Ecommerce Management Advisors
Ecommerce Management Advisors
Google released the August spam update, impacting search results worldwide. The rollout may take a few weeks to complete.
This post was sponsored by Bluepear. The opinions expressed in this article are the sponsor’s own.
Have you ever seen an ad that looks just like your favorite brand’s ad, but isn’t? Ad hijacking.
Ever clicked an ad expecting to reach Nike’s website but ended up on some random store you’d never heard of? Ad hijacking.
It happens to thousands of companies that run paid ads, and you’re not immune.
More people are buying products and services online.
With $6 trillion being spent by online shoppers in 2024 (CapitalOneShopping Research), the competition for ad placement is fierce.
If someone hijacks your ads, you:
Ad hijacking harms your brand and ad performance.
Learn how to detect ad hijacking, stop affiliate abuse, and protect your traffic in 2025.
Ad hijacking, by definition, is a form of advertising fraud where someone pretends to be your brand in paid search ads (like Google or other platforms). The fraudsters copy your brand name, your ad style, even your messaging, so the ad looks real.
But when a customer clicks, they’re sent somewhere else.
Image created by Bluepear, August 2025There are two common types:
Affiliate ad hijacking happens when partners in your affiliate program bid on your brand name.
They:
The Result: The customer thinks they’re clicking on your official site because the ad looks the same. But behind the scenes, the affiliate redirects the traffic through their own tracking link.
You end up paying them a commission for a customer who was already looking for you. This inflates your costs, pollutes your data, and makes it harder to measure real performance.
Example: A user searches for [Super Tools]. An affiliate runs an ad with the headline “Super Tools Official Site,” but the link is an affiliate redirect. You pay them a cut, even though they didn’t bring in new traffic.
Image created by Bluepear, August 2025Competitor ad hijacking is when a rival company copies your brand in search ads to steal your traffic.
They:
The Result: Customers click, thinking they’re going to your site. But instead, they land on the competitor’s website.
This tactic lets competitors capture high-intent traffic. As a result, you lose potential sales, while they gain market share. Without PPC brand protection, your brand presence can be weakened, allowing competitors to grow faster at your expense.
Example: A competitor bids on “Super Tools” and runs a lookalike ad. The user clicks, expecting your site, but lands on the competitor’s product page instead. You lose a sale and possibly the customer’s trust.
As you see, search hijacking is already a serious threat to your brand and budget. It’s made even worse by how well the violators hide their tracks.
Non-compliant partners use smart tactics to avoid being seen by brand owners or their teams. Here’s how they do it:
Without proper hijacking prevention, these tactics make it easy for hijackers to hide and hard for your team to catch them in time.
The impact of affiliate ad hijacking goes far beyond a few stolen clicks. It damages performance, costs money, and creates serious risks for your business:
Search hijacking doesn’t just hurt your numbers. It makes you question the data you rely on, wastes hours chasing false leads, and forces you to fight for traffic that was already yours.
Image created by Bluepear, August 2025What actually works on PPC brand protection? To uncover real issues, you need tools and methods that go beyond surface metrics:
By combining manual checks and scalable tools, you can take control before search hijacking quietly eats into your ad spend.
Manual checks can’t keep up with how ad hijacking works today. Hijackers often run ads only in certain regions, at non-working hours, or under specific conditions. They use cloaking and redirects that can’t be detected with regular checks.
Most teams lack the time and capability to ensure hijacking prevention through manual monitoring alone.
That’s why ad hijacking tools like Bluepear are essential – as PPC brand protection software, they automate continuous scanning of search results to catch every sneaky ad trying to hijack your traffic and budget.
Here’s how Bluepear helps to fight against ad hijacking:
Ad hijacking tools aren’t an excess. If you want to survive in a world of smart fraud, automated PPC brand protection is a must.
Image created by Bluepear, August 2025Ad hijacking quietly eats into your ad budget, distorts your performance data, and damages user trust. Manual audits rarely catch it. Hijackers use GEO targeting, dayparting, and cloaking to stay hidden while stealing high-intent traffic and commissions.
