In this case study on the airline industry, Pascal Burg of management consultancy Edgar Dunn & Company explains how customer data should drive not only profits but also true business intelligence...
Airlines always make for great case studies: they are high-profile brands, they were among the first businesses to launch loyalty programs, and we all have an "experience from hell" to share. But we chose to focus on airlines for this article because all businesses can learn from (a) two major market changes that airlines are facing, and (b) how airlines use customer data to drive the business decisions required to address these changes.
The airline industry is facing two major changes which also impact other industries: growing buyer empowerment and increasing levels of proactive sales and marketing by competitors.
Buyer empowerment is driven by increased consumer access to product and pricing information. Meta-search engines like Kayak and SideStep now enable buyers to search across multiple travel supplier and travel agent websites quickly and conveniently for the lowest fare. Kayak also offers a service called Buzz, which identifies the lowest fare that was actually paid by a customer and enables another customer to make the same lowest-fare booking. Farecast enhances this buyer empowerment by offering a forecast of future fares to help buyers decide when to fly and when to buy in order to get the lowest fare.
The second major market change is the increasing level of aggressive and proactive sales and marketing activities undertaken by competing airlines. "Push marketing" initiatives are an example of one successful proactive sales tactic.
Since early 2005, Southwest Airlines has generated more than US$130 million in revenue with DING!, a service that enables customers to receive alerts directly on their computer about special fares for airports of their choosing. Another example is the introduction of ancillary product sales. In 2005, RyanAir collected US$265 million in ancillary revenues, which represented 15% of their total revenues, from fees generated by baggage handling and hotel or car rental bookings.
These proactive sales and marketing activities are putting downward pressure on the profit margins of airlines that are not as proactive, and growing buyer empowerment is putting pressure on all airlines. Many airlines are reacting to these changes by developing data-driven marketing strategies as a way to generate incremental revenues and/or enhance their margins.
Customers were not created equal
Why should a business be concerned with data-driven marketing strategies ? Because customers were not all created equal: "Some customers are very profitable and loyal. Some customers are unprofitable and fickle. We are beginning to discover that we can increase profits by studying these variations, and by using this newly acquired knowledge of our customer base to market differently to each discernable profit group."
In other words, developing data-driven marketing strategies is an increasingly important option for maintaining and increasing margins. Of particular significance is that margin uplift is achieved by improving the mix of customers without necessarily needing to increase market share. Developing data-driven marketing strategies does this by supporting the development and delivery of a differentiated product tailored to a specific segment.
It also supports marketing activities that influence the appropriate customers towards the appropriate behaviour. If you want to influence future customer behaviour, it makes a lot of sense to develop a segmentation based on past and present behaviour. And for your existing customers, you have all the necessary data on file.
Why is it so difficult?
Given that many businesses have the necessary data for a segmentation analysis of existing customers and that developing data-driven marketing strategies can increase profits, why do companies find it so difficult to implement this approach?
A typical large enterprise such as a bank or an airline holds customer data in multiple legacy systems rather than in a consolidated central database. The first challenge is therefore to collect, cleanse and store the data in a customer-centric manner.
The second and greater challenge is to analyze the data in such a way that identifies the insights that will result in profit improvements. In many cases, businesses have too much data and do not know:
This third question creates the hardest challenge as businesses seek to operationalise the outcome of the data analysis at the individual customer level.
So what can we do about it?
As a result, there is a real need for a practical and pragmatic analytical approach to help companies realize benefits from developing data-driven marketing strategies. To meet this need, Edgar Dunn & Company (EDC) developed an approach called "Slice & Dice." Slice & Dice is a process used to analyze large quantities of transactional data in order to:
A typical Slice & Dice project works for the benefit of business (not IT) users, involves three steps - business analysis, datamart construction, data analysis - and creates outputs, including:
A customer behaviour and profitability analysis such as Slice & Dice addresses the three key marketing questions of who to target, what to offer and how to communicate that offer. And, best of all, it uses internal customer data to drive these business decisions.
Case study in the airline industry
Let's take the example of Slice & Dice applied to the Frequent Flyer Program (FFP) of a large international airline. Tom and Tim are twins and share the same profile: both are male, 45 years old, and earn US$100,000. They also share the same flying activity: both fly 100,000 miles each year, have achieved the highest flyer status and use the airline co-brand credit card. So both of them are the airline's best and most profitable customers. Or are they?
In order to answer this question, Edgar, Dunn & Company completed a Slice & Dice project that involved allocating all revenues and costs of the airline at the individual customer level. Revenue items included flight revenue as well as upgrade sales, lounge fees, and change fees. Cost items included fuel (based on aircraft type and flight lengths), aircraft, personnel, distribution (travel agency commissions, GDS fees, credit card fees), cost of miles earned, and overhead.
We then calculated the profit contribution for each member of this Frequent Flyer Program and split all members into 10 deciles from most profitable to least profitable. Surprisingly, Tom and Tim were at opposite ends of the profitability spectrum.
We looked first at the differences in revenues across these 10 deciles. "Profitable Tom" generated significantly more revenue for the airline (50% more than Tim). This higher revenue was driven by the type of product (business class) purchased by Tom, as well as by the timing and the channel used for these purchases.
We then looked at differences in costs. "Unprofitable Tim" generated significantly higher fixed costs (nearly 70% higher than Tom). This was mostly driven by the fact that Tim frequently leverages his elite status to get free upgrades to business class.
The 'size does not matter' law
The conclusions from this case study are similar to conclusions from many Slice & Dice projects. We refer to it as the "Size does not matter" law. Many businesses assume that "big" clients (i.e. clients that buy a lot) are always their best clients and that gaining market share is therefore the most important objective. This airline case study showed that this generic assumption can be wrong: some "big" clients (that were flying a lot of miles each year) were actually very un-profitable. As for many businesses, "big" is not enough to identify the most profitable customers, and that's when a Slice & Dice project will help.
In conclusion, you need to know who are your most profitable customers and do something about it.
Edgar, Dunn & Company's experience in analyzing customer data across industries shows that all customers are not created equal. Businesses should therefore understand their segments and market differently to each segment in order to increase overall profitability. We have seen limited success with the implementation of data warehouse and CRM systems, and we believe that businesses should take a more efficient and effective approach. Being pragmatic means undertaking a customer profitability analysis that does not take many years or a huge IT investment and that does yield practical results. The real test for any such initiative is whether it helps you identify initiatives that will actually increase your revenues and/or reduce your costs.
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