Fundamentals
Introduction to Predictive Analytics for Small Businesses
Predictive analytics, once the domain of large corporations with dedicated data science teams, is now accessible to small to medium businesses (SMBs). This shift is largely due to advancements in user-friendly tools like Google Analytics Meaning ● Google Analytics, pivotal for SMB growth strategies, serves as a web analytics service tracking and reporting website traffic, offering insights into user behavior and marketing campaign performance. 4 (GA4) and Looker Studio. For SMBs, the ability to anticipate future trends and customer behaviors is no longer a luxury but a competitive advantage.
Imagine knowing which products are likely to surge in demand next month, or identifying customers at high risk of churning before they actually leave. This is the power of predictive analytics, and it’s within reach for businesses of all sizes.
GA4, the latest iteration of Google’s analytics platform, is designed with predictive capabilities built directly into its core. It leverages machine learning Meaning ● Machine Learning (ML), in the context of Small and Medium-sized Businesses (SMBs), represents a suite of algorithms that enable computer systems to learn from data without explicit programming, driving automation and enhancing decision-making. to identify patterns in your website and app data, offering insights into future customer actions. Looker Studio then acts as the visualization engine, transforming this raw predictive data into digestible dashboards. These dashboards aren’t just pretty charts; they are actionable tools that can guide strategic decisions across marketing, sales, and operations.
Think of a local bakery. Traditionally, ordering ingredients was based on past sales data and gut feeling. With GA4 predictive dashboards Meaning ● Predictive Dashboards, in the realm of SMB growth, represent a strategic tool using data analytics to forecast future business trends and outcomes. in Looker Studio, the bakery can now forecast demand for specific pastries based on historical trends, seasonality, and even external factors like local events. This leads to optimized ingredient ordering, reduced waste, and maximized profits.
Similarly, an e-commerce store can use purchase probability Meaning ● Purchase Probability, within the context of SMB growth, automation, and implementation, quantifies the likelihood that a prospective customer will complete a transaction. predictions to target potential customers with personalized promotions, increasing conversion rates and revenue. The key is to understand that predictive analytics Meaning ● Strategic foresight through data for SMB success. is not about predicting the future with absolute certainty, but about making more informed decisions based on probabilities and trends. For SMBs, this translates to smarter resource allocation, improved customer engagement, and ultimately, sustainable growth.
Predictive analytics empowers SMBs to shift from reactive strategies to proactive planning, leveraging data to anticipate future trends and customer behaviors for a competitive edge.
Setting Up GA4 to Harness Predictive Metrics
Before diving into Looker Studio dashboards, the foundation lies in properly configuring Google Analytics 4 Meaning ● Google Analytics 4 (GA4) signifies a pivotal shift in web analytics for Small and Medium-sized Businesses (SMBs), moving beyond simple pageview tracking to provide a comprehensive understanding of customer behavior across websites and apps. to collect and process the necessary data for predictive metrics. GA4 automatically generates several predictive metrics, but to ensure their accuracy and relevance, you need to optimize your GA4 setup. This involves focusing on event tracking Meaning ● Event Tracking, within the context of SMB Growth, Automation, and Implementation, denotes the systematic process of monitoring and recording specific user interactions, or 'events,' within digital properties like websites and applications. and conversion configuration, as these are the data points that fuel GA4’s machine learning models.
Essential GA4 Configurations for Prediction
The first step is to ensure accurate and comprehensive event tracking. GA4’s event-based model is crucial for predictive analytics. You need to track key user interactions on your website or app, such as page views, button clicks, form submissions, and product views. Beyond basic events, focus on setting up enhanced measurement events and custom events that are specific to your business goals.
For an e-commerce store, this includes events like ‘add to cart’, ‘begin checkout’, and ‘purchase’. For a service-based business, relevant events might be ‘contact form submission’, ‘appointment booking’, or ‘service inquiry’.
Next, configure conversions in GA4. Conversions are the actions you want users to take on your website or app that indicate business success. These can be purchases, lead form submissions, newsletter sign-ups, or any other meaningful interaction. Accurately defining conversions is critical because GA4’s predictive metrics Meaning ● Predictive Metrics in the SMB context are forward-looking indicators used to anticipate future business performance and trends, which is vital for strategic planning. are often tied to the likelihood of users converting.
For example, the ‘purchase probability’ metric predicts the likelihood of a user making a purchase within the next seven days. This prediction is directly based on the conversion events you have defined in GA4.
Data quality is paramount. Ensure your GA4 implementation is free of errors and inconsistencies. Regularly audit your event tracking and conversion setup to verify data accuracy. Incorrect or incomplete data will lead to inaccurate predictions and undermine the value of your dashboards.
Utilize GA4’s DebugView to monitor events in real-time and identify any tracking issues. Also, consider using GA4’s data quality Meaning ● Data Quality, within the realm of SMB operations, fundamentally addresses the fitness of data for its intended uses in business decision-making, automation initiatives, and successful project implementations. features, such as data filters and data thresholds, to further refine your data collection.
Finally, give GA4 time to learn. Predictive metrics rely on machine learning models Meaning ● Machine Learning Models, within the scope of Small and Medium-sized Businesses, represent algorithmic structures that enable systems to learn from data, a critical component for SMB growth by automating processes and enhancing decision-making. that need sufficient data to train effectively. After setting up event tracking and conversions, allow GA4 to collect data for at least a few weeks before relying heavily on predictive insights.
The more data GA4 has, the more accurate and reliable its predictions will become. Initially, predictions might be less precise, but as data accumulates, the models will improve, providing increasingly valuable insights for your SMB.
Common GA4 Pitfalls to Avoid for Predictive Accuracy
Several common mistakes can hinder the effectiveness of GA4 predictive metrics. One frequent pitfall is incomplete or inconsistent event tracking. If you are not tracking all relevant user interactions, GA4 will have a limited view of user behavior, leading to less accurate predictions. Ensure comprehensive tracking across all critical touchpoints on your website or app.
Another mistake is poorly defined or too few conversions. If your conversion definitions are too broad or don’t accurately reflect your business goals, the predictive metrics tied to conversions will be less meaningful. Conversely, if you define too few conversions, GA4 might not have enough data to train its models effectively. Focus on defining specific, measurable, achievable, relevant, and time-bound (SMART) conversions that align with your SMB objectives.
