Today's targeted marketing is powered by data and automation

 Marketing is always more effective when it is more targeted. As a result of integrating data and algorithms, marketers are able to now deliver a personalized customer experience at scale. 

There are various ways to target specific customers, and approaches range from lumping customers into very broadly defined categories to getting a lot more fine-tuned about the segments and responsive to individual customer behavior .In collaboration with Google, Deloitte put out a Digital transformation through data: a guide for retailers to drive value with data that took a closer look at these gradations. 

It ranked them as follows:

• Limited segmentation: All users are analyzed in broad segments. 

• Basic segmentation: Uses standard characteristics (e.g., gender, geography) for segmentation.

• Detailed segmentation: Segments are based on personal and behavior

• Dynamic segmentation: The UX / UI can respond to a customer’s in-session behavior as he or she exhibits different segment characteristics.

Achieving the detailed level depends on much more data than the static kind that is used for basic segmentation, and advancing to the dynamic level requires a level of automation that will enable recommendations and responses to go out in real-time. 

 The coming AI revolution in retail and consumer products invoked the women’s clothing store,  Avenue Stores LLC as an example of dynamic segmentation. It explained that  it brings together “data across multiple touchpoints, including in-store activities and market trend analysis, to learn and reason about what customers want and when they want it.” On that basis it can reach out to customers with communication tailored to their situation in real-time, which makes it possible to capture their attention while in “‘shopping mode.” 

Marketing for loyalty

Being in touch with your customers to let them know you’re there for them without pressuring them to buy can pay off in winning their loyalty and business later. In this case, your automated messaging doesn’t have to respond to segment your audience, as you would be working off a general form of communication.

When you don’t have history

But what if you do need to sell your products now? Marketing recommendations can work even on the more basic level, not just for new customers for whom you have no history to flesh out a profile but for the type of marketing communication that depends on general trends. For example, a very broad segment of all people in the United States can work for promotions tied to events shared by all due to the calendar, whether it’s Mother’s Day, Memorial Day, July 4th, etc. 

You don’t need to know much about your customer other than that they’ll know what these days are because they are on their calendars due to living in the United States for the trending algorithm to work well. That makes using this approach ideal for customers for whom you don’t have first-party data.

It doesn’t matter so much what they are normally interested in or what they’ve bought before when you’re sending out a marketing message about buying their mother something before May 10. However, if you do have information about the customer, say you know they’ve ordered flowers for their mother last year, then you can combine the trending recommendation with what you know about their behavior.

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Advanced Segmentation and Automation Are Changing the Marketing Game

AI gets a boost from curiosity

As data analytics become increasingly driven by artificial intelligence (AI),

photo credit: https://c1.staticflickr.com/3/2332/2083892100_3e015d810a_b.jpg

researchers search for a way to drive machine learning. The key ingredient its future development may be a dash of curiosity.

There are all kinds of AI systems currently used by various businesses with different names like Alexa and Albert to personalize then. Perhaps it's time for an AI system named George after the monkey whose curiosity propels him into various adventures.

That would be an apt choice for the Intrinsic Curiosity Module (ICM) developed by a group of four researchers at University of California, Berkeley. The attempt to inject curiosity to achieve self-motivated advances in machine learning was the subject of their paper, Curiosity-driven Exploration by Self-supervised Prediction, that was just submitted to the 34th International Conference on Machine Learning (ICML 2017).

Their premise is that external rewards for learning are of necessity limited and actually rather rare in real life. That doesn't mean that people stop exploring or seeking out answers even when there are no prizes for doing. They are motivated by their own human curiosity. Infusing that kind of motivation in a virtual agent gets it to test things out for itself even when not directed to do so. The test of the effect was done in monitoring how far it would proceed in two video games, VizDoom and Super Mario Bros. as you see in the demo video here:

Read more in Machine Learning Taps Power of Curiosity

Written in the meta-data

Is it possible to identify an individual’s romantic partner on the basis of his/her social networks alone? That’s the question Jon Kleinberg, a computer scientist at Cornell University and Eric Bakstrom, a senior engineer from Facebook, teamed up to answer. After analyzing millions of Facebook data points, they came up with an affirmative response in Romantic Partnerships and the Dispersion of Social Ties: A Network Analysis of Relationship Status on Facebook -- they assert the answer is yes with a 60% probability. 

Read more in Your Romantic Attachments as Predicted by Metadata