Since the turn of the year, many website owners have been experiencing volatile rankings with sites facing instability in day-to-day keyword rankings. This volatility has gradually progressed throughout the year, with some sites now experiencing steep ranking drops with historically strong keywords, completely falling out of the rankings before appearing back the day after.

In his article on the current ranking volatility, Barry Schwartz shows several different Google Tracking tools that demonstrate the increasing volatility of the SERP in recent months.

Semrush Sensor is a tool that lets you track Google ranking volatility over a selected period. As we can see, since the middle of February, in the UK, the SERP has seen very high levels of volatility, which has not let up for the entirety of March.

This means that at the moment, despite your best SEO efforts, you may be seeing some strong ranking swings that are out of your control. It’s important at these times to weather the storm and wait for the ranking volatility to stabilise before assessing your performance. If you feel your rankings are suffering in the long term, not just the interim period, then looking into ways you can improve or pivot your SEO approach would be astute.

How to Contain Ranking Volatility

As mentioned, while rankings are currently volatile, you should be thinking about the macro rather than the micro, not focusing too much on your rankings on a day-to-day basis, but making sure you are taking care of things under your control, such as:

• Expanding Content
• Keyword Targeting
• Internal and External Linking
• Improving Site Speed
• Improving UI

If you’re consistently focused on improving your site from an organic SEO perspective and improving your authority, trustworthiness and content, your rankings will trend upwards if you’re doing a good job, but the rise will never be smooth, and all sites face fluctuations and ranking drops, especially in high volatility search environments, the key to maintaining your trajectory over time is focusing on refining your SEO approach.

Artificial intelligence is quickly becoming a new product discovery channel. More consumers are asking tools like ChatGPT for buying advice instead of browsing comparison sites or search results themselves.

A recent study analysing more than 43,000 products shown in ChatGPT recommendation carousels revealed a clear pattern. Around 83 per cent of the products recommended by ChatGPT also appear in Google Shopping results, while very few come exclusively from Bing.

For e-commerce brands, the takeaway is straightforward. Visibility in Google Shopping is now influencing whether products appear inside AI recommendations.

This does not mean AI has replaced search marketing. In reality, it means the fundamentals of e-commerce visibility, such as product feeds and shopping optimisation, are becoming even more important.

ChatGPT appears to source products from Google Shopping

The study suggests that ChatGPT retrieves product recommendations through a separate shopping retrieval process. Instead of analysing articles or blog posts to choose products, the system appears to pull candidate products from shopping indexes.

Researchers found that most products appearing in ChatGPT carousels were also present within the top 40 organic Google Shopping results for the same query.

Even more telling was the influence of ranking position. Products appearing higher in Google Shopping results were far more likely to appear in ChatGPT’s carousel. Around 60 percent of matched products were found in the top 10 Google Shopping results, and nearly 84 percent came from the top 20.

For e-commerce brands, this means Google Shopping visibility may now affect not only search traffic but also AI-generated product recommendations.

Optimise your Google Shopping feed as a core marketing asset

If AI systems are drawing heavily from Google Shopping, then the product feed itself becomes a critical ranking signal.

Many e-commerce brands treat product feeds as a technical task handled once during setup. In reality, they should be actively optimised in the same way as search content.

There are several practical tactics that can improve feed performance.

Write highly descriptive product titles

Product titles play a major role in how Google categorises and surfaces products. Instead of using short or vague titles, include key information that shoppers would search for.

Effective titles often include the brand name, product type, model, key feature and variant where relevant. For example, a generic title such as “Running Shoes” is far less useful than “Nike Air Zoom Pegasus 40 Men’s Running Shoes”.

Ensure every product attribute is completed

Google Shopping relies heavily on structured attributes to understand products. Missing attributes can reduce visibility or lead to incorrect categorisation.

Important attributes to complete include brand, product type, material, colour, size, gender, condition and GTIN or manufacturer identifiers. The more complete the feed is, the easier it is for Google to match products with relevant queries.

Use Google product categories accurately

Google allows retailers to assign categories from a predefined taxonomy. Selecting the most precise category helps Google understand where the product belongs in shopping results.

Many brands either leave this field blank or choose broad categories. Using highly specific categories improves relevance signals and can help products appear for more targeted queries.

Improve product imagery

Images are a key factor in product engagement and performance. Google prefers clear, high-resolution images with simple backgrounds that show the product clearly.

Avoid cluttered images, overlays, watermarks or heavy text. Strong product photography increases click-through rates and can improve ranking performance in shopping results.

Keep pricing and availability accurate

Google favours products with consistent and reliable data. If the product feed frequently shows incorrect pricing or items marked as available when they are not, this can affect performance.