Ignoring data quality is another significant pitfall. Data discrepancies, tracking errors, and inconsistencies can skew predictions and lead to misguided decisions. Implement regular data audits and quality checks to maintain data integrity. Utilize GA4’s data validation features and consider using data sampling controls when analyzing large datasets to ensure representative and reliable insights.
Rushing to conclusions is also a common mistake. Predictive metrics are probabilities, not certainties. Avoid making drastic decisions based on initial predictions without further analysis and validation. Use predictive insights Meaning ● Predictive Insights within the SMB realm represent the actionable intelligence derived from data analysis to forecast future business outcomes. as indicators and starting points for deeper investigation, not as definitive answers.
Combine predictive data with other data sources and business context to make well-rounded and informed decisions. Remember, predictive analytics is a tool to enhance your decision-making process, not replace it.
Connecting GA4 to Looker Studio ● A Seamless Integration
Once GA4 is properly configured and collecting data, the next step is to connect it to Looker Studio. This connection is straightforward and allows you to visualize GA4’s predictive metrics in customizable dashboards. Looker Studio’s user-friendly interface makes it accessible even for SMB owners without advanced data analysis Meaning ● Advanced Data Analysis, within the context of Small and Medium-sized Businesses (SMBs), refers to the sophisticated application of statistical methods, machine learning, and data mining techniques to extract actionable insights from business data, directly impacting growth strategies. skills. The integration is designed to be seamless, allowing you to quickly access and transform GA4 data into actionable reports.
Step-By-Step Guide to GA4 and Looker Studio Connection
The connection process begins in Looker Studio. Start by creating a new report in Looker Studio. On the data source selection screen, you will see a list of available connectors. Choose the ‘Google Analytics’ connector.
Looker Studio will then prompt you to authorize access to your Google Analytics account. Select the Google account associated with your GA4 property.
After authorization, you will see a list of your Google Analytics accounts and properties. Navigate to your GA4 property and select it as the data source for your Looker Studio report. You will be presented with a list of data streams within your GA4 property.
Choose the relevant data stream (typically your website or app data stream). Once you select the data stream, Looker Studio establishes a live connection to your GA4 data.
With the connection established, you can now access GA4’s metrics and dimensions within Looker Studio. Looker Studio’s drag-and-drop interface allows you to easily create charts, tables, and scorecards using GA4 data. You can search for specific metrics and dimensions using the search bar in the data panel.
To visualize predictive metrics, simply search for terms like ‘purchase probability’, ‘churn probability’, or ‘predicted revenue’ in the metric selection panel. These predictive metrics will be readily available alongside standard GA4 metrics.
Looker Studio offers a variety of visualization options to present predictive data effectively. You can use time series charts to track trends in purchase probability over time, bar charts to compare churn probability across different customer segments, or scorecards to highlight key predictive metrics at a glance. Experiment with different chart types to find the most impactful way to communicate predictive insights to your team. Remember to keep your dashboards clear, concise, and focused on actionable information.
Data Refresh and Connection Management
Looker Studio dashboards connected to GA4 are dynamically updated. By default, Looker Studio refreshes data periodically, ensuring your dashboards reflect the latest information from GA4. You can also manually refresh data within Looker Studio if needed. This real-time data flow is crucial for predictive dashboards, as it allows you to monitor and react to changing trends and predictions promptly.
You can manage your data source connections within Looker Studio’s resource manager. This allows you to edit connection settings, switch data sources, or remove connections as needed. For example, you might need to update the GA4 property connected to your dashboard if you migrate to a new GA4 setup. Proper data source management ensures your dashboards always pull data from the correct GA4 property and data stream.
Consider data blending if you want to combine predictive data from GA4 with data from other sources, such as your CRM or sales platform. Looker Studio’s data blending feature allows you to merge data from multiple sources into a single dashboard, providing a more comprehensive view of your business performance. For instance, you could blend GA4 purchase probability data with CRM customer lifetime value Meaning ● Customer Lifetime Value (CLTV) for SMBs is the projected net profit from a customer relationship, guiding strategic decisions for sustainable growth. data to identify high-value customers who are also likely to purchase again. This advanced technique can further enhance the insights derived from your predictive dashboards.
Creating Your First Predictive Dashboard ● A Quick Start Guide
Creating your first predictive dashboard in Looker Studio doesn’t have to be daunting. Starting with a simple, focused dashboard is often the most effective approach for SMBs. Focus on visualizing one or two key predictive metrics that are most relevant to your immediate business goals. This quick start guide will walk you through creating a basic dashboard to track purchase probability, a metric particularly valuable for e-commerce and businesses focused on online sales.
Essential Components of a Basic Predictive Dashboard
Begin by adding a scorecard to your dashboard. A scorecard is a simple yet effective way to display a single key metric prominently. Select ‘Add a chart’ from the toolbar and choose ‘Scorecard’. In the data panel, select ‘Purchase Probability’ as the metric.
This scorecard will now display the average purchase probability of your website visitors. You can customize the scorecard’s appearance, such as font size and color, to make it stand out on your dashboard.
Next, add a time series chart to visualize the trend of purchase probability over time. Select ‘Add a chart’ again and choose ‘Time series chart’. Set the metric to ‘Purchase Probability’ and the dimension to ‘Date’.
This chart will show how purchase probability has fluctuated over a selected time period, such as the last 30 days. Time series charts are excellent for identifying patterns and trends in predictive metrics, helping you understand if purchase probability is increasing, decreasing, or remaining stable.
Consider adding a table to segment purchase probability by key dimensions. Select ‘Add a chart’ and choose ‘Table’. Set the metric to ‘Purchase Probability’ and add dimensions like ‘Device Category’, ‘Traffic Source’, or ‘Country’.
This table will break down purchase probability by these dimensions, allowing you to see which devices, traffic sources, or geographic locations are associated with higher or lower purchase probabilities. Segmentation is crucial for identifying specific areas for optimization and targeted actions.
Finally, add a date range control to your dashboard. This control allows you to easily adjust the time period for which data is displayed. Select ‘Add a control’ from the toolbar and choose ‘Date range control’.
Place the date range control at the top of your dashboard for easy access. With a date range control, you can quickly analyze predictive metrics for different time periods, such as week-over-week, month-over-month, or year-over-year, facilitating comparative analysis and trend identification.
Remember to add clear titles and labels to all charts and scorecards on your dashboard. A well-labeled dashboard is easier to understand and interpret, especially for team members who may not be familiar with predictive analytics. Use concise and descriptive titles that clearly communicate the purpose of each visualization.
