Highlights:

  • To build a complete customer picture, marketers should unify structured and unstructured data.
  • Once customer data is centralized, companies must activate it to enable targeted marketing and personalized customer interactions.

Advanced market analytics has become crucial in B2B marketing. Marketers are increasingly interested in using data to make informed decisions. By analyzing large data sets from various channels, they can improve their strategies.

This article explains effective marketing analytics methods in simple terms and provides examples. It also discusses how to implement these methods smoothly and use them to drive better marketing results.

The 5-Step Blueprint for Advanced Marketing Analytics

Advanced marketing analytics is changing how businesses engage with customers and make data-driven decisions.

By using sophisticated techniques, marketers gain deeper insights into consumer behavior, optimize campaigns, and drive higher ROI. Beyond basic metrics, advanced analytics enables trend prediction, personalization, and resource allocation across channels.

We just need to carefully follow the below steps for market research advanced analytics:

Step 1: Develop a holistic view of customer data

Every customer interaction generates valuable data, but legacy systems often store purchase, website traffic, email, app, paid media, and loyalty program data in silos. This fragmentation hinders data scientists from building effective attribution models and complicates real-time personalization.

Business units access filtered data through separate dashboards, obscuring the complete picture. The expanding marketing technology stack further complicates data integration, as tools for web analytics, e-commerce, video, and email often generate siloed information.

To achieve a true 360-degree customer view, marketers must consolidate both structured (e.g., organization entries) and unstructured data (e.g., customer reviews) in a single data warehouse. Clean, merged data sets should be accessible to various teams without relying on engineering support, allowing for agile, advanced analytics.

  • Store and access unified data

Companies need a scalable cloud platform to store and analyze structured and unstructured data. A Business Intelligence (BI) platform can provide self-service analytics to most team members.

To achieve this, analytics teams should work closely with business users to understand their needs and provide easy-to-use dashboards and tools. Avoid BI solutions that are too complex for non-technical users.

Step 2: Boost ROI at every touchpoint

Customer journeys have become more complex over the past decade due to multiple devices and abundant online information, resulting in more touchpoints along the path to purchase.

  • Explore attribution modelling

As consumer journeys have become less linear, attribution modeling—assigning credit for sales to various touchpoints—has gained importance. Marketers must assess ROI for channels like Facebook and Google AdWords to adjust spending accordingly.

Different attribution approaches exist, from models giving full credit to the first or last touch to those distributing credit among touchpoints. The best choice depends on the sales cycle and touchpoint mix. Many organizations start with static allocations and then refine them using data-driven insights.

Before applying any attribution model, customer data from all marketing channels must be centralized.

  • ROI from attribution modeling

Build attribution models to determine the effectiveness of marketing channels. Use these models to allocate marketing spend based on actual revenue impact. Regularly monitor and adjust the marketing mix to optimize ROI.

Step 3: Maximize campaign effectiveness

Centralizing customer data is just the start. Companies must activate this data across marketing channels to enable large-scale personalization. This involves syncing data with platforms like e-commerce sites, social media platforms, and other marketing clouds to deliver tailored content, offers, and experiences, leading to higher conversions and increased revenue.

  • Avoid the engineering backlog

Personalization can be slow and expensive without a unified data source. Engineering resources are often needed to sync data between systems, which can create a backlog and hinder personalization efforts.

  • Sync data to marketing channels for incremental revenue

To personalize marketing, marketers need to be able to activate customer data on various channels themselves.

Building a customer data platform offers more flexibility but requires higher integration costs.

Empowering marketers with direct control over targeting and segmentation can significantly improve campaign performance.

Step 4: Maximize personalization via data science

Attribution modeling and personalizing content, offers, and experiences are important uses of advanced analytics, but there are many other ways it can be applied as well.

  • Harness predictive analytics

Advanced marketing data analysis can help marketers target ads more effectively, identify customers at risk of churn, and improve product recommendations. Machine learning can enhance ad bidding, product upgrade scoring, lead scoring, and next-best-offer recommendations.

  • Power algorithms with a single source of data

Algorithms thrive on high-quality data. A unified data platform empowers data science teams to access clean, integrated datasets, facilitating quicker model development and deployment across a range of applications.

Concluding Lines

As a marketing professional, the effectiveness of your campaigns depends on the quality of your data. With the right data, you can create more impactful campaigns and discover better solutions to real-world challenges. Advanced technologies and processes enable smarter analytics, elevating marketing to new levels of success.

Moreover, marketing organizations face pressure to cut costs and deliver ROI, increasingly relying on advanced analytics. Centralizing customer data is key to advancing analytics maturity. Organizations can follow three steps:

  • Ingest data: Use extract, transform, and load (ETL) tools with prebuilt connectors to avoid relying on engineers for ongoing maintenance.
  • Store unified data: A single platform that supports both structured and semi-structured data is essential for scalability.
  • Make data accessible: Analytics should be accessible to all users, not just data scientists. Requiring IT support for data access can hinder progress and lead to failure. Empowering business users with direct access fosters a data-driven culture and enhances decision-making.

Finally, though scaling to advanced marketing analytics requires investment, a unified data approach empowers teams to make real-time, data-driven decisions that significantly improve campaign performance and business outcomes.

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