Using Artificial Intelligence for Marketing Part 3

Welcome to part 3 of 4 of using artificial intelligence for marketing. A small review to start.

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: used to perform 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 final five Applied Propensity Models.

15 apps for content markeitng

The Final Five Propensity Models:

  1. Lead Scoring
  2. Re-Targeting
  3. Web and App Personalization
  4. Marketing Automation
  5. Dynamic Content Emails

Lead Scoring:

Certain propensity models are used to evaluate the value and potential of incoming leads. Sales teams use these models that are generated using historical data in order to evaluate if the lead will end up as a sale. This can be particularly important in B2B businesses with consultative sales processes, where each sale takes a considerable amount of time on the part of the sales team. By contacting the most relevant leads, the sales team can save time and concentrate their energy where it is most effective. The insights into a leads propensity to buy can also be used to target sales and discounts where they are most effective.

Lead Scoring falls under the ACT portion of the RACE framework.

Act: Draws visitors in and makes them aware of your product

Re-Targeting:

Much like with ad targeting, machine learning can be used to establish what content is most likely to bring customers back to the site based on historical data. My building an accurate prediction model of what content works best to win back different types of customers, machine learning can be used to optimize your retargeting ads to make them as effective as possible.

retargeting

Engage – Keep your customers returning

Re-targeting falls under the Convert portion of the RACE Framework.

Convert: involves getting your audience to take that vital next step which turns them into paying customers whether the payment is taken through online E-commerce transactions or offline channels.

Web and App Personalization

Using a propensity model to predict a customer’s stage in the buyer’s journey can let you serve that customer, either on an app or on a web page, with the most relevant content. If someone is still new to a site, content that informs them and keeps them interested will be most effective, if they have visited many times and are clearly interested in the product then more in-depth content about a product’s benefits will perform better.

Web and App personalization falls under the Convert portion of the RACE Framework.

Convert: involves getting your audience to take that vital next step which turns them into paying customers whether the payment is taken through online E-commerce transactions or offline channels.

Marketing Automation:

Marketing automation techniques generally involve a series of rules, which when triggered initiative interactions with the customer. But who decided these rules? Generally, a marketer who’s basically guessing what will be most effective. Machine learning can run through billions of points of customer data and establish when are the most effective times to make contact, what words in subject lines are most effective and much more. These insights can then be applied to boost the effectiveness of your marketing automation efforts.

One of the largest marketing automation examples is email marketing. Email is the arena for many marketing automation touchpoints, but getting prospects to click through is only the opening act — persuading them to fill out forms is where the real drama begins.

email-marketing-works

Constructing landing pages is an art form unto itself, and certainly, falls under the reach of marketing automation. Besides A/B testing, delivering dynamic content is the most powerful addition MAPs lend to landing pages, especially in the form of progressive profiling.

To combat form friction, it’s best to limit the length of your form on the initial landing page. This puts the prospect’s mind at ease and helps strike a balance between the information the customer is exchanging and the content you’re offering.

Marketing Automation falls under the Engage portion of the RACE Framework.

Engage: Developing a long-term relationship with first-time buyers to build customer loyalty as repeat purchases using communications on your site, social presence, email and direct interactions to boost customer lifetime value.

For more information on email marketing:

10 Reasons To Use Email Marketing

MailChimp: Email Marketing Part 2

Dynamic Content Emails:

In a similar fashion to marketing automation, applying insights generated from machine learning can create extremely effective dynamic emails. Predictive analytics using a propensity model can establish a subscribers propensity to buy certain categories, sizes, and colors through their previous behavior and displays the most relevant products in newsletters. The product stock, deals, pricing is all correct at the time of opening the email.

If you want to know more about using AI and machine learning for marketing, SmartInsights members can download our AI and Machine learning guide. The guide aims to help businesses of all sizes embrace Machine Learning and AI to benefit marketing efforts. It shows how businesses can effectively manage machine learning projects in place, and how to use the insights generated to improve marketing results.

Dynamic Content emails fall under the Engage portion of the RACE framework.

Engage: Developing a long-term relationship with first-time buyers to build customer loyalty as repeat purchases using communications on your site, social presence, email and direct interactions to boost customer lifetime value.

For more information on email marketing:

10 Reasons To Use Email Marketing

MailChimp: Email Marketing Part 2

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 https://www.linkedin.com/pulse/15-applications-artificial-intelligence-marketing-robert-allen
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