For instance, instead of just ‘Table’, use ‘Purchase Probability by Device Category’. Clear labeling enhances the usability and actionability of your predictive dashboard.
Basic Customization and Branding for SMB Dashboards
Looker Studio offers customization options to align your dashboards with your SMB’s branding. You can change the color scheme, fonts, and layout of your dashboard to match your brand identity. Consistent branding across your reports and dashboards reinforces your brand image and enhances professionalism.
To customize the theme, navigate to ‘Theme and Layout’ in Looker Studio’s report settings. You can choose from pre-set themes or customize colors and fonts individually. Consider using your brand colors for charts and scorecards to create a visually cohesive dashboard. Select fonts that are legible and consistent with your brand’s typography.
You can also add your company logo to your dashboard. Use the ‘Image’ tool in Looker Studio to upload your logo and position it appropriately on the dashboard. Placing your logo in the header or footer of the dashboard helps reinforce brand recognition when sharing reports with your team or stakeholders.
Layout customization allows you to arrange charts and controls in a way that optimizes readability and user experience. Looker Studio’s grid system helps you align elements neatly. Consider the flow of information when designing your dashboard layout.
Place the most important metrics and visualizations prominently at the top, and arrange related charts logically to guide the user’s eye through the data story. A well-organized layout makes your predictive dashboard more effective and user-friendly for your SMB.
Fundamentals Summary ● Key Takeaways for SMBs
Establishing a solid foundation in GA4 and Looker Studio is crucial for SMBs venturing into predictive analytics. Start by ensuring accurate GA4 setup, focusing on event tracking and conversion configuration. Connect GA4 seamlessly to Looker Studio to unlock the visualization power for predictive metrics. Create your first simple dashboard, focusing on key metrics like purchase probability, and customize it to align with your brand.
By mastering these fundamentals, SMBs can begin to leverage predictive insights to drive growth and efficiency. The initial setup may seem technical, but the long-term benefits of data-driven predictions are substantial for businesses of all sizes.
Solid GA4 setup, seamless Looker Studio connection, and focused dashboard creation are fundamental steps for SMBs to unlock the power of predictive analytics for data-driven growth.
Table 1 ● Key GA4 Predictive Metrics for SMBs
| Predictive Metric Purchase Probability |
| Description Probability that a user who visited your website/app will purchase within the next 7 days. |
| SMB Application Target high-probability users with personalized ads, optimize checkout process, forecast sales. |
| Predictive Metric Churn Probability |
| Description Probability that a user active within the last 7 days will not be active in the next 7 days. |
| SMB Application Identify at-risk customers, proactively engage with retention campaigns, improve customer service. |
| Predictive Metric Predicted Revenue (Top Spenders) |
| Description Predicted revenue from users in the top spending percentile over the next 28 days. |
| SMB Application Identify high-value customer segments, personalize loyalty programs, optimize upselling strategies. |
List 1 ● Common GA4 Setup Pitfalls
- Incomplete or inconsistent event tracking.
- Poorly defined or too few conversions.
- Ignoring data quality and accuracy.
- Rushing to conclusions based on initial predictions.
- Lack of regular data audits and validation.
Intermediate
Customizing Dashboards for Specific SMB Needs and Industries
Building upon the fundamentals, the intermediate stage focuses on tailoring Looker Studio predictive dashboards to the unique requirements of different SMBs and industries. Generic dashboards offer a starting point, but to unlock true value, customization is essential. This involves understanding specific business objectives, key performance indicators (KPIs), and the nuances of various SMB sectors. Whether you’re in e-commerce, services, or a local brick-and-mortar business, a customized dashboard provides more relevant and actionable insights.
E-Commerce Dashboard Customization ● Driving Online Sales
For e-commerce SMBs, predictive dashboards can be powerful tools for optimizing online sales and marketing efforts. Key predictive metrics like purchase probability and predicted revenue are particularly relevant. Customize your dashboard to focus on these metrics and segment them by crucial e-commerce dimensions.
For example, segment purchase probability by product category to identify product lines with high purchase intent. This allows for targeted promotions and inventory management.
Incorporate visualizations that track predicted revenue trends over time, broken down by marketing channels. This helps evaluate the effectiveness of different marketing campaigns Meaning ● Marketing campaigns, in the context of SMB growth, represent structured sets of business activities designed to achieve specific marketing objectives, frequently leveraged to increase brand awareness, drive lead generation, or boost sales. in driving predicted revenue. Identify channels that are attracting high-value customers with strong purchase probability and allocate marketing budget accordingly.
Use bar charts to compare predicted revenue across different customer segments, such as new vs. returning customers, or customers acquired through different channels.
Add interactive elements like filters and parameters to your e-commerce dashboard. Filters allow users to drill down into specific segments of data, such as users who viewed a particular product category or users from a specific geographic location. Parameters enable users to dynamically change dashboard metrics and dimensions, allowing for ad-hoc analysis and exploration. For instance, a parameter could allow users to switch between viewing purchase probability for different product categories or customer segments.
Consider integrating data from your e-commerce platform into Looker Studio. If you use platforms like Shopify or WooCommerce, explore connectors that allow you to blend e-commerce transaction data with GA4 predictive data. This blended data can provide richer insights, such as correlating purchase probability with average order value or customer lifetime value. For example, identify customer segments with high purchase probability and high average order value for targeted upselling and cross-selling initiatives.
Set up automated alerts based on predictive metrics. For example, create an alert that triggers when purchase probability for a specific product category drops below a certain threshold. This proactive notification allows you to investigate potential issues, such as increased competition or negative product reviews, and take timely corrective actions. Automated alerts ensure that your predictive dashboard is not just a static report but a dynamic monitoring tool that keeps you informed of critical changes in customer behavior.
Service-Based Business Dashboard Adaptation ● Enhancing Customer Retention
Service-based SMBs can leverage predictive dashboards to improve customer retention Meaning ● Customer Retention: Nurturing lasting customer relationships for sustained SMB growth and advocacy. and optimize service delivery. Churn probability is a particularly valuable metric for service businesses, as customer retention is often more cost-effective than customer acquisition. Customize your dashboard to focus on churn probability and related dimensions relevant to your service offerings.
