Analytical Engine

Accurately estimating demand at a granular level is critical for operations management in the travel industry. Traditional estimation models have used historical data to predict the demand. Recently, some companies have started experimenting with a novel source of data to augment the traditional models – the search data from their own websites.
Scandinavian Airlines has been a pioneer in this space.  It is using the volume of search activity on its website for flights on a particular day to estimate demand for the specific routes and flights.  They use this information to guide their operational decisions e.g. which type of aircraft to schedule for the flight segment.  As expected, there was a lot of resistance within the company to make decisions based on these numbers but the web analytics team collaboratively worked with the constituents to gain confidence of the operations team and bring about the change
 Online is already a significant channel in the travel industry.  Web analytics tool vendors like Web Trends and Omniture have made significant inroads and power most of the major travel websites. This means that there is a lot of data readily available for analysis. Travel companies should follow Scandinavian Airline’s lead and start exploring this data to make better operational and marketing decisions.

An interesting update on one of the examples we used in our Choice Optimization paper.

We mentioned how credit card companies offering a complex array of products risk overwhelming their potential customers with too many choices (‘purchase paralysis’). One way to reduce this risk is to ask customers questions about the product features that is important to them (reward card vs. low APR card) and then use the answers to narrow down the choice of products (explicit filtering). However, as we pointed out

“….this process indicates to customers that there are additional options available to which they are not being exposed. Also, explicit filtering still introduces the risk of regret…”

Capital One seems to have figured out a way to overcome the risk of regret through their new online Card Lab feature. Built for customers who are evaluating new cards from Capital one, it uses a very simple intuitive design to let a potential customer understand the various tradeoffs between features and choose the card that best suits his/her need.

As the customer tries out the various options, a huge amount of data will be collected. This can be used to generate insights to develop new products.

This tool is applicable to situations where consumers are making trade-offs between product features and options while shopping online. Whether it is buying auto insurance, cable/wireless service or a digital camera – it makes choosing the right product a simple and transparent process for the consumer and might result in less abandoned carts and more sales for the company.

Webmetricsguru points to two interesting studies done on impulse buying. One of the studies uses a behavioral economics framework (with brain scan evidence) to explain the buying process.

..consumers trade off the immediate pleasure of making a purchase against an immediate pain: the pain of forking out the money for the item.

Furthermore, to increase the likelihood of ‘impulse’ buys, online merchants are suggested to follow some principles which are very analogous to the ideas we have put forward in our recent Choice Optimization paper.

1. Expose the target audience to the “stimuli”:
   Priming the audience through targeted advertising and product placement
2. Figure out what the price point for buying “stimuli” items are for the target audience(s):
   Avoid price risk for consumer through market research, transparent pricing (showing competitor prices like Progressive) or price matching schemes
3. Offer the item(s) (stimuli) at a lower price than what the target audience thinks it’s worth:
   Relative framing by comparing the value of the decision compared to an alternative.

To make the point in tangible terms, here is an interesting example of relative framing which we found while researching for the whitepaper:

The Economist magazine once offered its three subscription options on its website,
(a) Online only for $59
(b) Print only for $125
(c) Online and Print for $125.

16% of customers chose the “Online Only” option and 84% selected the “Online and Print” option. No customer chose the “Print Only” option, so the company removed it. However, when only two choices were presented (“Online Only” and “Online and Print,” at $59 and $125, respectively), the number of customers choosing the lower priced “Online Only” product increased to 68%, while the percentage of subscribers choosing “Online and Print” dropped to 32%. While no one chose the “Print Only” option, having it available made the more expensive “Online and Print” option appear to be a bargain, and this drove a higher percentage of customers to select it.

Photo credit: PaysImaginaire

Professor Dan Ariely of MIT has done extensive research on human decision making using the framework of behavioral economics (go here to pre-order his forthcoming book on the subject). We recently collaborated with him to apply some of his research ideas in the online world. The result of the collaboration is the white paper: Predictably Irrational Customers: Optimizing Choices for How People Really Buy, Not How We Think They Buy.

As customers begin to make more financial decisions online either by conducting a transaction or researching a product online before buying at a store, it becomes critical for companies to have framework to understand customer decisions to generate potential ideas to improve the website. This coupled with the ability to rapidly baseline, test and evaluate the ideas using web analytics makes for a potent capability for companies to gain competitive advantage.