Are you sure no one is hijacking your branded ads?
Bluepear helps you catch what others miss. The ad hijacking tool automatically checks SERPs from different GEOs, devices, and browsers to keep your brand protected from fraud.
Try Bluepear free for 7 days to see if your brand is being hijacked – and stop the budget loss.
Image Credits
Featured Image: Image by Bluepear. Used with permission.
In-Post Images:Image by Bluepear. Used with permission.
Perplexity announced Comet Plus, a monthly subscription that pays participating publishers when people read their work and when AI systems use it to answer questions.
The company says subscriber payments go to partners, with a small portion retained to cover compute costs.
Comet Plus will be available for $5 per month. Existing Perplexity Pro and Max subscribers will have Comet Plus included.
Subscribers get direct access to participating publisher sites, answers informed by those sources, and agent workflows that can complete tasks on those sites. The offering is tied to the Comet browser and assistant.
Perplexity positions Comet Plus as a compensation model for an AI-centric web.
Publishers are paid for three interaction types:
Perplexity’s example of “agent traffic” is Comet Assistant scanning a calendar and suggesting relevant reading from publisher sites.
The idea is to reflect how people now consume information across browsing, AI answers, and agent workflows.
Perplexity wrote:
“Comet Plus is the first compensation model… based on three types of internet traffic: human visits, search citations, and agent actions.”
Interested publishers can email publishers@perplexity.ai to request to join the program.
For publishers and marketers, the model expands monetization and measurement beyond traditional clicks.
Websites are testing a range of responses to AI usage of their content, from blocking crawlers to signing licenses.
Comet Plus differs from flat-fee deals by tying payouts to actual user and assistant activity, which could align compensation more closely with real demand.
Perplexity says it will announce an initial roster of publishing partners when the Comet browser becomes available to all users for free.
Early adoption, reporting transparency, and real revenue for partners will determine whether this model becomes a viable framework or stays a niche experiment.
Boost your skills with Growth Memo’s weekly expert insights. Subscribe for free!
More data shows ChatGPT isn’t taking market share away from Google.
Instead, it’s expanding the range of use cases and blurring the line between searching for information and performing tasks.
I looked at Similarweb data to understand how this affects four different stages of the user journey across Google and ChatGPT:
What I found is that ChatGPT adoption is, essentially, a 400,000-pound locomotive barreling down the tracks with no intention of stopping anytime soon.
User conversations within ChatGPT are rich in context, which leads to higher conversion rates when intent shifts from information seeking or generating to buying.
Lastly, and also most notably for SEOs and growth marketers, ChatGPT is sending a lot more users out to the web.
Of course, all of these stats are still small in comparison to Google.
However, no effort from Google has been able to slow the momentum of ChatGPT’s runaway train. About the data I used in this analysis:
Image Credit Kevin IndigPeople are rushing to ChatGPT.
Over the last 12 months in the U.S., ChatGPT visits grew from 3.5 to 6.8 billion visits (+94%).
In the UK, it was even faster: 131% YoY, from 868 million to 2 billion.
Over the same time span, Google growth stagnated. Here’s what the data showed:
Image Credit: Kevin IndigTo put it into perspective: Google had almost 200 billion visits in the U.S. over the last 12 months, compared to ChatGPT’s 6.8 billion.
So, ChatGPT has a mere 3.4% of Google’s total visits.
However, if growth rates hold steady, in theory, ChatGPT could hit Google’s volume in the next five years.
My hypothesis: It’s almost guaranteed that ChatGPT won’t hit Google’s visit volume because there are too many moving parts (energy/chip limitations, training data, quality improvements, regulation, etc.).
But consider that Google has declined by -0.85% (~2 billion visits) year-over-year, and you can see where this is going.
Visits can only tell you so much.
Recent data from Semrush and Profound suggests that one-third to two-thirds of user intent when interacting with AI chatbots is generative, meaning users use ChatGPT to do and less to search [1, 2].
Leaked chats from ChatGPT and other AI chatbots confirm the aggregate data.