Regularly updating feeds ensures that stock levels, promotions and price changes are reflected accurately.

Add detailed product descriptions

While titles and attributes are critical, descriptions also help Google understand the product context. Clear descriptions that mention features, benefits and specifications can improve how products match to search queries.

Avoid duplicate manufacturer descriptions where possible. Unique descriptions help products stand out.

Improve Google Shopping visibility, not just SEO

Many marketing teams still separate SEO and product feed optimisation into different silos. However, this research suggests that shopping rankings may influence visibility across both search engines and AI assistants.

That means ecommerce brands should treat Google Shopping optimisation as a core growth channel rather than a secondary task.

Improving feed quality, ensuring accurate product data and strengthening product listings can increase the chances of ranking higher in Google Shopping results.

And increasingly, those same rankings may determine whether your products appear inside AI tools like ChatGPT.

AI product discovery still depends on search infrastructure

One of the most interesting insights from the research is that AI tools are not operating in isolation. Instead, they appear to be building on existing search ecosystems.

Rather than replacing search engines, AI platforms are currently layering intelligence on top of traditional product indexes.

For e-commerce marketers, that means the foundations of product visibility remain familiar. The brands that manage their product feeds well, optimise their shopping listings and maintain strong product data will be the ones most likely to benefit as AI-powered product discovery continues to grow.

Digital advertising continues to evolve, and businesses using Meta platforms like Facebook and Instagram should be aware of a new update that may slightly increase advertising costs in some markets. Meta recently announced it will introduce small location-based fees on certain advertising campaigns in Europe.

While this change may sound concerning at first, it is important to understand that the increase is not simply a platform price rise. Instead, it reflects government taxes applied to large technology companies operating in those regions. Understanding the reason behind the change helps businesses plan their advertising budgets more effectively and continue running successful campaigns.

A New Location-Based Fee on Meta Ads

Meta has announced that it will begin applying a small “location fee” to advertisements shown in certain countries. These additional charges will range between 2% and 5% of ad spend, depending on the country where the audience viewing the ad is located.

For example, ads delivered to users in the United Kingdom will include a 2% fee, while campaigns targeting audiences in France, Italy, and Spain will include around 3%, and Austria and Turkey could see fees of up to 5%.

Importantly, the fee is based on where the audience is located, not where the advertiser’s business operates. This means that even companies outside these countries could see the surcharge if their ads target users in those regions.

Why Governments Are Introducing Digital Services Taxes

Many governments have introduced Digital Services Taxes (DSTs) to ensure that large global technology companies contribute tax revenue in the countries where they generate significant income. These taxes typically apply to revenue generated from digital advertising, social media platforms, and online marketplaces.

Because companies like Meta generate advertising revenue from users worldwide, governments have introduced these taxes to capture a portion of that revenue locally. In response, Meta has decided to pass on a small portion of those costs to advertisers instead of continuing to absorb them internally.

What This Means for Businesses Running Ads

For most advertisers, the change will result in only a modest increase in campaign costs. However, it highlights the importance of regularly reviewing advertising strategies and budgets as the digital landscape continues to evolve.

The good news is that Meta advertising remains one of the most powerful and cost-effective ways for businesses to reach highly targeted audiences online. With the right campaign strategy, strong creative, and careful audience targeting, businesses can continue to generate strong returns on their advertising investment despite these small adjustments.

Staying informed about platform updates like this ensures businesses can plan ahead, maintain transparency with stakeholders, and continue making smart decisions when investing in digital marketing.

What is cringe content marketing?

Cringe content marketing is a type of marketing that plays on the emotional awkwardness of certain situations. Which can be wielded to create funny, tense, and even realistic types of content campaigns.

It goes beyond social norms to find a way to create an emotional response from the audience. Being able to make the audience feel something, even if it is second-hand embarrassment, proves brands can engage audiences.

Who does cringeworthy content marketing appeal to?

The power of cringe content marketing lies in its relatability. It draws on everyday moments of awkwardness and discomfort that most people have experienced at some point in their lives. Because these situations feel so familiar, audiences across generations can recognise them instantly and respond with that unmistakable “cringe” reaction.

In many ways, cringe content simply reflects the small, human experiences we often feel embarrassed about but secretly relate to.

Does cringe or awkward content marketing work?

Cringe marketing offers a different approach to creating content for audiences. Rather than appealing to comfortable or polished emotions, it embraces awkwardness to create memorable experiences for viewers, whether the cringe moment feels enjoyable or not.

Entertainment does not have to rely solely on humour or drama. Cringe marketing allows creatives to tap into more emotionally provocative experiences and generate stronger reactions from audiences. These intense responses can help make content campaigns more memorable.

