Framework to Build the Quant Model for Paid Acquisition Channels

Read time: 3 minutes.

Welcome to the 50th edition of The Growth Elements Newsletter. Every Monday, I write an essay on growth metrics & experiments and business case studies.

Today’s piece is for 5,100+ founders, operators, and leaders from businesses such as Shopify, Google, Sage, Hubspot, Zoho, RateGain, Zaggle, Servcorp, Apollo and more.

Happy Monday!

Paid Acquisition is getting extremely hard, expensive and challenging to crack.

It requires tons of data crunching, modelling and understanding of clicks to conversion data flow and user behaviour.

► It is key to streamlining paid acquisition channels, and one way to improve performance is via the “Quant Model.”

► Predictability is key when running paid ads and if businesses plan to scale customer acquisition.

Here’s the framework to build the quant model:

[1] 𝐃𝐚𝐭𝐚 𝐆𝐚𝐭𝐡𝐞𝐫𝐢𝐧𝐠

► Collect historical paid ads data (e.g. CTR, conversion rates, CPC) for accurate modelling.

[2] 𝐒𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐚𝐥 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬

► Use regression analysis to identify correlations and patterns in the data.

[3] 𝐑𝐮𝐥𝐞-𝐁𝐚𝐬𝐞𝐝 𝐒𝐜𝐫𝐢𝐩𝐭 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭

► Create scripts based on data insights, for instance, dynamically adjusting bids if CTR drops below a threshold.

[4] 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧 𝐓𝐨𝐨𝐥𝐬

► Leverage Paid ads automation tools to adjust bids automatically based on performance metrics.

[5] 𝐂𝐨𝐧𝐭𝐢𝐧𝐮𝐨𝐮𝐬 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧

► Regularly reassess and optimise the model based on market conditions, demand, consumer behaviour, and campaign performance.

𝐁𝐞𝐧𝐞𝐟𝐢𝐭𝐬

[1] 𝐄𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐜𝐲

► Streamline bid management, e.g. reducing CPC through dynamic bid adjustments.

[2] 𝐓𝐚𝐫𝐠𝐞𝐭𝐢𝐧𝐠

► Allocate resources to high-converting keywords identified through the model.

[3] 𝐂𝐨𝐬𝐭 𝐒𝐚𝐯𝐢𝐧𝐠𝐬

► Avoid overspending on underperforming keywords, e.g. by pausing those with high CPA.

[4] 𝐒𝐜𝐚𝐥𝐚𝐛𝐢𝐥𝐢𝐭𝐲

► Adapt bids to business growth automatically, ensuring efficient resource allocation.

𝐅𝐢𝐧𝐚𝐥 𝐖𝐨𝐫𝐝𝐬

► A Quant Model for paid ads enhances efficiency, targeting, cost savings, and scalability, which is crucial for predictability.

That's it for today's article! I hope you found this essay insightful.

Wishing you a productive week ahead!

I always appreciate you reading.

Thanks,
Chintankumar Maisuria