So, even when we compare visits to ChatGPT vs. Google, they’re not leading to the same outcome.
But, against that argument, I will say that Google morphs more into a mirror of ChatGPT with AI Mode – and every generative intent has a high chance of including information along the conversation journey that is sourced to other sites or creators.
The conversational nature of AI chatbots means intent is fluid and can change from one prompt to the next.
Along the way, it’s likely users come across information in their conversations that would’ve been a classic Google Search for products or solutions.
At the end of the day, ChatGPT is continuing its adoption as the fastest-growing product on earth to date.
What does that mean for you?
Quick reminder here: I recently transitioned my WhatsApp group over to Slack. I share ongoing news and learnings throughout the week openly and freely, so it’s a great place to stay updated without all the extra (and sometimes overwhelming) noise. No need to be a premium subscriber to get access to the main discussion channel. Join here!
Old habits are hard to break.
People are used to searching on Google a certain way, while ChatGPT is a green field.
For the overwhelming majority of us, our first experience with ChatGPT was a conversation, so we all adopted it as the default way to engage.
Image Credit: Kevin IndigAs a result, the average query prompt length on ChatGPT vs. Google is:
Even informational prompts are 10 times longer (~38 words) on ChatGPT than on Google. People ask more detailed questions, which reveal much more about themselves and their intent.
Together with a growing context window, ChatGPT returns much more personalized and (usually) better informational answers – I’m still waiting on consistently better commercial/purchase intent outcomes.[3] AI chatbots compress the user journey from many queries over several days to one conversation with lengthy prompts.
For you, this means it’s even more critical to monitor the right prompts.
(I shared a trick with premium subscribers for finding prompts in Google Search Console from AI Mode in Is AI cutting into your SEO conversions?)
As referral traffic from Google reached historic lows, ChatGPT’s referral traffic reached new highs.
Image Credit: Kevin IndigOver the last 12 months, ChatGPT’s U.S. referral traffic to websites jumped by +3,496% (UK: +5,950%), from 14 to 516 million (after cleaning up referrals to Openai.com, which are mostly authentications).
In comparison, Google’s outgoing referral visits grew only +23% in the U.S. and 19% in the UK.
When you consider Google referrals include navigational searches (people navigating to the homepage of a brand) and ad clicks (ChatGPT doesn’t yet have ads), 23% is not much at all.
ChatGPT’s referral traffic to external websites makes up ~27% of Google’s (1.9 billion, in the last 12 months), based on the data. That feels high, in my field observation.
Also consider that ChatGPT’s goal is not necessarily to send out traffic but to keep the conversation going until users have the optimal response.
That being said, referral traffic has grown and continues to do so. Until recently.
According to Profound, ChatGPT’s referral traffic was down -52% between July 21 and August 20. [4] And that’s significant.
Time will tell whether this is just an experiment or a final decision.
For you, this means you should see more ChatGPT referral traffic over the last 12 months if you optimize well.
You might not see an increase of +3,500%, but if you’re not seeing at least some growth, it’s likely your competitors are.
Conversions from ChatGPT are small in comparison with Google (in volume), but they’re growing rapidly.
The whole narrative of investing in AI visibility optimization (AEO/GEO/LLMO) banks on the fact that it will continue at the same pace and become meaningful.
So far, it seems like that bet will work out.
Image Credit: Kevin IndigWhen ChatGPT sends traffic to sites, the conversion rate is usually higher than Google’s. As of June 2025:
ChatGPT sends higher-quality traffic to websites, at least in the U.S.
I define quality in this context as “higher intent,” meaning visitors are more likely to convert into customers.
The reason ChatGPT traffic is of higher quality is that users get answers to their questions in one conversation. When they click out, they’re “primed” to buy.
To me, the bigger question is how purchase decisions are influenced before a click happens (or even when no click-out happens).
For you, this means:
ChatGPT has all the ingredients to become the next big user platform on which other companies can build – just like Google 25 years ago:
Now, traffic and conversations just need more volume.
They’re still tiny in comparison.