Segment churn probability by service type or customer subscription tier. Identify service offerings or subscription levels with higher churn rates. This allows you to pinpoint areas where service improvements or targeted retention efforts are most needed. Use tables to compare churn probability across different customer cohorts, such as customers acquired in different months or customers with different service usage patterns.
Visualize churn probability trends over time, segmented by customer lifecycle stage. Track how churn probability changes as customers progress through their lifecycle, from onboarding to long-term engagement. Identify critical stages where churn risk is highest and implement proactive interventions, such as personalized communication or enhanced support, to mitigate churn.
Incorporate customer satisfaction Meaning ● Customer Satisfaction: Ensuring customer delight by consistently meeting and exceeding expectations, fostering loyalty and advocacy. data into your predictive dashboard. Blend GA4 churn probability data with customer feedback data from surveys or customer support interactions. This blended view can help you understand the drivers of churn and identify specific pain points that are contributing to customer dissatisfaction. For example, correlate high churn probability with negative customer feedback related to a particular service feature or support process.
Utilize Looker Studio’s calculated fields to create custom metrics related to customer retention. For instance, calculate ‘predicted customer lifetime’ based on churn probability. This metric provides a forward-looking view of customer value and helps prioritize retention efforts for customers with higher predicted lifetime value. Create scorecards to highlight key retention metrics, such as average churn probability and predicted customer lifetime value, for quick performance monitoring.
Local Business Dashboard Strategies ● Optimizing Local Visibility
Local SMBs, such as restaurants or retail stores, can adapt predictive dashboards to optimize local visibility and attract nearby customers. While direct predictive metrics in GA4 might be less directly tied to local foot traffic, you can use them in conjunction with location-based data and online engagement metrics Meaning ● Engagement Metrics, within the SMB landscape, represent quantifiable measurements that assess the level of audience interaction with business initiatives, especially within automated systems. to gain valuable insights.
Focus on website engagement Meaning ● Website Engagement, for small and medium-sized businesses, represents the depth and frequency of interaction visitors have with a company's online presence, particularly its website, with strategic growth tied to this business interaction. metrics that indicate local customer intent, such as ‘directions clicks’, ‘phone calls from website’, and ‘local landing page views’. Track these metrics alongside purchase probability and predicted revenue to understand the online-to-offline customer journey. Visualize the correlation between online engagement and predicted purchase behavior for local customers.
Segment predictive metrics by geographic dimensions, such as city or region. Identify geographic areas with higher purchase probability or predicted revenue. This can inform localized marketing campaigns and targeted promotions within specific neighborhoods or service areas. Use maps in Looker Studio to visualize geographic distribution of purchase probability and identify high-potential local customer segments.
Integrate data from Google My Business (GMB) into your Looker Studio dashboard. GMB provides valuable insights into local search Meaning ● Local Search, concerning SMB growth, designates the practice of optimizing an SMB's online presence to appear prominently in search engine results when users seek products or services within a specific geographic area. performance, customer reviews, and business profile interactions. Blend GMB data with GA4 predictive data to understand how local search visibility influences online purchase probability and predicted revenue. For example, correlate GMB search impressions and clicks with website purchase probability for local users.
Utilize Looker Studio’s community visualizations to enhance local business dashboards. Explore community visualizations that offer map-based charts or local search performance reports. These visualizations can provide a more geographically focused view of your data and highlight local trends and opportunities. For example, use a map visualization to display customer density and purchase probability across different neighborhoods in your service area.
Consider tracking seasonal trends in predictive metrics for local businesses. Local businesses often experience seasonal fluctuations in demand. Analyze historical predictive data to identify seasonal patterns and anticipate future demand spikes or dips. This can inform seasonal marketing campaigns, staffing adjustments, and inventory planning for local SMBs.
Advanced Segmentation and Filtering for Deeper Predictive Insights
To move beyond basic dashboards, intermediate users need to master advanced segmentation and filtering techniques in Looker Studio. Segmentation allows you to analyze predictive metrics for specific groups of users based on shared characteristics, while filtering enables you to focus on subsets of data based on specific criteria. These techniques are crucial for uncovering granular insights and tailoring strategies to different customer segments.
Creating Advanced Segments in Looker Studio
Looker Studio offers various segmentation options. You can create segments based on dimensions, metrics, or even combinations of both. To create a segment, navigate to ‘Add a chart’ and select any chart type.
In the data panel, click ‘Add segment’. You can choose from pre-defined segments or create custom segments based on your specific needs.
Custom segments can be created using various conditions. You can segment users based on demographics, behavior, technology, or traffic sources. For predictive dashboards, behavioral segments are particularly valuable.
Segment users based on their engagement level, purchase history, or website activity. For example, create a segment of ‘high-intent users’ based on users who have viewed product pages multiple times or added items to their cart but haven’t yet purchased.
Use sequential segments to analyze user journeys and predict behavior based on specific sequences of actions. For example, create a segment of users who started the checkout process but abandoned it. Analyze the churn probability of this segment to identify potential drop-off points in your checkout funnel and optimize the process to reduce abandonment rates. Sequential segments provide a deeper understanding of user behavior flow and its impact on predictive metrics.
Combine segments to create even more granular analysis. For example, combine a demographic segment (e.g., ‘users aged 25-34’) with a behavioral segment (e.g., ‘high-intent users’). Analyze the purchase probability of this combined segment to identify specific demographic groups with high purchase potential. Segment combinations allow for highly targeted analysis and personalized strategies.
Save your segments for reuse across multiple dashboards and reports. Once you create a valuable segment, save it in Looker Studio’s segment library. This allows you to easily apply the same segment to different charts and dashboards without recreating it each time. Saved segments streamline your analysis workflow and ensure consistency across your predictive reporting.
Utilizing Filters for Focused Predictive Analysis
Filters in Looker Studio allow you to narrow down the data displayed in your charts and dashboards based on specific criteria. Filters can be applied at the chart level or at the dashboard level, affecting all charts in the dashboard. To add a filter, select a chart or the dashboard canvas, and click ‘Add a filter’ in the data panel.
Use filters to focus on specific time periods, dimensions, or metrics. For example, filter your dashboard to show data only for the last month or for a specific marketing campaign. Filter charts to display predictive metrics only for users from a particular geographic region or device category. Filters enable you to isolate specific data subsets for in-depth predictive analysis.
Create advanced filters using conditions and regular expressions. Advanced filters allow for more complex filtering logic. Use conditions to filter data based on metric values or dimension ranges.
