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“…and they lived happily ever after” is a popular happy-ending of many legends and fairy tales. In fact, this saying varies among languages and cultures. For example, in French it will be “…and they married and had a lot of kids” (see also this movie!). In Hebrew it will be “and they lived in happiness and affluence until nowadays”. Moreover, in Hebrew happiness and affluence sound very similar – “osh-er”. So the financial aspect of the Hebrew version is quite close to the essence of lifetime value, as described in my The Money or Your Life – Some Reflections About Lifetime Value (LTV) with the exception that it considers affluence until nowadays and stops there.

In this post, I will discuss using LTV for decision support, looking forward.

The common use of LTV is to introduce financial metric for customer relationship optimization. If we look schematically at the customer lifecycle, an organization spends in order to acquire a new customer, to serve her/him, to expand the relationship and eventually to retain and make a profitable relationship last. In each of these phases LTV estimation can play a role. For example, many banks will offer loans to students in prominent universities, in better terms than to the average walk-in customer that visits their branch office. These banks assume that such students are likely to become affluent customers (and stay alive for some years). LTV estimations are needed in order to justify the associated acquisition budget, e.g. the marketing spending on campus campaigns and the “better terms” of the loans.

LTV can also be a decision metric for selecting a better alternative, for example, selecting among several alternative campus campaigns – each with its associated costs and expected value (= the expected accumulated LTV of the recruited new customers). LTV is also a major consideration for spending on retention – the other end of the customer lifecycle. Spending on retaining customer should consider the impact on the customer loyalty, on its future profitability and the savings on keeping a market share (=need for more acquisitions). In between those two ends of the customer lifecycle, LTV can be a major support for differentiating service levels among customers and for decisions on customer expansions, e.g. which service should be promoted and in which pricing levels?

Please note, that using LTV impact for a decision on a single action at a specific point of time (e.g. deciding on the better acquisition campaign in a campus) is a simplification of reality. LTV will be also impacted by the next decisions of the organization (and the customer) – using LTV, I may have selected the best campaign but this expected LTV will change with each and every interaction and with each and every passing moment (lifetime is getting shorter unfortunately).

So considering the expected impact on LTV of a single action, enable a “locally” optimized decision and not necessarily a global optimal decision. Trying to stay practical, I will give some examples and dive into some of the approximations of LTV in a “local” decision making in my future posts.

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