In today’s digital economy, rising customer expectations and competitive pressures have produced a new reality for marketers: personalization is no longer a luxury but a minimum level of service.

Nobody can deny that addressing your consumers personally on your website, in e-mail campaigns, and in advertising makes sense. In practice, rule-based and machine learning marketers apply personalized approaches. Which approach do you employ when?

The rule-based customization, which uses if/then logic to modify the client journey as per a set of manually coded targeting criteria. It has traditionally been used by businesses to provide relevant experiences.

Using a manual method to find the best experience isn’t always efficient or practical for companies looking to expand their customization efforts. As a result, many companies are turning to machine learning algorithms to help them make decisions. Both techniques have different merits, which is why businesses should use them in combination rather than abandoning one in favour of the other.

Personalization’s utility can be seen and shown in a variety of ways. Segment, for example, investigated its impact on customers. Customers who get a personalized experience are more inclined to purchase, according to the study. They’ll also share their storey on social media and with family and friends.

Econsultancy, on the other hand, conducted research into which channels should be personalized to achieve the highest conversion rates. Even though most marketers personalize e-mail, it appears that personalization in search engine marketing has the most impact. According to another Hubspot study, personalizing the call-to-action boosts conversions by 202 per cent, regardless of the channel.

For a long time, we could go on and on with similar examples. Personalization is important, and no marketer will argue with that, but where do you begin? Personalization can be done in two ways: rule-based or machine-learning.

Rule-Based Personalization 

The majority of marketing automation programmes are rule-based, with if-then situations determining what happens next. When a website visitor is near Utrecht, for example, a nearby event will be displayed. If you solely use rules to arrange content and campaigns, you’ll have to consider all possible circumstances and make a lot of assumptions. That’s a lot of effort. And over two thousand variables play a role in personalization in a small retail or travel organization. You’ll be occupied for a while if you have to come up with all the correlations. As a result, rule-based personalization is ineffective for the item and content recommendations, as well as fine categorization.

The key benefit of rule-based customization is that it allows you to customize your message to specific groups as needed. You also have input and influence over the recommendations. The drawback is that the rules must be manually set up and maintained. Then there’s the matter of deciding which segments to personalize, which isn’t particularly practical. Furthermore, while setting up rule-based personalization, assumptions regarding correlations are frequently made that turn out to be incorrect in practice.

Personalization With Machine Learning

In machine-learning personalization, you give data to algorithms, which then look for trends. This determines what message the consumer or prospect will see. This goes much beyond personalization based on rules. Even down to one-to-one communication, the algorithm has more refined categories than a marketing person can come up with. Scarcity, for example, can motivate women in their fifties and sixties who reside in Groningen, have a low income, and visit the site on Monday. The better the outcomes, the more particular you are.

The most significant benefit for machine learning marketers to personalize content is that you can provide virtual information to each visitor individually. Furthermore, you may personalize the website/app/e-mail campaigns fast and automatically. When compared to rule-based personalization, only a small amount of manual labour is required. However, to get the most out of it, you must first devise a sound approach. Select a robust platform and devote time to training algorithms.

Combination of Rule-Based And Machine Learning-Based Personalization

Today, most marketing automation is still focused on rules, but machine learning marketers should eventually take the lead. Although the algorithm does most of the reasoning, you can override it if necessary. When you’re training an algorithm for something predictable like delivery time or somebody’s birthday, overruling isn’t a good idea. Then you may better design a rule to send a celebratory e-mail on that particular day.

For example, in the car industry, you may make it a regulation that summer tyres are not available for the following four months. Machine learning-based personalization can be used to accomplish this, but it takes a long time to establish a link between the time of year and the tyre type. The system should then be trained to hunt for patterns that aren’t as predictable. For example, in whatever weather prediction does that type of customer change his tyres, and whether his salary or the fact that he is a business driver has any bearing on this.

Within the fashion industry, for example, someone who buys a white t-shirt twice may switch to black in the third transaction. This correlation is immediately recognized by Machine learning-based personalization, which also understands what traits people who buy two times white and then black have. It would take far too long if you had to think of that scenario with your marketing team. Furthermore, you should check to see if black is still the popular colour so that your material keeps up with market trends. Because time is valuable, why not let technology do the heavy lifting for you?

Conclusion

Algorithm-based decision-making is not intrinsically better than rule-based targeting, despite its many advantages. Remember that marketers bring unrivalled knowledge and intelligence to the table, which will always be required to determine the plan and reasoning for these initiatives. The best outcomes will be achieved by combining rule-based and Machine learning-based personalization. Today’s brands must meet several objectives, including converting first-time visitors into customers, regaining churned consumers, and securing loyal customers with high lifetime value.