While this approach is not entirely new, it has become increasingly popular as younger audiences respond to content that breaks away from traditional and highly polished marketing styles.

Examples of cringeworthy content campaigns

“A Spicy, but Not Too Spicy Plumber” by Doritos (2025)

Doritos launched its Golden Sriracha flavour with a campaign that leaned heavily into awkward humour. The advert features exaggerated sexual innuendos and deliberately uncomfortable scenarios, creating an ad that feels both icky and attention-grabbing. By embracing this awkward tone, the campaign demonstrates how discomfort can be used to capture attention and spark conversation.

Go Compare

When we think of brands that have created recognition beyond visuals, GoCompare is one of the first that comes to mind. The over-the-top opera singer Gio Compario belting “Go Compare, Go Compare!” was intentionally designed to be an earworm. The character was loud, disruptive and deliberately irritating, ensuring the adverts immediately pulled focus.

The campaign proved that annoying or awkward advertising can still be highly effective at building brand awareness.

“Women belong in the kitchen” by Burger King (2021)

“Women belong in the kitchen” was perhaps one of the biggest failures in content marketing. While the phrase was written to grab attention, the subtext that followed, which aimed to highlight female empowerment in culinary workspaces, entirely backfired.

Instead of landing as a critique of misogyny and gender inequality, it reinforced a narrative using language that is deeply entrenched in sexism. This is a clear instance where sexism was used as clickbait rather than drawing attention to the scholarship the campaign intended to promote.

Cringe marketing sits in an unusual space within modern content strategy. While traditional advertising often focuses on polished messaging and positive emotions, awkward or uncomfortable campaigns show that strong reactions can be just as powerful. Whether intentional or accidental, cringe content can spark conversation, increase engagement, and make a brand more memorable. However, as some campaigns demonstrate, there is a fine line between capturing attention and creating backlash. For brands, the challenge is knowing when awkward humour will resonate with audiences and when it may ultimately work against them.

Digital marketing teams have never had access to more data. Advertising platforms, analytics tools, CRM systems and dashboards all promise insights into customer behaviour and marketing performance. Yet many organisations still struggle to answer a fundamental question: which marketing activity actually generates real customers and revenue?

One of the main reasons for this is that much of the customer journey still happens offline. Many high-value purchases, particularly in sectors such as healthcare, professional services and education, involve a phone call before a customer commits.

If those calls are not captured and analysed properly, marketing teams are left with an incomplete picture. Website traffic and form submissions may be tracked, but the conversations that actually drive decisions remain invisible.

By combining call tracking technology, artificial intelligence and CRM systems, businesses can build a far more complete view of the customer journey. Platforms such as Nimbata, HubSpot and Monday.com allow marketing teams to track enquiries, analyse conversations and connect those insights directly to revenue performance.

When these systems are connected properly, marketing reporting moves beyond traffic and clicks. It becomes a true commercial intelligence system.

Why phone conversations are critical marketing data

For many businesses, a phone enquiry represents one of the strongest indicators of buying intent. A person who calls a business is often much closer to deciding whether someone is browsing a website. Yet phone calls are historically one of the most poorly tracked parts of marketing.

Without call tracking technology, it is impossible to know which marketing channels generated those enquiries. A customer might have discovered the business through organic search, paid advertising or social media, but the marketing team cannot attribute the call accurately.

This is where call tracking platforms such as Nimbata play a critical role. By assigning unique phone numbers to marketing channels and campaigns, every call can be linked back to its source.

This immediately connects phone enquiries to marketing performance.

However, tracking calls is only the first step. The real insight emerges when those conversations are analysed and integrated into a broader CRM system.

A process-driven approach to connecting AI, phone tracking and CRM data

To make this system work effectively, it helps to think of the process as a structured series of stages. Each stage captures and enriches the customer data so that it becomes more valuable for both marketing and sales teams.

Step 1: Track where the phone call originated

The first stage focuses on identifying where the caller originally discovered the business. Call tracking systems such as Nimbata use dynamic number insertion on the website. This technology automatically replaces the phone number shown on the website depending on how the visitor arrived.

For example, visitors arriving from:

• Organic search
• Paid search advertising
• Social media campaigns
• Email marketing
• Direct website visits

will each see a different tracking number.

When a call is made, the system records the source of that visitor and links the enquiry back to the original marketing channel. This step alone dramatically improves marketing attribution. Instead of guessing which campaigns generate calls, the marketing team can see exactly which channels are responsible.

Step 2: Transcribe the conversation using AI

Once the call has been captured, the next stage is transcription. Modern call tracking platforms automatically convert phone conversations into text. This makes it possible for artificial intelligence to analyse the content of calls at scale.

