24 October, 2024
How to make your data work this Black Friday
We explored how to make your data work this Black Friday, predictive analytics and analytics frameworks.
Reading time 12 minutes
Hey there!
Welcome back. This edition is packed with actionable insights to help you make the most of your data. We’re exploring how predictive analytics in Power BI can transform your decision-making, giving you a competitive edge. We’ll also break down the steps to building a simple analytical framework, so you can turn data into decisions with ease. And with Black Friday around the corner, don’t miss our guide on making your data work harder to maximise sales and customer engagement during the busiest shopping event of the year.
Leveraging Power BI for predictive analytics can transform how you make decisions by providing clear, actionable insights into future trends. Here’s how you can use it to build future-proof strategies that help your organisation stay ahead.
Before diving into Power BI, identify the business outcomes you want to predict. Whether it’s sales, customer demand, or operational efficiency, clarity is key. Power BI’s ability to connect multiple data sources will allow you to draw insights from across your organisation, but starting with a clear goal ensures your efforts are focused.
Action Step:
Define a specific question you want to answer. For example, "How much will sales increase if we launch a new product in Q4?" or "What inventory levels will we need to meet seasonal demand?"
Data integration is the foundation of predictive analytics. In Power BI, you can connect to various data sources such as CRM systems, marketing platforms, and ERP systems to build a complete view of your business. The more comprehensive your data, the more accurate your predictions will be.
Action Step:
Use Power BI’s "Get Data" feature to connect to all your critical data sources. Ensure you’re pulling data in real-time by setting up automatic refreshes to keep your reports up to date.
Leverage historical data to build predictive models that highlight future outcomes. Power BI’s machine learning and AI visuals allow you to identify patterns, trends, and anomalies. Use these tools to create models that forecast sales, predict customer churn, or estimate market demand.
Action Step:
Start small. Use the "Forecast" feature in Power BI visuals to make simple predictions based on past trends. Once comfortable, integrate more advanced machine learning tools like Azure ML to refine your models and make predictions more accurate.
Power BI’s "What-If" parameters let you simulate different business scenarios by adjusting key variables. This capability is crucial for decision-making as it allows you to understand how changes—such as price increases, marketing spend, or new product launches—will affect outcomes.
Action Step:
Set up "What-If" parameters in your reports to explore potential business strategies. For example, test how changing your pricing model might impact revenue, or how increased marketing could influence customer acquisition.
Predictive analytics is only as valuable as the action it inspires. Use Power BI’s interactive dashboards to visualise predictions in real-time, making insights clear and actionable for your teams. Share these dashboards with relevant stakeholders so everyone has access to the same insights, reducing decision lag.
Action Step:
Create role-specific dashboards that focus on key performance indicators (KPIs) for each team. For example, your sales team might need future revenue projections, while marketing could benefit from predicted customer behaviour trends.
Power BI allows you to automate the delivery of reports and set up alerts for key metrics. This ensures you don’t miss important changes in predictions. For example, if your sales forecast suddenly shifts due to market conditions, Power BI can notify you immediately, allowing you to act fast.
Action Step:
Set up email alerts for critical thresholds—like when forecasted sales drop below a certain point or when demand exceeds inventory levels. These notifications keep your team proactive, ensuring you react quickly to new developments.
Predictive models should evolve over time. As your business generates more data, continuously refine your models to improve accuracy. Power BI’s ease of use means you can update your assumptions and adjust variables without needing to rebuild entire models from scratch.
Action Step:
Regularly update your models by importing the latest data and testing different assumptions. Consider using Power BI’s "What-If" feature to tweak variables and see how it influences your forecasts.
Power BI’s predictive analytics capabilities don’t just offer insights—they help you take informed action. Whether you’re planning your next marketing campaign, managing supply chains, or forecasting revenue, Power BI enables you to make smarter, faster decisions. Start by integrating your data, build simple predictive models, and scale up as you gain confidence. The key is to act on the insights Power BI delivers to keep your business ahead of the curve.
Next Step:
Start using Power BI’s predictive analytics for a small project or forecast. Once you’ve seen the value, expand its use across more areas of your business for a deeper, future-proof approach to decision-making.
Start by identifying the key questions you need answers to. Whether it’s understanding customer behaviour, optimising operations, or forecasting sales, clarity on the outcome is essential. This will shape the data you collect and the metrics you track.
Action: Write down specific goals, like “How can we increase customer retention by 10% next quarter?”
Next, gather data from all relevant sources—CRM systems, sales databases, or customer feedback. Ensure the data is clean, consistent, and easily accessible. Centralising data in tools like Power BI or Google Analytics can streamline analysis.
