It is safe to say that today’s marketing technologies present remarkable opportunities as well as enormous challenges. On the one hand, it is possible to scale campaigns and reach consumers in ways that were once unimaginable. On the other hand, consumers feel increasingly numb to the onslaught of messages and content they receive. As a result, they often disregard emails, promoted posts, and pop-ups without even glancing at them.
At the heart of all this is a basic fact: it is incredibly easy and relatively inexpensive to flood consumers with digital messaging, but a scattershot approach, even if it’s cheap, isn’t necessarily better. The goal of any marketing or sales team should be to deliver the right message in the right format at the right time. Strategies and campaigns must revolve around creating value for both the consumer and the business.
As organizations look to scale and automate marketing, it is critical to do so with pinpoint precision. Personalization, context, and timing are not abstract concepts; instead, they are at the center of transforming marketing into a strategic advantage. This is where emerging technologies like machine learning (ML), deep learning (DL), and artificial intelligence (AI) come into play.
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Marketing Is More Than an Asset
Marketers have never had so many powerful tools at their fingertips. Yet, despite clouds and increasingly sophisticated analytics and automation systems, too many campaigns fall short of business objectives. This is because there are still many challenges at all points along the marketing continuum, including data collection, digital asset management, and content delivery.
Too often, processes are siloed, and tasks are still handled manually. As a result, gaps and errors in the marketing process are commonplace, wasting time and money. Flipping this script to market faster and lower the costs to produce qualitative improvements requires smart automation. According to GartnerOpens a new window , by 2025, 70% of organizations will have operationalized AI architectures due to the rapid maturity of AI orchestration initiatives. Used effectively, these systems can significantly improve the customer experience, increase conversion rates, boost brand perception and loyalty, reduce retention, and fuel increased ecommerce revenues.
While AI refers to the broad array of technologies that allow machines to perform with human-like intelligence, ML and DL are particularly valid for marketers. ML uses computers and algorithms to detect patterns that are typically too complex for humans to spot, and ML systems continually learn and refine results. DL relies on neural networks — roughly mimicking the way human intelligence works — to analyze relationships and derive deeper insights.
Not surprisingly, these techniques are gaining traction among marketers, particularly as the signal-to-noise ratio increases and consumers become increasingly numb to blunt-force marketing methods. As Forrester vice president and research director Christina De Martini points outOpens a new window : The ultimate goal is “to optimize the internal experience [to] drive a better experience externally, for customers and prospects.†Doing this effectively requires an optimal combination of technology and business processes that create “an optimal path to attain revenue†through automation tools and signal detection.
The goal when using AI, ML, and DL to inform marketing decisions is to gather the right set of data points and deliver the right asset at the right time for a particular customer. Tying everything together across the entire customer journey is vital. When you’re able to identify all the essential variables and build an AI model that allows you to understand a customer, you are equipped to send the right combination of physical and digital assets. Once an organization has the right data and a clear understanding of the relationships between and among the various data points, it can plug all the data into a marketing automation platform, a customer relationship management system (CRM), and a content management framework. It can then take marketing initiatives to a new and more sophisticated level. With these tools, it is possible to identify and react to various signals and triggers and build marketing models that differentiate a business from its competitors. If a business can decode the signals, figure out customers, and target them more effectively, everyone wins.
AI Delivers Insights
It is important to recognize that while today’s highly automated systems allow companies to market faster and more efficiently, they also reduce the human contact that solidifies relationships and generates brand loyalty. According to Marcie Merriman, America’s cultural insights and strategy leader at consulting firm EY, “People desire better connections with companies, on the terms they desire.â€
Best practice marketing data does not simply appear, even with the best tools and technologies in place. Despite today’s sophisticated digital data collection and delivery methods — everything from email click-through rates, web analytics, surveys, point of sale data, and past buying history to geofencing and content delivery networks — it is incredibly easy
for marketers to miss the signals or wind up flying completely blind. According to Forrester’s 2022 predictionsOpens a new window , 70% of B2B marketers will adopt an “always on†digital engagement strategy in 2022, but 75% of their efforts to create automated, personalized engagement will not meet ROI goals due to inadequate buyer insight.