Rather than manually listening to hundreds of recordings, AI can process transcripts and identify patterns in the conversations. This step transforms phone calls from isolated conversations into structured data that can be analysed.

Step 3: Segment callers into meaningful categories

After transcription, AI is used to categorise each call. The first classification identifies the type of caller. Calls are segmented into three main groups:

• New customers
• Existing customers
• Non-relevant calls such as sales outreach or internal staff calls

This distinction ensures that marketing teams are analysing genuine lead activity rather than operational noise.

Once this classification is made, the system moves to the next layer of segmentation.

Step 4: Evaluate the strength of the lead

Not all enquiries represent the same level of opportunity. Artificial intelligence can analyse the tone and content of a conversation to estimate the strength of the lead. For example, callers can be categorised along a spectrum from warm to cold.

A caller asking detailed questions about booking or availability may represent a high intent enquiry, while someone gathering general information may fall into a lower intent category.

This classification allows businesses to prioritise their follow-up activity more effectively. High intent enquiries can be routed to the sales team immediately, while lower intent leads can enter nurturing workflows.

Step 5: Identify the product or service being discussed

Another important layer of analysis focuses on the topic of the enquiry. AI systems can identify which product or service the caller is interested in. This provides valuable insight into demand patterns across different offerings.

For example, if a large proportion of calls relate to a specific treatment or service, marketing teams can adjust campaigns and landing pages to reflect that demand. This also helps sales teams prepare for conversations because they understand the context of the enquiry before engaging with the caller.

Step 6: Understand where the caller sits in the marketing funnel

Phone conversations often reveal exactly where a customer is in their decision-making journey.

By analysing the transcript, AI systems can determine whether a caller is:

• Gathering general information
• Comparing prices
• Checking availability
• Ready to book

Understanding these stages helps marketing teams refine their messaging. If many callers are asking basic educational questions, the website may need clearer explanations or additional content.

If price discussions dominate calls, messaging around payment plans or financing options may need to appear earlier in the customer journey.

Step 7: Capture structured customer data

During most phone calls, certain pieces of information are exchanged between the caller and the business. This may include the caller’s name, phone number, location or other relevant details.

AI transcription systems can extract this information automatically and pass it into the CRM system. In many cases, this data feeds directly into platforms such as HubSpot.

The result is a fully populated contact record without requiring manual data entry. This step ensures that every enquiry becomes a structured lead that can be tracked throughout the customer lifecycle.

Step 8: Preserve the original marketing source in the CRM

Once the call data reaches the CRM, one of the most important tasks is preserving the original marketing source.

If a customer first discovered the business through organic search, that source should remain attached to their record even if they later interact with email campaigns or direct website visits. Maintaining this source allows businesses to calculate accurate return on investment for each marketing channel.

Without this connection, attribution becomes unreliable and marketing decisions become harder to justify.

Step 9: Evaluate call handling performance

Another powerful use of AI is evaluating the quality of calls handled by sales teams, reception staff or customer service agents. Predefined training models and evaluation algorithms can analyse conversations and identify whether important steps were followed.

For example, the system may detect situations where:

• A caller raised concerns about price, but financing options were not mentioned
• A customer could not find an available appointment but was not offered a waiting list
• Key questions were not answered clearly

The system can then provide suggestions for improving call handling. This allows businesses to improve both sales performance and customer experience without manually reviewing every conversation.

Step 10: Record the outcome and next step for the lead

Another crucial piece of information is what happens after the call. AI analysis and CRM workflows can record whether the lead progressed, converted or stalled.

If a caller decides not to proceed after discussing the price, that reason can be captured. If a caller converts immediately after learning about financing options, that insight can also be recorded. Over time, these patterns reveal which factors influence customer decisions. Marketing teams can then adapt campaigns to address those concerns earlier in the customer journey.

Step 11: Automate follow-up and lead nurturing

Once this information is stored in the CRM, automated workflows can take over. CRM platforms such as HubSpot allow businesses to trigger actions based on lead behaviour.

For example:

• Sales teams can receive automated reminders to follow up with high-intent leads
• Priority leads can be routed to specific team members
• Cold leads can enter longer-term nurturing campaigns

Instead of aggressive sales messaging, these nurturing campaigns might include educational content such as guides, FAQs or blog articles. This softer approach maintains contact with potential customers without overwhelming them.

Step 12: Use CRM data to improve advertising campaigns

CRM data can also enhance advertising performance. Customer email addresses and phone numbers can be used in customer matching tools across advertising platforms. This allows businesses to retarget leads more effectively or exclude existing customers from campaigns.

In addition, lookalike audiences can be created based on existing customers. Advertising platforms can then identify new users who share similar characteristics.

This improves campaign efficiency and ensures that budgets are focused on the most relevant audiences.

