It goes without saying that the explosive growth of eCommerce through the course of the pandemic managed to create new dynamics and choices for millions of consumers across a variety of marketplaces and channels, with new products, brands and subscription services.
The ever-growing DTC ecosystem created a democratized battleground in the fight for user acquisition and consumer loyalty. It quickly became clear that the brands that embrace this growth with a strategic approach to modern commerce will emerge as the differentiators. But what would it take for DTC brands to get that extra edge they need to achieve growth to the fullest potential? To put it bluntly, they need superpowers to give them that added edge. Thankfully, it is entirely possible.
The right kind of visibility doesn’t come easily
It takes more than throwing major budgets into ad networks to achieve the visibility needed for DTC brands to maximize the potential of their user acquisition campaigns. That is because on their own, networks such as Google and Facebook focus on short-term conversions that take place soon after the initial engagement, such as signups, purchases, the viewing of content, and day-7 login.
There is one major issue here. These short-term metrics don’t help us measure customer lifetime value (LTV), because subscriptions don’t have many touchpoints with customers in general, and therefore late purchases are less of a relevance. The purchase of a subscription by itself is not predictive for LTV, considering the fact that some users will stay loyal as others will churn. The goal here is to target people who will stay subscribed in the long term and not churn.
According to insights from PocketSense, once consumers realize they need or want something, they search for information, formulate options, and evaluate the options to narrow them down. Research elements include thorough inspection of the brands website, policies and content; interacting with sales and service employees at companies; and even social media/online searches for reviews. Questionnaires sent by brands to their customers also help evaluate the loyalty of users. Of course, the questionnaires themselves would need to be well-planned to make it as easy as possible for the audience to complete while allowing brands to gain the most relevant insights.
This is the context that is missing in the eyes of ad networks. This is the signal they need to ensure the creatives are being delivered to exactly the right users who are more likely to fit the model. This is the edge that DTC brands need to achieve the right kind of visibility for UA.
There is tremendous potential
The sky’s the limit when implementing a user acquisition strategy that focuses on long-term profits and scalability. As mentioned in a previous blog post, “using LTV measurements, which are the best indicator of profitability, makes much more sense for optimization purposes. However, these metrics need time to manifest — a luxury that most marketers don’t have.”
So now what? This is where predictive LTV UA Optimization comes in, to acquire valuable users at scale using the power of AI and machine learning technologies to create new signals based on the evaluation of a users value, instead of a single event sent online. This is ultimately made possible thanks to Facebook conversions API and Google’s Server-Side Tagging, both of which grant media buyers the integration that is essential to send server side signals to ensure optimized campaigns. While the signal possibilities are endless, the ROI uplift will come from sending out signals for LTV.
At this point, the question is what ROI brands should aim for. I dove into this in a previous post, but in a nutshell, it depends on the industry itself. For instance, you should aim for a 20 percent ROI by the first month if you are a SaaS company. eCommerce aims at 70–95 percent within the first month. It’s not one-size-fits all, because predicting LTV can be done in a variety of levels and ways.
Prepping for powers
In order to determine which is the best UA predictive solution that fits the team, budget, scale, and internal resources of DTC brands, it is recommended for team members to be aware of important factors worth considering before taking the first step. For instance, what metrics does the solution use to predict LTV? Because historical averages alone are merely the tip of the iceberg. Also, how many internal engineering resources would be needed to implement and use the solution? Codeless solutions are ideal, to avoid the delays that are commonly associated with having too many cooks in the kitchen.
DTC brands have much to consider in order to come out on top of their UA efforts, and the backing of an AI-powered LTV predictive solution will help them get optimal results in the long-term.