From Keywords to Conversations: How SEO Evolved into GEO in the Age of Generative AI
The search landscape has undergone a seismic shift. What once revolved around optimizing for keywords and backlinks has transformed into something fundamentally different: Generative Engine Optimization (GEO).
This evolution isn't merely incremental—it's revolutionary, reshaping how brands connect with audiences online.
The AI-Driven Search Revolution
Generative AI has permanently altered how we search for information online. The traditional model of typing keywords and sifting through blue links has given way to conversational interfaces that deliver direct, synthesized answers.
This shift goes beyond cosmetic changes. Search engines now understand context and user intent rather than just matching keywords. They provide AI-generated summaries pulling from multiple sources, creating a more intuitive, interactive experience that feels less like searching and more like having a conversation with a knowledgeable assistant.
Why GEO Demands Your Attention Now
For businesses, this transformation isn't optional—it's existential. Here's why GEO should be on every marketer's priority list:
· The zero-click reality. AI-generated answers often provide users with comprehensive responses, reducing the need to click through to websites. This creates a challenging new environment where visibility doesn't automatically translate to traffic.
· Citation economics. Your content's value is increasingly measured by whether AI systems deem it worthy of citation in their generated answers. Without optimizing for these citations, your carefully crafted content may never reach your audience.
· Authority is the new currency. Generative AI prioritizes sources that demonstrate genuine expertise and depth. Surface-level content optimized for traditional SEO metrics simply won't cut it anymore.
· Personalization at scale. GEO enables more tailored, relevant experiences by better understanding specific user contexts and needs, creating opportunities for deeper engagement—if you know how to leverage them.
· Reimagining Content Strategy for GEO Success
Successful GEO requires a fundamental shift in how we approach content:
o From keywords to comprehensive answers. Instead of structuring content around keywords, focus on thoroughly addressing the questions and needs behind those queries. Provide depth, context, and genuine value.
o Structure for AI comprehension. Clear headings, concise paragraphs, bullet points, tables, and semantic markup aren't just good for human readers—they make your content more easily parsed and referenced by AI systems.
o Multimedia integration. High-quality images, infographics, and videos don't just engage users; they provide additional context that helps AI understand and accurately represent your content.
o Data-driven authority. Incorporate up-to-date statistics, credible citations, and expert quotes to signal trustworthiness and establish your content as a primary reference source.
o Comparison and explainer content. Formats like comparison blogs, FAQs, and step-by-step guides directly answer user queries and are easily referenced by AI for concise summaries.
· What Hasn't Changed (And Never Will)
Despite these transformations, certain fundamentals remain non-negotiable:
o Quality still reigns supreme. Whether for human readers or AI systems, well-researched, thoughtfully crafted content that provides genuine value will always outperform shallow alternatives.
o User experience matters. Responsive design, fast load times, and intuitive navigation remain essential for converting visitors once they do click through to your site.
o Trust and credibility. Building authority through consistent expertise and reliability continues to be the foundation of digital success.
o Brand identity. Your unique voice and perspective remain critical differentiators in a landscape of AI-generated summaries.
SEO vs. GEO: Key Differences and Future Preparation
The transition from SEO to GEO represents a paradigm shift in digital marketing:
To future-proof your digital presence:
Audit your content for AI-readability. Is it structured logically? Can key points be easily extracted?
Develop topic authority. Create comprehensive content clusters around your core areas of expertise rather than disconnected, keyword-driven pages.
Integrate multimedia strategically. Use visuals not just for engagement but to enhance comprehension and context.
Focus on being citation-worthy. Ask not just "Will this rank?" but "Is this the best possible answer that deserves to be cited?"
Balance technical optimization with content quality. Continue technical SEO best practices while prioritizing depth and authority.
SEO vs. GEO in Practice: A Side-by-Side Comparison
Traditional SEO Approach:
"Best Budget Smartphones 2025 [Ultimate Guide]"
Looking for the best budget smartphones in 2025? Our comprehensive guide breaks down the top affordable smartphones on the market today. From camera quality to battery life, we've analyzed every feature to help you find the perfect budget-friendly phone. Read on to discover our top picks for every price point!
