Cost per action (CPA) remains the most popular metric to evaluate online marketing success. But is the stand-alone CPA really the right approach for measuring and generating long-term value?
John Wanamaker famously complained that "Half the money I spend on advertising is wasted; the trouble is I don't know which half." While measuring effectiveness of advertising must have been a challenge for Wanamaker back in the 19th century, today’s marketers have the tools and technology to measure return on investment (ROI) with a great deal of accuracy and react accordingly.
Display advertising in particular has come a long way in the past 10 years, with innovations like dynamic messaging and real-time bidding that leave online marketers swimming in innovation acronyms. However, one thing that has remained unchanged through all of this innovation is the way that these campaigns are measured - cost per action (CPA) remains the most popular metric to evaluate online marketing success. But is the stand-alone CPA really the right approach for measuring and generating long-term value?
Acknowledging the Opportunity
The problem isn’t so much that the CPA is the wrong way of looking at ROI, but rather that it’s too limited in scope, as it doesn’t account for the changing relationship of the marketer with the customer over time. While new-user acquisition may be critical to growing the customer base, retention and loyalty of existing customers are what enables marketers to grow sustainably. It’s time for marketers to realize that an investment today could - and probably should - be measured not only in terms of its immediate return, but also in terms of potential future gain.
Let’s take, for example, a fashion retailer with 500,000 unique visitors to its online shop each month. This site’s different types of shoppers create different action goals for the marketer: converting a serial bouncer to a first-time customer, bringing back a customer who hasn’t shopped in six months, or increasing the frequency of purchases from the most loyal customers. All of these actions are valuable for the retailer, but a simple CPA-based model might not provide the depth needed to show how a short-term investment might pan out in the long run. Today’s technology has transformed the way media is bought and sold — so key performance indicators (KPIs) need to keep up.
Measure Customer Lifetime Value – an Alternative Approach
How can marketing spend account for the expectation of customer loyalty? In order to do so, marketers must begin to measure and react to the potential lifetime value (CLV) of a customer. A simple example: Let’s assume the average basket of our fashion retailer’s online shop is $50, and that a new customer remains a customer for an average of 18 months, purchasing on average every three months. Based on these figures, we could calculate a CLV expectation of $300 for this new customer (6 x $50). In this light, we can see that the long-term value of this customer and therefore, the value of continuing to engage with this customer is much higher than the original $50 action.
In practice, CLV is calculated using three customer purchase attributes: recency, frequency, and monetization or RFM. While the first two are pretty easy to understand and measure (How recent was the customer’s last purchase? How frequently did she purchase over time?), monetization is a more complex metric that requires a combination of data about how many products a customer returned to the shop (a loss for the marketer) and the margin generated by the customer over time. Using these three dimensions, marketers can get a clear estimation of the value of that customer over the lifetime of purchases.
The ability to calculate CLV isn’t something new to savvy marketers. What’s new and exciting is the ability to finally use these calculations for real-time media buying and personalization. Programmatic display makes it a reality.
Programmatic Display: Bridging the Gap From Data to Actionable Data
Programmatic display technologies, specifically real-time bidding (RTB) and dynamic creative optimization (DCO), enable markers to use data to buy and build individually customized ads on-the-fly. Before an impression for our fashion retailer is served on a publisher site, an algorithm can use behavioral and CRM data to evaluate in about 5 milliseconds if the user in question is a new customer or existing customer and how far up or down that loyalty road she is. These programmatic technologies can dynamically adjust investments to be aggressive or conservative depending on the user’s existing purchases - or lack thereof .
Transitioning from pure-play CPA to CLV-driven marketing is no easy task, but marketers can start by using CLV to derive differential CPAs for different target groups. The ability to segment your campaigns is absolutely critical to long-term success, so it’s time for marketers to get moving in the right direction. Find the pieces of your marketing spend that can take this data and put it to work. For example, are you still using a single CPA for all of your retargeting? That’s an easy piece to fix. Start by splitting your site’s retargeting campaigns into groups of new and existing customers and you’ll already have a big win.
The more data you know about a given customer segment, the more finely you can tailor the strategy for that segment using the same segmentation strategies that are already familiar to most retailers from e-mail marketing. After that, it's about finding the right investment level relative to the value over time of these customers, and then making adjustments to that on as regular a basis as you can manage.
In the end, it’s about changing the mindset from thinking about the customer as a single transaction to respecting the value of a long-term investment in customer loyalty.
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Rohit Kumar joined Google in 2007, where he began as industry manager for Google’s automotive team. Later, as a founding member of Google’s EMEA Ad Exchange team, he led the commercialization of the product and helped bring real-time bidding (RTB) to the marketplace in early 2010. Prior to joining Sociomantic, Rohit ran his own ad tech advisory business. He holds a degree in economics from University of Madras in India and a Master’s degree in management from the London School of Economics. Today, Rohit leads the growth and development of Sociomantic in Southeast Asia. You can find him on LinkedIn and Twitter.
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