A frequent measurement challenge for businesses selling goods and/or services using a subscription model is to be able to estimate the net running total of spend by customers acquired in a given time period and then to compare this figure to the total spend on the original source of acquisition. For SaaS businesses, evaluating acquisition campaigns without taking a long-term customer spend view could literally amount to throwing money down the drain. This is particularly relevant for businesses with high customer acquisition costs that take time to recover or in scenarios where the first transaction revenue is not enough to cover acquisition cost and provide sustainable profit margins. The evaluation of media channels using first conversion ROI inevitably leads to flawed and irrational conclusions often resulting in missed optimization opportunities in addition to a loss of marketing credibility.

So why is it so hard to evaluate acquisition budgets against customer lifetime value? The answer lies almost always in the technical capabilities required for tracking customer spend over a lifetime and in allocating individual customers to specific campaigns. For example, customer lifetime value can be significantly complex to calculate in scenarios involving free trials. Customers cancel risk-free trials (many times via emails or phone), transactions deemed fraudulent are not completed even though they are recorded in the web analytics tool, chargebacks by customers need to be accounted for and in many cases, products are simply returned thereby canceling the original orders. Or consider the scenario of third-party advertising or co-marketing. A business that spends a significant proportion of its overall marketing budget on third-party email advertising whereby partner sites send out emails containing signup links that drive prospects to advertiser’s site. The advertiser would like to track how many users signing up through these links eventually go on to become paying customers and also estimate the lifetime total of their spend. These metrics are required in order to weigh up the performance of various third-party email advertisers against net revenue from customers.

These are just some of the common scenarios that mandate tracking the entire customer journey for properly evaluating acquisition marketing spend. From a technical perspective, this requires

  1. Collating data from across multiple touch points throughout an active customer lifecycle.
  2. Implementing a reporting and analytics setup to use these holistic datasets for assessing marketing performance using lifetime value metrics.

In the sections below, we focus on the data collection aspects and build a case for using cloud hosted solutions by comparing two alternative technology options. To simplify things, we further narrow our focus on pureplay digital businesses with no offline presence and where a bulk of the customer transaction data is already present in online applications and databases. For such businesses, an acquisition typically happens via the website though subsequent transactions may or may not be triggered via the web.

Technical options for tracking lifetime customer value

The architectural options for tracking customer lifetime value for digital businesses effectively boil down to two key approaches

Option 1 – Using Web Analytics tool to collect transaction data

This is a rudimentary approach (yet widely implemented) whereby website tagging (or batch data import) is implemented to send event data into the web analytics tool. The implementation is usually simple but its utility is largely limited to scenarios where all post-purchase transactions (returns, trial cancellations etc.) happen through the website. This, of course, is very rarely the case. Even a simple scenario such as tracking cancellations of repeat billings in third-party tools (e.g. Stripe, Zuora) becomes incredibly challenging to implement depending upon the data model and integration architecture of the web analytics tool used. Web/Customer Analytics tool vendors would, of course, have you believe otherwise but even if pushing data into the tool could be implemented with reasonable ease, building meaningful reports with this data would be practically impossible without extensive customization and vendor consulting. This is largely because off-the-shelf web analytics tools rarely provide the kind of complex drill-down data analysis and visualization capabilities that would be required for meaningful analysis.

Option 2 Creating offline mashups of web analytics data with other datasets

This is a more advanced architectural approach that can scale with business and also one that provides opportunities to implement advanced business intelligence on customer lifecycle data. Technically, it makes use of an intermediate data hub (typically cloud hosted) that combines acquisition marketing data with post-acquisition events such as account cancellations, returns etc.

Option 2 as outlined above should ideally be the ultimate goal of every Digital Marketer using the website/mobile app as an acquisition channel. For example, it is far easier to send data from digital marketing applications (ad servers, search advertising, social media etc.), billing applications, payment gateways, shipping providers, bespoke excel sheets etc. into an intermediate data store with custom designed data model rather than to aggregate all this data into a web analytics tool with rigid data model and more importantly where data ownership is lost. Once data is consolidated, customers can then build advanced business intelligence solutions for accurately tracking lifetime value metrics against a variety of dimensions including first/last touch channels, media partners, campaigns, and many more.

Article purpose

A short-form article designed to project domain authority around marketing measurement challenges in SaaS business scenarios. Articulates the data challenges and solutions for tracking acquisition spends against LTV as opposed to first touch attributions.

Target audience

Digital Marketing Managers, Web Analytics Professionals, Marketing operations staff.

About the Client

Digital Marketing Managers, Web Analytics Professionals, Marketing operations staff.