The Power of Aggregation

The competitive advantages of content aggregation in the B2B hotel space and how to keep ahead using RevenueNexus price Shopper.

Efficient supply chains are a key component in any successful high volume, low margin business model. Content aggregators have become more attractive to retailers who seek to simultaneously simplify their supply chain and broaden their product range. This is increasingly the case in hotel B2B distribution where the competitive advantage demonstrated by content aggregators is pressuring established wholesalers to adopt the aggregation model.

Aggregators are B2B distributors of hotel rooms to travel retailers, i.e. Travel Agents and Tour Operators. Unlike the traditional wholesalers who distribute hotels that they contracted with directly, Aggregators source the bulk of their product from other wholesalers. They also tend to focus on their national or regional outbound sales market.

In doing so they are able to provide their customers with a ‘one-stop-shop’ for a global product range and are more focused on the specific needs of their customers than a multinational wholesaler.

But how can an Aggregator compete on price against wholesalers who source product directly from hotels?

Our explanation for that is this:

  1. aggregators have more product and a greater chance of responding with the lowest-priced offer for any given search criteria (star, customer rating, amenities, micro-location),
  2. wholesalers do not have automated pricing systems and with millions of price points to manage manually they are often undercut significantly on price by other wholesale competitors. Aggregators are able to arbitrage these price discrepancies and beat individual wholesale competitors,
  3. absence of international sourcing and corporate infrastructure means low operating costs so that Aggregators can happily live with lower gross margins than multinational wholesalers.

Our Price Shopper service allows users to monitor and analyse their wholesale supplier performance. If you are an Aggregator, it will measure your competitiveness in terms of availability and price against your wholesale suppliers, indicating, for example, which destinations need more content or which suppliers to push for price reductions. If you are a Travel Agent or Tour operator, it will show you how much competitive content each supplier (or prospective supplier) is providing in each destination and determine the optimum mix that will minimise connections and maximise content. 

Case Study using RevenueNexus Price Shopper

The case study is based on real data from an Aggregator but also would be relevant to a Travel Agent or Tour Operator assessing the performance of its suppliers.

Price Shopper searches and then matches lead-in prices from the Aggregator and the Aggregator’s suppliers for the next 90 arrival dates. Matched lead-in prices are converted in to a KPI which we call ‘Meet Beat Ratio’ (see footnote).

The Aggregator in the case study is supplied by 2 global wholesalers (Supplier A and Supplier B), as well as other regional wholesalers.

If we compare the MBR for each supplier (the Aggregator and the 2 global wholesalers) against the other 2 suppliers for 2 cities, we can see the effect of having an unbalanced supplier profile has on competitiveness.

City 1, low Aggregator MBR
City 2, high Aggregator MBR

Although both supplier A and B have similar MBR vs. our Aggregator in both cities, the Aggregator’s MBR is significantly higher in City 2 because it has more unique hotels from other suppliers.

Supplier A clearly has the highest MBR for both cities but from the point of view of a Travel Agent or Tour Operator, the trade-off between the number of available hotels and MBR makes the Aggregator’s offering compelling in City 2 where the number of hotels offered is significantly higher than any single wholesaler while MBR is close to Supplier A and significantly ahead of Supplier B.

In terms of actions, the Aggregator should be seeking additional suppliers for City 1 and negotiating with Supplier B for price reductions to improve Supplier B competitiveness against Supplier A.

A Travel Agent or Tour Operator connecting all three (Aggregator, Supplier A and B) should be concerned that Supplier B is contributing such a low proportion of ‘Beats’ in both cities and the Aggregator is contributing a low proportion of ‘Beats’ in City 1.

footnote

Meet Beat Ratio (MBR) 

MBR is calculated by summing the number of matched prices where your price is equal or lower than the selected competitor’s prices and dividing the result by the sum of the matched prices. 

Example: Count of matched prices = 100 . Sum of matches where your price < = Competitors prices = 50.  MBR = 50/100 = 0.5 or 50%.  