[Keyword-stuffed introduction followed by a list of phones organized primarily for keyword coverage rather than user needs]
GEO-Optimized Approach:
"Budget Smartphone Comparison: Performance, Features, and Value in 2025"
Which budget smartphones offer the best balance of performance and value in 2025? We've tested 23 models under $300 to determine which deliver exceptional experiences despite their lower price points.
Our analysis focuses on four key metrics:
• Real-world battery life (measured through standardized testing)
• Camera quality in various lighting conditions
• Processing performance during multitasking
• Build quality and durability
Key findings:
[Data-driven comparison table with clear performance metrics]
For users prioritizing camera quality, the [Phone A] consistently produced the most accurate colors and sharpest details in our controlled testing environment, though it sacrifices about 2 hours of battery life compared to our overall top pick.
[Continues with specific, factual insights organized by user priorities rather than keywords]
The GEO approach emphasizes structured data, factual depth, and organization around user needs rather than keywords—exactly what generative AI values when selecting sources to cite.
The Path Forward
The evolution from SEO to GEO doesn't represent the death of search optimization—it signals its maturation into something more sophisticated and user-centric. By understanding these shifts and adapting strategically, forward-thinking marketers can position their content to thrive in this new landscape.
The future belongs to those who create content that deserves to be found—not because it's engineered for algorithms, but because it provides genuine value, demonstrates true expertise, and answers user questions more effectively than the competition.
Start implementing these GEO strategies today, and you'll build a foundation for sustainable digital visibility and presence in the age of generative AI.
Mad About Marketing Consulting
Advisor for C-Suites to work with you and your teams to maximize your marketing potential with strategic transformation for better business and marketing outcomes. We are the AI Adoption Partners for Neuron Labs and CX Sphere to support companies in ethical, responsible and sustainable AI adoption. Catch our weekly episodes of The Digital Maturity Blueprint Podcast by subscribing to our YouTube Channel.
Citations:
Bridging the Data Divide: The Untapped Power of Integrated Marketing and Customer Data
In the data-rich landscape of modern business, a curious paradox persists. While companies amass unprecedented volumes of customer information, they often operate with a fragmented view of their customers' journeys. Marketing teams track campaign metrics in isolation, while customer experience or service departments maintain separate records of interactions. The result? A disjointed understanding that undermines the potential for truly personalized experiences.
The Persistent Gap in Journey Mapping
Most organizations still maintain artificial divisions between marketing data (impressions, clicks, campaign performance) and customer-level information (purchase history, service interactions, preferences). This separation creates blind spots in journey mapping, where:
Marketing teams see campaign touchpoints but miss post-purchase experiences
Customer service lacks visibility into which marketing messages customers have encountered
Product teams develop features without complete context of acquisition channels
Finance departments struggle to connect marketing investments to customer lifetime value
The persistence of these silos isn't merely an organizational inconvenience—it's a strategic liability that prevents companies from delivering coherent customer experiences.
The Dual-Lens Advantage: Why Both Journeys Matter
When businesses integrate marketing and customer data, they gain a holistic view that reveals insights neither dataset could provide alone:
Enhanced Attribution Understanding By connecting pre-purchase marketing touchpoints with post-purchase behavior, companies can finally answer the elusive question: "Which marketing investments truly drive long-term customer value?" This moves beyond simplistic last-click attribution to a more sophisticated understanding of influence across the entire journey.
Contextual Personalization When customer service representatives can see which marketing campaigns a customer has engaged with, or marketing teams can target based on service history, personalization becomes meaningful rather than mechanical. This contextual awareness transforms generic interactions into genuinely helpful engagements.
Predictive Capabilities Combined datasets provide the foundation for predictive models that can anticipate customer needs based on patterns across both marketing engagement and customer behavior. This anticipatory approach allows businesses to be proactive rather than reactive.
Operational Efficiency Breaking down data silos enables organizations to eliminate redundant efforts across departments. The efficiency gains extend beyond marketing—informing product development, inventory management, and resource allocation.
