The data revolution in marketing

Integrating predictive analytics, AI, and machine learning unleashes significant — and in some ways astonishing — possibilities for organizations across industries.


Personalization: How brands spark & grow relationships

Personalization is how brands reach out to customers to spark, establish, and evolve a relationship that is not just more enjoyable — it is also significantly more profitable. Consider that:

  • 80 percent of customers are more likely to buy from a brand that offers personalized experiences.
  • 77 percent of customers will pay more to a brand that offers them personalized experiences.
  • 72 percent of customers will only interact with brands that offer personalized experiences

The best — and in many scenarios the only — way that brands can get closer to customers in an agreeable, scalable, and cost-effective manner is by delivering content, content, and yet more great content. This is even more critical in the B2B space, where the average prospect (which is typically composed of multiple professionals) accesses 13 pieces of content before making a final purchase decision. Any single piece of content — whether an ebook, webinar, white paper, etc. — that fails to inform, impress, and influence could stall or end the relationship.

However, it can be extremely difficult for brands to determine:

  • What content should we create?
  • To whom should we deliver content?
  • When should we deliver content?
  • Through what channels and touchpoints should we deliver content?

These are pivotal questions that cannot be ignored, because they do not just influence whether a brand will succeed — in the big picture and long run, they can determine whether a brand will survive. Fortunately, there is a proven and practical way for brands to get the answers and insights they need, and it starts with predictive analytics.

For brands that struggle to deliver great content, the root problem is not necessarily money, focus, or determination. It is data. Or rather, the lack of data.


Using the past to predict the future

In essence, predictive analytics involves using historical data to generate insights, which in turn help predict future outcomes. This is typically done by assigning a probability (also referred to as a predictive score) to the possibility that a certain unit or entity such as a customer segment will act in a certain way given a set of details and variables. There are three main types of predictive models:

  • Regression models: These identify patterns in large data sets to estimate relationships across variables. For example, a brand could use this approach to identify the influence of shipping costs on purchase decisions.
  • Decision trees: These are driven by algorithms that glean different ways of breaking data into branch and tree-like segments (hence the name). For example, a brand could use this approach to identify which of three possible new products to add to their roster.
  • Neural networks (a.k.a. artificial neural networks): These are used to solve highly sophisticated and multi-faceted pattern recognition inquiries. For example, a brand could use this approach to predict the likelihood that a potential customer will click on an ad.

Predictive analytics has practical applications in many fields such as healthcare, insurance, financial services, telecommunications, and cybersecurity. In marketing, different types (and sub-types) of predictive analytics are used by successful brands to capture metrics and key performance indicators (KPIs) such as:

  • Customer lifetime value
  • Marketing attribution
  • Product recommendations
  • Marketing campaign effectiveness and ROI

The good news is that predictive analytics can be extremely useful in forecasting events, as well as the likelihood changes will have on future outcomes. We could use regression modeling, for example, to tell us that if we reduce shipping fees by 10 percent, within one month we should see an increase in overall sales by 16 percent, which would offset our added costs and increase customer lifetime value.

However, the bad news is that predictive analytics can be less insightful in scenarios that are characterized by significant change — because historic data is not a reliable foundation upon which to forecast future events and outcomes. Of course, we don’t have to search long and hard for an example of this in reality: the pandemic that erupted in 2020 triggered complex and new problems that conventional predictive analytics struggled to forecast, since the paradigm was so dramatically different.

Thankfully, that is where AI and machine learning enter the story.


3 Types of AI systems

At its core, AI is about simulating human intelligence through computer systems (“smart machines”) that rely on external data and internal algorithms to make decisions. Generally, there are three types of AI systems:

  • Artificial General Intelligence (AGI), in which systems can perform any task that a human is capable of — but with much greater proficiency, speed, accuracy, and endurance. AGI is currently hypothetical.
  • Artificial Superintelligence (ASI), in which systems are self-aware and surpass the intelligence and capabilities of human. Alongside AGI, ASI is also hypothetical.
  • Artificial Narrow Intelligence (ANI), in which systems are designed to perform a singular objective (e.g. search the web, drive voice assistant apps, etc.).

One of the most popular types of ANI is machine learning. Similar to predictive analytics, machine learning is a method of data analysis, in that it automates the process of analytical model building by creating and refining algorithms to look for patterns and behaviors in data.

However, unlike predictive analytics, machine learning does not have to be told what to look for. In other words, machine learning is not anchored or limited by historical data. It literally “learns by doing,” which is why it is being embraced by marketers around the world. We explore why in the next section.


Predictive analytics + AI + machine learning = astonishing possibilities

So far, we have discussed that:

  • Predictive analytics is about using models rooted in historical data to forecast future events.
  • AI is about creating smart machines to simulate human thinking capability and behavior.
  • Machine learning is about smart machines learning from data, experiences, or both, but without having to be explicitly programmed to do so.

Integrating all three of these unleashes significant — and in some ways astonishing — possibilities for organizations in a variety of industries. For example:

  • Organizations in the financial services industry are using AI to detect and thwart fraud.
  • Organizations in the cybersecurity industry are using AI to prevent threats and respond to active attacks in real-time.
  • Organizations in the retail industry are using AI to plan what items to stock based on trends, season, and other factors.