Regular expressions enable pattern-based filtering, allowing you to filter data based on text patterns in dimensions. For example, use a regular expression filter to include only traffic sources that contain the word ‘social’.
Use interactive filters to allow dashboard users to dynamically filter data. Add filter controls to your dashboard, such as dropdown lists or checkboxes, that allow users to select filter values. Interactive filters empower users to explore the data and perform their own focused analysis. For example, add a dropdown filter for ‘Device Category’ that allows users to view predictive metrics separately for desktop, mobile, and tablet users.
Combine filters and segments for highly refined predictive analysis. Apply filters to segments to further narrow down the data being analyzed. For example, apply a filter to a ‘high-intent users’ segment to view purchase probability only for high-intent users from a specific marketing campaign. Filter and segment combinations provide maximum control over data analysis Meaning ● Data analysis, in the context of Small and Medium-sized Businesses (SMBs), represents a critical business process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting strategic decision-making. and enable highly targeted insights.
Leveraging Calculated Fields for Custom Predictive Metrics
Looker Studio’s calculated fields feature empowers intermediate users to create custom metrics and dimensions based on existing data. This is particularly valuable for predictive dashboards, as you can derive new insights by combining and transforming GA4 predictive metrics. Calculated fields extend the analytical capabilities of Looker Studio and allow for more tailored and sophisticated predictive reporting.
Basic Calculated Field Creation for Predictive Metrics
To create a calculated field, navigate to ‘Resource’ in the Looker Studio menu and select ‘Manage added data sources’. Edit your GA4 data source and click ‘Add field’. You will be presented with a formula editor where you can define your calculated field using Looker Studio’s formula language.
Simple calculated fields can involve basic arithmetic operations, such as addition, subtraction, multiplication, and division. For example, you can calculate ‘predicted average order value’ by dividing ‘predicted revenue’ by the number of predicted purchases. This custom metric provides a more granular view of predicted revenue potential.
Use conditional statements in calculated fields to create metrics based on specific conditions. For example, create a calculated field to categorize users based on their purchase probability. Use the CASE statement to define categories like ‘High Purchase Probability’ (e.g., purchase probability > 0.8), ‘Medium Purchase Probability’ (e.g., purchase probability between 0.5 and 0.8), and ‘Low Purchase Probability’ (e.g., purchase probability < 0.5). This categorization simplifies the interpretation of purchase probability and enables targeted strategies for each category.
Combine multiple predictive metrics in calculated fields to create composite metrics. For example, create a ‘customer engagement score’ by combining purchase probability, churn probability, and website engagement metrics. Weight each metric based on its relative importance to your business goals. Composite metrics provide a holistic view of customer behavior Meaning ● Customer Behavior, within the sphere of Small and Medium-sized Businesses (SMBs), refers to the study and analysis of how customers decide to buy, use, and dispose of goods, services, ideas, or experiences, particularly as it relates to SMB growth strategies. and predictive potential.
Use date and time functions in calculated fields to analyze predictive trends over time. For example, calculate the ‘week-over-week change in purchase probability’ using date difference functions. This metric highlights changes in purchase probability trends and helps identify periods of growth or decline. Time-based calculated fields are crucial for monitoring the dynamic nature of predictive metrics.
Advanced Formulas and Functions for Sophisticated Metrics
Looker Studio’s formula language supports advanced functions for more sophisticated calculated fields. Explore functions related to text manipulation, data aggregation, and statistical analysis. These advanced functions unlock the potential for creating highly customized and insightful predictive metrics.
Use text functions to manipulate dimension values and create new dimensions based on text patterns. For example, extract product categories from product names using text functions and create a calculated dimension for ‘product category’. This calculated dimension can be used to segment predictive metrics by product category, even if product category is not directly available as a dimension in GA4.
Utilize data aggregation functions to calculate aggregated predictive metrics. For example, calculate the ‘average purchase probability per session’ by dividing the sum of purchase probability by the total number of sessions. Aggregation functions allow you to derive metrics at different levels of granularity and gain insights into average predictive behavior.
Explore statistical functions for more advanced predictive analysis. While Looker Studio is not primarily a statistical analysis tool, it offers some basic statistical functions that can be used in calculated fields. For example, calculate moving averages of predictive metrics to smooth out fluctuations and identify underlying trends. Statistical functions can enhance the analytical depth of your predictive dashboards.
Test and validate your calculated fields thoroughly. Ensure that your formulas are correctly implemented and that the calculated metrics are producing accurate and meaningful results. Use Looker Studio’s formula validation feature to check for syntax errors and data type mismatches.
Compare calculated metric values with expected values to verify their accuracy. Thorough testing is essential for ensuring the reliability of your custom predictive metrics.
Intermediate Summary ● Taking Predictive Dashboards to the Next Level
The intermediate stage of Looker Studio predictive dashboards focuses on customization, segmentation, and calculated fields. Tailor dashboards to specific SMB industries and business needs, focusing on relevant predictive metrics and visualizations. Master advanced segmentation and filtering techniques to uncover granular insights for different customer segments.
Leverage calculated fields to create custom predictive metrics that provide deeper and more tailored analysis. By mastering these intermediate techniques, SMBs can transform basic dashboards into powerful analytical tools that drive data-informed decisions and optimize business performance.
Customization, advanced segmentation, and calculated fields are key intermediate steps for SMBs to create powerful, tailored predictive dashboards for deeper data analysis and informed decision-making.
Table 2 ● Dashboard Customization Examples for SMBs
| SMB Type E-commerce |
| Focus Metric Purchase Probability, Predicted Revenue |
| Key Dimensions for Segmentation Product Category, Marketing Channel, Customer Segment |
| Customization Strategy Visualize trends by channel, segment by product category, integrate e-commerce data. |
| SMB Type Service-Based |
| Focus Metric Churn Probability |
| Key Dimensions for Segmentation Service Type, Subscription Tier, Customer Lifecycle Stage |
| Customization Strategy Segment by service type, track trends over lifecycle, blend customer satisfaction data. |
| SMB Type Local Business |
| Focus Metric Website Engagement (Directions, Calls), Purchase Probability |
| Key Dimensions for Segmentation Geographic Location, Device Category, Traffic Source |
| Customization Strategy Segment by location, integrate GMB data, track online-to-offline journey. |
List 2 ● Advanced Looker Studio Features for Predictive Dashboards
- Advanced Segmentation (behavioral, sequential, combined segments).