You can download the white paper here

PS: You can also set up sometime to speak with our resident choice optimization experts by sending an email.

A recent article in WSJ talks about how offline segmentation schemes and data is being used for online targeting of advertisements. The sidebar of the article mentions how Acxiom executes on this strategy

1. Acxiom has accumulated a database of about 133 million households and divided it into 70 demographic and lifestyle clusters based on information available from public sources.

2. A person gives one of Acxiom’s Web partners his address by buying something, filling out a survey or completing a contest form on one of the sites.

3. In an eyeblink, Acxiom checks the address against its database and places a “cookie,” or small piece of tracking software, embedded with a code for that person’s demographic and behavioral cluster on his computer hard drive.

4. When the person visits an Acxiom partner site in the future, Acxiom can use that code to determine which ads to show.

5. Through another cookie, Acxiom tracks what consumers do on partner Web sites.

At a client engagement we also have utilized a similar strategy (however without using any personally identifiable information like email address) to understand how segments of customers consume content on a website (see image below). This information is used to develop and personalize content on the website and can also inform site layout and design.

What we are witnessing is that companies are trying to bridge the worlds of marketing and web analytics. As we have previously mentioned, third party segmentation schemes which are so pervasive in the marketing world will increasingly become the translation layer between the two worlds.

CPG companies spend a lot of advertising dollars to build and maintain their brands both in traditional media and increasingly in the online world. Directly measuring the effectiveness of such brand building efforts is challenging and companies default on sales volume as a proxy metric, knowing fully well that there are myriad other effects which influence sales. Other than that, survey based brand recall studies are also used to indirectly determine the effectiveness of advertising.

Online channel lends itself very well to measurement and CPG companies can exploit that fact. CPG companies should geographically segment (by DMA) their website traffic and correlate it with that particular market’s mass media spend to determine whether advertising is creating interest in their products. Media spend and mass market campaigns have a correlation with greater online activity and web analytics provides a way to measure the effect. The online activity metric also provides a real time campaign tracking ability to companies. Proper setup of test and control markets will provide insights into effectiveness of various forms of media spend (online only, online and print, only television etc.) for different geographies.

Setting up such a capability requires, a good understanding of baseline metrics of the website and also a solution which will help to link the IP address of the website visitor to a particular DMA.

While this method is also not full proof as it tries to measure effectiveness in terms of activity on the online channel (that too only on company’s own website), it provides a view from a channel where at present, activity is not systematically measured and very seldom correlated with cross channel initiatives and overall business metrics.

Jim Novo, who authors an insightful marketing blog, rightly identifies that PRIZM clusters (or other geo-demographic segmentation systems) are not as predictive as a behavioral data for desired ‘action’ in the online world.

e-commerce folks are usually looking for behavior from customers, and the fact demographics are not generally predictive of behavior by themselves

Segmentation and targeting are different and PRIZM is fundamentally not a targeting tool. Even in the world of direct mail where rich behavioral data from website is not available; it is rarely used as the sole targeting criterion.

However, geo-demographic segmentation systems like PRIZM, have some benefits for which web analysts should consider using them. Here are four reasons for which we have used PRIZM in an online setting:

1. Customer data is available
When customers login to your website and it is possible to link them to their PRIZM code, you have one more variable to put into your targeting models and an additional dimension of information on your customer.

2. Tie back to non-online world
In most organizations, web analytics is another marketing silo, and senior executives do not yet understand how to relate their online and offline customers/visitors with their media buying strategy, direct mail strategy etc.. A segmentation scheme like PRIZM plays the role of a standard translation engine for the various marketing organizations to describe a customer. Identifying your online visitors using PRIZM helps your marketing department create a holistic view of the customer.

3. Hypotheses to identify content gaps
If you are a content-based site, then having PRIZM segment of your visitors associated with its rich detail on media consumption and lifestyle interests, will help develop hypotheses on the existing content gaps on the website. You will need to augment it with good market research and do rigorous testing to identify what really works.

4. Monetizing the site
Advertisers understand PRIZM – so if you have advertising inventory, then you should consider segmenting the visitors based on it.

PS: PRIZM is one of the major “geo-demographic segmentation” schemes available in the market and we have used the two terms interchangeably in this post