Action: Standardise your data collection process to avoid inconsistent or incomplete data.
Once your data is organised, select the appropriate analysis method. This could be trend analysis, segmentation, or predictive modelling, depending on your goals. Keep it simple to start—focus on uncovering patterns that inform decision-making.
Action: Use basic visualisation tools to identify trends, such as line charts for sales over time.
Insights are valuable only when acted upon. Create actionable recommendations based on the data analysis. For example, if sales drop mid-month, offer targeted promotions to address this.
Action: Present data-driven insights in an easy-to-digest format, ensuring that decision-makers can act quickly.
By following this framework, businesses can transform raw data into clear, actionable decisions that drive growth and efficiency.
Black Friday is no longer just a day of discounts—it’s a data-driven opportunity for retailers to boost sales, attract new customers, and enhance loyalty. As competition intensifies, data has become the key differentiator, offering actionable insights that allow businesses to outsmart competitors and deliver an optimised shopping experience.
Here’s how businesses can harness the power of data this Black Friday to drive success.
Running out of stock on Black Friday can be a disaster, costing potential sales and damaging customer trust. By using historical sales data, businesses can predict which products will be in high demand and adjust stock levels accordingly. With modern tools like Power BI or Google Analytics, retailers can analyse previous Black Friday trends, pinpointing the exact items that tend to sell out fast.
Actionable Tip:
Review your historical Black Friday data to identify top-performing items and adjust your inventory orders accordingly. Use forecasting tools to simulate different demand scenarios, ensuring you’re well-prepared for any surge in sales.
Generic marketing messages won’t cut through the noise of Black Friday promotions. Personalisation is essential, and the best way to achieve this is through data analysis. By leveraging customer data such as purchase history, browsing behaviour, and demographic information, you can create targeted campaigns that resonate with specific customer segments.
For example, send tailored discounts to loyal customers or recommend products based on previous purchases. Data-driven marketing is proven to increase click-through rates and conversions, ensuring your campaigns generate maximum impact.
Actionable Tip:
Segment your email list based on purchasing patterns and interests. Use personalisation tools to offer tailored deals—such as "Since you loved [previous product], we thought you might like this 50% off deal on Black Friday."
Black Friday is a dynamic event where customer behaviour can shift rapidly, requiring real-time adjustments to pricing strategies. By monitoring competitor pricing and sales performance throughout the day, businesses can adapt their prices to stay competitive without eroding profit margins.
Advanced pricing algorithms and data analytics tools help retailers adjust prices based on real-time demand, competitor behaviour, and customer preferences. Data-driven pricing ensures you’re never under or overpriced, helping you maintain a competitive edge while maximising profitability.
Actionable Tip:
Set up dynamic pricing systems that use data from competitor monitoring tools and sales performance metrics to automatically adjust pricing throughout Black Friday.
Data isn’t just about driving sales—it’s also crucial for enhancing the customer experience. Analysing customer behaviour during Black Friday helps identify friction points in the shopping journey. Whether it’s through tracking abandoned carts, monitoring page load times, or reviewing customer support queries, data can reveal where customers are dropping off or experiencing issues.
For example, if data shows that many customers abandon their carts at the payment stage, you can introduce smoother payment methods or offer real-time customer support. Identifying and addressing these pain points in real-time ensures a seamless shopping experience, boosting satisfaction and conversions.
Actionable Tip:
Set up real-time monitoring for your website’s performance and checkout process. Use data to quickly identify and resolve bottlenecks, ensuring customers enjoy a smooth shopping journey.
Black Friday is a significant investment for most businesses, so it’s crucial to understand which marketing channels are delivering the highest returns. By tracking metrics such as click-through rates, conversion rates, and customer acquisition costs, data can help retailers fine-tune their marketing efforts for maximum impact.
Additionally, Black Friday provides a treasure trove of customer insights. Analysing who buys what, when, and how much can inform future campaigns and help businesses better understand their customer base. These insights can drive more effective retargeting efforts and shape your approach for the next big event.
Actionable Tip:
Use data analytics tools to track each marketing channel’s performance. Post-Black Friday, review these insights to optimise your ad spend and refine future marketing strategies.
Black Friday is a huge event on social media, and tapping into social media analytics can be a goldmine for businesses. Monitoring social media mentions, shares, and customer sentiment in real-time allows you to gauge how well your promotions are performing and make quick adjustments if necessary. Social listening tools can also help identify trending products, allowing you to capitalise on the latest buzz.
Actionable Tip:
Use social media analytics tools to track engagement and sentiment around your Black Friday deals. Respond quickly to both positive feedback and complaints to maintain a strong brand presence during the event.
Thanks
Issy @ OnMetrix
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