Online Marketing: Thinking beyond customer acquisition costs

As per Gartner “by 2015, 80% of discretionary buying from consumers will be driven through effective digital marketing.” The explosion of product choices and complex consumer decision journey with ever evolving digital touch-points, has made it necessary for every brand to focus on Digital Marketing.

The Measurement Challenge

While a lot of brands continue to embrace digital marketing channels as part of the marketing mix, only 5% marketers have an efficient marketing metric in place as per the CMO council’s report. Digital Marketing, specifically, online advertising is expensive, and so a poor metric results in wasteful advertising money spent and leads to wrong decisions. Most companies approach digital marketing with an agenda of acquiring customers at the lowest acquisition cost. The focus, instead, needs to be on customers who hold the greatest probability of being your most profitable customers. It’s important to analyze the efficiency of marketing campaigns based on the projected long term value of the customers acquired.

Focus on long term value of customers

So, the trick is to estimate the long-term value of a customer in every business. Think of job sites acquiring resumes online, or freemium services which acquire customers for a free trial intending to convert some of them into paying customers later. In most cases like these, the concept of long-term value needs to be quantified – you could call this a revenue score, or quality score, of an acquired customer. This score for a customer has to be an estimate of the ‘value’ or revenue this customer delivers to your business over a period of 3 months, 6 months, or a year or a lifetime. The computation of this estimate can be made by analyzing past data (such as value generated, purchases made, or the customer’s initial behavior or demographics) of customers who were most profitable for your business. It will be an equation, and arriving at the perfect equation/model is an iterative process and will evolve over time as more data comes in. The model will also have to be updated regularly.

Start with simple models and evolve towards more complete models

Let’s revisit the two examples, discussed earlier. For most job sites, members who upload resumes are not their paying customers. How do you go about measuring the campaign effectiveness for resume acquisitions? The job sites are building resume databases for the recruiters and a simple model could be to develop a score that quantifies how valuable that resume is for recruiters. This ‘value’ could be computed by taking into account variables, such as, say, the number the jobs searched by the candidate after sign-up and the popularity of this candidates resume among the recruiters. There may be more variables that can be built into the model. Freemium models typically focus the digital marketing campaigns to acquire free customers and mostly measure campaign performance based on the acquisition costs alone, as it might take long cycles for the customers to pay and upgrade. So if the revenue is a long time in the coming, what is the proxy for ‘revenue’ that can work? What is the next best measure of ‘value’ that a free member is generating? One way could be to see the activity and engagement of the user as a free member. For example, in case of a web hosting company acquiring customers to host free web sites and then offering upgrade options, the business could track the number of free websites hosted, the traffic these sites attracted and the number of times the user visited the control panel – and use a weighted equation to bring these variables together to arrive at a single score – and use this score for predicting the probability of converting into a paid member, and to measure the return on advertising spend used to acquire a particular customer. Similar models could be evolved for most businesses but it’s important to start thinking beyond minimizing merely the overall cost of acquiring customers.