Marketing in 2022: Compare Rule-based vs. Machine-learning
We know that personalization is the key to successful marketing. Personalization can have many forms. It can range from simple campaigns like “welcome back” messages when users return to your website or app, or “sign up for our newsletter”, to more advanced ones like individualized product recommendations based on a shopper’s unique tastes. These two examples, and all forms of personalization in between, can deliver significant value to your users and customers. The key to a successful personalization is matching the strategy to the goal to ensure that you are getting the most out of your personalization efforts.
By personalization, we don’t mean just adding the names of the customers to the messages you are sending out. Personalization means that the marketing channels and the timings should be tailored to each customer’s journey to be the most effective. However, it is impossible to check what each customer has been doing through your product and how they should be reached to prevent them from churning or enticing them to make another purchase (unless you only have 10 customers!!). It has been seen that customers who have a personalized shopping experience are more likely to make a purchase. They will also spread by word of mouth and tell about their experience on social media, and to family and friends.
Marketing automation is one of the tools businesses use to handle the aforementioned issue. Different pricing tiers are available for these tools, allowing them to be used both by small and large businesses. Most of these tools work with rules, in which marketers or business owners define specific rules and actions, and they perform accordingly. For example, you can set a rule that whenever a user has not made a purchase within 45 days from their signup or their previous purchase, they should receive an email with a specific message.
This is called rule-based marketing. Rules-based marketing is a process that allows marketers to deliver timely, relevant, and consistent communications to their customers via different marketing channels. Through rules-based marketing, the marketer creates the rules that will guide the marketing campaigns.
Machine-learning Marketing (Predictive Models)
The counterpart to rule-based marketing is predictive marketing or machine-learning marketing. In machine-learning personalization, you use modeling algorithms and improve the models with data. The messages that the customers will see are based on this. Since the steps are all done automatically by the machine, there are more refined segments than a marketing team can come up with. The more specific you are, the better the results will be — and machine-learning personalization will help you with this.
Predictive models are also so much faster than humans. They can learn about users, categorize them into many specified groups, and take action at the right time. However, keep in mind that these automation tools will also require a small amount of manual work from the marketing team. These manual works are usually coming up with messages and campaigns you want to promote through the marketing automation tool. Also, to get the most out of machine-learning marketing you have to set up a good strategy, choose a platform that suits your business’ needs, and take time to train its algorithms.
Machine-learning personalization provides a more scalable way to achieve the most relevant experiences for individuals, rather than generalized segments of people. It allows you to utilize algorithms to deliver these one-to-one experiences, typically in the form of recommendations for products or content. Machine-learning personalization can also be applied to recommending categories, brands, offers, and more, as well as dynamically modifying site navigation, search results, and list sorting.
To learn more about marketing automation tools, click here.
Rule-based Marketing vs. Machine-learning Marketing
We explained what rule-based marketing and machine-learning marketing are. Now, let’s compare them together:
Nowadays, we have a lot of access to data, since people are using digital widgets and platforms more than ever. Considering the huge amount of data for our decision makings manually is impossible. If we just choose to pick one or two variables that seem to have the most impact on the decision-making, we can work it out, but the decision model will probably be so far from reality. Picking the right predictor is indeed the most important step in modeling, and it is very hard to do manually by human resources. Machines and computers are the solutions here. They can find the best predictor variables so that the model gives out the best results.
Human error happens way more than computer errors. The whole process from gathering data, to segmentation and messaging can include errors or wrong information. As a result, the decisions and actions can be wrong and drive users away — which is obviously the exact opposite of what we want. Computers can figure out more precisely which independent variables have a positive or negative impact on the decision variable and provide the marketers or business owners with correct information.
To conclude, we think that predictive models and analytics work better than rule-based marketing tools. Machine-learning marketing platforms constantly learn from the data in the system and improve themselves, compared to those rule-based platforms that will stay the same unless you change them manually. People change, their desires change, and you need to change, too, to keep up with them.
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