Over the last year, we've seen remarkable advancements in the field of Artificial Intelligence. Unless you’ve been living under a rock these last few months, you probably heard about the competition between ChatGPT (Microsoft via OpenAI) and Bard (Google) or seen funky profile pictures generated by Midjourney. Maybe you’ve been contacted by content writers offering cheaper, AI assisted blog posts and articles. Possibly you’ve wondered how this might impact many different roles within marketing and advertising - and this is only the surface of tools and technology being developed as we speak.
However, it is worth noting that artificial intelligence has been aiding marketers and data analysts for quite some time now. The media might be loud from news about generative AI, but from chatbots and predictive analytics to image and speech recognition, AI technologies have become widely adopted by marketers to gain deeper insights into customer behaviour and preferences. In this blog post, we'll explore the roles of these specialised, narrowly focused technologies and how they help us. Hopefully outlining how their shinier (and without a doubt more complex) cousins might also become incredibly valuable tools in the hands of marketers and creatives - follow us on Linkedin, Instagram or Twitter to make sure you catch this second article in the series.
A Quick Glance
According to Forbes, top-performing companies are more than twice as likely to be using AI for marketing (28% vs. 12%). Whether it is inventory management or medical imaging analyses, technology is making things faster, simpler and boosting ROI.
But where do marketers rely heavily on AI and Machine Learning already? Artificial Intelligence is used to make automated decisions based on data collection, data analyses, and observations of audiences or economic trends. It is often employed in digital marketing efforts where speed is essential. Relevance had been incredibly important in ad targeting and messaging as well: personalisation without the need of human intervention had contributed to serving customers better, without a human intervention needed.
Some of the most common examples would include product or service recommendations, predictive analytics and pop-up chatbots.
Core Elements of AI Marketing
Let’s break down the different types of tools and technologies already in use, sometimes without a second thought. These tools and components help bridge the gap between the massive amounts of data being collected and the next steps businesses can take in future campaigns:
Machine learning involves computer algorithms that can analyse information and automatically improve digital marketing campaigns. Devices that leverage machine learning analyse new information in the context of relevant historical data, which can inform campaigns, based on what has or hasn’t worked in the past.
Big Data and Analytics
The emergence of digital media has brought on an influx of “big data”, which has provided opportunities for digital marketers to understand their efforts and accurately attribute value across channels. This has also led to an over-saturation of data, as many marketers struggle to determine which data sets are worth collecting. AI marketing can help quickly sort through data and filter it down to its essentials.
Application of AI technology in tools you (probably) use
Customer Relationship Management systems would be a great example of AI making marketing and sales processes much easier. These features all make use of artificial intelligence in smaller or greater degrees:
- Predictive lead scoring: AI algorithms can analyse customer data and behaviour to predict which leads are most likely to convert, helping sales teams focus on the most promising opportunities.
- Customer segmentation: AI can segment customers based on demographics, behaviour, and preferences, enabling businesses to tailor their marketing messages and offers to each segment.
- Chatbots and virtual assistants: AI-powered chatbots and virtual assistants can handle customer inquiries and provide assistance, reducing the workload on customer service teams.
- Sentiment analysis: AI can analyse customer feedback from various sources, such as social media and customer surveys, to identify patterns and sentiments, allowing businesses to respond proactively to customer issues.
- Personalization: AI can analyse customer data to provide personalised recommendations, content, and offers, improving customer engagement and loyalty.
- Next-best action recommendations: AI can analyse customer data to recommend the best next action for a sales rep or customer service agent, helping them provide a more personalised and relevant experience to the customer.
Advertising tools have also been using many machine learning and artificial intelligence tools behind the scenes:
- Automated bidding: AI algorithms can adjust bids in real-time based on historical data and user behaviour to maximise conversions.
- Programmatic advertising: AI-powered programmatic advertising platforms can automatically purchase and place ads in real-time, improving efficiency and reducing manual effort.
- Ad optimization: AI-powered tools can automatically create and optimise ad campaigns to improve performance and reach.
- Chatbots: AI-powered chatbots can handle customer queries and provide assistance, reducing the workload on customer service teams and improving customer satisfaction.
- Image and speech recognition: AI can analyse images and speech to identify patterns and sentiments, which can be used to improve ad targeting and messaging.
The most obvious use case? It is hard to operate today and not have any ads running in Google, so we have all used or interacted with the result of machine learning and predictive ad technologies. Google Ads has become more efficient, accurate, and effective in delivering highly personalised and targeted ads to the right audience. The automated bidding uses machine learning algorithms to adjust bids in real-time to maximise conversions based on historical data and user behaviour. The Smart Campaigns feature uses AI to automatically create and optimise ad campaigns, making it easier for small businesses to get started with advertising. Additionally, responsive search ads use AI to dynamically generate and test different ad variations, ensuring that the best-performing ads are delivered to the right audience.
Implementing AI Marketing Tools
Using artificial intelligence within your marketing strategy can be a given, if you are using tools we’ve previously mentioned. However, digging deeper and building tailored products for yourself or using the technologies mindfully may seem like a complicated task, but the improved efficiency and customer experience should be worth it. When leveraging the technology in campaigns and operations it is crucial to have the correct setup and a thorough long-term plan. This ensures cost and time is left to a minimum and maximum value is obtained.
When effectively implementing AI marketing tools into your strategy, it is imperative to consider the following key factors:
Start with a Pilot
Try an AI pilot with key members of your team to test and play around with the technology, identify best practices, and expand purposefully into functions with similar applicability. You need your employees to be enthusiastic, rather than fearful about the opportunities ahead and can identify use cases and business problems that AI will solve more efficiently.
Maintain Data Quality
As AI marketing programs consume more data, they learn how to make accurate and effective decisions. If the data being fed into your program is not standardised and error-free, the insights will not be useful and could affect decisions made further along in the cycle. Prior to implementing any AI marketing programs, marketing teams must establish a foolproof process for data cleansing and maintenance, and consider the six data quality dimensions:
- Completeness: The minimum information essential for productive engagement.
- Accuracy: The level to which data represents the real-world scenario and is confirmed by a verifiable source.
- Consistency: If the same information stored and used at multiple instances matches.
- Validity: The value attributes are available for aligning with the specific domain or requirement.
- Uniqueness: This dimension indicates if there is a single recorded instance in the data set used.
- Integrity: Attributes are maintained correctly, even as data is stored and used in diverse systems.
It is important to outline clear goals and marketing analytics for your AI marketing program at the beginning of your journey. Identify areas within your campaigns that need improving, then establish direct KPIs that will highlight the success of the AI-augmented marketing. This is especially important for qualitative goals such as “improve my customers’ experience”.
Begin applying AI to small samples of your data, rather than taking on too much too soon. Start simple, use AI incrementally to prove value, collect feedback, and then expand accordingly.
This is just the beginning
Chat GPT might be a state-of-the-art language model that emerged recently, but it only makes up a small portion of the larger AI puzzle. Artificial intelligence solutions can identify insightful concepts and themes across large data sets, incredibly fast.They can also interpret emotion and communication like a human, which makes these platforms able to understand open form content like social media, natural language, and email responses. Overall, while they might not look so shiny and new anymore, these tools are baked into many of our tools and support efficiency, understanding customers and delivering communication that is more personalised and more relevant.