How To Acquire More Profitable Users with a Long-term Optimization Strategy

Instant gratification is great. But a tunnel-vision on short-term metrics could distract you from a more lucrative audience when it comes to user acquisition. 

Google, Facebook, and the likes have been giving you the data to target short-term conversions that take place soon after the initial engagement (e.g., signup, purchase, content view, day-7 login.) 

But are we missing something if we don't look beyond the short conversion window?

The answer is “yes.”

These short-term metrics don't help us measure customer lifetime value (LTV), which is the key to long-term profitability. 

Now you may wonder, who are these customers with high LTV, how can you find them, and how do you incorporate insights about them into your targeting strategy?

Slow down, grasshopper! Before we dig into all that, let’s get down to the basics: who should care about LTV?

Tracking LTV is particularly important for business models that don't show immediate return on ad spend (ROAS.) These include SaaS subscriptions, freemium offerings, and businesses that rely on repeat customers or high-involvement purchases.

But you may say, "What choice do I have? That's all I can get from ad networks."

I get it. When you have a hammer, everything is a nail.

Growth marketers' strategies are steered by the metrics available to them through ad networks. Media channels only offer optimization for short-term events, so user acquisition teams have to structure campaigns based on data collected from a narrow timeframe (e.g., a few days.) 

As a result, campaign go/no-go decisions are mostly based on ROAS metrics obtained within the first 7 days of acquisition. 

Such an approach suffers from the “streetlight effect” — we only pursue outcomes we can measure. It’s limited in scale because we can only address a handful of short-term events measurable by media channels.

Let’s say your average customer will have a 2-year relationship with your brand. If you only measure their interactions with you during the first 7 days, aren’t you missing a big chunk of the puzzle?

Not to mention, marketers are constantly under the pressure to achieve quick wins. They need to justify how they're spending their budget to get more resources. Many organizations simply don't have the patience, foresight, and understanding to support long-term efforts that may not show immediate results.

Doesn’t seem fair, does it?

Furthermore, media channels push for short-term ROAS because they need to quickly produce data to feed their hungry machine learning engines with conversion data.

Ugh, that’s not a good place to be at.

Many growth marketers suffer from a sizable blindspot. They can't see the significant profits that lie just around the corner as we rush to generate quick ROI.

How do we stop leaving money on the table? 

Start by understanding the different types of users and recognize that not all customers convert at the same pace.

Early Birds vs. Late Bloomers

Most marketing channels have a conversion window of limited duration. Not all marketers can generate enough sales or sustain the purchase frequency so they can use the data to optimize campaigns for purchase events. 

Since every marketer in the same category is optimizing for the same audiences, i.e., those who tend to engage in the same short-term events, the bid prices continue to rise. 

The way ad networks determine conversion windows and measure conversion events means that the competition for Early Birds — users who convert quickly and fall within the conversion windows specified by ad networks — is much higher. 

In fact, the "Not Another State of Marketing Report" by HubSpot found that over 60% of marketers surveyed say that their customer acquisition costs have increased in the past 3 years. 

Thus, it's also more costly to acquire Early Birds than to target “Late Bloomers” — those who take longer to make the purchasing decision but are more profitable in the long run.

Yet, most marketers focus on selling to Early Birds at the expense of acquiring Late Bloomers. This is where they took the wrong turn.

Such tunnel vision is diverting them from reaping the rewards of implementing a user acquisition strategy that focuses on long-term profits and scalability. 

It's not surprising that the cost is going up while the results are staying flat.

Houston, we have a problem. 

We need new lenses to see the value of our customers. But how?

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. 

If marketers could accurately predict the true ROAS throughout a user's lifetime, then we won't be under constant pressure to generate immediate revenue. 

We can also double down on targeting audiences similar to current users with a high predicted LTV because we know that the effort will yield long-term profits.

But who can afford to sit around and wait weeks or even months to measure each user's LTV? 

Think about how you interact with brands and make your decisions. Do you usually take some time to check out a product or service, or do you buy right away?

If you’re like me, and most consumers, you’d take some time to do your homework.

Most people explore a product, try it out over an extended period, build interest, and engage with it on different levels before signing up for a subscription or premium features. 

Users who don’t take immediate actions represent an entire audience segment currently bypassed by advertisers. 

Here’s one example:

Game marketers tend to optimize for 1- or 7-day ROAS but this short-term optimization goal offers skewed measurements. Most gamers need time to learn the game mechanics and reach a point where they want to accelerate their experience with a purchase. This often takes more than a week, so the 7-day optimization timeframe may not be the most effective in identifying a desirable audience.

