Connected processes

This refers to different business or operational tasks that are integrated or linked together. In a company or system, processes that are “connected” work together in a way that enables smoother workflows, communication, and better outcomes. For example, in an enterprise software system, one department’s process (like sales) might be connected to another (like inventory), allowing data and actions to flow seamlessly between them.

Actionable intelligence

This refers to insights, data, or information that is gathered and analysed in a way that leads to a clear, practical decision or action. In a business context, “actionable intelligence” often refers to data-driven insights that can inform decisions, improve efficiency, or solve problems. For instance, a company might collect data on customer behaviour and analyse it to take actionable steps in marketing strategies.

Example

Connected Processes:

In a fashion brand, the marketing processes are interconnected to ensure a smooth flow of data and actions across multiple channels. Some of these processes might include:

• Customer Data Collection: Through online and offline channels, the company collects data about customers—such as demographics, purchase history, and online browsing behaviour. This data is often gathered through website interactions, loyalty programmes, and email sign-ups.
• Content Creation & Campaign Planning: The marketing team creates content (such as social media posts, ads, emails, etc.) to promote the brand and its products. They may use customer data to personalise the content.
• Advertising Channels: The campaign is executed across various channels such as social media (Instagram, Facebook), email newsletters, paid search ads, and even physical store promotions.
• Customer Interaction/Engagement: Customers interact with the campaign via ads, emails, website visits, or in-store visits. These interactions are tracked and logged.
• Post-Campaign Analysis: After the campaign, performance data is analysed to measure the effectiveness of the campaign—did it increase website traffic, sales, customer engagement, etc.?

All these processes are connected in a way that ensures consistent messaging, tracks customer behaviour, and measures the campaign’s effectiveness. For example, if a customer clicks on an Instagram ad, that data can be fed into the company’s CRM system, which helps tailor follow-up emails with personalised recommendations.

Actionable Intelligence:

The data from these connected processes is then analysed to create actionable intelligence—insights that can inform marketing decisions and drive real actions. Here’s how actionable intelligence can be used in this context:

• Customer Segmentation and Personalisation: Analysing customer data (purchase history, browsing behaviour, etc.) can help identify distinct segments, such as:
  • Customers who prefer casual wear vs. formal wear.
  • Customers who purchase frequently vs. occasional buyers.
  • Customers who engage with specific types of content (e.g., those who like fashion influencers or those interested in sustainable fashion).
• Actionable Insight: Personalised marketing campaigns can be created for each segment, such as sending exclusive offers for casual wear to casual wear buyers or offering a discount on eco-friendly products to those who follow sustainability-related content.
• Campaign Performance Optimisation: By tracking customer interactions (like click-through rates, engagement on social media, email open rates, etc.), the marketing team can determine which messages, products, or channels are performing best.
• Actionable Insight: If an Instagram ad featuring a new collection generates higher engagement than Facebook ads, the team can allocate more budget toward Instagram ads for the remainder of the campaign or extend the promotion on Instagram.
• Predictive Analytics for Future Campaigns: By analysing data from previous campaigns, such as seasonal trends or the performance of different product lines, the company can forecast which products are likely to be popular during the upcoming season.
• Actionable Insight: If data from past campaigns shows that certain colours or styles of clothing were popular during the spring, the marketing team can emphasise those products more in future campaigns, increasing the chances of success.
• Customer Retention Strategies: By analysing customer behaviour after the campaign ends (e.g., repeat purchases, engagement with post-purchase content, etc.), actionable insights can be drawn to improve customer retention.
• Actionable Insight: If data shows that customers who received personalised emails or follow-up messages post-purchase tend to return for repeat purchases, the company can create automated post-purchase email sequences to maintain engagement with all customers after they make a purchase.
• Budget Allocation: Data from the campaign (e.g., return on ad spend, conversion rates, etc.) can help marketers decide where to allocate future budgets.
• Actionable Insight: If the data shows that paid search ads generated the highest ROI compared to organic posts or influencer partnerships, the marketing team can redirect a larger portion of the budget into paid search for future campaigns.

Summary:

In this example, connected processes—such as customer data collection, campaign creation, ad execution, and post-campaign analysis—work together to ensure a consistent and effective marketing effort. Meanwhile, actionable intelligence is derived from analysing the data across these processes. This allows the marketing team to take real, impactful actions—like personalising campaigns, optimising ad spend, forecasting trends, and improving customer retention.