Step 13: Connect the data to reporting dashboards

The final step in the process is bringing all this information together in reporting dashboards. These dashboards combine marketing data with commercial performance metrics so that businesses can measure true return on investment.

When systems do not integrate directly, connectors such as Zapier can bridge the gap between platforms.

In some cases, business intelligence tools such as Microsoft Power BI can act as a central data source that aggregates information from multiple systems.

The result is a reporting environment that shows not just marketing performance but real business outcomes.

The practical and commercial benefits of this approach

When this process is implemented correctly, the impact goes far beyond better marketing reports. It fundamentally changes how businesses understand their customers and manage their sales processes.

Some of the most significant benefits include:

• Accurate marketing attribution so businesses can clearly see which channels are generating genuine leads and revenue.
• Better use of marketing budgets by identifying the campaigns and keywords that produce the highest quality enquiries.
• Improved sales performance through AI-driven feedback that highlights where call handlers can improve conversations.
• More efficient lead management by prioritising high intent enquiries and automating follow-up for colder leads.
• Stronger customer insights by analysing real conversations and identifying common questions, objections and motivations.
• Smarter marketing messaging because campaigns can address the concerns customers actually raise during calls.
• Better customer experience as businesses refine how enquiries are handled and improve their booking or purchasing processes.
• Full lifecycle reporting showing how long leads take to convert, how many interactions were required and which marketing channels initiated the journey.
• Clear ROI measurement by connecting marketing data with real commercial outcomes rather than just website metrics.

Ultimately, this approach allows marketing teams to move beyond vanity metrics and focus on what truly matters: generating customers and revenue.

Bringing your marketing and sales data together

The combination of call tracking, AI analysis and CRM integration represents a major step forward in marketing intelligence. Instead of analysing isolated metrics such as clicks or impressions, businesses can now track real conversations, understand customer intent and measure the commercial impact of their marketing activity.

Platforms such as Nimbata, HubSpot and Monday.com allow organisations to build a connected ecosystem where every enquiry becomes part of a structured data process.

The result is clearer reporting, better sales performance and more effective marketing decisions.

Want to implement a similar system for your business?

Many organisations already have some of the tools needed to build this type of process. The challenge is often connecting those tools in a way that captures the right data and turns it into meaningful insight.

If you would like help assessing how this could work within your organisation, we would be happy to review your current setup.

We can evaluate your existing marketing platforms, CRM systems and call handling processes to identify how a similar framework could be implemented using your current infrastructure. If the right systems are not already in place, we can also design and deploy a new solution tailored to your business.

If you would like to explore how this approach could help you better understand your customers, improve marketing attribution and increase conversion performance, get in touch with us, and we will be happy to talk through the possibilities.

Since their introduction to search in May 2024, AI Overviews have continued to grow in size and dominate Google search. In the past year, AI Overviews have grown by over 50% in terms of their coverage on the SERP. AI Overviews now appear in 48% of searches, meaning almost half of all searches use AI. This is a far cry from the past, where often your organic SEO efforts would see you appearing at the top of the SERP. In this article, we’ll look at what this means for marketers in the space.

Decline in Click-Through-Rate

With AI now dominating search, one of the most notable changes for digital marketers has been a decline in click-through rate. Click-through rate is a metric that tells marketers what percentage of people click on the link after seeing it. Historically, sites that appeared at the top of the SERP would see higher click-through-rates as links higher up the search engine are typically seen as more reliable and trustworthy by consumers. Now that searches are being dominated by AI Overviews, people are often finding the information they need pulled straight onto the SERP without having to click throught onto websites, causing a decline in click-through-rates.

This is especially apparent in certain sectors that answers peoples more informational queries on the SERP as opposed to e-commerce searches, for example. As Roger Monti states in the Search England Journal,

“The education sector experienced the strongest expansion in the number of queries triggering AI search results, from 18% of queries in May 2025 to 83% of queries triggering AI search results by December 2025.”

“Meanwhile, healthcare queries were already triggering AIO results by a large margin since 2024, at a rate of 72% of the time. By December 2025, however, the rate at which healthcare queries triggered AIO edged up to 88%.”

We can see that sectors that strongly favour informational queries are seeing a huge uptick in AI Overviews, which means marketers in these sectors need to try to use AI Overviews to their advantage, by appearing in them, to improve performance going forward.

Greater Competition for Citation

One of the ways to address declining click-through-rates for sites, as mentioned previously, is to appear in the AI Overviews themselves. AI Overviews often collate answers from several web pages and incorporate links showing users where they have collected the information from. However, because AI Overviews use fewer links than traditional Google searches, it is a more competitive space.

