Using Artificial Intelligence For Marketing Part 1

Artificial Intelligence is one of the most interesting but misunderstood upcoming trends in marketing. This is the first of a four part series on using Artificial Intelligence for marketing.

There are 3 main types of Artificial Intelligence:

  1. Machine learning techniques: using algorithms to learn from previous data or successful campaigns and adapt to current trends in the marketplace.
  2. Applied Propensity Models: used to predict events based on the probability to convert into sales.
  3. AI Applications: performs tasks that can also be done by humans. ex. answering human questions, writing content.

Each one has different advantages and disadvantages. Some are better for attracting customers; others conversation or re-engaging drifting customers. Today we are focusing on the machine-learning techniques.

15 apps for content markeitng

Before explaining these techniques in more detail, let us review the four main stages of marketing.

  1. Reach: Attracts visitors with a range of inbound techniques like content marketing and SEO.
  2. Act: Draws visitors in and makes them aware of your product or service using techniques like blogs, and websites.
  3. Convert: Nudges interested consumers to become customers using a variety of techniques like E-Commerce information, promotions etc.
  4. Engage: Keeping your customers coming back over and over. Common techniques include social media and referrals.

RACE Framework

3 Main types of Machine-learning techniques:

  1. Prosperity Modeling
  2. Dynamic Pricing
  3. Predictive customer service

Propensity Modeling:

This is the goal of a machine-learning project. Prosperity Models can be fed large amounts of data and uses it to predict new trends or niches.

It works by first inputting a target like $50,000 in sales for the upcoming weekend sale. Then uploading data from similar sales from previous years as well as any other relevant data. With this, the program generates an algorithm to predict if sales target will be hit.

Dynamic Pricing:

All marketers know that sales are effective at shifting more product. Discounts are extremely powerful, but they can also hurt your bottom line. If you make twice as many sales with a two-thirds smaller margin, you’ve made less profit than you would have if you didn’t have a sale.

Sales are so effective because they get people to buy your product that previously wouldn’t have considered themselves able to justify the cost of the purchase. But they also mean people that would have paid the higher price pay less than they would have.

Dynamic pricing can avoid this problem, by targeting only special offers only at those likely to need them in order to convert. Machine learning can build a propensity model of which traits show a customer is likely to need an offer to convert, and which are likely to convert without the need for an offer. This means you can increase sales whilst not reducing your profit margins by much, thus maximizing profits.

Predictive Customer Service:

It’s far easier to make repeat sales to your existing customer base than it is to attract new customers. So keeping your existing customers happy is key to your bottom line. This is particularly true in subscription-based business, where a high turnover rate can be extremely costly. Predictive analytics can be used to work out which customers are most likely to unsubscribe from a service, by assessing what features are most common in customers who do unsubscribe. It’s then possible to reach out to these customers with offers, prompts or assistance to prevent them from leaving.

For more information on artificial intelligence:

Chatbots for marketing

Thank you for reading!

Feel free to comment with your thoughts and ideas! I always welcome new ideas.

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Allen, R. (2017, June & July). 15 Applications of Artificial Intelligence in Marketing. Retrieved July & aug., 2017, from