- Interactive Filters (dropdowns, checkboxes, range sliders).
- Calculated Fields (custom metrics, conditional logic, advanced functions).
- Data Blending (combining GA4 data with CRM, sales data, etc.).
- Automated Alerts (notifications based on metric thresholds).
Advanced
Integrating External Data Sources for Enhanced Predictive Power
To truly push the boundaries of predictive analytics, advanced SMBs should consider integrating external data sources with GA4 and Looker Studio. GA4 provides valuable website and app behavior data, but combining it with data from CRM systems, sales platforms, marketing automation Meaning ● Marketing Automation for SMBs: Strategically automating marketing tasks to enhance efficiency, personalize customer experiences, and drive sustainable business growth. tools, and even external market data can significantly enrich predictions and provide a more holistic view of the customer journey and business landscape. This data integration Meaning ● Data Integration, a vital undertaking for Small and Medium-sized Businesses (SMBs), refers to the process of combining data from disparate sources into a unified view. unlocks more sophisticated analysis and more accurate forecasting.
CRM Data Integration for Customer-Centric Predictions
Integrating CRM data with GA4 predictive metrics allows for a deeper understanding of customer relationships and their impact on future behavior. CRM systems Meaning ● CRM Systems, in the context of SMB growth, serve as a centralized platform to manage customer interactions and data throughout the customer lifecycle; this boosts SMB capabilities. hold valuable information about customer demographics, purchase history, customer service Meaning ● Customer service, within the context of SMB growth, involves providing assistance and support to customers before, during, and after a purchase, a vital function for business survival. interactions, and customer lifetime value. Blending this data with GA4 data in Looker Studio creates a 360-degree view of the customer and enhances the accuracy of predictive models.
Connect your CRM system to Looker Studio using available connectors or data blending features. Many popular CRM platforms, such as Salesforce, HubSpot, and Zoho CRM, offer direct connectors to Looker Studio. If a direct connector is not available, you can export data from your CRM and upload it to Google Cloud Storage or Google Sheets, which can then be connected to Looker Studio. Ensure data privacy and security compliance when transferring and blending CRM data.
Blend CRM customer lifetime value (CLTV) data with GA4 purchase probability and predicted revenue metrics. Identify high-CLTV customer segments with high purchase probability for targeted retention and upselling campaigns. Conversely, identify low-CLTV customer segments with high churn probability for proactive churn prevention efforts. CRM data enriches the segmentation and targeting capabilities of predictive dashboards.
Incorporate CRM customer service interaction data into predictive models. Analyze the relationship between customer service interactions and churn probability. Identify customer segments with frequent customer service inquiries and high churn risk.
This allows for proactive customer support interventions and service process improvements to reduce churn. CRM data provides valuable context for understanding the drivers of churn and customer dissatisfaction.
Utilize CRM sales data to refine predicted revenue forecasts. Blend CRM sales pipeline data with GA4 predicted revenue metrics. Compare predicted revenue with actual sales pipeline performance to validate prediction accuracy and identify potential discrepancies.
CRM sales data provides a reality check for GA4 predictions and helps improve forecasting accuracy over time. Iterate on your predictive models Meaning ● Predictive Models, in the context of SMB growth, refer to analytical tools that forecast future outcomes based on historical data, enabling informed decision-making. based on CRM sales performance feedback.
Sales Platform Data Integration for Transactional Insights
Integrating data from sales platforms, especially for e-commerce SMBs, provides detailed transactional insights that complement GA4 predictive metrics. Sales platform data includes order details, product information, pricing data, and promotional data. Blending this data with GA4 data in Looker Studio offers a comprehensive view of the sales funnel and enhances predictive analysis of purchase behavior.
Connect your e-commerce platform, such as Shopify, WooCommerce, or Magento, to Looker Studio. These platforms often offer connectors or APIs that facilitate data integration. Use these connectors to blend sales transaction data with GA4 website behavior data in Looker Studio. Ensure data consistency and accurate mapping of data fields across platforms.
Blend sales transaction data with GA4 purchase probability to analyze purchase behavior at a granular level. Segment purchase probability by product, order value, and promotional offers. Identify products with high purchase probability and optimize product merchandising and pricing strategies.
Analyze the impact of promotions on purchase probability and predicted revenue. Sales platform data provides detailed product-level insights for predictive analysis.
Incorporate sales platform order fulfillment Meaning ● Order fulfillment, within the realm of SMB growth, automation, and implementation, signifies the complete process from when a customer places an order to when they receive it, encompassing warehousing, picking, packing, shipping, and delivery. data into churn probability analysis. Analyze the relationship between order fulfillment speed, shipping costs, and churn probability. Identify customer segments with high churn probability and poor order fulfillment experiences.
Optimize order fulfillment processes and shipping options to improve customer satisfaction and reduce churn. Sales platform data helps understand the operational drivers of churn.
Utilize sales platform customer review data to enhance sentiment analysis and predict future purchase behavior. Blend customer review data with GA4 predictive metrics. Analyze the correlation between customer sentiment expressed in reviews and purchase probability or churn probability. Identify products or services with negative sentiment and high churn risk.
Address negative feedback and improve product or service quality to enhance customer loyalty and improve predictions. Sales platform reviews provide valuable qualitative data for predictive analysis.
Marketing Automation Data Integration for Campaign Optimization
Integrating data from marketing automation platforms provides insights into marketing campaign performance and customer engagement Meaning ● Customer Engagement is the ongoing, value-driven interaction between an SMB and its customers, fostering loyalty and driving sustainable growth. that can be leveraged to optimize predictive dashboards. Marketing automation data Meaning ● Automation Data, in the SMB context, represents the actionable insights and information streams generated by automated business processes. includes email campaign metrics, ad campaign performance, social media engagement, and lead nurturing data. Blending this data with GA4 data in Looker Studio enables a closed-loop marketing optimization process driven by predictive insights.
Connect your marketing automation platform, such as Mailchimp, Marketo, or ActiveCampaign, to Looker Studio. These platforms often offer connectors or data export options. Blend marketing campaign performance data with GA4 website behavior data and predictive metrics. Track the impact of marketing campaigns on purchase probability, churn probability, and predicted revenue.
Analyze the effectiveness of different marketing channels and campaign types in driving purchase probability. Segment purchase probability by marketing campaign source and medium. Identify high-performing marketing channels and campaigns that generate high purchase probability leads.