Michael McCune, a senior director in the Gartner for Marketing Leaders practice, advisesOpens a new window companies to focus on metrics and key performance indicators (KPIs) in a few key areas. Broad triggers may include brand health, customer satisfaction, social sentiment and stagnant revenue. On a narrower scale, behavioral signals may include things like where a person resides in the product life cycle, the place and time of day a person is most receptive to receiving a message, and various other traits and idiosyncrasies.
With this information, it is possible to take a more enlightened and strategic approach to marketing. For example, if a person called technical support with a problem, it is not the best idea to follow up with a sales pitch the following day. On the other hand, once the product is functioning correctly, particularly if it’s nearing the end of its life cycle, that may be a highly opportune time to connect with the customer and offer an incentive to buy a new product. Context is key.
The goal is to establish personal, authentic and relevant conversations and ensure that these interactions are meaningful at every moment across the customer journey. When an organization accomplishes this task, a brand can adapt to changing conditions quickly and smoothly by scaling a marketing effort up or down based on trends in the marketplace, delivering a level of personalization and contextualization that ratchets up affinity. When it all comes together, customers are receiving a message that resonates.
AI, including its subsets ML and DL, are essential components in this advanced marketing framework. By analyzing high-quality data in the right way, it is possible to spot the patterns, relationships, trends, and nuances that might otherwise fly below the radar. However, it is also important to recognize that all AI, ML, and DL systems are not created equal. The best systems continue learning and refining algorithms to get better over time. The more data they digest, the better they get at delivering actionable information and insights.
AI is not a one-and-done proposition. It can take months to build effective models because it is a process of continual improvement through data collection, A-B testing, and other techniques. To achieve success, you have to be committed to the long game.
A Framework for Success
When marketers get the formula right, AI can prove transformative. Suddenly, it is possible to deliver the right mix of digital and physical assets to consumers. This may take the form of dynamic web pages that adapt to underlying behavior and a customer’s place in the buying cycle or a signal that the person will respond to a printed brochure or other forms of direct mail. It may translate into developing and delivering different coupons and promotions that appeal to specific individuals or adapting pricing dynamically based on trends in the marketplace.
For example, one company in the auto maintenance industry previously relied on a general approach to marketing, saturating local geographic areas with mailers and brochures. When the company switched to a machine learning framework, everything changed. With broader and deeper insights into customer behavior and responses, the company revamped its top-of-funnel mailers and began marketing in a more targeted and precise way. The result was a digital and physical campaign that generated six months of leads.
By analyzing factors such as buying histories, click patterns on websites, and more, ML and DL systems can identify the specific consumer signals that lead to purchases and ongoing brand loyalty. As a result, a business can tailor the tone and timing of its messages to match consumer expectations. “Such personalization can deliver five to eight times the return on investment on marketing expenditure, and can lift sales by 10 percent or more,†according to McKinsey and CompanyOpens a new window .
Make no mistake, an AI-centric approach that delivers an orchestrated blend of digital and physical assets can serve as the foundation for a smarter and more responsive marketing organization. As Gartner’s McCune explains: “The marketing organization can create an operational model around Event Triggered Actions. Media buyers, print designers, copy writers, and data interpreters can all keep their specializations, but also come together as a whole or in agile groups to develop and document ever more event triggered actions.â€
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The ultimate result of this can be remarkable; breaking down siloed databases, disconnected data points, sporadic automation, and subpar insights take organizations into a zone of transformation. Business management consulting firm McKinsey Opens a new window l=”nofollow noopener” title=”Opens a new window” target=”_blank”>reportsOpens a new window that organizations adept at understanding and leveraging customer behavioral insights outperform peers by 85% in sales growth and more than 25% in gross margin. These same organizations also ratchet up brand loyalty and become allies with consumers.
In the end, it is critical to build AI, ML, and DL into marketing. Not only do these techniques lead to better short-term results, including increased response rates and an uptick in revenue, but they also produce a more fluid framework for communicating and interacting with customers. The objective should be to connect to customers more effectively. When marketers build better relationships and enhance value, success usually follows.
How are you using AI to make your marketing more effective? Share with us on LinkedInOpens a new window , TwitterOpens a new window , or FacebookOpens a new window . We’d love to hear from you!