Adopting AI in Paid campaigns

AI-powered technologies offer unparalleled opportunities to optimize paid digital ads, maximize performance, and achieve remarkable business outcomes. AI brings a new level of sophistication and efficiency to online advertising by leveraging advanced algorithms, machine learning, and data analysis. It empowers marketers to make data-driven decisions, automate processes, and deliver highly targeted and personalized experiences to their audience.

Here are a couple of points that all digital marketers should keep in mind:

a) The AI that you are deploying for your digital ads is not self-aware (yet). It can perform complex calculations, analysis, and execute a campaign accordingly. But it cannot create or think for you. If your data is wrong or your objectives are flawed, you will get the wrong output.

b) AI-driven digital ads are like heavy machinery. They take time to reach full speed, implement any changes or come to a stop. So plan well before starting your campaign. Once your campaign starts, any major changes you make will send the campaign right back to the learning phase or require you to create a new campaign. Either way, you end up losing a week or more.

Now let us look at the key aspects of adopting AI in your digital ads efforts.

1. Identify your business objectives and align your marketing metrics

You should have a business objective designed to drive and measure business results, such as revenue, profit, or market share. Then your marketing metrics should closely align to those business objectives, such as total revenue return on ad spend, profitability per dollar invested, and number of quality customers acquired. Make sure you present your plan and align your leadership team, ideally before the beginning of a new year.

1. Keep the marketing and finance teams on the same page

By building communication and alignment between marketing and finance stakeholders, the message becomes clear that all online advertising efforts should be designed to drive the right results for the business, instead of optimizing toward more intermediary metrics, such as clicks or cost-per-conversion. Work with your marketing and finance teams to showcase how modern marketing isn't an expense on your balance sheet but an investment that directly contributes to the business goals your company has — profit, revenue, or maybe lifetime value growth.

Without this level of coordination and alignment on a new technology, you may find campaigns getting delayed or even getting shelved because the finance team is not convinced and withholds or delays release of funds.

To ensure alignment, you can encourage your C-suite to create a set of joint KPIs between marketing and finance teams to track on a regular basis and help them measure against these to demonstrate the impact of online advertising on company performance.

1. Build a strong data strategy and drive the right metrics

AI works best with strategic human input — including setting the right goal and fueling it with data and insights, such as audience or seasonality insights. Data can also provide inspiration for new ideas to test, including creative messaging, website and landing page experiences, and web-to-app experiences.

To drive the most value from marketing, AI needs advertisers to instruct it with the best data. Without the right guidance, it can’t deliver the best results. The more accurate data an advertiser provides, the better trained the algorithm will be to deliver stronger results.

Make sure data links back to your business objectives.

Some key data sources aligned to business objectives are:

Offline Data (if available)

Sales and walk-in data from a store, local branch, dealership, or other offline sites where your sales team directly interacts with customers.

Customer Segmentation Data

Telling your marketing engine who your high-value customers are is important to driving the results you're working toward. If your goal is to drive quality leads, segmenting your customers by value can help you identify even more customers like those who are already valuable.

Profit Margin Data

Not all of your offerings will be equally profitable, and if your business objective is to drive profit or value, your marketing strategy should reflect that. Using margin data in your value bidding strategy helps you drive total profit, which matters to all organizations.

Other examples of data can include gross merchandise value, incremental active accounts, lifetime value, predictive lifetime value, and 12-month profitability.

1. Data strategy and marketing automation

Integrate your marketing and data/customer management platforms to enable an automated flow of data. Data is spread out across campaigns, CRM systems, websites, apps, etc., and is also managed by many teams. The key is to create infrastructure that breaks down the organizational silos and connect the dots between data within your organization.

Combine previously siloed data to unlock insights that lead to timely and relevant customer messaging, surfacing behavioral predictions to help your advertiser invest more effectively in marketing. Import data that’s logged offline (in your database or CRM system) via enhanced conversions for leads, offline conversion import, store visits, or store sales.

Set up automated processes that allow for the seamless connection of data. Where possible, own your data. The more processes and tech that are done in-house, the more you can control within your own ecosystem. Make sure this data is uploaded for one to two weeks before beginning to activate within campaigns to make sure all data is funneling into an account properly and aligned with what you see internally.

1. Create a fool-proof measurement strategy

Make sure you have a solid foundation of online conversion measurement. Having the correct measurement in place lets you start tracking conversion actions before setting up attribution and optimization.

For Google Ads conversions, marketers can implement at least one of the following options, based on their business objectives:

Online Sales:

If you measure online sales with transaction-specific values and use the Google tag, you can track transaction-specific conversion values. You can also use Tag Manager to deploy conversion tags with values.

If you use Google Analytics to measure web or app values, you can import your conversions into Google Ads conversion tracking.

Lead Generation

If you measure values from your lead generation campaigns that have been qualified or closed, you should implement enhanced conversions for leads.

Offline Sales

If you measure the impact of your online advertising spend with offline sales, implement store sales measurement.

Other Offline Conversions

If your conversion value is only available offline or in your customer relationship management (CRM) system, you can also import offline conversions from ad clicks into Google Ads.

These are just some of the strategies and processes that you can implement internally to make AI work for your digital ads campaigns. Are there other strategies you can think of? Let me know through the contact page.