Allocate marketing budget to channels and campaigns with the highest predictive ROI. Marketing automation data informs marketing budget allocation and campaign optimization.
Incorporate email marketing engagement data into churn probability analysis. Analyze the relationship between email open rates, click-through rates, and churn probability. Identify customer segments with low email engagement and high churn risk.
Re-engage at-risk customers with personalized email campaigns and offers. Marketing automation data enables proactive churn prevention through targeted communication.
Utilize marketing automation lead scoring data to prioritize leads with high purchase probability. Blend lead scoring data with GA4 purchase probability metrics. Identify leads with both high lead scores and high purchase probability for prioritized sales follow-up.
Optimize lead nurturing processes to convert high-probability leads into paying customers. Marketing automation data streamlines lead prioritization and sales efficiency.
Leveraging Advanced Looker Studio Features for Complex Predictive Analysis
Advanced Looker Studio users can unlock sophisticated analytical capabilities by mastering advanced features such as parameters, data blending, and custom functions. These features enable complex data manipulation, interactive analysis, and highly customized predictive dashboards that go beyond basic visualizations.
Parameters for Interactive Dashboard Exploration
Parameters in Looker Studio allow for dynamic dashboard exploration by enabling users to change metric and dimension values interactively. Parameters create interactive reports that empower users to perform ad-hoc analysis and uncover hidden insights. Utilize parameters to create dynamic predictive dashboards that adapt to user queries and analytical needs.
Create parameters to allow users to select different predictive metrics to visualize. For example, create a parameter named ‘Predictive Metric’ with options like ‘Purchase Probability’, ‘Churn Probability’, and ‘Predicted Revenue’. Use this parameter to dynamically change the metric displayed in charts and scorecards. Parameter-driven metric selection enables users to focus on the predictive metric most relevant to their current analysis.
Implement parameters to enable users to segment data by different dimensions. For example, create a parameter named ‘Segmentation Dimension’ with options like ‘Device Category’, ‘Traffic Source’, and ‘Customer Segment’. Use this parameter to dynamically change the segmentation dimension in tables and charts. Parameter-driven segmentation allows users to explore predictive metrics across various dimensions interactively.
Use parameters to control date ranges dynamically. Create parameters for ‘Start Date’ and ‘End Date’ and allow users to select custom date ranges for analysis. Parameter-driven date range control provides flexibility for analyzing predictive trends over different time periods. Combine date range parameters with predictive metric parameters for comprehensive interactive analysis.
Combine parameters with calculated fields to create dynamic custom metrics. Use parameters within calculated field formulas to create metrics that adapt to parameter selections. For example, create a calculated field that calculates the average of a selected predictive metric over a selected date range. Parameter-driven calculated fields enable highly customized and interactive predictive analysis.
Data Blending for Multi-Source Predictive Insights
Data blending in Looker Studio allows you to combine data from multiple data sources into a single dashboard. This is crucial for advanced predictive analysis that integrates external data sources with GA4 data. Master data blending techniques to create comprehensive predictive dashboards that leverage the full spectrum of available data.
Blend GA4 data with CRM data, sales platform data, and marketing automation data in a single Looker Studio dashboard. Create blended data sources that combine relevant metrics and dimensions from each data source. Ensure proper join keys and data relationships are defined when blending data. Multi-source data blending provides a holistic view of customer behavior and business performance.
Use calculated fields within blended data sources to create cross-source metrics. For example, calculate ‘CRM-validated predicted revenue’ by blending GA4 predicted revenue with CRM sales data and creating a calculated field that reconciles predictions with actual sales. Cross-source metrics provide a more accurate and reliable view of predictive performance.
Visualize blended data using combined charts and tables. Create charts that display metrics from multiple data sources side-by-side. Use tables to compare data from different sources for the same dimensions.
Combined visualizations highlight the relationships and synergies between data from different sources. Data blending enhances the analytical richness of predictive dashboards.
Optimize data blending performance for large datasets. Data blending can be resource-intensive, especially with large datasets. Optimize data blending queries by selecting only necessary fields and applying filters early in the data blending process.
Consider using aggregated data sources for blending to improve performance. Efficient data blending ensures responsive and performant predictive dashboards.
Custom Functions and Community Visualizations for Extended Capabilities
Looker Studio’s custom functions and community visualizations extend its capabilities beyond standard features. Custom functions allow you to implement advanced data transformations and calculations using JavaScript. Community visualizations offer a library of pre-built visualizations created by the Looker Studio community, providing access to specialized chart types and interactive components. Explore these advanced features to create highly customized and visually compelling predictive dashboards.
Develop custom functions in JavaScript to perform complex data transformations or calculations not available in Looker Studio’s formula language. For example, create a custom function to implement a specific predictive model or algorithm within Looker Studio. Custom functions extend the analytical flexibility of Looker Studio and enable highly tailored predictive analysis.
Utilize community visualizations to enhance the visual appeal and interactivity of your predictive dashboards. Explore the Looker Studio visualization gallery and discover community visualizations that are relevant to predictive data visualization. Implement community visualizations for advanced chart types, maps, and interactive components. Community visualizations enrich the visual storytelling of predictive dashboards.
Contribute to the Looker Studio community by sharing your custom functions and visualizations. If you develop valuable custom functions or visualizations, consider sharing them with the Looker Studio community. Contributing to the community helps expand the collective knowledge and capabilities of Looker Studio users. Community contributions foster innovation and collaboration in data visualization.
Stay updated on new Looker Studio features and community developments. Google regularly releases new features and updates for Looker Studio. The Looker Studio community is constantly evolving and creating new resources.
Stay informed about the latest developments to leverage the full potential of Looker Studio for advanced predictive analytics. Continuous learning and adaptation are crucial for advanced users.
Advanced Automation and Reporting Strategies for Predictive Insights
To maximize the impact of predictive dashboards, advanced SMBs should implement automation and reporting strategies that ensure timely delivery of insights to relevant stakeholders. Automation reduces manual effort, ensures consistent reporting, and enables proactive decision-making based on predictive data. Implement advanced automation Meaning ● Advanced Automation, in the context of Small and Medium-sized Businesses (SMBs), signifies the strategic implementation of sophisticated technologies that move beyond basic task automation to drive significant improvements in business processes, operational efficiency, and scalability. and reporting workflows to transform predictive dashboards from static reports into dynamic decision-support systems.