Featured Image: Paulo Bobita/Search Engine Journal
This edited excerpt is from Ethical AI in Marketing by Nicole Alexander ©2025 and is reproduced and adapted with permission from Kogan Page Ltd.
Recent research highlights intriguing paradoxes in consumer attitudes toward AI-driven marketing. Consumers encounter AI-powered marketing interactions frequently, often without realizing it.
According to a 2022 Pew Research Center survey, 27% of Americans reported interacting with AI at least several times a day, while another 28% said they interact with AI about once a day or several times a week (Pew Research Center, 2023).
As AI adoption continues to expand across industries, marketing applications – from personalized recommendations to chatbots – are increasingly shaping consumer experiences.
According to McKinsey & Company (2023), AI-powered personalization can deliver five to eight times the ROI on marketing spend and significantly boost customer engagement.
In this rapidly evolving landscape, trust in AI has become a crucial factor for successful adoption and long-term engagement.
The World Economic Forum underscores that “trust is the foundation for AI’s widespread acceptance,” and emphasizes the necessity for companies to adopt self-governance frameworks that prioritize transparency, accountability, and fairness (World Economic Forum, 2025).
Consumer trust in AI marketing systems operates fundamentally differently from traditional marketing trust mechanisms.
Where traditional marketing trust builds through brand familiarity and consistent experiences, AI trust involves additional psychological dimensions related to automation, decision-making autonomy, and perceived control.
Understanding these differences is crucial for organizations seeking to build and maintain consumer trust in their AI marketing initiatives.
Neurological studies offer intriguing insights into how our brains react to AI. Research from Stanford University reveals that we process information differently when interacting with AI-powered systems.
For example, when evaluating AI-generated product recommendations, our brains activate distinct neural pathways compared to those triggered by recommendations from a human salesperson.
This crucial difference highlights the need for marketers to understand how consumers cognitively process AI-driven interactions.
There are three key cognitive factors that have emerged as critical influences on AI trust, including perceived control, understanding of mechanisms, and value recognition.
Consumer trust in AI marketing is deeply influenced by emotional factors, which often override logical evaluations. These emotional responses shape trust in several key ways:
The global nature of modern marketing requires a nuanced understanding of cultural differences in AI trust. These variations arise from deeply ingrained societal values, historical relationships with technology, and norms around privacy, automation, and decision-making.
For marketers leveraging AI in customer engagement, recognizing these cultural distinctions is crucial for developing trustworthy AI-driven campaigns, personalized experiences, and region-specific data strategies.
Research reveals significant disparities in AI trust across global markets. A KPMG (2023) global survey found that 72% of Chinese consumers express trust in AI-driven services, while in the U.S., trust levels plummet to just 32%.
This stark difference reflects broader societal attitudes toward government-led AI innovation, data privacy concerns, and varying historical experiences with technology.
Another study found that AI-related job displacement fears vary greatly by region. In countries like the U.S., India, and Saudi Arabia, consumers express significant concerns about AI replacing human roles in professional sectors such as medicine, finance, and law.
In contrast, consumers in Japan, China, and Turkey exhibit lower levels of concern, signaling a higher acceptance of AI in professional settings (Quantum Zeitgeist, 2025).
The Quantum Zeitgeist study shows that regions like Japan, China, and Turkey exhibit lower levels of concern about AI replacing human jobs compared to regions like the U.S., India, and Saudi Arabia, where such fears are more pronounced.
This insight is invaluable for marketers crafting AI-driven customer service, financial tools, and healthcare applications, as perceptions of AI reliability and utility vary significantly by region.
As trust in AI diverges globally, understanding the role of cultural privacy norms becomes essential for marketers aiming to build trust through AI-driven services.
As AI-driven marketing becomes more integrated globally, the concept of cultural privacy targeting – the practice of aligning data collection, privacy messaging, and AI transparency with cultural values – has gained increasing importance. Consumer attitudes toward AI adoption and data privacy are highly regional, requiring marketers to adapt their strategies accordingly.
In more collectivist societies like Japan, AI applications that prioritize societal or community well-being are generally more accepted than those centered on individual convenience.
