There has been a lot of ‘hype’ around how artificial intelligence (AI) has the potential to transform marketing and ultimately support more impactful customer experiences.
But there’s also been a lot of discussion about whether some AI applications for marketing are actually ready for prime time, or even if they have been used – or should be used – on their own at all.
Taking it down a notch, AI is really just the higher level, broader category of emerging technology that seeks to enable machines to do the business critical thinking that marketers traditionally have done manually. However, if marketers think about applying AI to their more tactical, day-to-day tasks, they could not only alleviate the manual work involved in these responsibilities but also execute them perfectly every time without the common distractions or fatigue that is inherent to humans.
It’s really these most basic types of AI applications for automating common but time-consuming tasks such as rote data processing and management processes that are actually already made big impacts in some organisations and evolved how they address their marketing.
Here are four specific ways AI has changed data processing and management for marketing:
1. Better optimisation of data in assets
AI has helped marketers automate the enrichment and tagging of marketing assets, such as content, images, and videos, so they can make better use of their assets and Digital Asset Management (DAM) solutions. The technology can also perform more critical thinking tasks, such a speech-to-text capture, sentiment analysis and even image use recommendations for assets.
By automating these essential but often time-consuming processes marketers not only now have the advantage of a more robust DAM solution but also have:
- Improved content reuse
- Delivery of richer experiences
- Decreased asset search time
- Decreased content creation costs
- Improved user satisfaction with their DAM because it’s now making their job easier when they need to find and choose assets
2. Enhanced capabilities for gathering and using ROI data
AI has helped marketers become more accurate in financial data processing and predictive modeling as it is able to combine content productivity metrics with creation costs to provide incredibly accurate and timely return of investment (ROI) analyses. This has helped organisations become more agile as they now have the ability to make better data-driven marketing decisions, such as adjusting activities in-flight to maximize performance or identifying and removing low-performing assets to ultimately improve top-line revenue and bottom-line performance costs.
3. New use of resource data
AI also has helped marketers make better use of previously stagnant resource data by processing and analysing it to determine how much time their organisation took to make an asset, the current workload, and capacity of each of its staff members, and its external agencies. Marketers then have taken such analysis to make such data-driven decisions as automatically routing or rerouting tasks to more available or better capable resources.
This has resulted in:
- An increased percentage of one-time projects
- Improved employee satisfaction
- Increased quality with first-time approvals on assets
- Increased production maximizing existing resources
4. Improved use of customer/prospect data
AI solutions have helped marketers better manage their customer and prospect data by performing an automated analysis of buyer persona content needs or previous content interactions. The technology then recommends content that would more accurately resonate with these groups based on such data.
This optimized data management has ultimately helped marketers deliver better customer experiences because it’s ensuring that they create and push out the type of content their customers and prospects want, which has often resulted in:
- Increased return on marketing investment
- Increased overall revenue
- Improvements in conversion rates and buyer journey progressions
- Increased budgets for content and campaign creation
While AI has many other potential applications in marketing, it will never completely replace the various roles involved in marketing teams. It can’t, for example, fully replace human creativity, common sense, or the emotional decisions that humans must make across an organization’s marketing activities.
But as some early adopters have seen, it has changed the basic, rote data processing and management tasks in marketing for the better by automating overhead and expediting analysis of performance data so organisations can have the most accurate and timely data at their fingertips to help them make better decisions across their enterprise.