Jun 13, 2025 26 reads

Effective Marketing during Ramadan: Strengthen Your Strategy with Insights and Trends

Effective Marketing during Ramadan: Strengthen Your Strategy with Insights and TrendsRamadan continues to be a period of significant changes in consumer behaviour. As daily lifestyles change, people's consumption habits evolve, but the underlying needs and motivations remain largely the same. By understanding these underlying needs and motivations, as well as the consumer's consumption journey, and developing marketing strategies specific to Ramadan, brands can successfully navigate one of the highest volume and most profitable periods of the year.First of all, we will examine how Ramadan consumer behaviour changes and category-based trends, and then we will take a detailed look at how we can shape marketing strategies during this period in the light of these insights and trends.Source: Google Ramadan Insights 2025How Does Consumer Behavior Change During Ramadan?Ramadan brings significant shifts in shopping habits and digital content consumption. According to Google Insights 2025, interest in Ramadan begins earlier each year. This highlights the importance of focusing not only on Ramadan itself but also on the Pre-Ramadan period.Key Areas of Interest Before Ramadan Increased searches related to home cleaning, kitchenware, and food stocking. A surge in interest in Ramadan packages, special recipes, and table decorations. Rising Mobile Usage and Content Consumption Screen time on mobile devices increases significantly. YouTube sees a more than 50% rise in views for cooking tutorials, series, and movies. Users spend more time on mobile devices, particularly during suhoor and iftar hours. Short-form video consumption reaches record levels. Category-Based Changes in Consumer Behavior Food and recipe searches peak in the first weeks of Ramadan. Interest in kitchenware and small home appliances rises. Skincare products experience the highest click-through rates of the year. Towards the end of Ramadan, fashion and footwear shopping spikes to levels comparable to Black Friday. How to Strengthen Your Marketing Strategy During RamadanPlan Your Campaigns and Promotions in AdvanceConsumers start shopping 2–3 weeks before Ramadan, making it essential for brands to: Launch early campaigns to be part of the decision-making process. Offer flexible payment options and special discounts to encourage conversions. Develop Ramadan-specific product bundles and promotional campaigns to stand out. Optimize Advertising Strategies for the Right Times and ChannelsSince mobile usage peaks during suhoor and iftar, brands should: Schedule advertising campaigns around these peak hours. Use Reminder Ads to keep promotions top-of-mind for consumers. Leverage specialized solutions like YouTube Select Ramadan Line-Up and TikTok Pulse Ramadan Category to target highly relevant audiences. Create engaging and interactive campaigns on TikTok, where 81% of users try new things inspired by Ramadan-related content. Enhance Your Video and Content StrategyThroughout Ramadan, consumers engage with: Cooking tutorials, personal vlogs, and content focused on spirituality and self-improvement. Influencer collaborations that help brands reach a broader audience. Short-form videos (YouTube Shorts, Instagram Reels) that drive dynamic engagement. Focus on Eid Shopping Sales of clothing, footwear, and gift items surge before Eid. Email and SMS marketing strategies can be used for timely reminders. Ramadan offers unique opportunities for brands to establish deep and meaningful connections with consumers. During this period, brands that correctly analyse changes in consumer behaviour and shape their strategies with these insights can stand out from the competition and create a loyal customer base.By adopting innovative and data-driven approaches during Ramadan, you can ensure that your brand builds stronger bonds with consumers and develops strategies in the spirit of this holy month.

Jun 13, 2025 106 reads

Google Meridian MMM & Facebook (Meta) Robyn MMM: In-depth Analysis of Modern Marketing Modeling Tools

