Another blog about AI? Really?
We know. But we promise this one is different. It’s not about ChatGPT or Adobe Firefly (although those are exciting for creating high-quality content at scale).
As CXM consultants, we’re more interested in how marketers gain insight into their customers.
So we’re looking at the role of AI in customer experience management (CXM), especially how AI marketing tools enable personalised marketing at scale.
A simple definition of Artificial intelligence (AI) is the use of computer science and software to analyse large data sets and automate time-consuming tasks.
AI marketing is the application of this concept to marketing processes, freeing up marketers’ time to focus on strategic and creative tasks.
The leap forward enabled by AI is the speed and scale at which data can be processed.
Traditional market research methods often rely on sample data or surveys, which can be time-consuming to analyse and limited in scope. AI – in particular machine learning – processes vast data sets to unearth insights and perform complex analytical tasks.
“Chances are you’ve used AI, although you can be forgiven for taking the technology in stride. Enterprise marketing tools like Adobe Marketing Cloud solutions have become so sleek that many AI-powered tasks are considered standard features nowadays.”
We’re currently seeing the bubbling-up of AI as the technology becomes mainstream and more people understand (and exploit) its potential.
By leveraging AI-powered analytics, marketers can:
By combining strategy and software, marketers can develop highly targeted campaigns that resonate with individuals on a personal level. And they can do it at scale.
Advanced analytics and attribution modelling enables marketers to track and measure cross-channel customer journeys to understand what’s working and where to optimise.
Essentially, AI marketing tools shortcut data analysis workflows to:
Adobe’s Attribution AI feature is a prime example of the difference between regular data processing and AI. Attribution AI quantifies the impact of each marketing touchpoint, both active and passive, at every level of granularity.
As a result, brands can understand the true impact of marketing efforts and optimise campaigns to generate the most impact.
By employing AI algorithms for data integration and identity resolution, brands can stitch together fragmented data and build a comprehensive profile for individual users.
This holistic, unified view helps brands personalise omnichannel customer experiences. Adobe Real-Time Customer Data Platform (CDP) leverages AI to unify customer data, collecting information from online and offline touchpoints.
Apteco FastStats is another powerful platform marketers use to understand their audience, segment customers and predict buying behaviour. Our extensive experience with FastStats has helped our clients connect multiple data sources to organise data and gain actionable customer insights.
Our goal, whether with Adobe-first martech stacks or best-in-class solutions like FastStats, is to furnish our clients with the right tools to deliver personalised marketing.
By analysing large volumes of data such as demographics, purchase history, customer journeys and conversion triggers, AI can uncover insights that enable marketers to anticipate customers’ needs. This insight is incredibly valuable for creating relevant content and experiences.
But there’s more to personalisation than creating relevant content. AI also enables dynamic segmentation, predictive analytics and optimised delivery strategies. The right message reaches the right person at the right time, improving engagement, conversions and customer satisfaction.
Adobe Target is where all these features coalesce. Target is built for brands to dynamically tailor content, offers and experiences to individual customers. Adobe Sensei’s machine learning capabilities enable brands to deliver personalised omnichannel experiences and interact in real time.
Machine learning has long been pivotal in bidding for and serving online advertisements.
“AI is foundational to Google Ads. For many years, it has been quietly helping in the background, supporting advertisers in maximizing their time and return on investment.”
~ Google (source)
Generative AI is also entering the mix as Google rolls out Bard across many of its services.
But these are all publisher-side features. What about ad buying and marketing spend optimisation?
Adobe’s Advertising Cloud ad buying platform utilises AI algorithms to analyse audience data, identify high-value segments and optimise ad targeting and placements. By crunching an enormous data set, Advertising Cloud can allocate budgets more effectively to maximise their ROI in advertising campaigns.
Adobe’s Contribution Analysis is a great example of AI analytics. Available in Adobe Analytics, Contribution Analysis is a machine learning process designed to map the contributions to observed anomalies.
This makes Adobe Analytics more useful than other analytics platforms, which alert marketers to an anomaly but can’t provide hints as to what happened.
The comparison is important because most web analytics platforms offer some version of error or anomaly detection. As we outlined in a previous article comparing GA4 to Adobe Analytics, AI-powered intelligence gives users better data-driven insights.
AI-powered automation capabilities in Adobe Experience Platform streamline and accelerate various business processes like content creation, experience delivery and asset handling.
By automating repetitive tasks and workflows, brands can improve operational efficiency and deliver better personalised marketing experiences.
Another solution currently in the spotlight is Adobe Workfront. Taking inspiration from resource optimisation and project management software seen in other industries, Workfront brings automation and resource management to marketing.
It’s not the first of its kind, but it’s an Adobe solution, so you know it’s got oomph.
Almost all of these solutions are tailored to enterprise-scale businesses. Adobe Marketing Cloud is geared toward global organisations, such as those bringing in 1m+ monthly website visits or operating several high-profile brands.
That’s not to say there aren’t AI marketing tools available to SMEs and scale-ups. For example, Tableau is an industry-leading BI and data visualisation tool that’s often more cost-effective than Adobe counterparts.
However, the real power of AI comes in connecting different systems to create seamless data flows.
So growing businesses are faced with an investment decision. Eventually, they will benefit from ‘graduating’ up to enterprise solutions, but transitioning too soon could result in overengineered systems and high martech costs.
We recommend revisiting the organisational strategy to see where AI can add value. Almost every company is using ChatGPT to create content, but few are talking about the value-add of automating business processes.
That could be your opportunity to get ahead of the competition. While others are busy prompting generative AI, focus on scaling analytics, collecting first-party data and building a loyal customer base.
In other words, invest in customers first.
AI marketing tools come later.
If you’re weighing up an investment or upgrade, TAP CXM will help you make a decision that puts your business on a path to growth. We provide expert CXM consulting and database marketing services, with a team of skilled technicians ready to develop and deploy your bespoke solution.