Defining the 360-Degree Customer Profile
The term "360-degree view" has become something of a business cliché, but its essence remains valid. A true 360-degree customer profile integrates:
Identity Information: Who they are (demographics, psychographics)
Interaction History: How they've engaged (website visits, app usage, store visits)
Transaction Records: What they've purchased (products, services, frequency)
Marketing Exposure: Which campaigns they've seen (ads, emails, social)
Feedback Data: What they've said (reviews, survey responses, support tickets)
Social Sentiment: How they talk about your brand publicly (mentions, comments, shares)
Contextual Factors: Relevant environmental conditions (location, season, economic indicators)
Predictive Indicators: Likelihood of future behaviors (churn risk, upsell potential)
The power lies not in collecting these data points separately but in connecting them to reveal the interplay between different aspects of the customer relationship.
Common Challenges in Integrating Online and Offline Data
Despite its clear benefits, implementing a truly integrated view faces several persistent challenges:
Technical Hurdles
Data Architecture Limitations Legacy systems often weren't designed for cross-channel data integration, creating fundamental structural barriers to unified views.
Identifier Fragmentation Tracking the same customer across devices, platforms, and physical locations requires sophisticated identity resolution capabilities many organizations lack.
Real-Time Processing Constraints Meaningful personalization requires rapid data processing, but many systems struggle with the velocity requirements of true omnichannel integration.
Organizational Barriers
Departmental Silos When marketing, sales, and customer service operate as separate fiefdoms with distinct KPIs, data integration becomes politically challenging.
Skills Gaps Many organizations lack the analytical talent to extract meaningful insights from integrated datasets, even when technically available.
Budget Allocation Conflicts Investment in data integration infrastructure often falls between departmental boundaries, making funding difficult to secure.
Compliance Complexities
Regulatory Restrictions Privacy regulations like GDPR and CCPA create legitimate constraints on how customer data can be integrated and utilized.
Consent Management Tracking consent preferences across channels adds another layer of complexity to integrated data management.
Practical Approaches to Integration
Despite these challenges, forward-thinking organizations are making progress through several strategic approaches:
Technical Solutions
Customer Relationship Management (CRM) as Integration Hub Modern CRM platforms have evolved far beyond basic contact management to become central nervous systems for customer data integration. When properly implemented, a robust CRM serves as the authoritative record of customer interactions, providing:
Unified contact records that marry transaction history with marketing engagement
Workflow automation that bridges departmental processes
Integrated service ticketing that maintains contextual awareness
Custom objects that capture industry-specific relationship nuances
The true power of contemporary CRM lies not in contact storage but in relationship orchestration across marketing, sales, and service functions.
Customer Data Platforms (CDPs) Purpose-built integration platforms that unify customer data from disparate sources provide the technological foundation for integrated views. While CRMs excel at structured relationship data, CDPs specialize in:
Anonymous-to-known identity resolution
Behavioral event processing at scale
Real-time audience segmentation
Cross-channel identity stitching
The most sophisticated organizations leverage both CRM and CDP capabilities in complementary fashion.
Social Listening Integration
Forward-thinking brands are now connecting social listening platforms directly to their customer data infrastructure. This integration transforms scattered social mentions from marketing curiosities into actionable relationship intelligence by:
Mapping public conversations to individual customer records
Identifying advocacy potential among existing customers
Spotting service recovery opportunities before formal complaints
Detecting emerging sentiment shifts within specific customer segments
When social listening moves beyond the marketing department to inform customer experience strategy, companies gain unprecedented insight into unstructured feedback that would otherwise remain invisible.
Unique Identifier Strategies Implementing consistent customer identification methods across channels (like logged-in experiences, loyalty programs, or sophisticated identity resolution) creates the connective tissue between datasets.
API-First Architecture Moving toward flexible, API-driven systems enables more seamless data exchange between previously siloed platforms.
Organizational Strategies
Cross-Functional Teams Creating dedicated teams with representation from marketing, product, and customer service ensures integrated data serves multiple stakeholders.
Unified Metrics Developing shared KPIs that span traditional departmental boundaries encourages collaborative data utilization.
