Algorithmic marketing is empowering marketing leaders with the AI and automation knowledge and tools they need to embrace the future of marketing operations.
This paper, the first in our ‘Future of B2B Marketing: Navigating the Algorithmic Landscape’ series, demystifies the concept of algorithmic marketing. We’ll explore the plethora of benefits that come from the effective implementation of these techniques - from improved efficiency to increased job satisfaction to heightened productivity, all whilst making data-driven, real-time decisions, ultimately driving significantly increased ROI.
Practical advice includes:
- What is algorithmic marketing?
- Preparing for successful algorithmic marketing implementation
- Training your automation tools
- Ethical considerations for utilizing algorithmic techniques
- Workforce augmentation as a skills gap solution
We’ll evidence how forward-thinking B2B brands are leveraging advanced algorithmic techniques such as AI and machine learning to empower their marketers and reshape marketing operations, accelerating growth to deliver their strategic goals.
Later articles in the series will delve into:
- Thriving with imperfect data: harnessing algorithmic marketing within modern enterprise marketing operations
- Addressing the B2B enterprise marketing AI skills gap: building a sustainable operating model for the future
- Training the building blocks of optimal algorithmic marketing: tools, platforms and integrations for automated workflow optimization
- The AI maturity curve in B2B enterprise marketing: assessment, access and progression
- Ethical AI in marketing operations: mitigating tomorrow’s risks
- AI fears and hallucinations: maintaining human control through robust interfaces
- Democratizing AI: the two halves of algorithmic marketing and our solution
What is algorithmic marketing?
Algorithmic marketing is a ground-breaking approach to marketing that leverages big data, algorithms, and advanced analytical techniques to utilize cutting-edge technologies like machine learning, NLP, and robotic process automation. It drives superior customer experiences, relieves resource-intensive tasks, and improves budgeting strategies.
Algorithmic marketing is undoubtedly the future of B2B marketing, providing marketers with the ability to target customers more effectively, in a logical, data-driven, and informed manner. Without it, businesses will quickly lose competitive edge and traction in growth strategies.
What outcomes can you expect?
Working in partnership with Algomarketing, a global enterprise client recently delivered the following sales outcomes through implementation of AI and automation techniques through their marketing operations.
The Algomarketing approach
Using our ground-breaking methodology, we transform the marketing operations of some of the world’s biggest brands using cutting-edge technologies and advanced automation.
Utilizing the most advanced AI techniques, optimized customer and marketer experiences are delivered through elevating MarTech solutions and streamlining operations:
- Greater marketing efficiency
- Faster, informed decision-making
- Optimization of your MarTech solutions
These data-driven approaches provide deep insights and fast execution to drive unparalleled impact and maximum returns.
Empowering marketers through algorithmic techniques
The past few years have brought about a new industrial revolution for the digital age, with AI able to take on more complex tasks than ever before.
For the first time in history, advances in technology are enabling highly skilled workers across various industries to automate repetitive, laborious tasks – a feat that not only enhances operational efficiency and heightens productivity, but also has significant potential to increase employee engagement and morale.
The use of algorithmic techniques is increasing operational efficiency while enhancing reward from work:
- Agile methodologies: Enhancing workflow efficiency by removing reliance on productivity-limited humans.
- Automation of menial tasks: Knowledge workers can focus on more complex and rewarding tasks, improving productivity and satisfaction.
- Streamlined workloads: Employees have more focused responsibilities thanks to their ability to delegate many previously time-consuming tasks to machines.
- Bringing joy back to work: Too many tedious tasks can make workers feel undervalued and burnt out. By automating these tasks, workers have the space to deliver their best work.
From increasing productivity to helping to hold onto key talent, the merits of deploying algorithmic techniques, underpinned by automation, are clear: enabling organizations to strike the right balance between human value and supportive technologies.
Transitioning from static to modular workflows to optimize performance
Most B2B enterprise marketing operations currently use static workflow processes to guide delivery of their campaigns. This traditional approach provides a prescribed journey to follow in a linear fashion, with analysis and learnings typically coming at the end of the process to be considered and implemented into future workflows.
By introducing machine learning and automation into these journeys, AI can constantly learn and reflect on what might fit the audience better at any given time. This can be done to the workflow as a whole, or by breaking the static workflow into modules in order to identify the biggest automation opportunities. These can be identified by considering challenges such as bottlenecks that occur within a particular module or the timings of manual processes that could benefit from acceleration.
The introduction of AI techniques to even one module within a previously static workflow can provide improved customer and colleague experiences, whilst driving increased speed to the whole process.