This is evident in Japan’s Society 5.0 initiative – a national vision introduced in 2016 that seeks to build a “super-smart” society by integrating AI, IoT, robotics, and big data to solve social challenges such as an aging population and strains on healthcare systems.
Businesses are central to this transformation, with government and industry collaboration encouraging companies to adopt digital technologies not just for efficiency, but to contribute to public welfare.
Across sectors – from manufacturing and healthcare to urban planning – firms are reimagining business models to align with societal needs, creating innovations that are both economically viable and socially beneficial.
In this context, AI is viewed more favorably when positioned as a tool to enhance collective well-being and address structural challenges. For instance, AI-powered health monitoring technologies in Japan have seen increased adoption when positioned as tools that contribute to broader public health outcomes.
Conversely, Germany, as an individualistic society with strong privacy norms and high uncertainty avoidance, places significant emphasis on consumer control over personal data. The EU’s GDPR and Germany’s support for the proposed Artificial Intelligence Act reinforce expectations for robust transparency, fairness, and user autonomy in AI systems.
According to the OECD (2024), campaigns in Germany that clearly communicate data usage, safeguard individual rights, and provide opt-in consent mechanisms experience higher levels of public trust and adoption.
These contrasting cultural orientations illustrate the strategic need for contextualized AI marketing – ensuring that data transparency and privacy are not treated as one-size-fits-all, but rather as culture-aware dimensions that shape trust and acceptance.
Hofstede’s (2011) cultural dimensions theory offers further insights into AI trust variations:
For marketers deploying AI in different regions, these insights help determine which features to emphasize:
Understanding the cultural dimensions of AI trust is key for marketers crafting successful AI-powered campaigns.
By aligning AI personalization efforts with local cultural expectations and privacy norms, marketers can improve consumer trust and adoption in both individualistic and collectivist societies.
This culturally informed approach helps brands tailor privacy messaging and AI transparency to the unique preferences of consumers in various regions, building stronger relationships and enhancing overall engagement.
While cultural differences are clear, overgeneralizing consumer attitudes can lead to marketing missteps.
A 2024 ISACA report warns against rigid AI segmentation, emphasizing that trust attitudes evolve with:
For AI marketing, this highlights the need for flexible, real-time AI trust monitoring rather than static cultural assumptions.
Marketers should adapt AI trust-building strategies based on region-specific consumer expectations:
The data, drawn from the 2023 KPMG International survey, underscores how cultural values such as collectivism, uncertainty avoidance, and openness to innovation, shape public attitudes toward AI.
For example, trust levels in Germany and Japan remain low, reflecting high uncertainty avoidance and strong privacy expectations, while countries like India and Brazil exhibit notably higher trust, driven by optimism around AI’s role in societal and economic progress.
As AI becomes central to how brands engage customers – from personalization engines to chatbots – measuring consumer trust in these systems is no longer optional. It’s essential.
And yet, many marketing teams still rely on outdated metrics like Net Promoter Score (NPS) or basic satisfaction surveys to evaluate the impact of AI. These tools are helpful for broad feedback but miss the nuance and dynamics of trust in AI-powered experiences.
Recent research, including work from MIT Media Lab (n.d.) and leading behavioral scientists, makes one thing clear: Trust in AI is multi-dimensional, and it’s shaped by how people feel, think, and behave in real-time when interacting with automated systems.
Traditional metrics like NPS and CSAT (Customer Satisfaction Score) tell you if a customer is satisfied – but not why they trust (or don’t trust) your AI systems.
They don’t account for how transparent your algorithm is, how well it explains itself, or how emotionally resonant the interaction feels. In AI-driven environments, you need a smarter way to understand trust.
