Table of Contents

What Is Marketing Analytics?

Current State of Marketing Analytics: Trapped in the Legacy Cycle

Seeking for Contemporary, Forward-thinking Alternatives

Urgent Need for Marketing Decisioning Automation

The Future of Marketing Analytics: Beyond Meaningless Dashboards

The Role of Data-Driven Analytics in Marketing

Artificial Intelligence and Machine Learning Capabilities in Marketing Analytics

Data-driven Marketing as a Business Strategy

The Role of Advanced Cross-channel Marketing Analytics

Drive Long-term Growth with Intelligent Marketing Analytics

Most Important Growth Marketing Metrics to Watch

How Narrative BI Helps Enhance Marketing Intelligence & Boost the Value of Data

The Future of Marketing Analytics

future-marketing-analytics

What Is Marketing Analytics?

Marketing analytics is a strategic component of marketing that enables teams to build and implement more accurate and efficient data-driven strategies. 

As the key players in shaping the future of marketing analytics, CMOs and marketing teams invest more time and money to figure out how their marketing efforts affect growth and performance. To shine a light on this route, let's take a closer look at what marketing analytics is, why it is crucial, and how to apply it to your organization.

Marketing analytics is designed to inform on the past, illuminate the present, and guide the future.

Marketing data analysis is critical in a fast-paced market, but the data becomes irrelevant fast, and as a result - has little or no use. Technological innovations, particularly in the field of generative AI for marketing, have led to a shift in the traditional customer journey. The relationship between brand and buyer is now owned by marketers rather than the sales team. Consequently, senior executives must access comprehensive market intelligence solutions that continuously track, collect, and provide real-time insights based on precise and reliable data.

Instead of traveling back in time and looking through old records, marketers can leverage real-time analytics and generative AI for marketing to understand what is occurring right now and develop a strategy for moving forward.

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Current State of Marketing Analytics: Trapped in the Legacy Cycle

Legacy marketing analytics tools can't keep up with the ever-increasing demands for data within every business today. Marketing teams spend more time analyzing data than generating insights, since they lack the resources and tools to execute their vision.

According to Gartner survey The State of Marketing Budgets, when asked what they’d prioritized to reduce costs, one of CMOs’ top responses was: to gather data-driven insights from digital channels (39%).

Top Marketing Intelligence Challenges in 2024

  1. The time-consuming data integration processes undermine data accuracy, authenticity, and actionability. 
  2. Leverage real-time insights to navigate the uncertainty.
  3. Marketing performance data continues to grow across numerous channels and platforms, resulting in performance measuring complexity.
  4. Timely, accessible, and actionable data and insights are difficult to obtain.
  5. Lack of speed in reporting and communicating results and drawbacks to C-suite.
  6. Centralizing data across all channels and sources intended to optimize every customer journey stage is time and effort-consuming.
  7. Optimizing marketing spends.
  8. Gaining greater and faster insights to understand and engage with consumers.
  9. Providing customers with instant, personalized messages in the proper channels.
  10. Measuring performance across marketing channels and implementing large-scale data-driven campaigns.
  11. Fast data processing since real-time use cases require lightning-fast marketing data processing.

Aligning marketing analytics insights with company strategy can add tangible benefits and helps solve tough issues — from defining which questions to ask to instantly accessing the relevant data and generating queries.

Seeking for Contemporary, Forward-thinking Alternatives

Growing demand for data-driven marketing analytics is fueling a lot of software initiatives in the data space. There is a tremendous amount of marketing data there. The challenge now is to determine how to leverage this marketing analytics trend effectively.

The limitations of traditional business intelligence platforms have prompted marketing executives to seek contemporary, forward-thinking, and adaptable alternatives.

Modern marketing analytics tools powered by Generative BI address the new challenges with self-service instruments, empowering non-tech users with intelligent yet easy-to-use solutions. Generative BI enables marketing professionals to analyze unstructured data and achieve more with it, offering unprecedented scale and speed.

Generative AI: the future of marketing analytics

Exploring the future of marketing analytics, we see a shift towards more dynamic and predictive solutions.

The development of BI solutions using generative AI for marketing welcomes us with the promise of hyper-intelligent marketing analytics tools with intuitive cognitive capabilities that will transform the marketing landscape in unexpected ways.

In 2020 the marketing technology landscape grew by 13,6%, up to 8,000 MarTech Solutions.

