Exploring Marketing Technology

Tag: AIDA

  • AI boosts ecommerce conversions by compressing evolution

    AI boosts ecommerce conversions by compressing evolution

    Every sale on your ecommerce site travels through four phases: Attention, Interest, Desire and Action (AIDA). Before AI, optimizing each meant months of randomized controlled tests, slow feedback loops, and lots of guesswork. AI compresses that personalization cycle dramatically. Marketers using it have a distinct competitive advantage

    To explain how, I’m pulling from two creatures found in nature. They evolved over millennia to do exactly what your ecommerce experience needs. One of them might be the strangest and most instructive thing you’ve never heard of.

    I described those two creatures recently on another blog site, Dyslexic Data. The title: Using data to visualize evolutionary forces. There I explained how the pair have evolved to catch and eat the optimal number of bugs, in a feat of natural selection that evolutionary biologists have visualized.

    Although you should not expect either showing up in an upcoming Pixar feature, in my mind’s eye I do see them, above, fitting in nicely outside The Haunted Mansion. (I describe the significance of this Disneyland attraction in another post, also on Dyslexic Data). 

    First, the spider: A creature so reviled it even has a phobia named after it. I asked AI to make it look friendly. Still, all those legs! It’s hard to avoid the creep factor.

    The other isn’t shown, because, 1.) It is genuinely hideous, and 2.) It spends its phase of life hiding in loose sand, at the bottom of funnel-shaped depressions like the three shown in the foreground. 

    This shy, larval stage of the lacewing, is called the ant lion.

    Growing qualified visits by optimizing attention and interest

    AI is rewriting the economics of all four phases of AIDA. The first two, before modern LLMs, were addressed by the human optimization of paid and organic search, as well as digital display ads. Algorithms helped, but decisions based on feedback loops were slow and data was thin.

    You can think of this process, pre-AI, as similar to the silk spun by the very first ancestors of modern spiders. They weren’t very efficient. 

    The computer simulator described in my evolution blog post makes a case that natural selection and food scarcity inevitably drive generations of spiders to spin webs optimized for the lowest calorie expenditure yielding the greatest caloric intake. If you ran that NetSpinner simulation a thousand times, over multiple generations, each pass would iteratively, gradually evolve to what you see above: the classic Charlotte’s Web bug-catcher. 

    Every single time. Now that’s optimization!

    Similarly, modern machine learning works to optimize your campaigns for better attraction of attention and generation of interest. Here are just three of the mechanisms:

    1. It does a better job of stitching identities in your customer data platform (CDP)
    2. It refines your organic and paid campaigns by providing more actionable data
    3. It continually interprets that data to launch and test multiple ad variants

    Those actions include look-alike modeling, to find more prospects similar to your best customers, and suppression models, to ensure you aren’t “wasting silk” — in this case, your ad dollars — on people who are already customers.

    In these ways and the others below, AI compresses evolution by quickly learning the best placement of its own “sticky silk strands” to capture the attention and interest of ideal prospects.

    From search rankings to AI recommendations: the new attention engine

    Organic search marketing, also known as search engine optimization (SEO), is still important. 

    AI helps there too. 

    It can optimize your product description pages to rank higher in search engines for the phrases your customers use to find the products.

    Rapidly emerging as a counterpart to SEO is agentic engine optimization (AEO … sometimes called GEO for “generative engine optimization”). AEO / GEO acknowledges that attention and interest take place in ways we marketers find difficult to quantify, in recommendations made by ChatGPT and other chatbots.

    Agents such as Optimizely’s Opal can build the data schemas within your product description pages that are invisible to human visitors but instructive to bots.

    Those schemas serve as training datasets for the LLMs. They help persuade chatbots to recommend your products over competitors when consulted by your prospects.

    Measuring Success

    Success metrics in a world of AEO / GEO can be tricky. 

    In the old world, interest was measured by the raw number of people who arrived at your site. Since the emergence of Google Gemini, ChatGPT and their ilk, marketers are noticing significantly fewer visitors. That’s because consideration for many is happening on search engine answer sections and the recommendations served up by the chatbots. 

    Here are two metrics that still work:

    1. An increased return on ad spent (ROAS)
    2. A reduced number of clicks leading to conversion, since people are generally arriving more knowledgeable about your offerings, with fewer questions

    Boosting desire and action through optimized conversion funnels

    I’ve known about ant lions since I was a morbid little boy. As a digital marketer, I recognized they’ve evolved to produce a literal, perfect conversion funnel.

    The ant lion hangs out at the bottom. When it senses an ant has stumbled onto the rim, it flings its shovel-shaped head to toss grains of sand up and over.

    The unwitting ant works against the avalanche as it begins a downward slide. The ant lion counters with more targeted shoveling. In stages, similar to the steps of conversion funnels that are your site’s shopping experiences, any slip-up would mean escape, and a ruined dinner.