MIT Media Lab’s work on trust in human-AI interaction offers a powerful lens for marketers. It breaks trust into three key dimensions:
This is about what customers do, not what they say. When customers engage frequently, opt in to data sharing, or return to your AI tools repeatedly, that’s a sign of behavioral trust. How to track it:
Trust is not just rational, it’s emotional. The tone of a voice assistant, the empathy in a chatbot’s reply, or how “human” a recommendation feels all play into emotional trust. How to track it:
This is where understanding meets confidence. When your AI explains itself clearly – or when customers understand what it can and can’t do –they’re more likely to trust the output. How to track it:
Today’s marketers are moving toward real-time trust dashboards – tools that monitor how users interact with AI systems across channels. These dashboards track behavior, sentiment, and comprehension all at once.
According to MIT Media Lab researchers, combining these signals provides a richer picture of trust than any single survey can. It also gives teams the agility to address trust breakdowns as they happen – like confusion over AI-generated content or friction in AI-powered customer journeys.
Customers don’t expect AI to be perfect. But they do expect it to be honest and understandable. That’s why brands should:
Building trust is less about tech perfection and more about perceived fairness, clarity, and respect.
Measuring that trust means going deeper than satisfaction. Use behavioral, emotional, and cognitive signals to track trust in real-time – and design AI systems that earn it.
To read the full book, SEJ readers have an exclusive 25% discount code and free shipping to the US and UK. Use promo code ‘SEJ25’ at koganpage.com here.
More Resources:
References
Featured Image: Rawpixel.com/Shutterstock
Brave published details about a security issue with Comet, Perplexity’s AI browser, that enables an attacker to inject a prompt into the browser and gain access to data in other open browser tabs.
Brave described a vulnerability that can be activated when a user asks the Comet AI browser to summarize a web page. The LLM will read the web page, including any embedded prompts that command the LLM to take action on any open tabs
According to Brave:
“The vulnerability we’re discussing in this post lies in how Comet processes webpage content: when users ask it to “Summarize this webpage,” Comet feeds a part of the webpage directly to its LLM without distinguishing between the user’s instructions and untrusted content from the webpage. This allows attackers to embed indirect prompt injection payloads that the AI will execute as commands. For instance, an attacker could gain access to a user’s emails from a prepared piece of text in a page in another tab.”
A post on Simon Willison’s Weblog shared that Perplexity tried to patch the vulnerability but the fix does not work.
A developer posted the following on X:
“Why is no one talking about this?
This is why I don’t use an AI browser
You can literally get prompt injected and your bank account drained by doomscrolling on reddit:”
Things aren’t looking good for Comet Browser at this time.
The wildfires that swept through Los Angeles County in January 2025 left an indelible mark on the Southern California landscape. The Eaton and Palisades fires raged for 24 days, killing 29 people and destroying 16,000 structures, with losses estimated at $60 billion. More than 55,000 acres were consumed, and the landscape itself was physically transformed.
Researchers are now using lidar (light detection and ranging) technology to precisely measure these changes in the landscape’s geometry—helping them understand the effects of climate disasters.
Lidar, which measures how long it takes for pulses of laser light to bounce off surfaces and return, has been used in topographic mapping for decades. Today, airborne lidar from planes and drones maps the Earth’s surface in high detail. Scientists can then “diff” the data—compare before-and-after snapshots and highlight all the changes—to identify more subtle consequences of a disaster, including fault-line shifts, volcanic eruptions, and mudslides.
Falko Kuester, an engineering professor at the University of California, San Diego, co-directs ALERTCalifornia, a public safety program that uses real-time remote sensing to help detect wildfires. Kuester says lidar snapshots can tell a story over time.
“They give us a lay of the land,” he says. “This is what a particular region has been like at this point in time. Now, if you have consecutive flights at a later time, you can do a ‘difference.’ Show me what it looked like. Show me what it looks like. Tell me what changed. Was something constructed? Something burned down? Did something fall down? Did vegetation grow?”
Shortly after the fires were contained in late January 2025, ALERTCalifornia sponsored new lidar flights over the Eaton and Palisades burn areas. NV5, an inspection and engineering firm, conducted the scans, and the US Geological Survey is now hosting the public data sets.
Comparing a 2016 lidar snapshot and the January 2025 snapshot, Cassandra Brigham and her team at Arizona State University visualized the elevation changes—revealing the buildings, trees, and structures that had disappeared.