One of the ways marketers are finding to appear in AI Overviews is by focusing on answering-based content. Answering users’ questions explicitly and in a clear and concise manner gives marketers a much better chance of appearing in AI Overviews. 

What this means for Marketers

Ranking in AI Overviews is fairly similar to ranking in traditional search in that search engines still value authority and credibility of sources, users have to slightly shift how they produce content to more clear answer-focused content that can pull through directly into AI Overviews if they are going to battle a declining click-through-rate.

Is there a gender bias in digital marketing? (the hidden agenda)

When we think of gender bias, we often think of the pink tax on products. The disparity in cost between products marketed to men and women, where a simple change in colour or packaging can increase the price without adding real value.

And, while that is a common issue with gender-bias in marketing, there are more subtle ways that brands tend to sneak in gender-based biases.

·         Imagery – men are frequently positioned as leaders or decision-makers, while women are portrayed as caregivers or supporters

·         Product naming – phrases like “boss babe” aimed at women, compared with words like “power,” “defence,” or “extreme” used for men

·         Tone of voice – messaging directed at women may emphasise emotion or appearance, while men are addressed through performance or strength

·         Audience assumptions – marketers often assume who the decision-maker is based on gender

The cost of business when being gender biased

Gender bias affects businesses in two key ways: brand perception and commercial performance.

When marketing relies on gender-exclusive messaging, brands risk alienating part of their potential audience. Excluding prospective customers ultimately limits revenue opportunities.

Consumers who feel misrepresented or overlooked may disconnect from the brand, eroding trust and reducing engagement. This is particularly relevant given generational shifts around diversity and inclusion, where younger audiences increasingly expect brands to reflect broader identities.

Perhaps the greatest commercial risk is competitive disadvantage. As more brands prioritise inclusive messaging, those that fail to adapt may lose relevance and market share.

Algorithmic bias

Algorithmic bias occurs when AI systems are trained on historical data that already contains social biases. As a result, these algorithms can unintentionally reinforce stereotypes or existing inequalities.

In digital marketing, this may influence who sees certain advertisements, which audiences are targeted, or how content is distributed. In some cases, biased datasets have resulted in patterns that exclude minority groups or reinforce existing disparities.

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Intersectionality: Gender isn’t one-dimensional

Women are more than just their gender. While this may seem obvious, many digital campaigns still fail to reflect that reality.

Intersectionality recognises that people’s identities are shaped by multiple factors, such as age, ethnicity, ability, and social background. These dimensions influence how individuals experience the world and how they respond to marketing messages.

When campaigns treat women as a single, uniform audience, they risk overlooking the diversity of real experiences. A one-size-fits-all approach cannot reflect the wide range of identities that exist within any demographic.

Understanding intersectionality allows marketers to design campaigns that resonate more authentically with their audiences. Ignoring these identity dimensions, however, risks creating content that feels disconnected from the people it aims to reach.

The February Open Event for the Leiths School of Food and Wine marked a key moment in the recruitment cycle for the newly evolved Culinary Diploma. With the course redesigned to offer greater flexibility, affordability, and choice for aspiring chefs, the objective for this year’s marketing activity was clear: reach a wider audience and convert interest into meaningful engagement.

Working closely with Leiths, Intelligency Group led the paid media strategy to maximise visibility for the Open Event and drive high-quality registrations. The results demonstrated strong demand for the programme and significant improvements in campaign efficiency compared to previous years.

Strong Attendance from High-Intent Audiences

The February campaign generated hundreds of leads, resulting in record attendance for the Open Event and achieving a lead-to-attendee conversion rate of 37.82%. This represents a substantial improvement on the previous year and highlights a growing level of commitment among prospective students who registered to learn more about the diploma.

Over the last year, we have consistently observed the same registration pattern across our open days. Interest typically builds gradually during the early weeks of promotion before accelerating sharply in the days leading up to the event. The most significant spike in registrations usually occurs three to five days before the event, highlighting the impact that proximity and urgency have on prospective students’ decision-making.

Based on this insight, we have sought to optimise our budget allocation for each open day this year by concentrating spend closer to the event date, when audiences are most likely to convert. As a result, the majority of investment was placed in the final two weeks of promotion, aligning activity with the period of highest conversion efficiency and ultimately contributing to the results achieved.

Google Ads: The Standout Channel

Across the campaign, Google Ads emerged as the most effective paid acquisition channel. The platform generated record numbers of leads and attendees, delivering the highest conversion rate of 43.43% among all marketing channels.

This performance underscores the strength of search-based marketing in reaching audiences actively researching culinary education and career pathways. By capturing users already demonstrating intent, such as those searching for professional cooking courses or culinary diplomas, Google enabled the campaign to reach potential students at the most decisive stage of their decision-making journey.