    The Action at the end of the Desire phase is, of course, a sale. Improving your conversion funnels to optimize sales, before AI, was slow and imperfect. 

    True, funnel-optimizing A/B and multivariate testing succeeded in the pre-AI world, by using complex math to measure improvements. But today, AI makes these calculations and decisions in an instant, based on more variables than you could ever hold in your mind (the segment of that prospect, the visit source, day and time, and purchase history, to name just a few attributes).

    AI ensures that every method to reduce conversion funnel attrition is put to practice in real time. The result? A greater share of your prospects pass from Desire to Action.

    Measuring Success

    What metrics would tell you if your conversion funnels are performing like a well-dug ant lion trap? Here are two:

    1. Conversion rate, as measured from sales divided by entries into the relevant product description page.
    2. Shopping basket size, since AI can also supercharge your “You may also like” recommendations

    Better webs, smarter funnels

    AI doesn’t change what a sale is. It compresses the time it takes to get there … to serve up the right message and experience for the right person at every stage of the process. 

    Chances are some of your competitors are already using AI to spin better webs and dig more efficient funnels. The question isn’t whether to adopt AI. It’s which phase of your AIDA cycle you optimize first. 

    Start there.

  • New web metric puts the I in AIDA and helps optimize content

    The first marketing class I ever had in college taught me the AIDA model of advertising. It’s still used today, lo these many years later. The AIDA model goes like this: Once you attract Attention, you must generate Interest, create Desire, and enable your market to take Action. Do all of those things and you’re golden.

    Back then the only way you could actually measure any of these (except for the last A, which was making a sale) was by employing expensive and time-consuming research. The web changed all that. It allows us to measure each of these steps — except for that pesky thing called Interest.

    Until now, the interest that people exhibit in a site’s content has been impossible to accurately measure through current techniques. What has changed? I and my team have come up with a workable solution — a simple way to plug the hole in that famous AIDA acronym.

    We’ve found a way to reliably measure web visitor interest.

    That’s a bold statement, but here’s why I make that assertion. After analyzing a critical mass of data from the site of one of our largest clients, I can say with confidence that we have a metric that is the rarest of creatures. It is a measurement that arms content managers with real, actionable feedback about the changes they’ve made to web content over which they have responsibility.Of all the content changes year-to-date, those in page #2 were most effective, as measured by CIIThe metric is called CII: Content Interest Index. I’ve been wrong more than once in my career. Sometimes I’ve been wrong in spectacular ways. But I firmly believe from what I’ve witnessed that this is a unique and valuable tool for managers of many types of sites.

    The CII is most valuable for those who manage large amounts of product and service information, delivered over a site with a content management system (CMS) handling a ton of impressions. Volume is really key. The biggest constraint of this metric is it requires high levels of traffic to the pages being measured.

    The graphic above is a sample from an analysis for a client — who will remain anonymous — who manages that critical mass of traffic I was referring to, and has provided two year’s worth of data to analyze. In the graphic, the CII illustrates to a single content manager how five product pages are doing, this year compared to last. Specifically, it shows that for two of the pages, CII has dropped in real numbers, while in all five cases page views have risen.

    These CII comparisons help a content manager optimize pages using real feedback provided directly by the user (through the measurement of two behaviors, as described below). In the hands of the right content manager, CIIs for the pages managed should progressively climb, as a positive feedback loop continues to reward well-targeted content. It’s the equivalent of a public speaker talking to real audiences, and getting real applause (and real yawns), as opposed to merely guessing at what people want to hear by speaking to an empty room.

    How is CII Calculated?

    As this white paper (a PDF file) on the CII explains, this is a simple metric that can be built into just about any web site. Here is how it is described there:

    The CII counts instances of a page’s “Printer Friendly Format” or “Email a Colleague” icon receiving a click. This observes what are arguably the two most common ways that visitors save or share information – either through printing or emailing content. To factor out a page’s level of overall readership, the sum of clicks is divided by page views.

    Page views still come into the equation of analyzing CII, and they are excellent measures of the first A of a site’s AIDA. In other words, the number of people who view a page can be a proxy for the amount of attention you have to play with for that page.

    As the white paper explains, the D of AIDA can also be measured using existing web metrics, because desire is exhibited as prospects circle your product or service more closely — and more often — investigating things like delivery options, pricing variables and means of payment. And, as with the “real” world, the online marketplace has always measured the A — as in action, well. This is a transaction.

    That leaves interest as something without a good yardstick. Until now.

    Check out my white paper and feel free to adopt the system on your own site.

    I’d like to extend a special thank you to all of my marketing and technology friends and colleagues who have helped by commenting on drafts of this white paper. Your help has been invaluable.