In today’s marketing landscape, data-driven decision-making is a key factor in strategic success. Marketing Mix Modeling (MMM) is a crucial analysis technique that helps measure the impact of advertising expenditures on sales and other key performance indicators (KPIs). Traditionally, MMM was accessible only to large corporations with significant budgets, but thanks to open-source solutions, it has become available to businesses of all sizes.Google’s Meridian MMM and Meta’s Robyn MMM represent two advanced approaches to modern MMM. This article provides an in-depth comparison of these tools, examining their methodologies, use cases, advantages, and limitations.What is Marketing Mix Modeling (MMM)?MMM is a statistical regression technique that helps businesses understand how marketing activities influence sales, conversions, and traffic over time.Its core functions include: ✔ Measuring the impact of advertising spend, promotions, seasonality, and economic factors on sales. ✔ Modeling adstock (delayed effects) and saturation (diminishing returns) to better reflect long-term marketing impact. ✔ Providing predictive insights for budget allocation and strategic planning. Google Meridian MMM and Meta Robyn MMM offer different approaches to tackling these challenges.Google Meridian MMM: In-Depth ReviewKey Features & MethodologyLaunched by Google in 2024 as an open-source project on GitHub, Meridian MMM introduces a Bayesian regression approach, making it highly flexible in handling uncertainty and incomplete data.Key features include: ✔ Bayesian Regression: Provides probability distributions rather than fixed estimates, improving robustness with limited data. ✔ Adstock & Saturation Modeling: Captures how marketing spend accumulates over time and reaches diminishing returns. ✔ Geo-Level Analysis: Incorporates regional marketing data to evaluate local trends. ✔ Google Data Integration: Direct access to Google Search volume and YouTube reach data for more precise insights in digital advertising. Use Cases & AdvantagesMeridian is ideal for companies with strong in-house data science teams and those investing heavily in the Google ecosystem: ✔ Optimized for Google Ads & YouTube campaigns – making it particularly useful for brands reliant on these channels. ✔ Geo-specific modeling capabilities, helping businesses assess performance in different markets. ✔ Supports measurement-driven marketing cultures, where ROI and incrementality testing play a central role. Limitations & Challenges ❌ High technical requirements: Requires expertise in data engineering and advanced statistical modeling. ❌ Executive-level communication challenges: Outputs can be complex, making it harder to generate clear business insights for non-technical stakeholders. ❌ Documentation & adoption hurdles: Being a new solution, documentation and user guides are still evolving. Facebook (Meta) Robyn MMM: In-Depth ReviewKey Features & MethodologyMeta’s Robyn MMM modernizes MMM by incorporating advanced data science techniques while maintaining accessibility for a wider audience.Key features include: ✔ Prophet Integration: Automatically detects seasonality, trends, and holiday effects for more accurate forecasts. ✔ Ridge Regression: Reduces overfitting and improves generalizability. ✔ Hyperparameter Optimization: Uses Nevergrad for continuous model fine-tuning and performance enhancement. ✔ Adstock & Saturation Effects: Models how media spend drives sales over time and when additional spending loses efficiency. Use Cases & AdvantagesRobyn is particularly valuable for businesses looking to implement MMM with limited budgets: ✔ Open-source accessibility makes it an attractive choice for small and medium-sized businesses. ✔ Real-time optimization capabilities allow dynamic adjustments to marketing campaigns. ✔ Advanced visualization tools generate clear, easy-to-understand reports for marketing teams. Limitations & Challenges ❌ Technical complexity: Requires proficiency in R programming, making implementation more challenging for non-technical users. ❌ Fixed media coefficients: Certain seasonality and campaign-based fluctuations may not be captured accurately. ❌ Limited documentation for advanced customization: Features like calibration and priors lack detailed examples, making full utilization difficult. The Future of MMM StrategiesBoth Google Meridian MMM and Facebook (Meta) Robyn MMM offer advanced solutions for measuring marketing effectiveness and optimizing media budgets. Meridian leverages Bayesian approaches and Google’s proprietary data sources to provide deep insights into digital campaigns, while Robyn offers a more flexible, real-time optimization framework through Prophet and Ridge regression.The choice between these tools depends on factors such as data availability, marketing budget, and internal technical expertise. As machine learning and AI-driven modeling continue to evolve, both tools are expected to become even more sophisticated, making marketing strategies more dynamic and data-driven.

