Personalized marketing is the practice of analyzing and leveraging data from your digital marketing mix to deliver unique experiences (including messages and offers) to your customers. In recent years, it has become an increasingly important part of any organization’s marketing strategy. Especially in light of COVID-19, it can be a vital tool to establish a meaningful connection with customers in lieu of more traditional in-person marketing tactics. Further, effective personalization strategies can deliver value to your visitors and customers, establishing relationships and progressing them through the sales journey.
Today, most personalization strategies are a combination of manual rule-based approaches and machine learning through algorithms. If you’re new to personalization or are looking to elevate your organization’s existing strategy, understanding the difference between these approaches is essential. To help get you started, here’s an outline of the differences between manual and machine-driven approaches to personalization.
Manually Targeting Segments with Rule-Based Personalization
Today, most manual approaches involve some form of rule-based personalization. Rule-based personalization allows businesses to display specific experiences to customer segments based on their behaviours. Through the manual creation and manipulation of rules and experiences, marketers can deliver offers, promotions, and other value-adding content to a specific audience persona.
What does this look like in practice? Let’s say you manage an online shoe store. If a first-time customer visits your website, you may want to offer them free shipping on their first pair of shoes. If they’re a returning customer familiar with your products, it may make more sense to show them a direct link to your newest arrivals and styles for the upcoming season. If they’re a frequent shopper and a high-value customer, a “welcome back” message and an incentive for your store’s loyalty program is a way to cement their long-term relationship with your business.
The best way to think of rule-based personalization is as a series of “if/then” statements; “if” a customer meets specific criteria or performs certain behaviours, “then” they fall into a pre-defined customer segment and are given the corresponding experience.
Keeping track of the “ifs” is easier than ever, with most modern web platforms featuring plugins and other tools to keep track of customer behaviours and sort them into segments. In particular, HubSpot offers a host of tools that integrate with their (free) CMS to create personalized experiences on your website and beyond.
However, managing the “thens” can be challenging for even the most experienced marketing professionals. Creating individual experiences for each customer segment is a manual process, requiring a high level of time and expertise to manage effectively. As a result, rule-based personalization and other similar approaches are challenging to scale effectively as your business grows. Furthermore, no matter how many unique experiences you set up, you’re still ultimately sorting your audience into large segmented “buckets,” preventing your marketing from delivering a truly personalized experience.
Machine-Learning Personalization: A Scalable Solution
Machine-learning personalization has emerged as a scalable alternative to rule-based personalization, utilizing algorithms and predictive analytics to present the most relevant content or experience to each potential customer. While most people think of these strategies as used by giants like Amazon, Netflix, and Spotify, businesses of all sizes can now easily work machine-learning personalization into their marketing strategy.
Commonly used algorithms include recommending products or content based on what has been popular, what is similar to what a visitor has viewed so far, what content has been recently added to the site, and more. These can be supplemented by advanced algorithms such as collaborative filtering (grouping visitors together based on common likes and dislikes and interaction with content), decision trees (to identify the most common paths taken through a site to reach a conversion point), and other types of filters and boosts.
Algorithms can be employed nearly anywhere on your website to personalize the experience. Tools like Segment, Dynamic Yield, and Optimizely leverage AI and machine learning to deliver highly personalized experiences on your site. From organizing information on your homepage, pulling specific blog posts that are likely to be relevant to your customer, even sending personalized emails, there are very few places machine-learning can’t deliver high-level personalization.
Key Differences in Approach
Machine-learning personalization differs from rule-based approaches in two critical areas. First and foremost, it completely automates the process of defining the “ifs” and “thens” in rule-based personalization, removing much of the labour required to establish a personalized marketing strategy. As a result, machine-learning is significantly more scalable than a manual approach.
Second, because machine-learning is capable of much greater depth and specificity, it’s possible to deliver highly-personalized experiences that simply aren’t possible with a manual approach. If your customers need a highly-personalized marketing strategy to convert, machine-learning personalization offers a much higher ROI than rules-based methods.
Which Approach is Right for You?
Your business’ approach to personalization is a critical component of your digital marketing strategy. The right plan will allow your marketing to achieve consistent, scalable performance over time.
Machine-learning personalization offers many advantages over rule-based manual approaches in terms of scalability and the depth of personalization possible. While it requires more strategic insight to get started, effectively-placed algorithms will get the right content in front of the right visitors to drive sales while requiring less manual input (and allowing your team to focus on other marketing tactics).
That said, manual rule-based personalization still has a place in digital marketing strategies. Certain strategies don’t necessarily need to be driven by an algorithm, and some audiences may start to feel uncomfortable if a website seemingly knows everything about them.
Fortunately, it’s not necessary to choose one over the other. Platforms like HubSpot, WordPress, and other modern web development tools offer a host of plugins and utilities to support your personalization strategy, no matter what approach you choose.
Marketing professionals know that personalization is a time-consuming strategy. Even when using machine-learning tactics, it can become an endless task that can take away from other areas of your business’ marketing strategy. Nonetheless, especially with recent consumer behaviour changes, personalized marketing is poised to become vitally important for companies to connect with potential customers.
Atrium Digital offers a consistent, reliable approach to personalized marketing through Marketing Systems Engineering. Our programs span all digital marketing strategies and tactics, delivering consistency that allows your business to achieve reliable results to enable growth over time.
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