Data Democratization Implementing self-service analytics tools makes integrated customer data accessible to business users across the organization.
How Generative AI Transforms Integrated Journey Analysis
The emergence of generative AI represents a step-change in how organizations can leverage integrated customer and marketing data:
Enhanced Pattern Recognition
AI excels at identifying complex correlations within large datasets that human analysts might miss. By processing integrated marketing and customer data, generative AI can reveal subtle journey patterns and unexpected causal relationships that drive business outcomes.
Social Sentiment Analysis at Scale
Generative AI has fundamentally transformed social listening capabilities, evolving them from basic keyword monitoring to sophisticated sentiment understanding. Today's AI systems can:
Process millions of unstructured social conversations to extract meaningful patterns
Distinguish between casual mentions and urgent service needs
Identify emerging reputational threats before they become crises
Map social sentiment to specific product features, marketing messages, or customer segments
When integrated with structured customer data, this AI-powered social intelligence creates unprecedented visibility into how public sentiment influences individual customer journeys.
Natural Language Interfaces
Gen AI systems can translate technical data queries into natural language, making integrated journey data accessible to business users without SQL expertise. Marketing managers can simply ask questions like "Show me customers who engaged with our social campaign but didn't complete purchase" and receive meaningful visualizations.
Predictive Journey Orchestration
Beyond analysis, generative AI can recommend next-best actions based on integrated journey patterns. This enables real-time journey orchestration that adapts to emerging customer behaviors rather than following rigid campaign rules.
Automated Insight Storytelling
Perhaps most powerfully, generative AI can transform raw journey data into narrative insights that explain customer behavior in business context. Instead of presenting disconnected metrics, AI can generate explanatory narratives that help teams understand why certain journey patterns emerge.
Simulation Capabilities
Advanced generative AI systems can simulate how changes to marketing tactics or customer service approaches might influence end-to-end customer journeys, creating virtual "journey labs" for testing strategies before deployment.
Moving Forward: The Integration Imperative
The competitive advantage of integrated customer and marketing data will only grow more significant as customer expectations continue to rise. Organizations that bridge this divide will deliver more coherent experiences, allocate resources more effectively, and build deeper customer relationships.
The journey toward integration is neither simple nor quick, but it is essential. By acknowledging the current gaps, addressing the challenges systematically, and leveraging emerging technologies, businesses can transform fragmented customer understanding into a genuine competitive advantage.
In a landscape where customer experience increasingly determines market success, the ability to see and respond to the complete customer journey may be the most valuable capability an organization can develop.
Mad About Marketing Consulting
Advisor for C-Suites to work with you and your teams to maximize your marketing potential with strategic transformation for better business and marketing outcomes. Catch our weekly episodes of The Digital Maturity Blueprint Podcast by subscribing to our YouTube Channel.
Citations:
Marketing and Customer Analytics: 8 Integration Techniques - Insight7 https://insight7.io/marketing-and-customer-analytics-8-integration-techniques/
Use Case: Combining First and Third-Party Data - Revelate https://revelate.co/use-cases/combining-first-and-third-party-data/
The Power of Marketing Data Integration: Boosting Business Success https://diggrowth.com/blogs/data-management/marketing-data-integration-for-business-success/
Data Analytics and Market Research: How to Combine Them - Insight7 https://insight7.io/data-analytics-and-market-research-how-to-combine-them/
Integrate Strategies for Best Online and Offline Marketing https://www.digitalauthority.me/resources/strategies-connecting-online-offline-marketing/
Unlocking the next frontier of personalized marketing | McKinsey https://www.mckinsey.com/capabilities/growth-marketing-and-sales/our-insights/unlocking-the-next-frontier-of-personalized-marketing
The Art of the Queue: How Brands Turn Waiting Lines into Marketing Gold
In an era of instant digital gratification, there's something peculiarly fascinating about seeing hundreds of people voluntarily waiting in line for hours or even days. From the latest iPhone launches, exclusive streetwear drops to a seemingly humble bubble tea, these queues have become a powerful marketing phenomenon that continues to shape consumer behavior and brand perception.
