Tired of Deciding? Let Machine Learning Algorithms Help You Out!
A guest article by Ilke Karabogali, co-founder of Perzonalization, the AI-powered personalization engine for Shopify.
As a decision maker, every day you come to a point where you need to make a new decision. Developing a new marketing campaign might seem like a simple task you need to finalise each day, but it needs analytical analysis and decision-making capabilities.
If you solely listen to your intuition, you know that it may drive you to a place where you haven’t intended to reach. After all, relying solely on gut-based, experience-driven decision-making in marketing is foolish in the digital age.
In today’s dynamic marketing landscape, data-driven marketing approaches are praised. Luckily as a Shopify merchant, you have the chance to benefit from the state-of-the-art rule-based marketing approaches as well as ML (machine learning) powered marketing.
What is rule-based marketing?
If we talk about “Rule-based systems” for dummies, we may easily comment that these approaches involve “if this, then that” type of reasoning. As a decision maker, you first set your business rules then the tool helps you automate the actions. Some examples of rule-based marketing tools may be:
- Advertising campaign automation: PPC management tools help advertisers set basic rules on positioning or quality score that will help them to optimize their campaigns. In attribution modeling, rule-based approach finds a good ground. These models are based on easily understood assumptions we have such as first and last touch, equal touch and other clear-cut scenarios. For example, it was the last exposure that drove the sale or the first and last equally.
- Rule-based personalization: Rule-based personalization allows marketers to deliver experiences to specific groups or segments of people based on the manual creation and manipulation of business rules. This is also the expert area of marketing automation systems. Before setting the rules, a basis – such as a clickstream behavior, demographics or location - for rule creation shall be defined. Callouts, info bars, pop-up and push messages are usually used to convey the message. We may list some examples here:
- Display a 10% discount coupon to a visitor if she does not add any products to her cart after viewing 10 pages on your online store
- Greet your visitor in his native language (based on the visitor’s location)
- Welcome a repeat visitor with a special message (“hey, glad to see you again!”)
- Push dynamic subscription on your website (“leave us your e-mail your address and receive a discount on your first purchase”)
- Send triggered emails via a Shopify app, such as Perzonalization, to your site’s visitors given that they make a certain action (send a reminder when she’s viewed a certain product but has not added the item to her cart)
- Behavioral retargeting: Your visitor spends only a few minutes on your online store but that person is visiting hundreds of pages all across the web, every month. You may define a set of business rules based on their previous internet actions and use one of the retargeting platforms such as Criteo , Adroll, or Retargeter to display ads to those visitors on several websites.
What is ML powered marketing?
There are no absolutes in marketing; there is just a lot of mushiness in the middle. By using machine learning, we’re just trying to get help from algorithms and the power of computers to make sure that we do not get lost in the fuzzy corners of our minds. Data may be incomplete or it may just reflect history, but with the help of state-of-the-art technologies, we may shed a light on the complex purchasing decision process of a shopper.
From automated data visualization to semantic analysis, ML has the potential to equip marketers in several ways. What rule-based marketing cannot achieve – i.e. real-time analysis and decision-making, learning from historical behaviors – can become real and valuable via ML. To make better decisions, leaders will need to use machine learning and analytics to find actionable patterns in the data.
Machine learning is a vast subject with many methods and applications, but it is typically used to solve problems by finding patterns that we cannot see ourselves.
Machine learning may sound something for massive online retailers but with the help of SaaS marketing solutions, Shopify merchants are able to make the best of ML for their eCommerce websites. You may use an AI-powered personalization solution, that can analyze your visitors’ behaviors in real time and come up with personalized product recommendations both on web and emails – tailored to individual customers’ purchase intentions.
I have a Shopify store, which approach is the best for me: rule-based or ML?
We may say, both. Combining the best of both worlds may be the safest route for a Shopify merchant to take. Machine learning helps you to discover patterns in your data and predict the next best action of your shopper whereas rule-based approaches help a professional use his/her human intelligence, experience, and observations.
There are also handy SaaS solutions using both ML algorithms and rule-based triggers to help Shopify merchants.
In case you’re wondering; Perzonalization offers a 14-day free trial. After the trial - if the app helps you generate extra revenues - the monthly fee is equal to 4% of the additional revenue via Perzonalization. (No upfront costs or hidden fees) Start your free trial now using this link.