Serendipity35

When I first started consulting on social media in 2005, I was introducing blogs, wikis, podcasts and the newly -emerging social networks such as Facebook. Both with my academic colleagues and with clients, one of the persistent questions was "How do I know I'm getting any benefit from these social tools?"

Seeing the impact of your social marketing relies on attribution, which is similar to the older metric of ROI (return on investment). Both are sometimes difficult to quantify.

As someone who taught writing for many years, when I first heard the term attribution I thought of giving credit to the original source of information, ideas, images, or language used in a piece of writing. Attribution in writing is important because it shows respect for the work of others, helps to prevent plagiarism and those sources often provide additional information. (see my attribution at the end of this post)

That ROI (return on investment) is a much older dollars-and-cents measurement used well before the Internet and social media For example, you invested $1000 for an advertisement and it produced $5000 in sales. (Some might call that ROAS - Return on Ad Spend - but I'm being simpler here.) Or perhaps, you spent a $1000 on an ad and saw no increase in sales.

Attribution in the social media sense assigns value to the channels that drive an outcome. That might mean dollars but it coukd also be a measurement of a purchase, web visit, download, or subscribing to the site or a newsletter.

It is a bit of reverse engineering or backward design in that you are looking at the effect and trying to determine the cause.

My own tracking of the referring sites for posts on this site allows me to see if traffic to a post came from LinkedIn, Facebook, Twitter, one of my blogs or just a search engine. When someone finds me via Google, I can see what search terms they used. Those results can be surprising. I might get a surge of traffic from a search that found the mention of "Erik Satie" or "flat web design" or "social media attribution."

I have little control about search engine attributions, but I can control what I post on social media and how I word the posts.

Attribution is generally broken down as being in three modes:
Last-touch,
First-touch
Multi-touch attribution.
(Take a look at this diagram from digitalthought.me about more on multi-touch models called Even, Time Decay, Weighted, Algorithmic, etc.)

The first-touch attribution credits the first marketing touchpoint. For example, you run an ad and monitor how many contacts came from that ad.

 

Last-touch attribution credits the channel that a lead went through just before converting. Maybe you ran an ad on Facebook which someone later tweeted and the lead came from the Tweet that linked to your site for a purchase, so Twitter gets the attribution.

Last-touch is easier to measure, but both single-touch models fail to show the complete and sometimes circuitous customer journey. That's why multi-touch attribution is used. This gets much more complicated and more difficult to track. More complicated than the scope of this post. But as an example, the time decay attribution gives more weight to touchpoints closer to the final conversion event. If your original ad is the starting point but the final purchase came after a tweet that was retweeted and then posted as a link in someone's blog a week later, the blog gets more credit (as a personal endorsement) than the ad although obviously none of this would have happened without the ad.

Back to that question I started getting in 2005. It is important to remind clients that social media used for marketing and as engagement and brand-building may not always generate leads or sales directly but rather indirectly. Getting visitors to your site alone is a kind of success. It may not lead to sales (ROI) immediately, but it increases awareness of your brand for the future.

I will crosspost this on my business blog, Ronkowitz LLC, and measure which post gets the best results.

Attribution is more complicated than this primer, so you might want to check out these sources:

• A Comprehensive Guide to Attribution
• The Complete List of Marketing Attribution Models
• What is Marketing Attribution?
• Social Media Attrbution,

ChatGPT has received a lot of attention for about a year, and it has also garnered competition. Google's entry into the AI for the masses is Gemini which has excellent web browsing and Google app integrations. Gemini provides results with often cited sources and links and has ‘Search Related Topics’ feature under some of its results allowing you to explore other search avenues you might have not considered initially. ChatGPT's paid version crawls the web but is not as effective as Gemini and for citations you need to explicitly prompt it to do so.

Gemini and ChatGPT generate images but Gemini does it for free while ChatGPT limits this function to paid plans with access to DALL-3. Then again, ChatGPT paid version might be worth it because the quality of DALL-3 images are better than those of Gemini.

Here is a features comparison from another site.

 

I saw the term " rhizomatic learning" used in an article about digital pedagogy. I know about rhizomes because I am a gardener but the use of it for learning was new and not immediately clear.