The Strategic Queue: A Marketing Masterstroke or A Tacky Stunt?
Yes, companies do pay people to queue for their launches – a practice known as "line sitting" or "professional queuing." This tactic has evolved from a spontaneous occurrence into a sophisticated marketing strategy that creates buzz, generates media attention, and fuels FOMO (fear of missing out) among consumers.
Masters of the Queue: Brands That Set the Standard
Several brands have perfected the art of queue-based marketing:
1. Apple: The tech giant's iPhone launches are legendary, with companies paying line-sitters $100-250 per day. Apple subtly encourages these queues by providing amenities to these sitters and having staff engage with the crowds, creating a festival-like atmosphere.
2. Supreme: The streetwear brand has built its entire business model around artificial scarcity and long lines. The "Supreme drop" has become a cultural phenomenon, with professional line-sitters earning substantial amounts to wait for limited releases.
3. Gaming Console Launches: Both Sony and Microsoft orchestrate elaborate launch events for their PlayStation and Xbox releases, combining long queues with midnight launch parties and exclusive giveaways.
4. F&B Launches: Food and beverage is an essential item and in places where they are the first to be launched in the country, especially if it’s a renowned brand elsewhere, be it doughnuts, cream puffs, burgers or bubble tea, you can expect queues of people that help add to the hype of the official launch. Some are puzzling while some might be ‘genuine’ buzz created organically; you be the judge of that!
The Asian Queue Revolution
The practice of professional queuing has reached new heights in Asia, where it's not just a marketing tactic but a legitimate service industry:
Japan
- Professional line-sitters ("yoyaku-tetsuke") are in high demand for limited-edition food items and restaurant openings
- Sushiro famously paid people to form queues when launching new locations to create a "popular restaurant" image
- Pokemon merchandise releases regularly generate massive queues
China
- "Paipai" (professional queuers) are organized through sophisticated apps and WeChat groups
- Luxury brands frequently employ this tactic for product launches
- Real estate developers use paid queuers to create artificial buying frenzies
- Some malls and restaurants hire fake customers to appear consistently busy
Singapore
- The "kiasu" (fear of missing out) culture drives queue marketing
- Property launches and restaurant openings regularly employ professional queuers
- The Shake Shack opening saw paid queuers waiting for days
- Hello Kitty promotions at McDonald's led to the development of professional queue management systems
The Rise of Queue-as-a-Service
A fascinating spin-off of this phenomenon is the emergence of professional queuing services where consumers pay others to wait in line for them. In Bangkok, "queue-fixers" charge around 700 baht ($27) to secure spots at popular Michelin-starred restaurants. Singapore's iQueue startup offers services ranging from $20 for one hour to $250 for 18 hours of queuing.
Digital Evolution: The Virtual Queue
Modern brands have adapted queuing psychology to the digital realm:
- Harry's razor company generated 100,000 sign-ups in a week through a virtual waiting list
- Robinhood gained nearly a million users pre-launch through a gamified referral queue system
- Monzo created engagement through a transparent waiting list where users could see their position
Effectiveness and Considerations
When executed well, queue marketing can:
- Generate substantial earned media coverage
- Create social proof of product demand
- Build community among brand enthusiasts
- Drive social media engagement through user-generated content
- Establish product exclusivity and desirability
Key Considerations Before Implementation
It might sound like a quick win and low hanging fruit to take advantage of but is it suitable for all brands?
1. Authenticity: While paid queuers can jumpstart interest, the strategy works best when there's genuine consumer demand to sustain it.
2. Market Fit: Queue marketing is most effective for products with strong appeal against scarcity and/or affordability.
3. Cultural Context: What works in Singapore might not work in New York – understand your market's relationship with the queuing culture.
4. Resource Management: Ensure proper crowd management, safety measures, and amenities for waiting customers as this might backfire on you socially if the other organic customers are unhappy and start complaining.
5. Digital Integration: Consider how physical queues can be amplified through social media and digital engagement.
6. Brand Alignment: The strategy should align with your brand's positioning and values. Not all brands think “queues” equal desirability.
