Predictive analytics in advertising: How data science is shaping the future

Predictive analytics is the fusion of data science with digital marketing, and it could work wonders for your next campaign. It uses data science to forecast future outcomes, enabling marketers to make informed decisions about their strategy.

With traditional data collection methods such as cookies being phased out, predictive analytics offers a way to navigate the changing digital landscape. 

But how do you navigate this changing landscape, whilst making the most of what predictive analytics has to offer? We’re here to help guide you through this transition, leaving you in a stronger position to navigate the future of marketing.

• Challenges of operating in a cookieless landscape
• Bridging the data gap
• Attribution in a cookieless world
• AI-driven targeting
• Optimising PPC campaigns with predictive insights

Challenges of operating in a cookieless landscape

In a move that has shocked some legacy marketers, Google has been phasing out cookies over the last couple of years. This has had a significant impact on how data is sourced, what data can be collected and just how accurate that data is.

The demise of third-party cookies means fewer direct insights into user behaviour across your campaigns. This could be a sticking point in the accuracy of your targeting if you don’t find a suitable replacement source for your data.

These untracked conversions and metrics result in reduced visibility into how users interact with your ads, complicating the measurement of ad effectiveness. This can make it difficult to know what campaigns, ad types or ad variations are bringing in the most conversions or engagement to your brand.

An alternative (but slower) option
Creating PPC or SEO campaigns in a cookieless environment requires a new approach to tracking and attribution to avoid incomplete data, or data sets that are simply too minimal to be of use.

One potential solution is privacy-first, consent-driven strategies using alternative tracking methods such as first-party data collection. This is where you encourage your audience, customers and followers of your brand to provide information about themselves that you can store and use in your marketing. This could be their name, email or address, or more developed data, such as their style preferences, likes and dislikes or favourite seasons for shopping.

However, as useful as this can be, many customers are not happy in today’s digital era to provide such information. They’ll simply decline the offer or ignore it entirely. This leaves many marketers in the dark. So what’s an alternative to this alternative solution?

Bridging the data gap: leveraging predictive analytics for missing conversions

If you’re working with smaller data sets, you are bound to find gaps in your dream data. This is where predictive analytics can come in handy. Predictive analytics fills gaps left by missing conversion data in a cookieless world.

But how does predictive analytics work? Simply put, machine learning algorithms use historical data and user behaviour patterns to predict future actions, and fill gaps in data sets. It looks for patterns and trends in your data, and the wider context of your campaign, to do this.

Models estimate the likelihood of conversions based on proxy data, such as device type, location, and content engagement.

If you’ve already made a start on collecting first-party data (CRM systems, website interactions, etc) you can also use these data sets to inform any predictive analysis over time creating robust models for predicting outcomes.

Attribution in a cookieless world: harnessing data science for smarter campaigns

Introducing single-touch attribution and multi-touch attribution. These are your scientific partners that will help you source the most useful and accurate data from your user base. They are a way of measuring the impact of users' interactions with your brand across various touchpoints. Each interaction is assigned a credit, and there are lots of different attribution models that you can use to get the most out of your tracking.

Multi-touch attribution
This is the term given to the model where predictive analytics supports multi-touch attribution by assigning credit to multiple touchpoints in the buyer journey. For example, if your customer comes across an ad, clicks through to your site, signs up for a mailing list and then makes a purchase via an offer email, this is all tracked as one complete journey.

• With fewer cookies, data science models help identify which interactions (organic search, paid search, email, social media, etc) drive conversions.

• This approach provides a more comprehensive view of campaign success across various platforms.

• Of course, due to its detailed and diverse nature, this can take some time to set up in full, as so many platforms need to be tracked at once.

Single-touch attribution 
Here, predictive models identify patterns in consumer behaviour, attributing conversion credit even when data is missing. It fills in the gaps based on data-driven estimations , bridging the gaps between your existing metrics.

• Statistical techniques like Bayesian models help marketers optimise campaigns based on estimated conversion pathways.

• By simulating different attribution scenarios, advertisers can adjust spend and improve ROI.

• You can pick the attribution model that works best for your customer journey, ranging from first-touch, to lead conversion and last non-direct attribution.

AI-driven targeting - maximising campaigns without full conversion data

One of the key challenges with a lack of cookies is the gaps in your data, but predictive analytics make use of AI to help solve some of these little mysteries. 

AI uses patterns in consumer data to predict likely buyers, even without full conversion details. By analysing historical trends, machine learning algorithms can suggest optimal audience segments, ad placements, and creative content. So it goes beyond predicting a click-though-rate or conversion trend, and picks out wider elements of your campaign too.

You can also use AI to feed into your ad generation and targeting. AI-driven targeting analyses contextual signals such as time of day, device type, and geography to deliver relevant ads. Whereas dynamic creative optimisation (DCO) can enhance performance by delivering personalised ads based on predictive insights. In short, AI can be useful, if you know how to get the most out of it.

Optimising PPC Campaigns with predictive insights and first-party data

Let’s cut to the chase. How can predictive insights, paired with first-party data, become a powerful force in your marketing campaigns?

Reach a new audience
Working with machine learning brings predictive analytics to your targeting. This makes it more manageable to source relevant new audiences across a huge range of countries and demographics. It’s also useful for larger campaigns where you don’t always have the time to be individually tweaking targeting for each ad group.

Predictive analytics can also unlock more details of your customer journey, bringing overlooked stages of the funnel to your attention with a fresh perspective.

Improve the relevancy of your content
If you're matching the data you collect on what products your customers are most interested in with predictive analytics, chances are you’ll uncover more relevant recommendations to share with your audience. We know that customers respond positively to ads that reflect their interests and preferences, so let predictive analytics get you one step closer to what they’re craving at the moment.

Save budget for when it really matters
Real-time adjustments based on predictive data can improve your bidding strategies, reducing wasted ad spend. If you take it a step further and team your PPC management with tools like Google Analytics 4 (GA4), you’ll discover predictive metrics (e.g., purchase probability) that can also inform your budgeting decisions.

It’s like making an educated guess, but this time it’s backed up by machine learning, AI capabilities and predictive analytics, all in one.

In Conclusion

Predictive analytics offers a solution for navigating the challenges of a cookieless landscape. By teaming up first-party data and AI-driven models, you can continue to deliver targeted, effective campaigns.

Optimising PPC and attribution strategies using predictive insights ensures smarter, more resilient advertising in an increasingly data-privacy-conscious world. So stay competitive and cutting edge, with predictive analytics at the core of your advertising strategy.