Jun 12, 2025 77 reads

Proactive SEO: Reporting and Analysis with Screaming Frog Scheduling

Manually conducted technical SEO audits, especially for large-scale websites, often turn into repetitive and time-consuming processes that can take hours or even days. Spending this valuable time on data gathering instead of analysis and strategy development decreases overall efficiency. At this point, automation becomes one of modern SEO’s strongest allies, enabling a shift toward proactive site health management. It helps you discover which pages to prioritize.This is where Screaming Frog's scheduling feature steps in as a true lifesaver—allowing you to replace exhausting manual audits with an automated system that keeps track of your website’s technical health 24/7.What is Screaming Frog Scheduling and Why is it a Game-Changer?Screaming Frog Scheduling is a feature available in the paid version of the SEO Spider tool that allows you to automatically run pre-configured crawl settings at your specified times and frequencies. In short, even if you're not at your computer, Screaming Frog can crawl your website on your behalf.The core purpose of this feature is to automate repetitive auditing tasks, eliminate human error, standardize processes, and give you back your most valuable resource—time.- First, it eliminates the weekly hours spent on manual crawls, giving you extra time for strategy development and data analysis.- Second, since each crawl is run with the same saved configuration file, your data is 100% consistent. This makes it easy to track changes over time (such as increasing 404 errors or broken redirect chains).- Finally, and most critically, it provides real-time detection—allowing you to prevent a potential organic traffic disaster before it even begins.Step-by-Step Guide to Setting Up Scheduled CrawlsStep 1: Save Your Crawl Configuration Template:Start by saving the settings of the crawl you want to automate. Configure crawl settings such as the crawl mode, tags and links to be included, crawl depth, and elements to exclude.Step 2: Integrate Google Analytics 4 and Google Search Console APIs:Under Configuration > API Access, integrate Google Analytics 4 and Google Search Console to enrich crawl data with real-time Google performance metrics tied to each URL.Once done, save this configuration via File > Configuration > Save As into a .seospiderconfig file. This file will serve as the blueprint for all future automated crawls.Step 3: Create a Scheduled Task:Under File > Scheduling, click the Add button to create a new task. Use the Date/Time field to set how frequently the crawl should run—options include Daily, Weekly, and Monthly.Step 4: Choose the Crawl Mode:You can opt for Standard or List Mode. In List Mode, you can crawl a specific list of URLs using a locally stored .xlsx file.Step 5: Define the Output Location:Decide where the crawl output should be saved—either locally or to your connected Google Drive account.Step 6: Export to Google Drive or Google Sheets:To export reports directly to Google Drive or Sheets, simply link your Google account under the Google Drive Account menu.Step 7: Select Reports and Export Settings:In the Reports and Exports tabs, select which predefined reports (e.g., Redirects, Title Errors, etc.) should be automatically generated and saved after each crawl.Step 8: Choose Export Format:Specify the format in which you want the data to be saved (e.g., CSV, XLSX).Optional: Integrate with Looker Studio to visualize and analyze your exports more interactively.Data Enrichment: Google Search Console & GA4 API IntegrationThe