MarTech :
Marketing Transformation – The MarTech Effect
By: Lisa Horwich @ Pallas Research
So far, in our series of Marketing Transformation – The #MarTech Effect, we have explored the background of why companies are investing so heavily into marketing technology solutions. And then, last moth we took a deeper look into some of the technologies and how they are being used and bringing about a transformation of how we do marketing.
Now let’s dig deep into the Four Pillars of MarTech – Artificial Intelligence, Machine Learning, Business Intelligence and Real-Time Analytics to more clearly understand what they are, their relationship to each other and even the limitations they pose.
To understand the relationship between AI, ML, BI and RTA, I found it helpful to visualize how these technologies are interrelated.
Everything starts with Data – whether its transactional like sales data, observational from watching what customers are doing in their daily lives, or experimental as one would get from running a survey or other structured methodology. The data then feeds directly into a Business Intelligence or Real-Time Analytics dashboard or over to an Artificial Intelligence algorithm, supported by Machine Learning. As you can also note, often an AI tool can feed its output directly into an analytics platform as well.
Real-Time Analytics
Definition: “Unified customer data platforms incorporating predictive analytics and contextual customer journey interactions – delivering real-time customer interactions across touch points and devices.” Marketers are turning to RTA in order to meet consumer demand for targeted engagements connecting customers with the products or services they need at the precise moment they need them.
The benefits companies enjoy when using an RTA solution include:
I would argue the last item is the primary reason companies have begun implementing RTA solutions – as discussed in Chapter 1, being able to measure ROI is a key rationale for increased MarTech deployments.
Business Intelligence
Definition: “BI is an umbrella term that includes the applications, infrastructure and tools, as well as the best practices that enable access to and analysis of information to improve and optimize decisions and performance.” The importance aspect of this definition is that Business Intelligence isn’t just the platform or application but includes the best practices of collecting and analyzing the data. Too often people think of BI as the dashboard and forget that data gathering, and analysis are equally as important as the tool itself.
The key benefits of a business intelligence tool include:
If you consider that busy executives aren’t going to spend the time culling through reports, the last benefit can have the most impact in an organization – making the data easy to consume is key.
Artificial Intelligence
Definition: “The theory and development of computer systems able to perform tasks normally requiring human intelligence – such as visual perception, speech recognition and decision-making.” Personalization of marketing campaigns is a key commercial application of AI – companies have so much data on their current customers, they are turning to AI to retain and upsell customers and reduce churn.
Some of the primary benefits companies realize with AI include:
In the quest for greater personalization, companies are relying on AI-powered solutions to help match the appropriate content to the audience that will find it most relevant. The key for optimizing AI, is to not only match content to the relevant audience, but companies must also identify the key performance indicator (KPI) that they are wanting to achieve – whether it’s increasing revenue, engagement or retention. By giving the AI tool a "goal" it allows the company to harness the power of AI to its extent.
Machine Learning
Definition: “Machine Learning is the science of getting computers to learn and act like humans do, and improve their learning over time in autonomous fashion, by feeding them data and information in the form of observations and real-world interactions.” As my diagram above shows, I tend to think of Machine Learning as a subset of AI – sort of the engine that powers AI solutions.
The key benefit of Machine Learning is the ability for the computer to highlight or find patterns or correlations in the data that would have otherwise been missed by human beings. One example is an AI tool that optimizes messaging, the machine "learns" by observing how customers interact with emails that are sent.
Limitations
All of these tools have limitations centered around data – whether it is quality or quantity. In any tool, quality of the input is key and for many of the AI tools, they require vast amounts of data. One tool I explored requires at least 1 million customers data sets – not every company has that kind of information, especially those that operate in a B2B market.
The other issue marketers face when using tools is having enough content for optimal personalization. Gartner predicts that 90% of brands will practice at least one form of marketing personalization, but content will be the bottleneck and cause of failure by 2020. Marketing teams must produce exponentially more content than they used to in the past.
Be sure to watch for the final chapter in our MarTech Effect Series next month where we look at the value of the Human Element in all of this and why it is not always only the tech that makes MarTech work well.
ABOUT THE AUTHOR
Lisa specializes in sophisticated, strategic market research and competitive intelligence. With over 15 years of marketing experience, including communications/brand management and product marketing, Lisa uncovers the ‘why’ behind data helping companies get the information they need to make smart decisions. Lisa specializes in business-to-business research helping companies understand their customers’ point of view through data analysis, focus groups and in-depth interviews.