Making Sense of Wholesaler Demand

How mapping visualisation reveals hotel clusters within cities and explains why some hotels achieve exceptional volumes for their wholesale distributors.

When I look at individual hotel performance in any city I always see a few outperforming hotels. So, how do you explain the sheer extent to which certain hotels in a wholesaler's portfolio outperformed the others? 

I asked a lot of experienced sales and contracting people and this is what they told me…. 

  1. Travel Experts at the wholesaler's B2B customers tended to recommend a limited selection of hotels to their clients. That’s why hotels offer ‘Fam trips’ (familiarisation trips) to get their hotels in to this 'selection'. So, price may not the only factor here.
  2. Customers often consolidated rates from multiple wholesalers on their own systems and de-duplicated rates based on lowest price. Our client’s systems then usually ranked hotels by ascending lead-in price. Lowest price is definitely a factor here.
  3. Our client’s systems provided the usual sorting and filtering functionalities found on on-line travel sites. Web analytics show that the most used filters are star rating, brand and location within the city. So these filters also needed to be factored in.

I had looked for correlations between average room rate and room nights sold but could never find the strong correlations that I was expecting, even when I filtered for star ratings and separately for branded / unbranded hotels. 

Recently I gave this another go but this time I was able to use average lead-in prices rather than the average rate booked. Lead-in price (or lowest available price for a room at the hotel) more accurately reflects the relative ranking of what is seen on the booker’s computer screen. Again, I was disappointed to find only low correlation and looked for a way to add micro-location within a city destination in to the analysis. 

Here is an example of what I found when I used mapping software to visualise price and booking data:

In this image we are looking at a single star category in a busy city. The columns represent the number of bookings for the hotels in a week. 

Clearly there are a few stand-out hotels (column height represents the number of bookings).

And when I combined lead-in price using a heat map layer  with the number of bookings as column height, some interesting things become apparent:

 note - high price is red, lowest is blue.

  1. there is noticeable clustering of hotel across the city,
  2. the hotels getting booked the most are often the lower priced ones in each cluster,
  3. it’s easy to spot the hotels generating exceptional booking volumes for the wholesaler and there are one or two in each cluster.

Here is another city which I believe shows similar characteristics. 

In each cluster, more bookings are produced by the lower priced hotels. 

The most booked hotel is the lowest priced and is located away from the main clusters.

Examining some of the clusters in more detail:

For this cluster there is still low statistical correlation between price and volume of bookings. 

But 2 of the lowest priced hotels generate 74% of bookings and other lower priced hotels are getting booked more than the higher priced ones.

In this cluster we see a similar situation where 2 lower priced properties generate 67% of the total bookings.

Conclusions:

 I think the first conclusion is that lowest price does have an influence but particularly within the context of the hotel’s cluster. By cluster I mean hotels in the same micro-area and of the same star rating. 

Perhaps equally important is the influence of the Travel Experts or ‘bookers’ who seem to favour certain hotels. There seems to be no other obvious reason for these hotels outperforming their competitor set. While they seem to be priced at the low end of their competitive set, they are not the absolute cheapest and while they have good guest satisfaction scores, not always the top score. 

Any super-performing hotel should be closely monitored to ensure there is always sufficient availability and pricing remains competitive relative to wholesale competitors. 

But can you increase mark-ups for these hotels? Well, perhaps not, since low price is probably one of the reasons they are so high performing in the first place. 

If there are clusters that don’t have one or two high performing hotels, maybe you don’t have them in your portfolio, or if you do, your price is uncompetitive or you have insufficient availability. Your customers can probably help you identify high performers that you are missing out on in each micro-area. 

If you have lowest price but low booking hotels in a cluster, it may be possible to improve bookings by promoting the hotels with your customers and arranging 'Fam trips' for your high volume customers. 

Finally, all of the data used in this post was generated by the RevenueNexus Revenue Management and Price Shopper services. Price Shopper enables near real time competitor price and availability monitoring.