technical insights Screaming Frog provides (URL structure, HTTP status codes, meta tags, etc.) are extremely valuable, but they only tell half the story. The other half is how users interact with those pages and how Google evaluates them in SERPs.That’s where Google Search Console (GSC) and Google Analytics 4 (GA4) API integration becomes crucial. This integration merges technical data with performance and behavioral metrics—empowering you to move from basic queries like:“Which pages have titles over 60 characters?” to “Which pages have high impressions, low CTR, and also a poor Largest Contentful Paint (LCP) score?”To enable this, API authorization must be completed for the relevant sources in Screaming Frog. Once integrated and configured, save your settings as a .seospiderconfig file or upload it to your Drive account.When you assign this configuration to your scheduled task, Screaming Frog will fetch real-time data—clicks, impressions, CTR, average position, users, sessions, conversions, etc.—from GSC and GA4 for each crawl. This allows you to prioritize technical SEO tasks based on actual business impact.Automated Reporting: Advanced Export SettingsScreaming Frog’s scheduling feature lets you fully automate your reporting process. Instead of dealing with dozens of export files after every crawl, you can predefine which specific reports should be automatically created.For example, for a weekly “Health Crawling Check,” you might only export:- 4xx Error Pages- Redirect Chains- Canonical Issues- Non-Indexable PagesAnalyticaHouse Pro-Tip: Practical Use Cases for Scheduled CrawlsThe key to maximizing the benefits of scheduled crawls is to apply them in the right scenarios. At AnalyticaHouse, we frequently use scheduled crawls for:Weekly Crawling CheckMonitor critical SEO issues weekly:- 5xx Server Errors- New 4xx Pages- Indexability Issues: Detect mistakenly noindexed or robots.txt-blocked pages- Canonical Tag Errors: Identify duplicate content issues due to incorrect canonicalizationCompetitor AnalysisSet up weekly or monthly crawls of competitors to automatically monitor their strategic moves:- Changes in site architecture or URL hierarchy- New meta titles and descriptions reflecting targeted keywords- Newly added content sections or blog posts- Implementation of new structured data types (Schema)Post-Migration ChecksAfter a site migration, it’s vital to ensure that 301 redirects from old to new URLs work correctly. Manually checking thousands of URLs is unrealistic.Instead, schedule a List Mode crawl using a list of old URLs. This ensures every legacy URL redirects properly. If a redirect breaks (e.g., leads to a 404), you'll be alerted immediately.Centralizing All SEO Data in Google BigQueryThe main objective here is to build a scalable and queryable data warehouse that combines data from Screaming Frog, GA4, and GSC. This allows you to build fully automated, detailed, and customizable reports using tools like Looker Studio or Google Sheets.Key Components:- Schedule Timing: Customize frequency and timing of crawls- GA4 & GSC Integration: Real-time Google metrics per URL- BigQuery & URL Breakdown: Store and categorize URLs by path type (e.g., /blog, /category, /product)- Metric Selection: Choose which performance metrics to pull (e.g., GA4 Sessions, Purchases, Revenue, Conversion Rate; GSC Impressions, Clicks, CTR, Average Position)- Functional Reporting: Upload bulk URL lists to monitor their performance over time- Looker Studio & Sheets Integration: Use the BigQuery table to create interactive dashboards and automated reports updated in real time

