Profiting from the Big Picture

Why Lifetime Value (LTV) Should Drive Your Online Ad Strategy

By Mitsunaga Kikuchi, CEO, Shirofune

Offer these choices to a young child – one cookie now, or two an hour from now – and you can predict the outcome. Most of us, children and adults, crave immediate gratification. Professionals with clients to satisfy also feel the need to show good results today, rather than promise better ones tomorrow. Yet the most successful businesses are those that think and act for the long term, and the advertising industry is recognizing that maximizing profits happens along a continuum.

With the escalating need for strategic online ad campaigns to reach targeted audiences, the success of these has traditionally been measured through Return on Advertising Spend, a metric calculated based on individual conversions. As tools such as AI allow advertisers to gather and predict more data, a paradigm shift is leading the industry from the one-time ROAS standard toward a more longitudinal view. Ad strategies are increasingly driven by Lifetime Value (LTV), helping advertisers and agencies invest in better-performing ads and reap bigger profits.

The ABCs of LTV

Until recently seen as the ungraspable Holy Grail of predictive tools, Lifetime Value (LTV) takes into account initial conversion, repeat purchases, upsells, cross-sells and customer retention in order to estimate the end-to-end value of one customer. In actuarial terms, which factor in risk and uncertainty, it’s the total revenue that a company forecasts from any single customer over their entire relationship with that business. The more time that a customer with a high average purchase value patronizes a company, the higher their LTV.

Consider the following scenario: Person A buys a product from a company for $100 but never returns. Person B initially purchases another of the company’s products for only $50 but then makes three subsequent purchases of $50 each. The first customer generated an immediate return of $100, which through a traditional ROAS lens would be satisfactory. The second one took more time, but spent twice as much. In the process, they developed a longer relationship with the company and perhaps even recommended it to others. The long-term approach affirms the greater potential of LTV.

LTV-Based Ad Management

By optimizing campaigns to identify and target customers with higher LTV, businesses can enhance their overall profitability and achieve sustainable growth. What’s required is to allocate advertising budgets based on the performance of ad platforms, campaigns and ad creatives that track lifetime value, rather than solely relying on ROAS. So with the clear benefits of LTV, what does it take to convert more ad professionals? Beyond the quagmire of inertia (“It’s the way we’ve always done it!”), there are some key challenges that require the time, tools and tenacity to embrace this approach:

Data Accessibility: LTV-based ad management requires more robust data from a breadth of sources. For example, Google knows when people buy products on e-commerce platforms through its advertisements. Shopify, on the other hand, can identify whether customers are new or existing and can track if customers come in organically and when they make return purchases. Google does not have access to this range of data, so both of these sources would better enable LTV. Capturing and analyzing customer data benefits from integrating different elements, from customer relationship management (CRM) software to analytics tools, in order to better track and measure buyer behavior.

Attribution Complexity: From auto accidents to marriage counseling to Covid-19 contact tracing,

causality is a tricky factor in determining the backward-looking geometry of a situation. What led a consumer to purchase a particular product? Beyond answering a marketing survey, there are any number of catalysts, from seeing an ad to heeding a friend’s recommendation. Tracing sales and revenue to specific ad campaigns or platforms can be complex, and particularly when customers interact with multiple touchpoints. Employing advanced attribution models and utilizing tools that provide comprehensive data insights can help overcome this challenge.

Predictive Modeling: Recent advances in artificial intelligence have increased the capabilities of predictive modeling, not only in advertising and marketing but across medicine, transportation, agriculture, manufacturing, education and cybersecurity. Predicting customer LTV accurately is crucial for making informed advertising decisions. By leveraging machine learning algorithms and historical customer data, businesses can build better predictive models to estimate LTV and allocate resources strategically.

Testing and Optimization: No matter how advanced technology tools become, humans will always need to fine-tune systems and course correct as needed. Witness the fallibility of AI, including the recent news story about the lawyer who filed a brief in a federal lawsuit that leaned on citations and quotes sourced by ChatGPT, “facts” which proved fictitious. The proof for advertisers and their clients is in the results. LTV-based ad management requires continuous testing and optimization. Experimenting with different ad creatives, platforms and targeting strategies is essential to identify the best-performing combinations that maximize LTV.

About the Author

Mitsunaga Kikuchi is Founder/CEO of Shirofune, the #1 automated advertising management platform in Japan which launched in North America earlier this year, using human intelligence rather than AI to deliver significant time, cost and performance benefits.