As introduced by the philosophers Gilles Deleuze and Felix Guattari, the rhizome takes that botanical term that refers to a root structure that expands and connects in multiple directions. It creates a decentralized, horizontal structure. Applying it to learning, particularly in higher education, means that students navigate their learning based on the cognitive conflicts they encounter.

Rhizomatic learning encourages students to acquire knowledge through the interconnectedness of curricular content, prompting them to explore diverse perspectives and methods. This is not a traditional approach or path but one that can lead to a critical, reflective learning experience.

Rhizomes help plants spread and survive in various conditions. Rhizomes often store nutrients and energy, allowing the plant to regrow if above-ground parts are damaged or destroyed. Unlike roots, rhizomes have nodes from which new shoots and roots can emerge. In my garden, I am most familiar with the types of iris plants that have rhizomes. Other examples are ginger and the part of ginger we use as a spice is a rhizome. Many species of bamboo spread via rhizomes, which can form dense clusters and cover large areas. Near water, you often find cattails (Typha spp.), a wetland plant that has rhizomes that anchor them in muddy soils and help them spread across wetlands.

Applying this concept to the principles of critical pedagogy and to generative AI could offer a new dimension to the relationship between learning situations and the digitization of learning processes. The rhizome, in this framework, symbolizes a non-hierarchical, decentralized network of ideas and knowledge, in contrast to traditional, linear models of learning.

The term is new to me but the idea is not completely new. I have used approaches that seem to fit into this framework.

Platforms like forums, social media, and MOOCs (Massive Open Online Courses) and Online Learning Communities often embody rhizomatic principles, where learners can pursue diverse interests and create their learning paths.

PBL (project-based learning) students explore real-world problems and collaborate on projects, allowing for a more flexible, student-driven approach to acquiring knowledge.

Inquiry-based learning is an approach that encourages students to ask questions, conduct research, and explore topics of interest, promoting a more decentralized and learner-directed way of learning.

Learning that can be described as Self-Directed Learning where individuals take charge of their own learning journeys, choosing what and how they learn based on their personal goals and interests, are engaging in rhizomatic learning.

I read an article from Rutgers University-Camden that begins by saying that most people now realize that artificial intelligence has become increasingly embedded in everyday life. This has created concerns. One of those lesser-spoken-about concerns is around bias in its programming. Here are some excerpts from the article.

Some AI tasks are so innocuous that users don't think about AI being involved. Your email uses it. Your online searches use it. That smart thermostat uses it. Are those uses frightening? But as its capabilities expand, so does its potential for wide-ranging impact.

Organizations and corporations have jumped on the opportunity presented by AI and Big Data to automate processes and increase the speed and accuracy of decisions large and small. Market research has found that 84 percent of C-suite executives believe they must leverage artificial intelligence to achieve their growth objectives. Three out of four believe that if they don't take advantage of AI in the next five years, they risk going out of business entirely.

Bias can cause artificial intelligence to make decisions that are systematically unfair to particular groups of people, and researchers have found this can cause real harm. The Rutgers–Camden researcher Iman Dehzangi, says that “Artificial intelligence and machine learning are poised to create valuable opportunities by automating or accelerating many different tasks and processes. One of the challenges, however, is to overcome potential pitfalls such as bias.” 

What does biased AI do? It can give consistently different outputs for certain groups compared to others. It can discriminate based on race, gender, biological sex, nationality, social class, or many other factors.

Of course it is human beings who choose the data that algorithms use and humans have biases whether they are conscious of them or not.

"Because machine learning is dependent upon data, if the data is biased or flawed, the patterns discerned by the program and the decisions made as a result will be biased, too," said Dehzangi, pointing to a common saying in the tech industry: "garbage in, garbage out." “There is not a successful business in operation today that is not using AI and machine learning,” said Dehzangi. Whether it is making financial investments in the stock market, facilitating the product development life cycle, or maximizing inventory management, forward-thinking businesses are leveraging this new technology to remain competitive and ahead of the curve. However, if they fail to account for bias in these emerging technologies, they could fall even further behind, remaining mired in the flawed data of the past. Research has revealed that if care is not taken in the design and implementation of AI systems, longstanding social biases can be embedded in the systems' logic.