks.
Pre-launch checklist
First and foremost, it would be a good idea to build a business case: that way you’ll immediately see the benefits of adopting RM, everything else will be easier from then on. Draw a sketch with the costs, the timeline, and the possible output: suppose that an analytics tools (forget Excel, please) costs you about 100 bucks per month, plus you need to dedicate one month/4 hours per week to the project completion (say, a one-time 350$ expense) and the result should be to get at least 4 more bookings per month. Given that each booking on average brings a 100U$ margin, from the second month onwards, the net gain would be 300$. Makes sense, right? Realistically, though, you won’t improve your numbers by just four bookings per month with RM in place. A safe assumption would be a six percent improvement YoY in the first half-year, or a much higher percentage if your pricing or distribution strategy is sucking big time right now. At any rate, defining goals to get via RM is not a bad idea before starting.
Ok, now that we established RM means money, the next steps before implementation are:
- Timing: Allow for a certain building and adaptation period, which will depend on your company size, resistance from bosses or subordinates, tech tools, etc.- RM does not happen overnight, it is a process.
- Manager: at least one person must be assigned the data-master role, in charge of collecting, managing and processing the data that will be distilled into the RM system. The larger the company, the bigger the datasets, the more experienced must be the data-master… up to the point of needing a whole data-science team. That will happen when you are neck to neck with TUI, though. More importantly, the data-master must possess an irrepressible honesty: original data has to be absolutely trusted, otherwise insights and forecasts will not be accurate!
- Communication: all stakeholders and decision-makers must be informed that from now on, exchange of insights and KPI measures are a company policy. That’s the first step towards eliminating information silos: you don’t want sales acting independently from operations, marketing going astray with no attachment to contracting… Everyone must be aware of the big picture, while focusing of course in their own contribution, because RM will involve all departments, even human resources. Don’t believe it? I challenge you to ask me examples, if you’re not able to think a few on your own.
Fine, let’s proceed with simple implementation guidelines now.
Revenue Management’s simplest implementation guide ever
It can’t be more abridged than this, in fact it should be taken at face value, because the process is not as simple. Notwithstanding, I’d rather give you a general guideline to understand that this RM thing is not that complicated either.
1) If a larva of data-driven culture has infected your organization, supposedly data collection was already addressed. The collecting method depends on your various systems; it must ensure it’s cost-efficient, precise (more than reliable!) and redundant (backed up). To properly apply RM techniques (especially forecasts), I’d recommend a dataset worth at least a year of transactions (bookings and searches alike). Of course, it is assumed your systems are in perfect order condition: if dates come out as strings and money values as dates, your data-master will have to fix things a bit, but if those amounts or dates come out completely wrong, you’re in trouble.
2) Commence immediately segmentation studying and calibration. To know who your clients are -not their names, but their will to buy at a price range- is paramount. Check what they look for, define their buying patterns, calculate how much they plan to spend in the next trip so you can even predict what they’ll buy next time. From there, sending offers at the right time for the right price will be like selling cookies outside a college dorm after party night.
3) Verify demand: by dates, destinations, segments, etc. Use not only your own booking systems, also external sources like social media, airline route stats and industry reports.
4) Combining demand with segmentation as well as benchmarking are the first steps to build a competition-killer pricing strategy. See my article about Dynamic Pricing strategies >>, if you didn’t up to now. Forecasting is next: it will mainly answer to the when/where/who questions
5) Ponder on the insights fetched by the analytics and forecasts, to devise strategies for pricing, inventory control, segmentation, etc.
6) Act upon what was decided on the previous step, of course measuring the results and learning from the lessons brought forward. For instance, marketing campaigns would be laser-targeted towards specific segments.
It is important to note this is a cycle, so once the sixth step was completed, it starts all over again, continuously. See below a representation of this cyclic process.
Tools for the Job
Can it all be done in-house, manually? you might ask. Well, if your datasets are really small and you limit yourself to the above tasks and calculations, you should be able to come up with a DIY homemade solution ideal for your needs. However, I’m ready to bet that, considering the time and the skills needed to achieve that, it would be wiser and cheaper to resort to a ready-made solution like REVVA. I am sorry to inform you, though, that real-world RM is hundred-fold more complex than that. To illustrate its complexity, I took the liberty to modify a fantastic scheme created by my colleague (hopefully soon to be client) Mr. Thuan Dao, CEO of BedLinker.com, a technology solutions provider that includes on its portfolio a hotel B2B e-commerce portal.
Mr Dao is a proven Revenue Management Ninja, and he’s kindly agreed to republish his flowchart, which originally targeted the accommodation industry, now adapted to the tour operating business. If you follow his advice, you can’t go wrong!
It should be evident that something like that cannot be properly performed using the brains and pencil method. In particular, to obtain really accurate forecasts, machine learning algorithms (a kind of artificial intelligence) should be employed because, season after season, the system will actually learn how to predict with higher precision. There’s no such thing as “zero risk/zero error”, but with these predictions you’ll certainly get better results over betting the farm as usual.
Conclusions
I really hope the above diagram didn’t scare you off RM; quite the opposite should be expected. As many hoteliers of your acquaintance can confirm, it took some time and effort, but it wasn’t that hard and now they can’t imagine life (and thriving business) without RM. That’s exactly the main reason it is a pain in the neck for them to sign fixed rates contracts with you!
I realize it is difficult to grasp all this in a single take, so find below the three main takeaways from this article:
- Your company is amassing a huge volume of data, which is profitable only if you convert it into actionable information. Otherwise it is worth nothing.
- The RM implementation process is not easy-peasy, nor the investment irrelevant, but the rewards hugely compensate for the nuisance. Especially profit-wise!
- Applying a fixed mark-up to all your products and services was the only option when you acquired your booking engine, ages ago… but if you don’t work with dynamic mark-ups, you’re leaving money on the table for your bigger (or smarter) competitors to take.
The current state of affairs allows for automated analysis, decently accurate forecasts and dynamic pricing recommendations, while not yet for automated application of business rules based on demand/segment/dates, etc. (except big boys like Expedia, already experimenting with these things). But we are currently exploring such feature with prestigious solution providers… [ctt template=”3″ link=”dvcB3″ via=”no” ]Soon enough, your booking engine will automatically advance specific offers and display a different price for each segment -or even user-, based on their preferences, search/book history, date ranges, etc.[/ctt]
Mark my words. You read it here first!
Special thanks to Mr Thuan Dao for his contribution. Check this article to know more about BedLinker >>.
And thank you for sharing this text, if you find it interesting.
Marcello Bresin