As seen in the picture above, with a 25.5% increase, data solutions are by far the fastest-growing market segment. Today's business intelligence landscape is quickly evolving and shows no signs of slowing down.

BI-powered self-service marketing analytics platforms shift the focus away from IT and data scientists, offering intuitive and flexible set of features to many not-data-savvy business users. It allows marketing teams to produce reports and actionable recommendations on the fly while sharing data with C-suite executives to speed up decision-making.

Urgent Need for Marketing Decisioning Automation

Marketing data is continuously growing throughout the marketing cycle; it is created in website visits, downloads, advertising clicks, social media reactions, shares, transactions, purchases, streaming content, and much more. When properly analyzed, marketing data can help you better navigate in a speeding up world. However, it is impossible to analyze vast amounts of data manually, so many growth leaders leverage Generative AI for marketing: advanced artificial intelligence systems that can automatically analyze data and generate insights and predictions.

According to Gartner, “though CMOs understand the importance of applying analytics throughout the marketing organization, many struggle to quantify the relationship between insights gleaned and their company’s bottom line”.

While having access to marketing data is no longer a difference, what matters most is what marketing teams do with it, how they are using data-driven insights to make better and faster decisions, improve customer experiences, and drive revenue. The marketing teams that succeed in today's environment are the ones who excel at harnessing data to drive intelligent action and deliver measurable real-world outcomes.

Marketing teams must make significant digital adjustments, leveraging data-driven marketing analytics to deal with today's complexity and unpredictability while being competitive in the future.

Image courtesy: Gartner

The Future of Marketing Analytics: Beyond Meaningless Dashboards

Marketing campaign analysis is one of the top priority areas for senior marketing leaders. Reliable marketing data is required for intelligent decisions and delivering exceptional client experiences. It isn't beneficial to collect data analytics and business intelligence insights if you keep them locked up in your head. 

Pushing beyond meaningless dashboards and using business intelligence to translate data-driven insights into actionable strategies, enhancing your business is essential. To trust data, marketing executives must stay on the right side of controls, maintaining customer trust. It would be best to have the senior marketers analyze performance and gain data-driven insights to make the right judgments, which is challenging for growth teams.

One of the most valuable assets of a good marketing analytics solution is its return on investment, which may have far-reaching effects on your business. When marketers can accurately report and track their program's success, they'll be able to answer questions from the C-suite confidently.

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The Role of Data-Driven Analytics in Marketing

Top 10 Analytics Barriers for Growth Marketing Teams

  1. Lack of data and analytics governance/management
  2. Lack of real-time actionable insights
  3. Lack of reliable, trustworthy analytics to support managing change
  4. Misalignment on marketing measurement and reporting that affects executive decision making
  5. Lack of resources and talent
  6. Low return on marketing investment
  7. Gaining stakeholder buy-in to launch new campaigns
  8. Ensuring data availability
  9. Choosing the right marketing analytics solutions
  10. Inability to accurately measure and adapt to changes

Artificial Intelligence and Machine Learning Capabilities in Marketing Analytics

AI and machine learning are expected to play a significant part in marketing efficiency and become increasingly important as marketers seek multichannel marketing solutions. Generative AI for marketing is becoming increasingly significant, offering capabilities like automated content generation and predictive analytics.

The critical secret of generative AI in marketing analytics is turning data into instant insight, which enables businesses to make better decisions for the future. This is the fundamental principle of data-driven marketing. Data is the basis for all significant decisions about campaigns, audiences, and competition in marketing.

Marketing intelligence helps teams to take the straight path to smarter decisions across all your business efforts.

Marketing Analytics Trends to Watch in 2024

Among the key marketing analytics trends to watch in 2024 are AI/ML-powered solutions and data democratization:

  • Data Collection and Governance
  • Data Democratization
  • Automation
  • Self-service - low IT involvement
  • Future-ready tools: AI/ML-powered solutions
  • Generative AI for marketing

There are many ways marketing can use artificial intelligence (AI), including alerts, anomaly detection, machine learning (ML), natural language processing (NLP), predictive analytics, and other AI technologies. These technologies are crucial because they allow marketers to act immediately on new insights, automatically serving dynamic content. 

Dynamic pricing, automated sales funnel projections, automated content generation, and real-time personalization are available with predictive analytics, machine learning, and AI-powered marketing capabilities. The Generative BI data revolution is here, disrupting all the legacy systems, concepts, and approaches.

