1. wns.com – Insights – Blogs – Research & Analytics Services Outsourcing
Improving Marketing Effectiveness by Enhancing Long-term Customer Value
By Shravan Pai
Can a car be driven by looking only in the rear-view mirror? The answer is an obvious ‘no’! You drive a car by focusing
mostly on what lies ahead. In the same vein, would you market to your customers based on how they have performed
historically, or, based on how they could potentially perform in the future?
Although past performance is a very good indicator of how customers might behave going forward, it does not paint the
complete picture. In particular, there are two key areas that don’t get addressed using this approach:
• It underemphasizes customers who could potentially move up the value chain in the future.
• It does not give adequate importance to the Incremental impact of marketing activities on different types of
customers. For example, the incremental impact of marketing spend on a low-value customer could actually be
higher than that on a high-value customer.
I have noticed, in my discussions with marketing professionals, a very good understanding of the fact that customers are
like financial assets and the real value of a customer is based on the revenue a customer would potentially yield in the
future. However, when it comes to the actual campaign execution, this understanding is often dulled by the challenges in
quantifying the effect of a campaign on the Future Value of customers. Consequently, a lot of models used for campaign
execution end up focusing more on historically high-value customers.
Different approaches could be considered to better capture the impact of the Future Value of customers depending on the
type of industry. It could be a simple qualitative cognizance, or a better segmentation scheme. It could even be a
quantitative Future Value model as highlighted in the example below. The example would best resonate with firms which
sell non-commoditized products / services to a member base.
Consider a firm XYZ that works on a membership based business model. As with any membership based business model,
all customers are not equal. The customers vary in terms of the frequency and value of transactions, the length of their
engagement with XYZ and their understanding of XYZ’s offerings. To get the biggest bang for its buck for its marketing
campaigns, the firm uses predictive models to evaluate which of its customers are most likely to transact and
subsequently prioritizes marketing spend based on this understanding. The financial effectiveness of the marketing
programs is evaluated after the campaigns, using standard ROI models. Even after taking into consideration the impact of
incrementality, the model regularly shows that targeting members with a higher likelihood to make transactions provides
the best return on marketing investments.
But is this really the best business strategy? Based on the observations we made at the start of this article, we know it is
not. For example, in XYZ’s case, by focusing its marketing spend on its top members, it ends up under-marketing to its
lower members. Many of these lower members are often relatively newer and are not fully conversant with the benefits
that XYZ offers, and hence their transaction volumes are relatively lower. These small transactions do not justify
maintaining the membership and over a short period of time many of these members attrite. With the right marketing
stimuli, many of these low performing members could have graduated to high performing members in the future. The
second aspect of many models used in businesses is that they do not adequately account for the halo effect of the
activities. Going back to firm XYZ’s example, not only did it lose out on potential high transactors, it also ended up losing
out on the membership revenue that the attrited members would have brought in had they continued as members.
How could XYZ have improved the effectiveness of its marketing campaigns?
• Recognize the fact that every activity has long-term effects on consumer behavior. Even though in the
short term every transaction has the same value, when considered from the long-term perspective, every
transaction does not. For example, a transaction that makes a relatively newer low-value member more familiar
with XYZ’s offerings could, in the long term, make her a high-value member.
• Incorporate halo effects. For example, in XYZ’s case, the loss of transaction revenue among lower tier
members could also result in a loss of their membership revenue.
1

2. • Increase the time-span over which campaign ROI is calculated. Many of the high transacting members would
have transacted even if they had not received a particular marketing campaign. Often in firms, incremental
revenue is measured by holding out control groups among members belonging to similar segments and looking
at difference in transactions pre- and post-campaigns. Part of the reason for higher incremental revenue
observed among higher tier members is due to a demand pull forward because of a particular campaign. When
looked across multiple campaigns and over a longer time period, the impact of demand pull forward is reduced
and the true incremental increase in transactions is better captured.
How did an experienced analyst service provider help a client address these issues?
The service provider helped develop a Transaction Propensity model, and a Future Value model using a combination of
Survival and Logistic techniques. The Future Value model could estimate the change in Lifetime Financial Value of
different members for different transactions (for example, it would estimate that the change in Lifetime Financial Value for
member A, making transaction type M would be $X).
As expected, the change in Lifetime Value was not the same for all members for a particular transaction. It was observed
that for certain groups of lower tier members the incremental Lifetime Value of making a transaction was significantly
higher than those for the higher tier members. The different Lifetime Values of the transactions for different members were
then used in association with the member Transaction Propensity model. Whereas, earlier the Transaction Propensity
model would rank-order members based on propensity to transact only, the new approach could rank-order members
based on a combination of the Transaction Propensity and incremental value of each transaction.
Thus, using this approach it was possible to place more focus on the value different customers would give in the future
(rather than in the past) and better gauge the true incremental financial contribution of marketing activities.
http://blogs.wns.com/tabid/127/Category/9/research-and-analytics-services-outsourcing.aspx
2