How This Trend will Evolve
As consumer behavior continues to evolve, the art of queue marketing adapts accordingly. While some brands are moving away from physical queues in favor of digital alternatives, others find continued value in creating these obvious spectacles of demand.
The key lies in understanding your audience and crafting experiences that transform the simple act of waiting into a memorable brand moment. Hai Di Lao does this pretty well and turn it into almost like their trademark queuing experience for customers by providing them with snacks, refreshments and even nail services.
Whether physical or digital, the psychology behind queue marketing remains powerful: people value what they have to wait for, and the sight of others waiting makes us wonder what we might be missing out on.
Mad About Marketing Consulting
Advisor for C-Suites to work with you and your teams to maximize your marketing potential with strategic transformation for better business and marketing outcomes.
Citations:
https://kickofflabs.com/blog/5-small-businesses-made-it-big-with-prelaunch
https://www.prefinery.com/blog/referral-programs/prelaunch-campaign/examples/saas/
https://www.convinceandconvert.com/digital-marketing/how-to-create-buzz/
https://fastercapital.com/topics/creating-a-buzz-with-exclusive-launch-events.html
https://viral-loops.com/blog/buzz-marketing/
https://queue-it.com/blog/influencer-marketing-strategy-product-launch/
https://www.straitstimes.com/asia/se-asia/queue-fixers-help-tourists-stomach-long-lines-at-bangkok-s-michelin-rated-eateries
https://newsroom.airasia.com/news/2023/3/2/say-goodbye-to-restaurant-queues-with-airasia-super-apps-queuing-service
https://sg.news.yahoo.com/new-service-singapore-lets-pay-someone-queue-100357551.html
https://www.asiaone.com/business-wires/because-everything-also-need-queue-singapore-startup-will-do-it-you-20-hour
https://cnalifestyle.channelnewsasia.com/living/htb-service-help-buy-professional-queuer-concert-tickets-392956
Everyone Loves Some Data But…
The million dollar question is - what exactly do you want to get out of the data?
Everyone has been talking about data for a good decade or so and depending on your level of data maturity, you are either still trying to find where are all of your data sources are located or you are now trying to monetize the insights gathered from your data.
Woe to you if you’re in the former bucket but no surprise many organizations, especially non digital native ones are still sadly in this bucket. Wow to you if you’re in the latter bucket, so what can you do to monetize it?
Customer data platforms, data management platforms and customer relationship management platforms suddenly became the talk of town thanks to Google’s flippant stance on third party cookies, that kept rolling back and back. Companies realized their archaic customer data collection methods and storage methods (often just in excel spreadsheets (horrors!)) are not quite cutting it.
Some are even confusing the whole customer data terminology and what it means when we talk about cookies, first party data, third party data and personal information level data. Some have all but sitting in silos or disconnected platforms that don’t talk to each other while others have none (more horrors!).
Some used to think a good data visualization and analytical tool is the holy grail to get all the answers they need by simply plugging it onto of their so-called data sources. But they soon wonder - how to plug, what to plug, where to plug and why can’t it just be plugged and played?!
Things like:
is the data clean, updated or accurate?
is the data in the format that is even retrievable., extractable or readable?
do you even have the data sitting where you thought is sitting?
is your data even categorized in the logic, classification and format that is aligned with your decision-making algorithms?
million dollar question - what exactly do you want to get out of the data? What is the truth that you’re after?
If these were not considered before your so-called plug and play approach, then you get a ton of data yes and a ton of outputs yet but hardly any useful insights. You get more of what we call, data outputs in a format that looks like you just downloaded a gigantic excel spreadsheet or a bunch of fancy looking graphs to make you feel good about some visually appealing data formatted in a presentable manner
E.g. you might see things like:
xx customer transactions performed over xx period
xx customer spent over xx period
That is still not data insights, it’s just data outputs telling you how many transactions and spent over a certain period of time. What are you going to do with that without other insights around:
who are these customers in terms of their interests and life stage needs and what is the co-relation between this and what they are spending versus not spending on?
what did they exactly spend on and why that might be the case?
what are their other needs and what is the possibility for that?
what else have they spent on and why that might be the case?
are they spending more or less on the same products/period and why that might be the case?