“We said, what would be a useful product for people to have as quickly as possible, since we’re doing this a couple weeks after the end of the fires?” says Brigham. Her team cleaned and reformatted the older, lower-resolution data and then subtracted the newer data. The resulting visualizations reveal the scale of devastation in ways satellite imagery can’t match. Red shows lost elevation (like when a building burns), and blue shows a gain (such as tree growth or new construction).
Lidar is helping scientists track the cascading effects of climate-driven disasters—from the damage to structures and vegetation destroyed by wildfires to the landslides and debris flows that often follow in their wake. “For the Eaton and Palisades fires, for example, entire hillsides burned. So all of that vegetation is removed,” Kuester says. “Now you have an atmospheric river coming in, dumping water. What happens next? You have debris flows, mud flows, landslides.”
Lidar’s usefulness for quantifying the costs of climate disasters underscores its value in preparing for future fires, floods, and earthquakes. But as policymakers weigh steep budget cuts to scientific research, these crucial lidar data collection projects could face an uncertain future.
Jon Keegan writes about technology and AI, and he publishes Beautiful Public Data (beautifulpublicdata.com), a curated collection of government data sets.
Merchandising is an ongoing process of presenting products to boost sales. Every ecommerce site merchandises its products either consciously or by default.
Many ecommerce platforms have basic merchandising built in directly or in popular themes. These built-ins make merchandising easy, but not necessarily optimized.
Nike’s home page hero slide is seasonal. It features spots or products, such as this example of inspiring shoppers to dress like tennis star Carlos Alcaraz.
Merchandising techniques vary. Some focus on visuals or product curation. Others use behavioral economics and personalization for maximum persuasion.
Still others think about merchandising as it applies to a buyer. The result can be a five-step framework, where each step defines a set of tactics that move shoppers from curiosity to purchase.
The first set of tactics aims at product inspiration. It seeks to stimulate the shopper, to help imagine a lifestyle or a need your products solve.
These tactics often manifest themselves as:
For example, the recipes found on Le Creuset’s site help a shopper imagine making the meal for a special occasion and experiencing the joy of sharing food with folks you love. The content (merchandising) evokes a feeling and, ultimately, sells the enamel-covered cast iron cookware needed to bring that feeling to life.
Content marketing can be a powerful form of product merchandising. Le Creuset uses recipes to inspire shoppers.
The second set of tactics in this merchandising framework aims to reduce friction and guide shoppers toward products they are likely to buy.
These techniques often take the form of:
Many ecommerce platforms include these sorts of navigational features, but they require optimizing so that a search results page is a thoughtful arrangement of relevant items, not a keyword-based SKU dump.
Origin allows shoppers to filter apparel for color, size, and activity, such as jiu-jitsu and hunting.
Next come the tactics meant to influence choices and encourage shoppers to make complementary purchases.
While a merchant will measure the success of these efforts in revenue and average order value, persuasive merchandising should seek the shopper’s best interest.
Don’t make frivolous recommendations or use tricks, but do describe your products in a way that encourages the sale. For example, persuasion merchandising might include:
A low inventory count can be persuasive.
On-site merchandising often culminates near the buy-now button or on shopping cart and checkout pages. Here, the aim is to reassure and close the sale.
Conversion messages might be:
A simple, well-placed message that removes a shopper’s final doubts or concerns effectively closes the sale.
These three small graphics — American made, easy returns, and free shipping — make a final argument to buy.
A final set of merchandising tactics enables retention marketing. It is the conversion after the sale.
While each category of merchandising tactics in this framework requires human initiative and planning, AI can certainly help with execution, including content generation and personalization.
Thus for Inspiration, an ecommerce merchandiser might prepare a seasonal banner for back-to-school or the football season. Better still, AI personalization could select or generate dynamic banners to match a shopper’s personal profile and previous behavior.
Merchandising shapes how shoppers discover, evaluate, and purchase products. A sound framework ensures that each step of the shopping journey contributes to sales and customer relationships.