Change is never easy. Any organization looking to make the shift to algorithmic marketing is likely to encounter some resistance. It is therefore vital that this transition is managed effectively and the right skills are employed to support the transition.
Preparing for successful algorithmic marketing implementation
For many organizations, marketing is a well-oiled machine reliant on a carefully concocted formula of tried and tested techniques. However, marketers focused on competitive advantage and cut through never stand still. They’re focused on new strategic avenues and technologies that free up their time and fuel efficient growth.
The Algomarketing maturity matrix
When it comes to AI and automation, it’s useful to understand how you’re performing against industry benchmarks, as well as when and how to invest. Our maturity matrix provides a global enterprise benchmark based on recent market leader research.
Use our tool to see how your organization is performing against these benchmarks and assess your current level of advanced automation and AI maturity.
By understanding where you are on this journey, and by considering how open your organization is to change, you’ll be in a good position to explore the tools, skills, and expertise needed to accelerate your automation ambitions.
Request a complimentary consultancy call with our team of experts to discuss how we can help you progress your AI and automation maturity.
Training your automation tools: The hows and whys
AI and machine learning-led tools are rarely plug-and-play. To extract maximum value, they must be tailored to each specific use case. That means significant training through machine learning and data modeling delivered by experienced experts.
AI training is the process of teaching an AI system to perceive, interpret, and learn from data. In doing so, the AI becomes well-placed to make logical, accurate, and informed decisions based on specific datasets provided to it. The results of which can in turn be passed onto your people as clear and concise recommendations.
Giving this step the time and expertise it needs is imperative, as the training phase has a major impact on a machine learning model’s overall performance.
Algomarketing AI training model:
- Data collection: gathering the right data to train your AI model
- Data preprocessing: cleaning and preparing data for training
- Capturing human wisdom: collecting insight from the people closest to the data and processing to help eliminate model bias
- Model training: the process of feeding the prepared data into the AI model
- Model evaluation: assessing the performance of the trained model
- Model optimization: fine-tuning the model for better performance
Not only is the process methodology of AI training key. So too is the quality of the data used during those training processes. There is considerable nervousness around data quality, however, most data challenges can be easily overcome with the right expertise and tools to build trust through small incremental experiments with a training data set.
Ethical considerations for utilizing algorithmic techniques
Utilizing these technologies comes with the ethical responsibility that data be high quality enough to ensure it can accurately deliver appropriate and balanced outcomes.
Recognizing that inequality and unfairness stem from bias in the decision-making process, it’s now well-documented that this can also occur when implementing complex algorithmic techniques.
Algorithmic bias must be eliminated through robust data cleansing and training. Unless they are trustworthy, they will deliver unfair or unethical outputs that can lead to brand reputation damage and even leave board members personally liable.
Creating a robust data ethics framework specifically for your company
AI ethics is far more than regulation, it’s about setting a shared vision and framework around how your company uses data, closely aligned to your organizational values. This should strike an impactful balance between meeting user demands and ensuring data is only used for its intended purpose.
Data ethics should be embedded into your organizational governance through the creation of a comprehensive and bespoke data ethics framework, which is then implemented by a cross-functional data ethics committee responsible for upholding it.
Workforce augmentation as a skills gap solution
Ensuring you have the right skills and capabilities to support your teams to implement these technologies confidently and ethically is critical to long-term organizational success.
With demand for data scientist skills and expertise at an all-time high, many B2B enterprise brands are recognizing the value of an augmented workforce - bringing those skills into their businesses through workforce solution models that are fast and flexible, providing immediate access to sought-after skills, whilst building in-house capacity.
Here at Algomarketing, we augment the capacity of marketing operations teams in some of the world’s leading tech brands, helping to implement advanced automation techniques, whilst building in-house capability and bridging hiring gaps.
Algorithmic marketing is a critical area in which a plethora of major benefits can be realized – from improved personalization and efficient customer segmentation to predictive analytics and ROI improvements.
Those that embrace it will gain a competitive advantage, enabling marketers to target customers more effectively, in a logical, data-driven, and informed manner. Those that don’t, meanwhile, will be at risk of losing their competitive edge.
It can be both daunting and difficult to embark on this transformative journey. That’s why we’re here to help. Although shortages of skills or access to budgets can commonly be cited as barriers, there are workforce solutions to push forward faster than you think.
Currently operating in 27 countries across six continents, our unique methodology unlocks the power of algorithmic marketing through innovation, big data, and optimal tech stack performance.
By harnessing the power of algorithms, we unlock incredible efficiencies, drive innovation, and scale operations like never before through AI, automation, and machine learning expertise. We empower marketing leaders to confidently make data-driven decisions, ultimately driving increased ROI.