In February of this year Duke University, in conjunction with Deloitte and the AMA, published its annual CMO Survey. It’s a very comprehensive survey with lots of different insights. However, one of its newest categories is Marketing’s use of artificial intelligence (AI). The results are a little surprising in some respects, but quite predictable in others. Regardless, the results should serve as a benchmark for marketing teams to assess where they stand in their use of AI across the organization compared to their peers.
Across all AI use-categories, on average, more than 56% of respondents have implemented AI for content personalization and predictive analytics.
The sector that uses the most AI, on average, is B2C Service companies. On the other hand, B2C Product companies top out the other segments on their use of AI in customer segmentation and autonomous objects, on average. B2B companies lead the pack on adoption of augmented and virtual reality.
Below are the AI use-categories and their definitions featured in the Duke study:
- Content personalization – is a strategy that relies on visitor data to deliver relevant content based on audience interests and motivations. It ranges from a highly targeted call to action to a revolving landing page based on geographic or industry-specific segments. Source: CMI
- Predictive analytics for customer insights – is a form of advanced analytics that uses both new and historical data to forecast activity, behavior and trends. It involves applying statistical analysis techniques, analytical queries and automated machine learning algorithms to data sets to create predictive models that place a numerical value — or score — on the likelihood of a particular event happening. Source: TechTarget
- Targeting decisions – is the process of identifying customers and promoting products and services via mediums that are likely to reach those potential customers. Source: Techopedia
- Customer segmentation – is the process of dividing customers into groups based on common characteristics so companies can market to each group effectively and appropriately. Source: Shopify
- Programmatic advertising and media buying – Programmatic media buying, marketing and advertising is the algorithmic purchase and sale of advertising space in real time. During this process, software is used to automate the buying, placement, and optimization of media inventory via a bidding system. Source: StateofDigital.com
- Optimizing marketing content and timing – the systems and software that transform content data and business data into actionable insights for content strategy and tactics with impact. Source: AMA
- Conversational AI for customer service – refers to the use of messaging apps, speech-based assistants and chatbots to automate communication and create personalized customer experiences at scale. Source: Georgian Partners
- Next best offer – is a customer-centric marketing paradigm that considers the different actions that can be taken for a specific customer and decides on the ‘best’ one. Source: Wikipedia
- Augmented and virtual reality – is defined as an enhanced version of reality created by the use of technology to add digital information on an image of something. Source: VOA
- Autonomous objects – is an emerging term for the technological developments that are expected to bring computers into the physical environment as autonomous entities without human direction, freely moving and interacting with humans and other objects. Source: Wikipedia
- Facial recognition – is a biometric software application capable of uniquely identifying or verifying a person by comparing and analyzing patterns based on the person’s facial contours. Source: Techopedia
- Biometrics – is a technological and scientific authentication method based on biology and used in information assurance. Source: Techopedia
It’s worth noting from personal experience with many of these categories that there’s a lot of overlap between them. For example, a content intelligence platform might do personalization, customer segmentation and predictive analytics. Will share another example down below shortly.
It’s likely that many surveyed didn’t take note of the crossover between AI use-categories when they made their selections. That said, the below still represent good markers for marketing teams to be aware of.
Below is the study’s complete breakdown of the AI use-categories by B2B and B2C service/product brands:
Raw Data by Industry Vertical
Here’s where the data gets broken down even further – by industry vertical. The Tech Software/Biotech vertical leads the pack in AI use with adoption in every category except Biometrics. Consumer Packaged Goods and Consumer Services have adoption in all but the final two categories. Check your industry’s adoption in these AI use-categories.
Click the charts below to expand
When discussing AI solutions with an audience I always remind them to ask themselves before investing whether it actually provides real business value. If the answer is yes, then consider investing. Often, software companies simply use the term AI as a marketing ploy and don’t actually provide value with it.
It’s like the facial recognition Facebook uses on images. That’s AI. If you had to pay for it would you?
The reason the crossover, mentioned above, is important to consider when exploring AI solutions is simple. Rather than investing in multiple solutions in order to cover desired AI use-categories marketers can get many of them rolled up into one package.
For example, if someone wanted AI-driven programmatic advertising and media buying they might contact inPowered to partner with. They may not even consider our solution for AI-driven next best offer if they weren’t thoroughly educated first. Our technology falls in half of the categories defined above. Here’s a list of AI use-categories our technology falls in:
- Content personalization
- Targeting decisions
- Customer segmentation
- Programmatic advertising and media buying
- Optimizing marketing content and timing
- Next best offer
If one of our clients filled out this study, they would need to recognize all six of the above AI use-categories. Therefore, the topics of crossover and business value are mentioned above. Technology that falls under multiple categories have a good chance of providing better business value versus one-off AI solutions.
Duke University’s CMO study isn’t just about AI in marketing, either. It fully packed with 71 pages of insightful information gathered from CMOs across industries. Its companion raw data pack is 151 pages. In this article, however, we focused on AI because its an emerging technology with vast potential for every marketer in the world. We hope you can use the benchmarks and information above to help guide your current and future investments in AI.