When it comes to marketing, the main focus today is around shiny concepts like AI and ML. However, going back to the basics of marketing automation is often overlooked. In this article, Christopher Martin, co-founder and chief operating officer, MightyHive, discusses the five steps needed to integrate AI into the fabric of enterprise marketing automation.
Enterprise Marketing Automation (EMA) is not being widely talked about, but it absolutely should be. EMA is the concept that marketing is part of the supply chain of a brand. This new category will give rise to an entire class of software, services and industry best practices in the coming decade. All these will be focused on connecting the marketing stack into the entire enterprise, similar to how Oracle, SAP and other Enterprise Resource Planning (ERPs) digitally transformed other corporate functions in prior decades.
The buzzwords thrown about today seem to be focused on shiny, new-age concepts like Artificial Intelligence (AI) and Machine Learning (ML). But going back to the basics of marketing automation is often overlooked as the step that is required to get to those stages. Automation is the stage enterprise marketers would ideally be at today but automating an enterprise is no simple feat. The digital transformation needed to adapt an enterprise’s “frankenstack†to be fully automated is highly complex and will certainly take years to deploy. As I continue to listen in on the industry buzz, it’s occurred to me that most enterprise marketers have probably received misinformation about the sprint to AI & ML. They are also struggling with the solutions available today that can immediately help accelerate their marketing efforts.
Many marketers and clients alike are fond of the idea of using artificial intelligence to optimize their marketing efforts; it’s trendy and futuristic. They think it’s precisely what they need to be doing to keep up with the competition, and they are ever eager to implement it in any way they can. However, there is no turnkey option for complete AI-enhanced marketing or even machine learning today. It will most certainly require some form of custom engineering/integration and human involvement. Furthermore, adding AI to a system that isn’t optimized for it may only have a very limited impact.
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The biggest hurdle clients face in unlocking ML and AI is in the standardization and systemization of data. To truly reach a stage where AI or ML can reach its maximum potential, enterprise organizations first need defined taxonomies for data collection strategies, activation and reporting. This will allow for the creation of automated pipes to streamline properly structured data to be ingested by an AI or ML API service. But first, I encourage you to ask yourself if you’re trying to do the following:
- have decisions be made better than they currently are (Better)
- or create efficiencies and accelerate business outcomes (Faster)
If your goal is to enhance decision-making, ML can be incorporated into parts of your stack to generate insights for human review and decisioning today. If your goal is to streamline business outcomes and enable bulk execution of relatively simple tasks, then automation is the ticket. If your goal is to combine both, that is, to make complex decisions a human would normally perform at an accelerated rate and action on them, AI is what you need. In short, automation is used in circumstances where trivial tasks can be executed repeatedly, and ML is used to generate insights within data and train algorithms to recognize patterns. And AI is the combination of automation and ML that replaces humans in calculated decisioning-making and execution.
The process of reaching that machine-made choice is no small task, but in the big picture, it can be broken down into five distinct steps.
1. Standardization
Manual processing is a major bottleneck to efficiency. The first step in the process of automation and beyond is to embrace digital capture and processing, a key step in digital transformation. Keep in mind that the marketing department needs policies and procedures in place for the legality of privacy for data collections. So part of the digital process needs to be protected for privacy compliance. Once a capturable universe of data is made available, taxonomy creation follows. We’re still in the manual process in this stage, but standardization will help improve workflows by introducing expected inputs/outputs and repeatability. Both are key ingredients to predictability.
2. Enable Data Mobility
In this stage, the pipelines are architected and built that will allow for the rapid exchange of data between systems. This is the time to introduce out-of-the-box tooling or sometimes bespoke solutions to facilitate the transfer; ETL (extract, transform, load), making sure inputs are clean and have the data you need to be ingested by services. API services or software platforms, for example, all have their own standards and expect data to be retrieved in specific formats for processing: centralized CRM systems, unified IDs, operational, client management, billing, fulfillment, web/mobile, etc.
For maximum marketing effectiveness, an enterprise’s data systems should be architected with an API first approach, that is, to allow for services within (and where approved, outside) the enterprise to plug in and retrieve the data needed to join together and report / action upon. Being able to quickly mobilize your data allows for automation that will significantly improve the rate at which your marketing teams operate.
3. Automate Workflows
To create an effective automation strategy, the enterprise will need to evaluate the needs/outcomes desired from respective marketing teams, understand where repetitive tasks exist, map all the services and data available, and find any disconnects and opportunities in operational efficiency. The data architecture plan may include a host of internally built services and 3P integrations that allows for trigger-based logic to action on received data or flags within the system. Some simple examples include data exports, report generation, audience segmentation, database refreshing for dashboards, email notifications, flagging systems, etc.
Once a general automated workflow exists within teams, maximizing automated effectiveness across the enterprise is accomplished through the orchestration of automated services working together. Actions like real-time ecommerce retail inventory management linked to dynamic display ads, audience pool generation and distribution to live campaigns based on user properties, toggling line item strategies based on a variety of inputs from 3P sources can all be carried out by automated systems that work together.
Events should be orchestrated in such a way that the right people/services get the right information at the right time. Also, automation rules should be co-developed with experts within their field of expertise to provide the possible outcome for action. Automation, when done properly, is a process that can be executed with little or no human intervention since the operations they carry out are predictable and expected based on the conditions defined by experts.
4. Analyze With the Power of Machine Learning
Once systems are automated, we can now analyze faster and smarter. Depending on the use case (e.g., site side analytics, social campaign performance, conversion propensity models), you might elect to use human analysis or machine learning.
Data set size is less relevant when determining when to use human analysis vs. machine learning. Machine learning is best suited for pattern detection that can then result in identifying specific trends or even suggest a prediction. Machine learning models train themselves based on the input data provided. So, having good, clean, standardized data is key.
Could a human provide similar capabilities? Of course! However, machines are able to operate on a much larger scale and can accelerate the decision significantly. What’s more is once a proper machine learning model is set up, it can continually run a series of tests using its algorithm or model, continually perfecting the model toward optimal performance.
5. Decision by Artificial Intelligence
In this final and most mature (yet to be realized) stage, the machine is using the information above to make a decision or recommendation then, if desired, the machine can carry it out. Essentially, AI is a machine making a choice. At this time, no clients have a turnkey AI solution, but they can easily begin the journey to get there by strategically following these steps toward ultimate marketing automation.
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The modern enterprise is extremely complex, so there really is no perfect out-of-the-box solution for marketing optimization. With all the moving parts, successful and compliant marketing approaches have become tremendously complex as well. The best way to scale marketing successfully is via automation. This isn’t to say that ML and AI can’t be used in certain parts of your tech stack. Rather, today these technologies are best used when integrated as a part of your larger automation strategy (which comes first). Allowing the processes to be synced and streamlined saves the human component for higher-level cognitive tasks and quality client interactions. In the automation stage, humans are not fully removed from the puzzle; they are simply greatly assisted. The good news is that businesses can be fully okay with just automation for the next 5-10 years; there’s no rush to move to the next machine learning and AI steps just yet. If you’re an enterprise marketer, master automation now, and you’re just where you need to be.