Overall, the campaign maintained a strong blended cost per lead of £22.77, demonstrating that high-quality engagement could be achieved even with a modest budget and limited flight time.

A Clear Year-on-Year Improvement

When compared to February 2025, the campaign delivered significantly stronger outcomes despite similar lead volumes. Attendance increased and our lead-to-attendee conversion rate rose from +14%pp.

At the same time, marketing efficiency improved, with both cost per lead and cost per attendee decreasing compared to the previous year.

From October–February 2025/26, a total ad spend saw increased ROAS from 15.32 to 27.81 (81.5% improvement compared to the same October–February period in 2024/25), driven by our optimisation of audiences and keywords, alongside leveraging warm audiences and activating new cold audiences.

Supporting the Next Generation of Chefs

With the Culinary Diploma now offering greater flexibility, more accessible pricing, and new finance options, the programme is opening the door to a broader audience of aspiring chefs.

The success of the February Open Event demonstrates how a focused paid media strategy, particularly through high-intent platforms like Google, can effectively connect prospective students with one of the world’s most respected culinary schools.

For Intelligency and Leiths alike, the event represents a strong foundation for continued growth throughout the recruitment cycle. 

Google has quietly updated its Google Ads support contact form to include a mandatory checkbox that advertisers must tick before submitting a help request, and that box gives Google the right to make actual changes inside your ad account. The update was first spotted on social media by PPC specialist Arpan Banerjee and reported by Search Engine Roundtable. The wording says you authorise a “Google Ads specialist on behalf of your company to make the changes above directly to your company’s Google Ads account to reproduce and troubleshoot the issue.” But crucially, it also clarifies that any such changes are undertaken at your own risk and that Google doesn’t guarantee any particular outcome.

Why This Matters to Marketers

This tweak effectively forces advertisers to permit Google to touch live campaigns before receiving human support. Without checking the box, you cannot submit a support request at all, so advertisers now face a trade-off between getting help and maintaining full control over their account settings. The fine print makes it clear that any negative impact on campaign performance or spend is the advertiser’s responsibility, not Google’s.

Industry reaction has been mixed. Some view it as a troubling shift toward less control and more risk for advertisers, while others point out (as Google’s Ads Liaison did on X/Twitter) that this practice has been in place for years, with advertisers already requesting changes from support.

How Advertisers Should Approach It

For PPC managers and business owners, this change underscores the importance of precise problem descriptions and careful monitoring. When submitting a support request under this new system, it’s wise to:

• Document your current account settings and performance before you submit anything.
• Write very specific issue descriptions so the scope of authorised changes is clear.
• Monitor changes closely after support interaction to catch unintended impacts quickly.

The update also highlights the value of exploring alternatives like Google’s help documents, community forums, or working with certified partners who can navigate support channels.

Historic social media vetting is the process of reviewing an individual’s past online activity to assess potential reputational, ethical or operational risk. It goes beyond scanning recent posts. In many cases, organisations analyse years of content across multiple platforms, including comments, images, shared articles and public interactions.

The growth of this practice reflects a structural shift in how reputation works. Social media is now an informal archive of personal opinion and behaviour. Content that once felt fleeting can resurface in moments of heightened scrutiny, often detached from context.

For businesses, this creates a new category of due diligence. Digital history is treated as part of professional identity, sitting alongside CVs, references and formal background checks. The application of this vetting varies across sectors, but the strategic intent is consistent: reduce avoidable risk.

Professional sport: protecting club brands and sponsorship value

In professional sport, historic social media vetting has become standard practice in athlete recruitment. Players are commercial assets as much as competitors. Their public image influences ticket sales, sponsorship contracts and media narratives.

Clubs typically review an athlete’s online history for red flags that could conflict with organisational values. These may include:

• Past discriminatory language or content
• Public disputes
• Endorsements of controversial causes
• Criticism of players, managers, staff or regulatory bodies
• Content that may cause embarrassment for the club or player

The review is rarely limited to the previous season. Digital footprints from academy years are often examined. The consequences of overlooked content can be severe. Sponsors may withdraw. Fan bases can fracture. Governing bodies may intervene.

As a result, sport has moved towards a preventative model. Many academies now incorporate digital literacy into player development programmes. The message is clear: online conduct is inseparable from professional responsibility.

Corporate recruitment and executive social media screening

In corporate settings, historic social media vetting is most visible in hiring, particularly for senior leadership roles. When a new CEO or board member is announced, external scrutiny is immediate. Journalists and competitors often conduct their own digital searches within hours.

To mitigate risk, organisations increasingly carry out structured digital due diligence before appointments are finalised. This review may explore tone, consistency and judgement rather than focusing solely on isolated controversial posts.

