ove Fast, Succeed Faster: MarTech Innovation Holds the Key to Removing Failure from the Equation
Machine learning, AI and IoT – just a few of the quickly evolving data science technology tools available to today’s marketers. However, understanding how these innovations work both individually and together to generate meaningful results is still unclear. Influence Health’s, Senior Vice President of Marketing, Kyra Hagan, shares her vision about what the future of marketing holds including the role data plays in delivering seamless and relevant customer experiences (CX) to drive profitability.
Many marketers live by the mantra “move fast, fail fast,” priding themselves on being able to turn on a dime when campaigns don’t hit their mark. But what if instead of having to fail in the first place, marketers could move fast, and succeed faster?
Technological innovations – from the Internet of Things (IoT) and blockchain, to artificial intelligence (AI), machine learning (ML) and deep learning (DL) – are among the many new weapons available in a marketer’s arsenal that have the potential to significantly improve precision targeting and development of compelling content and appealing deals that translate into real, practical, and profitable, results.
To benefit most from these advancements in data science, marketers need a clear sense of what each technology can, and cannot, do. The key is understanding how best to leverage each type of technology, and how they can work hand-in-hand supporting each other, to get the best results.
To start, let’s step back, clear up some misconceptions that result in the use of these terms interchangeably, examine each technology, how they differ and their ideal use cases:
Artificial Intelligence – AI is the ability of machines to mimic human behavior, much like Siri or Alexa takes verbal input from an individual, and then responds in a human-like fashion. AI is enabling the rapid rise of voice communications and chatbot applications, which can offer your audience a more personalized experience, expand your reach and move customers seamlessly through the sales funnel. AI also is resulting in massive amounts of unstructured data in the form of verbal communications, which marketers need to figure out how to harness.
Machine Learning – ML relies on high-powered computing to make machines and applications think like a human. It combines science, statistics and computer coding to make predictions based on patterns discovered in data, much faster than humanly possible, to improve productivity by speeding common tasks like segmenting customers, generating branded collateral, extracting and classifying relevant content, predicting churn and forecasting customer lifetime value.
Deep Learning – DL is a more advanced subset of ML, in which multilayered neural networks learn from vast amounts of data. DL is also capable of speech recognition and independent decision making, in which the answer to one question inspires the machine to ask and answer other questions. DL extracts more meaningful insights from quantitative and qualitative customer research, and gains a better understanding of customer intent to power recommendation systems based on individual consumer’s explicit behaviors and implicitly derived preferences.
Internet of Things – With IoT, the internet is constantly present, collecting data from everyday objects – like digital scales and thermostats – and how we interact with them. Consumers are leaving a valuable, digital footprint that is so massive in scale that it’s almost impossible to comprehend how to manage. That’s where ML and DL come in, taking in these vast mines of data and delivering insights in real time. According to Marketo, information gathered from IoT can help marketers analyze customer buying habits across platforms, understand where a customer is in the buying journey, provide real-time point-of-sale notifications and targeted ads, and assist in quickly resolving service issues to improve the customer experience.
Blockchain – Perhaps one of the most confusing emerging technologies, many believe blockchain has the potential to reshape how marketers work in the near future. Blockchain is a digital method of economic transaction record-keeping – like a ledger – and its utility could go beyond banking and financial transactions. The Forbes Agency Council earlier this year examined 10 ways blockchain could change the marketing industry, including enabling brands to better target consumers, appeasing privacy concerns and building advertiser trust by giving users more control over the personal information they reveal, improving the quality of influencers and making advertising more transparent.
Applying These Technologies to Omnichannel Experience Delivery
Today, marketers are still struggling to ensure customer experience (CX) is seamless and relevant as consumers move between online and offline channels, systems, and a multitude of devices. The average enterprise uses 15 to 20 different marketing execution systems in their environment in an attempt to be everywhere their customers want to be. While many of these systems have redundant data, some may hold unique information that cannot be found in other systems. There is no universal source of truth regarding a customer – their needs, motivations, intents and behaviors. Most CRMs only work with structured data, making it a challenge to pull in information generated from IoT devices, AI applications and other sources churning out unstructured data. Looking ahead, organizations will be in hot pursuit of big data solutions that enable them to make better sense, and use, of all the various new types of data coming forward in real-time.
AI, ML, DL, IoT and blockchain will be the catalyst for changing the archaic infrastructure on which marketers currently rely. New architectural approaches are required to put the information together in a meaningful way to enable more real-time, precisely targeted marketing strategies. In healthcare, for example, this means leveraging both clinical and digital behaviors to more accurately predict condition propensity and potentially intercept patients with undiagnosed medical conditions – targeting them for appropriate medical screenings that boost profitable volume for the health system, but that also lead to early detection and timely intervention to improve health and longevity for at-risk individuals.
AI will also mature marketer’s approach to customer journey design so they are no longer dependent on A/B testing to determine what works best, but rather can depend on machine learning (ML) to produce insightful journey analytics. A/B testing offers great insights into which messages resonate best with consumers. But because they must be deployed in real-world settings, marketers may lose potential conversions with the subset of customers that receive a less effective message. Journey analytics leverages DL and neural networks to do simulations that test the next logical steps depending on where consumers are in their journey. With journey analytics, marketers can capture customer feedback every step of the way to learn how customers engage, what they want to accomplish and where problems may arise to derail the process. These insights into the customer journey help provide confidence when changes are made and new ideas implemented.
A Voice Revolution Underway
AI is also driving the voice revolution, as much of a consumer’s digital footprint becomes conversational. When mobile phones were introduced, marketers saw opportunities to reach consumers through this new channel. However, there was a monetary barrier for consumers; they needed to first buy a device before marketers could reach them. Social grew quickly from mobility, since consumers had their devices in hand and easy access to their preferred channels. Voice is growing exponentially faster than social, with mobility as its gateway since speaking to chatbots to conduct searches and discover information is easier than typing on small screens. Voice also enables greater refinement in searches, producing more insights into consumers’ specific desires and needs. DL and natural language processing are required to take these queries to the next step, translating this unstructured conversational data into actionable insights.
Marketing strategies, tools and practices continue to evolve at a break-neck pace, fueled by these data science technologies that are still in their infancy. The true impact it will have on the marketing profession is still not clear, since mass market adoption for many is at least three to five years away. Yet some early adopters, like Amazon and other large e-commerce companies, are showing the promise of these technologies, and how leveraging them helps marketers move fast and succeed even faster.