By Himanshu Gupta
For years, the mobile app economy chased a single vanity metric: downloads. In today’s saturated market, that strategy falls short. The goal has shifted from acquiring the most users to acquiring the right users — those with a high intent to engage, spend and stay.
This is where in-app value-bidding comes in. Instead of chasing the cheapest install or a single action, you teach campaigns to find users who create value over time. Think of it as paying for results that matter to the business, not just activity.
Two options make this work. Set a target return on ad spend so the system aims, for example, for $4 back per $1 spent. Or ask the system to maximise total conversion value within a fixed budget. Both align bidding with revenue, not volume.
This suits today’s privacy-centric environment. With more limits on tracking across apps, clean identifiers are less common. That shifts the weight to context and conversion signals. The winners are channels that can read those signals quickly and place accurate bids within each auction window.
An intelligence layer powers this. Strong models predict the expected value of a potential user from many signals and price the impression accordingly. High-value opportunities get a higher bid. Low-value ones do not. Scale and learning speed make the difference. Partners trained on broad, rich data and refreshed models tend to price more precisely and waste less media.
The Practical Guide For Advertisers
Pick one success metric. If margin is hard to share, start with revenue. For gaming, use a day 7 payer or an early lifetime value proxy. For subscription, use plan value and expected tenure. Document it so product, growth, and data share one target.
Send value with every conversion. Pass the actual dollar value or a sensible proxy with your postbacks. Cleaner signals help the model learn who is truly valuable.
Launch one always-on campaign on ROAS. Keep budgets steady for at least one learning cycle. Resist heavy segmentation. Give the system enough traffic to discover pockets of value.
Treat creative as a performance lever. Build a small set of messages tied to value. Refresh on a fixed cadence so the model always has new options to test.
Unify acquisition and re-engagement. The same prediction engine can find past users who are ready to convert. Use value bidding in retargeting with windows that match your payback period. It often delivers cheaper revenue than only new users.
Calibrate measurement for reality. Add holdouts or geo splits so you can see incremental results, not just last click. Expand your lookback for higher ticket items. Align finance and growth on how ROAS is calculated and when a campaign is judged.
Choose partners for their machine learning, not just reach. The success of value-bidding depends on the quality of the prediction layer. Avoid fixed price buys and affiliate setups that reward volume but miss lifetime value. Pick channels that can score each impression in real time, learn quickly from your postbacks, and keep performing when identity signals are thin. Ask how often models refresh, what auction volume they evaluate daily, and how they generalise when data is sparse. Do not over-target with narrow segments; let the system explore.
Build a clean feedback loop. Audit events, fix duplicates, and keep naming consistent. Share purchase type, tier, or category so models can learn which cohorts drive value. Small data hygiene wins often move ROAS more than fancy tactics.
Common pitfalls (and how to avoid them)
• Wrong or noisy value signals. If every add to cart counts the same as a purchase, the system will chase the wrong people. Send the real value, or at least a tiered score.
• Starving the model. Tiny budgets, frequent resets, and strict targeting keep the system blind. Give it a steady runway and room to explore.
• Relying on affiliates and fixed CPM buys. These often reward volume over quality. Work with channels that have a strong intelligence and prediction layer and can price each impression by expected value.
• One creative for everyone. High spenders respond to different prompts than casual users. Keep a small, diverse set and rotate on schedule.
• Lack of granularity in reporting. A single ROAS line hides a lot. Break results by cohort, market, and placement to see where value comes from.
• Expecting instant miracles. Models improve as they see your data. Set phased targets and revisit after each learning window.
Value-bidding is not a magic trick. It is a practical way to align spend with revenue. Define value, pass it back, choose partners with real predictive strength, and let the system do the work. Teams that run this playbook see steadier ROAS, better retention, and fewer surprises in the finance review. That is the kind of growth that lasts.
(The author is the Growth Director at Moloco India.)