In practical terms, screening often considers:

• Evidence of harassment or discriminatory commentary
• Disclosure of confidential or commercially sensitive information
• Repeated engagement in extreme or polarising debates

However, responsible employers avoid blanket assumptions. Context matters. A single poorly worded comment does not necessarily indicate character risk. Patterns of behaviour are typically more significant than individual posts.

The aim is not to police personal opinion but to assess whether public conduct aligns with corporate culture and stakeholder expectations.

Financial services and investor due diligence

Within financial services, historic social media vetting has been integrated into investment due diligence frameworks. Investors assessing founders or executive teams increasingly consider digital behaviour alongside financial metrics.

From an investor perspective, social media can reveal attitudes towards governance, regulation and accountability. It can also signal temperament. Publicly reckless or inflammatory commentary may suggest reputational volatility.

In this sector, digital screening often complements more traditional risk assessments. For example:

• A founder’s online commentary may be cross-referenced with regulatory filings
• Public disputes may indicate potential litigation risk
• Contradictions between stated values and digital behaviour may raise governance concerns

Financial institutions themselves apply similar scrutiny internally. Employees in regulated roles may be subject to conduct policies that extend to public platforms. In markets built on trust, digital history is increasingly regarded as part of professional integrity.

Media, entertainment and influencer brand alignment

The media and entertainment industries operate under intense public visibility. For actors, presenters and influencers, historic social media content can significantly influence casting and endorsement decisions.

Before signing contracts, brands and agencies frequently conduct digital audits. These reviews assess not only past posts but the broader narrative an individual has built online. Consistency, tone and long-term positioning are all considered.

Unlike other sectors, vetting here serves a dual purpose. It identifies risk, but it also identifies opportunity. A creator with a sustained record of advocacy for mental health or sustainability may be an ideal partner for a brand with similar priorities.

At the same time, resurfaced historic content can derail campaigns quickly. In influencer marketing, especially, authenticity is central to audience trust. If past behaviour contradicts present messaging, credibility suffers.

This has led to more nuanced screening processes that evaluate digital identity holistically rather than through isolated incidents.

Government, public sector and security clearance

In government roles, historic social media vetting is often embedded within formal security clearance procedures. Positions involving national security, sensitive infrastructure or classified information require comprehensive background assessments that include digital activity.

Authorities may examine public content for signs of extremist affiliation, hostility towards protected groups or susceptibility to coercion. The stakes extend beyond organisational reputation to national interest.

Political life offers a parallel example. During election campaigns, candidates’ historic posts are routinely surfaced and analysed. In many cases, digital history shapes public perception as strongly as policy proposals.

For public institutions, legitimacy is foundational. Digital vetting has therefore become part of broader integrity and trust frameworks.

Small businesses and franchise networks: localised reputation risk

Historic social media vetting is not limited to multinational corporations. Small businesses and franchise networks are increasingly aware of the reputational impact of individual behaviour.

In franchise systems, one operator’s controversial post can affect the entire brand. Franchisors may therefore review digital footprints before approving new partners. Similarly, small business owners often assess the online presence of potential hires, particularly in customer-facing roles.

At a local level, reputational damage can spread rapidly through community forums and review platforms. The scrutiny may be informal, but the commercial consequences are real.

This demonstrates that digital risk is not solely a concern for high-profile industries. It applies wherever trust underpins customer relationships.

Ethical challenges and proportionality in social media background checks

As historic social media vetting becomes more widespread, ethical considerations intensify. Businesses must ask difficult questions about proportionality and fairness.

Key issues include:

• How far back should reviews reasonably extend
• Whether deleted content should influence decisions
• How to distinguish between isolated mistakes and consistent harmful behaviour

Data protection laws in the UK and Europe impose legal constraints on how personal data can be processed. Organisations must ensure that screening practices are transparent, relevant and non-discriminatory.

There is also a broader cultural conversation about redemption. Digital footprints capture moments in time, not necessarily enduring values. Many organisations are beginning to emphasise patterns and evidence of growth rather than relying on single historical posts.

The evolving future of historic social media vetting

Historic social media vetting is likely to become more technologically advanced in the coming years. Artificial intelligence tools already assist in scanning large volumes of content and identifying potential risk indicators.

However, automation cannot fully replace human judgment. Context, humour and cultural nuance are difficult to interpret algorithmically. Businesses will need to balance efficiency with thoughtful review.

Across sectors, from sport to finance, education to government, digital history has become inseparable from professional identity. The application of historic social media vetting will continue to evolve, but its underlying role is now clear.

In the digital age, the past remains accessible. For businesses, understanding that the past is not about surveillance. It is about managing risk, protecting reputation and ensuring that public conduct aligns with organisational values.