Data-driven Marketing as a Business Strategy

Many marketing leaders are thinking about data-driven transformation as a future activity. Increasing market share, expanding into new customer groups, and enhancing product positioning are just a few of the objectives a Chief Marketing Officer is supposed to achieve each year. In a never-ending cycle of fighting to succeed, it's easy to get caught up in tactical, day-to-day operations and lose focus on long-term strategic goals.

Proper marketing metrics and BI-powered analytics allow marketing leaders to see the big picture of marketing analytics trends, determine which campaigns performed better and why, keep track of trends over time, calculate ROI for each activity, and accurately predict future results.

Today's marketing leaders leverage data to support business objectives and strategies throughout the innovative data-driven marketing analytics cycle:

  • Data Collection.
  • Data Transformation.
  • Data Analysis.
  • Data Communication.
  • Data-driven Decision & Actions.

Augmented marketing analytics may provide a wealth of valuable information that can be used to boost business success. Data-driven marketing analytics can make it easier to determine the ROI of marketing efforts, justify marketing expenses, and locate areas in which performance needs improving.

The Role of Advanced Cross-channel Marketing Analytics

Many marketers find themselves behind the curve when embracing augmented analytics, facing challenges across key campaign phases:

  1. Identifying and reaching audiences at the right time with the right message.
  2. Improving the data quality to gain more valuable insights.
  3. Selecting the best advertising and content delivery channels.
  4. Determining correct marketing attribution across multiple channels.

Marketing teams must integrate and analyze marketing performance data from all touchpoints to reach today's digital-first customers while optimizing growth.

Customer journeys can be seamless by leveraging cross-channel marketing strategies integrating advanced analytics into multiple marketing channels. 

Customer engagement across several touchpoints allows marketers to use people-based marketing to develop comprehensive profiles that enhance the innovative data-driven marketing analytics cycle, customer experience, funnel conversion, loyalty, and product retention. Creating a data-driven marketing culture will result in rapid business growth.

How to Create a Successful Cross-Channel Marketing Campaign Leveraging Data:

  1. Employ a unified marketing measurement strategy.
  2. Take advantage of data-driven decision-making.
  3. Create Customer Personas to discover trends across your audience.
  4. Align your content to a particular stage of the customer journey, delivering it via best-performing channels.

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Drive Long-term Growth with Intelligent Marketing Analytics

Generative business intelligence systems enable marketers to leverage large volumes of information in entirely new and innovative ways that were previously impossible. Users will be able to ask and answer any question, with any data, when they want. Marketing metrics alone make you feel good, but they don’t offer clear guidance for what to do.

As self-service marketing analytics technology continues to provide marketers with greater power and influence, it becomes increasingly essential to manage advanced marketing analytics properly to optimize performance, impact, and customer loyalty to drive growth.

Soft metrics (also known as "vanity" metrics) such as brand recognition, impressions, organic search ranks, and reach are significant, but only if they can be tangibly linked to hard metrics such as pipeline, conversion rates, and revenue.

Organizations can save time, money, and improve performance by embracing the power of data to become intelligent, data-driven, insight-driven companies. Marketers need to transform and analyze relevant data to build a comprehensive picture and follow the most critical marketing metrics we listed below.

Most Important Growth Marketing Metrics to Watch 

  • Customer acquisition
  • Sales-qualified leads/pipeline
  • Marketing-qualified leads
  • Customer acquisition cost
  • Customer engagement
  • Funnel conversion rates
  • Customer satisfaction or NPS
  • Sales/revenue
  • Customer retention
  • The lifetime value of a customer
  • Customers/client volume
  • Return on marketing investment
  • Marketing spend per user/customer
  • Market share

How Narrative BI Helps Enhance Marketing Intelligence & Boost the Value of Data

Narrative BI is a no-code augmented analytics platform for growth teams that instantly generates actionable data insights. Using generative AI for marketing, it seamlessly and effortlessly integrates with existing data sources, such as marketing, product analytics tools and CRM systems.

Being designed for non-tech-savvy growth team members (non-data scientists), Narrative BI removes complexity and provides value from day one. The platform shifts the insight knowledge from a handful of data experts to anyone in the organization, boosting wider adoption of advanced analytics across teams.

Narrative BI streamlines anomaly detection and helps companies address business incidents in real-time. Narrative BI conversational analytics tool removes complexity, being as easy as a conversation with a virtual data-science assistant.

Try Narrative BI here

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