Even the most data-savvy of marketing teams can make mistakes in thinking about the use of data in optimizing campaigns for financial success. Let’s face it, marketing data is rife with issues and is never perfect, and it is easy to put on blinders based on the data you have available, the systems you use, the marketing channels you work with, or even the directives of senior executives. Despite that, everyone wants to be able to connect the dots from ad impression to profit. Here are a few common data traps we see even smart companies easily fall into. How do you rank on this list?
1. Using Average Value CPA Targets
Cost-Per-Acquisition targets are fertile ground for data issues. For example, are your targets even based on customer value, or are they merely a “seems reasonable” guess? Smart data-centric companies will base target CPAs on actual customer value to ensure that their marketing programs don’t risk over-paying for customer acquisition. However, even smart companies can fall into using a single average CPA target for all their customers. In doing so, they underpay for audiences that provide higher-than-average value customers and overpay for low-value customers. Companies can avoid this by using targets tied to segmented customer values, not averages.
2. Using Last-Click Attribution
The attribution question has long been mired in a false discussion of “who gets credit?” when there is more than one user touch leading to a sale or lead. Some companies still use last-click attribution, often in a mistaken belief that this is somehow a “truer” view of acquisition, or that it just allows them to avoid thinking about attribution at all. Google hasn’t made things easier by offering multiple views of attribution with little guidance on when and where the different options should be used. Here is our take: Avoid last-click attribution at all costs unless you are evaluating retargeting assists. Last-click attribution will severely over-inflate your brand and direct numbers and cloud your ability to see high-value first-mover channels.
3. Not Considering Out-Of-Channel Effects
Everyone looks at their channel-specific numbers, but an astonishingly few companies continually examine their direct and brand channels, looking for influence from other areas. While everyone logically understands that users did not wake up with magical knowledge of a company’s brand, it often feels like that’s the assumption of marketing teams who put on “channel blinders” when evaluating their programs. Smart companies view their data holistically, looking for out-of-channel trends that increase or decrease direct and brand engagement. While only 5% of users in a typical search campaign are likely to bounce over to direct or brand, it is not unusual for a whopping 50%-90% of sales from social media or display campaigns to come through direct or brand traffic.
4. Over-Valuing Metrics Not Tied to Profit
What is the value of a Like? Most data-driven marketers have moved on from directly equating social media engagement as revenue-related events, but many metrics that don’t correlate well to revenue are still held as sacred cows. Any metric used for campaign optimization should be well understood in how it relates to revenue before it becomes a key KPI. Data-driven marketers with their eyes on the profit prize quickly realize that Time-On-Site, Impression Share, Cost-Per-Click, or other common metrics are not as tightly aligned with profit as they might think when other factors such as volume, customer value, out-of-channel influence, or profit margin are taken into account. Easy rule of thumb: Use post-marketing profit as your marketing KPI.
5. Assuming Traffic Equals Sales
We’re two decades into the digital revolution, and it’s still incredibly common for people to assume that eyeballs equal profit. Back in the days of traditional media the best shot you could make in media buying was the most eyeballs for the lowest cost. That approach doesn’t work in digital because of the competitive auctions, and yet “Let’s get more traffic to this page/product/site” is not at all an uncommon marching order, particularly from executives who don’t understand the auction effects in digital media buying. Smart data-driven marketers know that their job is as much about when NOT to buy traffic as it is in finding the areas of success, and continually evaluating how to increase the quality of an audience by peeling away the “eyeballs” that are not their target audience. This allows them to compete more aggressively in the auctions while protecting the bottom line. Sometimes less really is more.
These are samples of marketing data traps that are very easy to find in almost any campaign. Most of these issues can be avoided through three core practices: 1) By adhering to a holistic financial lens in optimizing the entire marketing program against financial targets; 2) By working backwards through the path that led from advertising to revenue and; 3) By not ignoring revenue that falls outside of the “channel buckets”.