AI revolutionizes the ABCs of digital advertising

AI is not just a concept of the future; it is very much a reality and is thriving in the realm of digital marketing. In today's landscape, every marketing campaign you run is being continuously monitored, analyzed, and optimized by AI technologies to enhance performance and achieve better results.

Let’s Explore how AI is transforming the ABCs of digital advertising: Audience, Bidding, and Creative.

Audience: - Traditionally Marketing audience targeting methods primarily relied on demographic data (Such as age group, gender, and location). However, these methods often fail to predict customer preferences accurately. AI is revolutionizing the way brands identify, understand, and engage their target audiences. The foundation of AI to identify target audiences lies in gathering and processing massive data sets.  It can be sourced by demographic information (i.e. age, gender, location, income level), social media activity, browsing & search history, mobile app or loyalty card usage & purchase history & transactional data. All this data uncovers trends, patterns, and invaluable insights into understanding your audience preferences and actions.

By applying advanced machine learning algorithms to massive datasets, AI enables more predictive, personalized, and effective ad targeting than ever before. It significantly improves audience identification and insights for digital campaigns by enabling deep data analysis, precise segmentation, personalized experiences, real-time adjustments, and predictive capabilities. This leads to more effective and efficient marketing strategies, higher engagement rates, and better overall campaign performance. AI can create lookalike models to identify new potential customers who exhibit similar behaviors and characteristics to existing high-value customers & this helps in expanding the reach of campaigns to audiences that are more likely to convert.  

Another crucial point to consider is the role of AI in enhancing customer lifecycle management & Churn predication. AI tools can track and analyses each stage of a customer's interaction with a brand, from initial awareness to post-purchase behavior. By understanding these touchpoints, AI can help brands tailor their messaging and offers more precisely to each stage of the customer journey. This means not only reaching potential customers more effectively but also improving retention and loyalty among existing ones by providing relevant, timely, and personalized interactions. Second one is Churn Prediction and Prevention as AI algorithms are identifying patterns that indicate a user may stop engaging with a brand, and with this information in hand brands can take proactive measures, such as personalized offers or re-engagement campaigns, to retain those users.

Bidding: - As digital media landscape evolving, led by the dominance of platform world & increasing preference of programmatic, brands are looking for opportunity to get more out of their digital ad investments. In digital buying pivotal challenge lies in determining the optimal bid for a desired outcome. Winning bid that drives the outcome at optimal cost is the balancing act that requires speed, intelligence, advance optimization & automation. However, the sheer volume & complexity of campaign data & multiple platforms present significant constraint, limiting the extent to which a programmatic traders can independently navigate these intricacies. Initially walled garden platforms were limited to manual or automated bidding strategies; however, landscape has changed by opening to offer third party AI based intelligence for optimization & automation. This transformation gave brands an opportunity to shift towards “Custom bidding or AI-Based bidding” function.  AI based bidding allow brands to dynamically adjust the bids for ad impression in real-time, optimizing their chance of winning auction that drives business specific KPI. AI Based bidding do provide forecasting capabilities to illustrate how many conversions are expected & can integrating third party & first party data signals like location, device, site behavior, demographics & so on.

While walled garden platforms offer automated bidding that may seem akin to AI- based bidding, there exists a distinction in their modus operandi.  Automated bidding uses algorithms & pre-defined rules to automatically adjust bids based on specific criteria. AI- based bidding goes a step further by incorporating machine learning & predictive modelling. it allows bidding algorithm to learn & use broader range of data, identify patterns that may not be explicitly defined by rules. Automated bidding often adopts one size fit all approach to purchasing impression, whereas AI based bidding takes a bespoke & tailored approach, designed to deliver outcome to specific business goal.

AI-based bidding empowers brands to transcend a singular & devise multi layered bidding strategies. Brands can go beyond a single action and create a multi-layered bid. Let’s understand this with an example of a D2C Brand and the users who land on website/app can complete one of the following key actions.

• Sign in with already a customer or single sign on with google/FB account.
• Add to cart.
• Contact Us

A compelling feature of custom bidding known as weighted conversion is particularly impactful. The provided example outlines distinct stages of the customer journey, such as a user clicking "Add to cart" indicating proximity to a purchase or completing a "single sign-on with Google/FB" signifying a new customer & contact us means seller or buyer inquiries. In the AI-based bidding, this approach allows for the optimization of a combination of goals, assigning varying weights to each action. For instance, a click on "Add to cart" may carry a higher weight due to its proximity to a purchase, while a "single sign-on with Google/FB" may be weighted differently to signify the acquisition of a new customer. Leveraging weighted conversion, AI based bidding will craft unique scripts and tailor bids & optimization for each specific action to maximize desired outcome.

The future of AI- based bidding in digital advertising holds promising development. As Artificial technology continues to advance, custom bidding is likely to become more sophisticated & tailored to specific brand needs. In cookies-less world, AI can delve into intricacies of user data to create more refined audience segment & bid them basis their chance of driving higher ROI. Cutting-edge AI integration may enable advertisers to optimize bidding strategies seamlessly across multiple channels, providing synchronized approach to reaching audiences across diverse platforms such as display, DOOH, video, audio, and social media. By integrating first-party data brands can strategically adjust bids based on where a user is in the marketing funnel, whether they are exploring products, adding items to the cart, or completing a purchase. AI will play a pivotal role in shaping more intelligent, adaptable & effective digital advertising buying strategies.

Creative:- Future of digital advertising depends upon being able to balance data & creativity. Personalization is key to powerful marketing & in digital advertising; it is possible to give unique experience to consumer. AI lead Dynamic Creative Optimization (DCO) is the real time process by which advertisers can personalize creative & with prevailing machine learning technology & data at your fingertips, you can ensure that your ads continuously change according to each user preferences and browsing history. Machine learning at the heart of the dynamic creative optimization help marketers  to use variation of creative over time and with a flexibility of experimenting at large scale and understanding which creative resonate with audience. AI lead DCO can also help brands nurture customer loyalty & significantly improve campaign performance. It allow marketers to break a creative apart into multiple pieces, and create different versions of those pieces for different context.

AI lead Dynamic creative optimization uses two different strategy.

• Personalize re-targeting: - This strategy uses data, which pass by pixels on advertiser website or App. These ads show dynamic content based a person’s website/app browsing history such as products viewed or added to a shopping cart.  Personalize re-targeting can be exceptionally effective to drive more conversions to your online store or website/app and to build customer loyalty. By using, personalize re-targeting advertisers get a second chance to connect with visitors, but also because they can create relevant messages for audience.  With personalized re-targeted ads you can pinpoint the stage in the sales funnel that each visitor is in, and tailor your marketing efforts accordingly.
• Rules- Based re-targeting: - Rules based re-targeting relies on situational & 3rd party data even if consume have not visited your website/app. The rule-based re-targeting can use data points like location, weather, time of day, day of the week.

AI led DCO is an effective strategy for any business that wants to deliver personalized, relevant messaging to consumers and verticals such as retail, ecomm, travel and auto making good use of this technology. One of the misconceptions around DCO is its too complex & expensive. Marketers will have to ensure that media and creative team is working together & there is no hesitation in DCO adoption during the campaign process.

The digital marketplace is jumbled & if an advertiser wants to get through to a consumer, their messaging must make a connection. If it can be a more personal connection, the chances of a consumer reacting to it are greatly increased. Together humans and machines can make beautiful things, happen and create ideal brand experiences that consumers reminisce.