But what about content delivery, which as we noted earlier is the key to personalization? Does AI apply here? Absolutely. Here are some of the remarkable ways that marketers are leveraging AI (and its subset machine learning) to drive engagement and experience across the customer journey:

  • Automated customer segmentation: AI can analyze the browsing behavior of website visitors (what they do, when they do it, how long they do it for, whether they have done it before, and so on) and automatically place them into pre-built audience segments.
  • Automated new segment creation: AI can detect if the browsing behavior of website visitors does not align with pre-built audience segments, and create new ones (this is a perfect illustration of the value of machine learning).
  • Automated personalization: AI can selectively deliver content to customers in each segment, and at the optimal time in their journey. For example, customers in the “awareness stage,” who are largely or wholly unaware of their options, could be prompted to download an e-book providing them with a checklist of how to optimize their research and evaluation process. Alternatively, customers who are further along their journey and ready to make a final purchase decision can be prompted to watch or read testimonials, case studies, or other forms of social proof that enable them to confidently move ahead into a transaction.
  • Automated A/B testing: AI can run split A/B tests on various content assets — e-books, white papers, checklists, infographics, banners, images, etc. — and identify which are converting effectively, and which need to be upgraded or retired.
  • Automated content tagging: AI can glean what an image or video portrays, and automatically tag them accordingly (e.g. “train,” “beach,” etc.). This not only helps break down content silos, but it also frees marketers to spend less time on tedious manual tasks, and more time on high-value priorities.

As we can see, predictive analytics, AI, and machine learning have the potential to help brands dramatically improve marketing results, and perhaps even more importantly given recent events, flourish and seize new opportunities — rather than struggle and face setbacks — during uncertain and volatile conditions.

However, attempting to establish and apply predictive analytics, AI, and machine learning without the right architecture can lead to chaos instead of clarity, problems instead of profits. This brings us to another pivotal piece of the puzzle: customer data platforms (CDPs).


The right architecture creates clarity and prevents chaos

CDPs are packaged software solutions that create a persistent, unified customer database that is accessible to other systems. The Customer Data Platform Institute breaks down this definition:

  • "Packaged software": a CDP is a prebuilt system that is configured to meet the needs of each client. Some technical resources will be required to set up and maintain the CDP, but it does not require the level of technical skill of a typical data warehouse project. This reduces the time, cost, and risk while giving business users more control over the system.
  • "Creates a persistent, unified customer database": a CDP creates a comprehensive view of each customer by capturing data from multiple systems, linking information related to the same customer, and storing the information to track behavior over time. The CDP contains personal identifiers used to target marketing messages and track individual-level marketing results.
  • "Accessible to other systems": data stored in a CDP can be used by other systems for analysis and to manage customer interactions.

This definition is a good starting point. But it is not the full story, because it triggers a critically important question: what is the essential functionality of a CDP, and what must it be capable of doing? Without this background information, marketers run the risk of choosing the wrong solution.

Thankfully, trial and error isn’t required. A superior CDP solution is built with three layers: segmentation, decisioning, and optimization

  • Segmentation: this layer tracks every click, search, and buying signal from both known and anonymous customer profiles. It also consolidates and connects all customer data in one place, builds customer profiles and segments with real-time data, and integrates data seamlessly from across the organization. Robust and reliable segmentation can be viewed as the "engine" of a CDP solution.
  • Decisioning: this layer uses predictive analytics, AI, and machine learning to drive positive interactions across the customer journey. It also leverages customer and business data alongside real-time context, uses decisioning technology to make powerful data-driven decisions, and enables A/B testing on any digital channel — which is critical for intelligent optimization and experimentation based on reliable data versus gut feelings. This powerful, reliable, and evolving (i.e. “learning by doing”) decisioning capability is what makes a CDP solution "smart."
  • Optimization: this layer orchestrates every interaction across all your channels, creates seamless experiences that jump from channel to channel, delivers hyper-relevant experiences for every customer, and triggers personalized messages to help customers take action. The optimization layer can be viewed as the "hub" of a CDP solution.

While some CDP solutions are extremely sophisticated (and may strike some folks outside of the marketing world as science fiction given how powerful, effective, useful, and accurate they can be), choosing the right one does not require a PhD.

Marketers that focus on a CDP solution built with the three layers described above — segmentation, decisioning, and optimization — can be assured their investment will be rewarding rather than regrettable.


Unleashing game-changing rewards

In recent decades — and especially since COVID-19 erupted —marketing has undergone tremendous changes. Customers want to know the brands they choose can still treat them as the individuals they are. And brands want customers to know they’re committed to building, protecting, and evolving a genuine relationship — one that’s tailored to their needs while maintaining their safety and privacy.

Predictive analytics, AI, and machine learning — grounded in and powered by a superior CDP solution — helps fulfill these expectations and ambitions, while unleashing game-changing rewards for both brands and customers alike.

Learn more about Sitecore's AI solutions.