The difference as you can see is in terms of the why and the co-relation between the transactional data and the rationale behind it.
We first need to know what it is that we want to see and how that will help us to better understand our customers’ behavior or potential to engage more with us. It helps to have these in mind, and then work backwards to derive what we then need to have in terms of data types and sources in order to arrive at the desired insights.
It’s equivalent to knowing what is that treasure you’re seeking for so you know which location, treasure map, equipment, skills, knowledge and coordinates to get there.
So, do you know the treasure you’re after?
About the Author
Mad About Marketing Consulting
Ally and Advisor for CMOs, Heads of Marketing and C-Suites to work with you and your marketing teams to maximize your marketing potential with strategic transformation for better business and marketing outcomes.
The Case of the Misunderstood MarTech and more…
Has marketing technology, content marketing and need for customer driven insights changed all that much in the last 4 years since I first wrote this post in 2020?
In 2020, I observed that companies were moving into Adobe experience management as their go-to content management platform. Come 2024, I am still experiencing some late bloomer companies especially in the content marketing game, now only moving into Adobe experience management or AEM for their content management platform in a bid to get ahead of the game in personalization of the customer experience and engagement.
They will soon be in for a surprise as AEM alone will not differentiate them from their competitors who are doing the exact same thing or have done the exact same thing as it’s after all a technology and a platform. It is merely an enabler but not the solution itself.
It doesn’t negate the need and the fact that it still boils down to having insightful and forward looking content that is useful to their customers. It certainly doesn’t negate the need for them to first have a close connection with their new and existing customers in order to know what kind of content matters to them above all the noise in the market. It certainly doesn’t remove the fact that you need a robust content pipeline to feed the hungry beast of a machine to fully maximize its capabilities especially in organic SEO and to supplement your SEM strategy.
That unfortunately is still a missing piece in lots of companies. Why is it so hard to get that thought provoking viewpoint? Why do so many so-called subject matter experts still behave and think they know it all when the truth is, they are merely regurgitating facts and what others are already saying or just passing the content strategy buck to their agencies? Why are companies who claimed to know their customers, not asking them the right questions in order to help them get the right answers?
Another common mistake is when companies don’t really know the full potential of a particular technology, including MarTech or marketing technology that they have and what they are investing in next.
What then happens is they start shopping for the next latest technology without first reviewing and fully understanding what they already have, how it’s being used, who has been using it and how it else it should actually be used. Often times, you’ll find the technology is perfectly fit for purpose but being used either by the wrong people or the wrong way. In addition, the existing organizational structure and culture might also not provide an ideal process of supporting its use.
But instead of changing that first, they start looking at the next big thing, adding to the mess of integration, implementation, adoption and usage problems that their employees and sometimes customers need to deal with. This leads to stack bloat.
4 years on and stack bloat is still a problem; in fact it has worsen and will continue to as even more MarTech tools get added to the market.
Therefore, instead of blindly investing in all sorts of MarTech tools and platforms, companies should also make sure they have the right objectives, people, processes and plans in place to fully maximize the capabilities of the MarTech. Else, they will end up with yet another white elephant and a misconception that it wasn’t a good enough technology. A case of the blind leading the blind is anything but fine.
Same goes for having the right expertise in who they hire to be thought leaders, spokespeople and making an effort to invest in getting consistent feedback and sentiments from both customers and prospects alike. This is to avoid an echo chamber situation, which is common in hierarchical organizations.
Ultimately, companies who wish to embark on their MarTech journey especially to better support their content marketing efforts need to look at it holistically and not cut corners on doing the needful. Start with your customers, then be clear with your objectives and then plan with a view to buffer for the what, who, where and how in terms of tools, processes and people in your organization.
About the Author
Mad About Marketing Consulting
Ally for CMOs, Heads of Marketing and C-Suites to work with you and your marketing teams to maximize your marketing potential with strategic transformation for better business and marketing outcomes.