Scheduled Report Delivery and Email Automation
Schedule automated email delivery of predictive dashboards to key stakeholders on a regular basis. Looker Studio’s scheduled report delivery feature allows you to automatically email dashboard reports in PDF format to recipients at specified intervals. Schedule daily, weekly, or monthly reports based on reporting needs and decision-making cycles. Automated report delivery ensures timely access to predictive insights for relevant teams and individuals.
Customize scheduled report delivery options to tailor reports to specific recipients. Create different dashboard versions or apply filters to scheduled reports to deliver targeted insights to different teams or departments. For example, schedule a marketing-focused predictive dashboard for the marketing team and a sales-focused dashboard for the sales team. Customized report delivery ensures report relevance and actionability for each recipient group.
Implement email alerts based on predictive metric thresholds. Looker Studio’s alert feature allows you to create email notifications that trigger when a metric value exceeds or falls below a specified threshold. Set up alerts for critical predictive metrics, such as purchase probability drops or churn probability spikes. Email alerts provide proactive notifications of significant changes in predictive trends, enabling timely interventions.
Integrate predictive dashboard alerts with workflow automation Meaning ● Workflow Automation, specifically for Small and Medium-sized Businesses (SMBs), represents the use of technology to streamline and automate repetitive business tasks, processes, and decision-making. tools. Connect Looker Studio alerts to workflow automation platforms like Zapier or Integromat. Trigger automated actions based on alert conditions, such as sending notifications to customer service teams when churn probability increases for specific customer segments. Workflow automation transforms predictive insights into automated actions and proactive responses.
API Integration for Data-Driven Applications
Looker Studio’s API enables programmatic access to dashboard data and functionality. Leverage the API to integrate predictive dashboard data into other business applications and systems. API integration transforms predictive dashboards from standalone reports into data sources for broader data-driven applications.
Integrate Looker Studio API with CRM systems to embed predictive dashboards directly within CRM interfaces. Embed customer-specific predictive dashboards within customer profiles in your CRM system. Provide sales and customer service teams with direct access to predictive insights within their daily workflows. API integration enhances the usability and accessibility of predictive data within operational systems.
Utilize the API to extract predictive data for use in custom data analysis or machine learning models. Programmatically extract predictive metrics and dimensions from Looker Studio dashboards using the API. Use extracted data for advanced data analysis in external tools or as input features for custom machine learning models. API integration enables advanced data science applications of predictive dashboard data.
Implement API-driven dashboard updates and automation. Use the API to programmatically refresh dashboard data or update dashboard configurations. Automate dashboard maintenance and updates using API scripts.
API-driven automation streamlines dashboard management and ensures data freshness. Combine API-driven updates with scheduled report delivery for fully automated predictive reporting workflows.
Advanced Summary ● Mastering Predictive Analytics for Competitive Advantage
The advanced stage of Looker Studio predictive dashboards focuses on data integration, advanced features, and automation. Integrate external data sources, such as CRM, sales platforms, and marketing automation systems, to enrich predictive models and gain a holistic view of customer behavior. Master advanced Looker Studio features like parameters, data blending, custom functions, and community visualizations to create sophisticated and interactive predictive dashboards.
Implement advanced automation and reporting strategies, including scheduled report delivery and API integration, to ensure timely delivery of predictive insights and drive data-driven actions. By mastering these advanced techniques, SMBs can achieve a significant competitive advantage Meaning ● SMB Competitive Advantage: Ecosystem-embedded, hyper-personalized value, sustained by strategic automation, ensuring resilience & impact. through proactive and data-informed decision-making, leading to sustainable growth and operational efficiency.
Advanced SMBs achieve competitive advantage by mastering data integration, Looker Studio’s advanced features, and automation, transforming predictive dashboards into dynamic decision-support systems for proactive growth.
Table 3 ● Advanced Tools and Techniques for Predictive Dashboards
| Category Data Integration |
| Tool/Technique CRM Data Connectors, Sales Platform APIs, Marketing Automation Integrations |
| SMB Benefit Enhanced prediction accuracy, 360-degree customer view, richer insights. |
| Category Advanced Features |
| Tool/Technique Parameters, Data Blending, Custom Functions, Community Visualizations |
| SMB Benefit Interactive exploration, multi-source analysis, custom metrics, visual appeal. |
| Category Automation |
| Tool/Technique Scheduled Reports, Email Alerts, API Integration, Workflow Automation |
| SMB Benefit Timely insights delivery, proactive notifications, data-driven applications, efficiency. |
List 3 ● Cutting-Edge Predictive Strategies for SMBs
- Predictive Customer Lifetime Value (pCLTV) Modeling.
- AI-Powered Churn Prediction with External Data.
- Dynamic Pricing Optimization Based on Purchase Probability.
- Personalized Product Recommendations Driven by Predicted Revenue.
- Automated Anomaly Detection in Predictive Metrics for Proactive Issue Resolution.
References
- Provost, Foster, and Tom Fawcett. Data Science for Business ● What You Need to Know About Data Mining and Data-Analytic Thinking. O’Reilly Media, 2013.
- Siroker, Jim, and Pete Koomen. Web Analytics 2.0 ● Empowering Customer Centricity. Sybex, 2010.
- Varian, Hal R. Big Data ● New Tricks for Econometrics. Google, 2014.
Reflection
The adoption of Looker Studio for GA4 prediction dashboards by SMBs signifies a critical shift in business strategy ● a move from reactive operations to proactive anticipation. While the technical implementation of these dashboards is increasingly accessible, the fundamental challenge lies not in the ‘how’, but in the ‘why’ and the ‘what next’. Are SMBs truly prepared to integrate predictive insights into their core decision-making processes, or will these dashboards become yet another reporting tool, admired but underutilized? The real discord emerges when considering whether the operational culture of SMBs, often characterized by agility and intuition, is ready to embrace the data-driven, probabilistic nature of predictive analytics.
The success of these dashboards hinges not just on technical proficiency, but on a deeper cultural transformation towards data literacy and a willingness to challenge established business norms with forward-looking, statistically informed strategies. The question remains ● Can SMBs bridge the gap between technological capability and organizational readiness to truly unlock the transformative potential of predictive analytics, or will the predictive power remain latent, a tool on the shelf rather than a driver of daily operations and strategic direction?
Unlock growth with Looker Studio & GA4 prediction dashboards ● Actionable SMB insights, no data science degree needed.
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