Can you explain what you do for a living, in one short sentence?
We all know that there’s more to growth marketing than what meets the eye. It takes usage of both the left and right brain to be able to view product development, marketing, and advertising as one big happy family, instead of silos. Then there’s that balancing act of keeping customer and user acquisition, activation, retention, and upsell in mind.
And what’s that one thing that’s at the core of it all? Data.
Of course, many types of data can be presented to growth teams on a silver platter, but it would hardly mean anything without the knowledge of how to utilize it to achieve maximum results.
Data: the building blocks for growth
Data is at the heart of the growth function, which is why growth executives everywhere invest a significant share of their resources to create the infrastructure that enables the analysis of user behavior, experimentations, and targeted promotions.
Growth teams need to understand how to interpret and capitalize on data, to ensure that their user acquisition campaigns reach their full potential. User data, especially LTV data, sets the tone in terms of what the growth objective should be. For example, should resources go towards user acquisition, or towards combating churn?
As a growth marketing leader, you would also need to help your brand (and its execs) quantify and understand progress against goals. This is typically accomplished through the selection of key performance indicators, and the development of reports on these metrics for consumption across the organization.
Loyalty marketing, and thinking long term
There are endless campaign ideas that can come to mind to spur growth. In an effort to narrow down (and prioritize) the list of ideas, growth executives should consider the impact of the change if the campaign realistically goes as planned, in addition to the confidence level towards the success of the campaign, and the costs associated with the campaign (or test campaign).
One fool-proof approach to growth and UA campaigns is to implement loyalty focused marketing campaigns, which serve to be far more strategic than hit and runs. It’s natural for companies to try to gain new and unique customers, but not enough are taking full advantage of the existing base. It’s a missed opportunity considering the fact that it is easier to monetize on existing customers through loyalty marketing.
When it comes to user acquisition, it is most beneficial for brands to think (and measure themselves) long term, instead of the default short-term conversion events that are presented by the major ad networks. Long-term optimization strategies yield more profitable users by default. This is a practice that is already being used by the biggest brands, who have realized that they can't maximize profits at scale if they let media platforms optimize for explicit early revenue signals. Instead, they build models to project LTV and make keep-or-kill decisions about their campaigns based on those predictions.
Brands are beginning to leverage AI-driven technologies to predict long-term profitability using third-party data alongside internal historical data without a large internal team of data scientists and programmers.
LTV-based marketing = empowerment for great business results
As we’ve stated in a previous post, we are all slaves to ROAS, though the numbers can vary between industries. When backed by user-level LTV prediction technologies, SaaS companies can aim for 20 percent ROAS by the first month. Casual gaming is 2–10 percent from IAP in the first week. Hyper casual gaming is along the range of 20–50 percent in the first week. eCommerce aims at 70–95 percent within the first month.
When it comes to utilizing LTV data to maximize the potential of user acquisition campaigns, there is much that needs to be considered beforehand. With that in mind, we at the Voyantis team decided to create a guide for growth teams to get started on that path.
Here are some of the insights you will gain by downloading the guide:
- Different types of LTV data
- Breakdown of user types
- How to use LTV data to bring out the full potential of UA campaigns
- What you need to factor before choosing a LTV predictive solution
- Best practices
Please click here to access the guide: The Full LTV optimization Guide, so you can share it with the rest of the team!