Jun 4, 2025 64 reads

E-Commerce Sites and "AI Mode" Visibility

E-Commerce Sites and "AI Mode" VisibilityE-commerce sites are undergoing a transformation with AI-powered "AI Mode" technologies, offering critical opportunities for brands aiming to stand out in visibility and competition. Simply listing products is no longer enough; how the site interacts with users and what it offers to algorithms has become much more important."AI Mode" is a next-generation approach in e-commerce that personalizes user experience, provides data-driven recommendations, and helps achieve better rankings in search engines. In an environment where search engines like Google prioritize advanced AI-based systems, adopting the right strategies is essential to gain visibility.What is AI Mode and Why is it Important for E-Commerce Sites?AI Mode, or Artificial Intelligence Mode, is a technology suite in e-commerce that analyzes user behavior to deliver personalized content, product recommendations, and shopping experiences. From the moment users enter the site, they encounter customized products, campaigns, and interfaces. This leads to:Increased user satisfactionHigher cart creation ratesShortened shopping timeFor example, an e-commerce site using AI Mode can customize its homepage based on the visitor’s previous shopping data, achieving up to a 30% increase in cart creation rates.Additionally, AI Mode is not just a sales booster; it plays a vital role in SEO. Search engines determine page value based on user interactions. Content enriched with AI Mode helps to:Reduce bounce ratesIncrease page engagementImprove Google rankingsThe Impact of AI Mode on SEO in E-CommerceAI Mode stands out in SEO by enhancing page experience based on user behavior. Google’s emphasis on user-focused metrics like Core Web Vitals makes investing in this area crucial for e-commerce sites. For instance, with AI Mode:Product pages can load fasterUsers find desired products quickerPersonalized suggestions in search boxes can reduce bounce rates by 20-35%All these factors directly affect SEO performance.Moreover, e-commerce sites using AI Mode generate richer data. This data helps restructure content and optimize pages with more relevant keywords. For example, the system can automatically identify the most searched keywords by users and incorporate them into titles and descriptions to boost organic traffic. Terms like “AI-driven SEO,” “AI-powered search optimization,” and “AI-supported SEO strategies” fit well with this content.The Evolution of Product Recommendation Systems with AI ModeTraditional product recommendation systems usually operate on fixed rules and past sales data. However, AI Mode technology has transformed these systems into dynamic, real-time, behavior-driven models. For example, when a user visits a site for the second time, AI Mode analyzes their previous browsing behavior and places relevant products on the homepage. This significantly boosts revenue, especially through strategies such as cross-selling and upselling.Traditional product recommendation systems often rely on static rules and sales history. But with AI Mode, these systems become dynamic and behavior-focused in real time. Users now encounter personalized products based on their past preferences whenever they revisit the site. This facilitates strategies like:Cross-sellingUpsellingand leads to increased revenue. To illustrate with numbers:System TypeClick-Through Rate IncreaseIncrease ConversionTraditional Recommendation Systems10-15%5-10%AI Mode Supported Systemsup to 60%Up to 35%Advanced recommendation systems using AI Mode can provide up to 60% higher click-through rates and up to 35% higher conversion rates compared to traditional systems. You can also use keywords with high search potential such as “personalized recommendation systems” and “product recommendation with artificial intelligence in e-commerce” within this title. Such systems have become applicable not only to large e-commerce giants; but also to small and medium-sized sites. Integrated AI tools are rapidly spreading, especially on platforms such as Shopify and WooCommerce.User Experience and AI Mode Integration in E-Commerce SitesUser experience (UX) is one of the key factors determining the success of an e-commerce site. AI Mode transforms an ordinary shopping experience into an unforgettable journey by customizing this experience for the individual user. For example, when a visitor is searching for sneakers, they may encounter not only the price filter, but also additional filters such as “comfort level” or “purpose of use” based on their previous preferences. This shortens the decision-making process and saves the user time. This micro-customization offered by AI Mode keeps the user on the site longer and increases the view rate per page.This dynamic structure, which contributes to UX, also has a positive effect on SEO. Search engines like Google rank by taking into account the time users spend on a site and the level of interaction on the site. Thanks to the AI-supported user experience, an e-commerce site becomes more valuable both in the eyes of users and in search engines. Searches such as “UX design with AI” and “AI solutions that increase user experience” can also be targeted under this heading. Visual optimization, smart chatbots and behavior-based notification systems can also be evaluated within this scope.Ways to Increase Conversion Rates in E-Commerce with AI ModeThe conversion rate is one of the most critical indicators of the success of an e-commerce site. AI Mode increases this rate by getting to know users better and offering them special offers and experiences. For example, when a user adds a product to their cart, AI Mode shows similar products in real time and triggers the purchase decision with messages like “you may also like this”. Similarly, personalized email campaigns or instant discount offers can be offered for abandoned carts. Up to 25% increase in conversion rate can be observed with these strategies.In addition, thanks to A/B tests supported by artificial intelligence, it is possible to automatically analyze which campaign messages, banners or CTA (call-to-action) buttons provide more interaction. These automations produce results 50% faster than manual tests. Terms such as “AI-supported conversion increase tactics”, “ways to increase sales in e-commerce”, “customer behavior analysis with AI” can also be evaluated within the content compatibility with this title. When success examples are examined, it is seen that the return on investment (ROI) of companies working with AI Mode is 30% higher on average.How to Integrate AI Mode for E-Commerce Sites?Integration of AI Mode into e-commerce sites is possible with the right tool selection and a solid data structure. As a first step, the current infrastructure of the site should be analyzed and how user data is collected should be evaluated. This data enables AI systems to learn. Then, AI software (e.g. recommendation engines, chatbots, dynamic pricing systems) that are suitable for the needs should be determined and integrated. These tools are usually connected to the site via API. Ready-made integrations are available for platforms such as Shopify, Magento, and WooCommerce.Another issue that is as important as technical integration is continuous optimization. The performance of AI systems should be analyzed regularly, and models should be updated in light of incoming data. This allows for better responses to user behavior over time. In addition, data security laws such as GDPR should be taken into consideration during integration. Search terms such as “How is artificial intelligence integrated?”, “How are AI systems integrated into e-commerce infrastructure?” can also be included in this title. Improvements in conversion increase, visitor satisfaction, and SEO metrics after integration can be easily observed.