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Impression’s Mike Wickham says as the sands shift around digital marketing, it may be time to rethink how you target customers online.
Good marketing should always be a win-win. Consumers should win because they are offered choices that are relevant to whatever is on the market. Brands need to win by meeting that need and delivering their products and services to the right audience, preferably at the right cost.
As someone who traverses both the worlds of marketing and consumerism, I’ve noticed an alarming trend in which paid media platforms across the board are offering fewer options and less relevance.
Algorithms are not always on our side
Let’s take an example. I was recently on a journey to find the perfect shoes. Versatile, suitable for all seasons, suitable for both smart and casual wear, and durable for many years. Unfortunately, I’m still figuring it out, and not just because I’m incredibly picky.
My customer journey started like most people, with a broad search on Google, which presented a wide range of options from boots to sandals. It’s not completely accurate, but when I went to the Shopping Him tab, I found a few items that were close to what I had in mind.
After clicking through several options from different brands and browsing catalogs, I still haven’t found the pair of my dreams, but at least I’ve narrowed down the style I’m looking for. So I went back to Google and specified a little more detail for my next search (long-tail search still exists). The carousel now displayed almost the same list of items as before.
The results came almost exclusively from the three brands I just visited. Browsing the web over the next few days and weeks yielded limited new suggestions. I was offered the same product again and again. Inappropriate use of frequency capping is partially the problem here, but the crux of the problem is that my behavior signaled that I was interested in these items, so the algorithm used the leather to convert me. I pushed hard.
My heart goes out to these brands, and advertisers in general, who are facing similar challenges. As audiences become more defined and we rely more on machine automation, we are a little more at the mercy of algorithms that distinguish who is the right customer.
How to identify your most likely customers
So how do you differentiate between someone who clicks on a visual ad for a product, goes to your website, and decides the product isn’t for them, and someone who clicks on a visual ad for your product, goes to your website? Is it okay? Are you looking at a website and likely making a purchase, but want to compare prices on other sites first and wait until payday?
Ultimately, a deeper understanding of consumer behavior and psychology is key. There are often more reasons not to buy something than there are reasons to buy it, so you have to start digging deeper.
It starts with research. Understand consumer behavior The “why” behind engagement and the “why not”. Is it because of affordability, lack of urgency, or too many options? Or is it because of concerns about compromise, distraction, likeability, trust, principles, ethics, etc.? A sense of inability to move on? The list of physical and subconscious reasons is wide-ranging.
Behavioral insights often start the old-fashioned way: actually talking to people. Focus groups, surveys, and surveys are often thought of as outdated for digital-first businesses, but they can provide insights that can help you identify where to start looking at your data.
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Don’t chase every potential buyer
We need to measure it differently. Analyzing small but important signals of consumer intent, such as attention mapping, depth of engagement, dwell time, and frequency of interactions, creates a clearer picture among genuinely interested buyers and passersby. will help you build.
By identifying and eliminating those who show signs of opposition, you can better focus your energy on more qualified customers, and from the same data, make the most of your “likely buyers.” Learn how to adapt your customer journey to
Ultimately, we need to better understand our customers’ wants and needs. And a key part of that is knowing when to track them down and when to let them go. Algorithms make it difficult to do the latter because they miss the context and cognitive reasoning in the decision maker’s mind.
These are the gaps we need to fill, and the combination of behavioral insights and machine learning tools will not only help marketers spend their ad dollars more effectively, but also increase relevance to consumers. It also helps you get back your .
Like I said, win, win.
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