The good news is that there are many signals you can use to identify Late Bloomers. 

You can use historical data (e.g., all of a user's product engagement data) to model the probable LTV of each user. Then, you'll just send this score to ad networks for optimization purposes. Boom, done and done!

Sounds good in theory. But how do you do it? 

Finding the Right Balance — Optimizing User Acquisition Through LTV Predictions

News flash: Major brands have already moved away from optimizing for short-term ROI. 

They realized that they can't maximize profits at scale if they let media platforms optimize for explicit revenue signals. Instead, they build models to project LTV and make keep-or-kill decisions about their campaigns based on those predictions. 

This approach is feasible for big companies because they can use business intelligence (BI) and LTV predictive models based on internal data lakes and attribution data for each campaign cohort. 

The most advanced companies have switched to user-level predictions (which is much more complex than cohort-level predictions.) They’re sending signals through the networks’ server-side APIs to control their targeting.

This is how you get back into the driver’s seat.

So, the big boys have the resources to do all these to target Late Bloomers and take advantage of the untapped revenue potential. 

But what about the rest of us?

Most media buyers don't have advanced in-house data science capabilities. (This is kind of crazy, considering that they spend millions on user acquisition!)

They can't reap the scale and ROAS potential associated with Late Bloomers because building and maintaining such a user-level model is very hard:

1. You need a “continuous” real-time value model that can constantly check the prediction as users advance within the product experience.
2. Your predictive models need to be monitored, retrained, and analyzed frequently.
3. You need a whole new set of algorithmic expertise (which is very different from today’s cohort-level analysis best practices) to send the right signals to ad networks' optimization engine.

Fortunately, change is coming.

End-to-End Plug-and-Play Solution for Long-Term Profitability Prediction

New AI-driven technologies empower user acquisition leaders to predict long-term profitability using third-party data alongside internal historical data without a large internal team of data scientists and programmers.

Brands — even the little guys — can now leverage these solutions to identify users with high long-term value to the business. They can also optimize their targeting for new users who are similar to these high-LTV customers.

Marketers can leverage media platforms’ new server-to-server APIs to send signals that represent LTV predictions to optimize their campaigns. They can execute segmentation down to the individual level and target users who are most likely to bring the most value to the business.

Yep, you can be in total control!

One example of such a solution is Voyantis end-to-end, zero coding platform. It leverages AI, data science, and a few other goodies to let growth managers and user acquisition teams effectively target users based on long-term LTV predictions.

With Voyantis’ Signal Optimization solution, marketers can target users with high revenue potential at a significantly lower cost than targeting Early Birds who may or may not offer LTV profitability down the road. 

And it's all possible without begging for R&D resources. Phew!

Targeting Early Birds vs. Late Bloomers isn't an either/or decision. Instead, we should look at it as a continuum of user behaviors. Without being artificially constrained by conversion windows, we can target any user on the profitability spectrum to meet business objectives. 

While targeting Late Bloomers will not replace your business-as-usual (BAU) optimization tactics, you can leverage insights on user LTV  to devise a mix-and-matches strategy and strike the right balance. 

It's like building an investment portfolio. You wouldn't put all your money in bonds while ignoring the stock market, would you? The same goes for your user acquisition strategy. 

A holistic user acquisition strategy should cover various audience segments, each with its expected risk (e.g., how quickly you can see ROI) and reward (e.g., potential ROAS and scale.) You also need to diversify and restructure your strategy as business objectives shift.

Conclusion

For years, media platforms’ algorithms have offered immediate rewards for strategies that target Early Birds, which became the low-hanging fruit every marketer focuses on. 

Fast-converting audiences have become more in demand and, therefore, much more expensive to acquire. This status quo isn’t good for growth. 

If you want to scale up, you need to do something different...

… and step up your game. 

Yet, this game was rigged to all but a few iconic brands. They were the only players with the resources to leverage historical data, long-term metrics, and predictive models to identify high potential users.

The rest of us had no choice but to use the metrics available from ad networks, despite the blindspots and incomplete coverage.

Not any more.

New AI-driven solutions are disrupting this status quo by enabling user acquisition teams to optimize their strategies. They can gain high-value users for long-term profitability without significant BI, data expertise, and R&D resources. 

Using these new solutions, growth marketers can manage and diversify their strategies on a sliding scale to balance risk-reward between short-term ROI and long-term LTV to achieve the best outcomes.

Let’s make sure you’re targeting the smart way.