Actionable Analytics Practical Analytics for Practical People

Airline Gate Optimization

People who are used to quite a bit of long distance air travels with one or several transits in between, would have at some point in their lifetimes definitely experienced the “moments of anxiety” of missing the connecting flights because of the arrival delays of the flights they are currently travelling – not to forget most of the connecting flights are timed to less than a couple of hours and the worst could be as close as about 45 minutes (source: Frankfurt Airport). This stress especially increases to unbearable levels if the transiting airports happen to be of type as the Frankfurt, Heathrow, Chicago ORD airports etc.

Ever wonder the reasons why? Airports such as the ones described above have multiple terminals and given the distances that passengers need to travel to take the connecting flights in different terminals only increases this stress. To top it all, security measures at the airports have now increased manifold times that sometimes it becomes rather impossible to take the connecting flights should there be even a slightest delay in the arriving flights.

Does that mean we experience instant ‘nirvanic’ feeling should we somehow manage to sneak through all these barriers of distance, security gates etc. and manage to take the connecting flights? The answer would be still a ‘NO’ since then immediately there creeps this thought  ‘Oh but what about my baggage?’ This would leave the passenger almost transfixed for the rest of his/her journey wondering the status of the baggage.

So is this all about the passengers alone and do the airlines need bother not this problem at all? Well, not exactly! The airlines in fact take the maximum hit out of this sub or poorly optimized gate allocations. The costs involved in providing a passenger who missed a connecting flight with an alternate airline connection, providing accommodations in cases where alternate connections are not possible at that time, cost due to lost/misplaced baggage etc. increase exponentially with the number of passengers with missed connecting flights.

And, finally is there a way where this problem impacting both the passenger and the airlines company be addressed at one go?  Yes, this is where Operations Research Optimizations or OR as it is widely known comes handy.  The OR takes into consideration all the input variables that impact the outcome – in this case to minimize the distance travelled by passengers bound for connecting flights (and thereby ensure the number of passengers failing to catch a connecting flight is minimized – impacting the airline operation costs)

Linear/Non linear Optimization techniques can be used to compute the best possible gates that can be assigned to an incoming flight such that the total number of people who fail to catch connecting flights is kept to a minimum. Number of passengers bound for connecting flights, time and distance required for the baggage to be moved from the incoming flight to the connecting flight, number of personnel required, costs of providing a passenger with accommodation, costs of providing a passenger with alternate connection etc. will then on become the constraints to solving this gate assignment problem.

The gate optimization problem requires to take into consideration that the operations performed would be dynamic as the time delay of arriving flights delays are not known before hand(at least in most cases) .

LatentView SAS to PMML Converter

LatentView Analytics has launched SAS-2-pmml (TM), a free, desktop-based application to generate PMML representations for your SAS models.

PMML is an XML-based language developed by the Data Mining Group, which provides a standard way to represent data mining models so that these can be shared between different statistical applications.

Representing your SAS models in PMML form gives you the flexibility to deploy your models using a whole range of applications that can "consume" PMML files, thus eliminating the need for expensive SAS licenses in the production environment.

The application takes SAS model datasets (EST-type datasets containing the parameter estimates) as input, and generates a PMML file (as per the PMML v3.2 specification) corresponding to each model.

You can use SAS-2-pmml to generate PMML files for regression models built using the REG and LOGISTIC procedures.

To check out more about SAS-2-pmml and download a copy of the application, please visit http://www.latentview.com/sas-2-pmml.html.

Multi-Channel Analytics

Today I received an SMS from a popular flight career stating that base fares have been slashed to by 50%.I called the customer care to understand the terms and conditions of the offer and finally made online bookings of my holiday tickets online. What struck me was that I had used three channels of communication to make the purchase, and even more interesting was that I made the final purchase online, and the offer came to me through a SMS campaign. Further, how is the carrier going to attribute my ticket purchase to a successful conversion on the SMS campaign?

With advent of new channels of communication such as internet, mobile etc. in addition to traditional channels, customers can now potentially receive campaign information through one channel to make a purchase on another channel.

Increasingly companies are finding it difficult to tag the successful transactions to the right campaign. Also, sometimes it is difficult to identify the true value of a customer.

Why is it important?

A noted website conducted a study on its direct revenues and pull-through revenues- to its surprise, it realized that the pull-through revenue exceeded its direct revenues by three times. Let us look at a simple case of two campaigns

If the online ad was evaluated only on the online purchase, we would make the conclusion that Campaign 1 was twice as effective as Campaign 2. However in reality, if we measure the direct and pull through revenues, Campaign 2 in fact has generated twice the revenue as Campaign 1.

The tables have turned! Wrong attribution of campaign revenues would mean sub-optimal allocation of marketing budgets leading to potentially inferior campaigns!

So what is the answer?

Multi-channel Analysis – Multi-channel analysis is the science of using data across different channels, identify common elements and study the true impact of channels, campaigns and resultant revenues.

Why is it so hard?

The answer is the “common” element. In the earlier example, the popular flight carrier has no way of knowing whether I heard about the reduction in fares through the SMS campaign. So is there no way? Well, there is - had the SMS campaign given the user a coupon code to use, and if I had used it to make the online purchase, the pull through revenue could be assigned to the SMS campaign (the “common” element here being the coupon code)

Data is the key

A key starting point in any multi-channel analysis process is to identify all the Data sources, from which the information needs to be integrated. It is also important to capture customer demographic data, in addition to channel data.

Once the data sources are identified, the most important step is to create the linkages in data through common elements. These could be as simple as using unique coupon codes (as illustrated earlier), service tags, reference numbers etc. Increasingly organizations are trying to use multiple 1-800 numbers to understand when multiple campaigns are run through different channels, how to attribute call volume and resultant sales to a specific campaign in a channel.

In the absence of these, creative methodologies would be needed to identify linkages; however these would be at a macro level but still can give good insights.

Conclusion

Organizations, which interact with their customers through multiple channels, would increasingly need to leverage multi-channel analysis to get a true picture of the customer and gain a competitive advantage in this economic climate.