This is going to be the first – of a couple of posts – about this metric. Lately, we have seen that Enterprise Web Analytics vendors have put some thought into a Visitor Lifetime Value metric (KPI) – I would like to add the following comments:

First and foremost, I think we should use a stronger description (skipping the traditional Analytics Vendor description of Visitor) and call it: Customer Lifetime Value (which is also the more general and common marketing term) – Secondly, I think one have to accept that a Customer Lifetime Value metric (without even debating how we are supposed to calculate it) directly referenced to e.g. a campaign is far from telling anything about either ROI (we must expect multiple campaigns attribute to a sale/s) or even whether we reached a high value customer segment (as the metric does not recognize whether a customer is frankly lost, have not bought anything in years or otherwise not represented rightfully).

That said; Introducing a Customer Lifetime Value metric, I suggest that the following accompanying metrics be introduced as well:

Customer Lifetime Metrics (KPI’s)

• Customer Lifetime Value (definition: Aggregated revenue SUM)
• Customer Lifetime Acquisition Cost (definition: aggregated campaign costs SUM
• Customer Recency (definition: Time since last sale)

Furthermore, given the strong filter and segmentation opportunities in Web Analytics solutions (that compared to e.g. more traditional CRM systems) – defining “Lifetime” (usually a very difficult task) as ALL tracked data so far, seems quite appropriate to me. We can then use this as a proxy for the absolute customer lifetime value (and we probably do not have the time to wait it out OR even the history of enough customer life cycles to have an educated idea anyway).

Then again; coming back to the ever returning dialogue about “attributing” a given sale to e.g. the Original campaign, The Direct Campaign or perhaps something even more glamorous or Intelligent. Does this matter and is this really a concern? – should we not “just” present the aggregated SUM of all Customer Lifetime Values of those visitors who reacted to a campaign. As in, if 19000 visitors reacted to a campaign (as per a campaign summary) then a SUM of all the visitors Customer Lifetime Value is presented alongside the other campaign metrics. The period chosen (e.g. February) is used to present active campaigns and their visitors in the period, but will otherwise not have any implication on the SUM calculation of all the visitors Customer Lifetime Values.

Then more importantly; one can filter/ segment on Customer Recency (dependant on business-model) to create a better understanding of what kind of customers (and their value) we are looking at, thus more an indicator on whether you are talking to the intended audience (as in; is this your typical repeat buyer, the lost customer coming back etc.). AND if one want to generate a true (or probably just better) ROI calculation; a Customer Lifetime Acquisition Cost metric can be applied.

This is a good beginning – leading me into the second post about the Customer Lifetime subject. How one should consider using the described Customer Lifetime Value metric (KPI) as part of ones SEM optimization efforts.