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Advanced Marketing Analytics: Examples, Tools & Courses

Explore use cases, resources, and the right tools to support your organization with accurate marketing insights.

How To
August 31, 2023
Advanced Marketing Analytics: Examples, Tools & Courses
Explore use cases, resources, and the right tools to support your organization with accurate marketing insights.

Advanced marketing analytics help you simplify multitouch attribution, create tailored marketing campaigns, and maximize return on investment (ROI) across channels. It helps you seek patterns and insights to improve marketing performance. 

These days, you can’t do online marketing without advanced analytics. Moreover, you need the right tools and skills to make sense of data scattered across multiple apps. 

In this guide, we’ll walk you through examples, tools, and courses that will help you become a more data-driven marketer and eliminate the necessary guesswork. 

#getsmarter
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Automate your marketing analytics and reporting with Keboola

But first, let’s make sure we’re on the same page…  

What is advanced marketing analytics? 

Imagine deciphering the intricate puzzle of customer behavior, mapping out their journey across multiple touchpoints, and predicting future trends with pinpoint accuracy. 

This is the essence of advanced marketing analytics - a strategic blend of data analysis, machine learning, and visualization techniques that go beyond traditional reporting dashboards and lagging marketing insights. 

While regular marketing analytics focuses on reporting and business analytics (creating reporting datasets and tracking metrics such as clicks, impressions, conversion rates, and basic demographic information), advanced marketing analytics focuses on actionable insights and data products.

You can think of the difference between regular and advanced analytics as the distinction between different analytic types. 

  • Regular analytics is descriptive analytics. It involves examining historical data to understand past events and trends. 
  • Advanced marketing analytics includes both predictive analytics, which anticipates future outcomes based on data patterns, and prescriptive analytics, which recommends optimal strategies for desired results. 

The key difference is in the focus: regular analytics looks at the past, while advanced analytics focuses on building tools and insights that propel marketing initiatives into the future.

So, how do you move your data initiatives from being used as a lagging indicator of marketing campaign performance to a leading force for shaping marketing strategies?

You need 4 things:

  1. Quality data: This is a must. If you start an analytics initiative without high-quality marketing data, you’ll end up with deceptive insights and useless data products.
  2. A clear idea of what to build: Don't fret if you don’t know yet, we’ll show you many successful industry examples below.
  3. The right marketing analytics tool.
  4. Know-how to turn your ideas into trustworthy insights and products.

Let’s explore each one in detail.

How to prepare data for advanced marketing analytics? 

The old machine learning proverb still rings true: “Garbage in, garbage out”.

Behind every successful analytics initiative lies a foundation of well-prepared data. Without it, both advanced and regular data initiatives will fail.

You must ensure your data is clean, consistent, and accurate. 

One of the most effective ways to do this is to set up an end-to-end ETL workflow:

  1. Extract data from all its data sources.
  2. Transform data by filtering out irrelevant data, removing outliers, performing normalization, aggregating granular data, and calculating metrics.
  3. Load data into your preferred database, data warehouse, or BI tool.

Not familiar with data preparation using ETL? Check out these resources: 

  1. Data Preparation: 7 Easy Steps to Deliver High-Quality Data
  2. How to Clean Data: The Ultimate Guide (2023)
  3. Data Transformation in ETL Process Explained (2023 Guide)

6 examples of advanced marketing analytics

1. Multitouch attribution 

In the past, advanced analytics leaned heavily on marketing mix modeling. This statistical model provided insights into how many customers were acquired from different marketing channels like TV, radio, etc., based on how much marketing budget we allocated to each channel. Marketing mix modeling helps optimize marketing efforts and budget allocations to channels that bring the most customers.  

But in the era of digital marketing, social media, and Google Analytics, we don’t have to analyze marketing budgets to understand the customer journey.

Modern platforms collect data across every touchpoint on the customer journey, giving you extremely high-granularity insights. They reveal which interactions, content, channels, and other touchpoints can be attributed to converting a lead into a customer.

Real-life example: 

Marketing Intelligence, a business intelligence firm specializing in marketing, uses Keboola to map customer journeys along multiple touchpoints and across different attribution models.

They constructed a dataset of different customer segments across multiple (sometimes 150+) touchpoints and pathways to understand how different segments convert with different interactions. 

The results? With Keboola, Marketing Intelligence helped save their client 30% on marketing costs while increasing acquisition by informing them what conversion paths work best. Read the full story here

2. Clustering, segmentation, and (hyper-)personalization

Don’t you hate it when you see an ad for skiing while all you want to do is drink a Piña Colada at the beach? So do your customers. 

By using clustering algorithms, you can segment customers and personalize communication, ensuring they get messages that resonate with them at the right time and at the right place.

Tailoring the messages to the right audience has a massive business impact as shown in the example below. 

At Rohlik, a leading e-commerce unicorn, data scientists and marketing teams have bound together to build a product recommender strip. 

When customers reach the checkout page, this tool offers product suggestions to add to their cart. These suggestions are based on segments of e-commerce users who have similar browsing and buying patterns. 

The result? Customers really liked the recommendations and clicked the “add to cart” button. As a result, The Average Order Value (AOV) increased by a swooping 1%. For a company with over 1 million customers, this translates to a substantial increase in revenue. 

Check out the full story here. Or learn how to prepare data for personalization by building the Customer 360.

3. Forecasting 

"It’s difficult to make predictions, especially about the future." - Danish physicist, Niels Bohr

Luckily, peering into the future has never been more easier and accurate. Use historical data and predictive analytics to anticipate market trends and customer demand. This enables you to proactively adjust your marketing activities.

So, how can forecasting amplify your marketing? 

Here are insights from two of Keboola’s clients: 

  1. Sportisimo: A sports retailer with 190 stores in 5 different countries, builds local weather forecasts to understand what products to stock and advertise based on the weather outdoors. Is a rainy weekend on the horizon? They will promote equipment for indoor sports. Sunny forecast? It’s time to advertise swimwear.
  2. Rossum: This tech firm, specializing in document processing, uses Keboola to predict which customer is likely to say goodbye. Using predictive analytics, they identify high-risk churn candidates and send this data to SalesForce. This enables rapid outreach with churn prevention emails and mitigates the potential losses.

4. Sentiment analysis 

Sentiment analysis uses Natural Language Processing (NLP) to determine the emotional tone behind text data. In marketing, it uncovers customer sentiments from social media posts, reviews, and comments. 

These insights help marketers tailor campaigns, refine messaging, and address concerns effectively. The result? Enhanced brand perception and better engagement.

NLP is a branch of machine learning that requires advanced skills. Luckily, we’ve built tools to automate a lot of the heavy lifting for you. 

Check this webinar that guides you through building your own NLP sentiment analysis with Keboola. 

5. Pricing analytics and optimization 

Pricing isn't about arbitrary numbers; it's a data-driven decision. 

With advanced analytics, you can analyze historical data, competitor insights, and market trends to optimize pricing strategies. The optimal pricing strategy will increase your revenue, profitability, return on investment (ROI), and competitive advantage.

Case studies to get you inspired:

  1. Rohlik: This e-commerce food retailer uses Keboola and AWS to suggest real-time discounts for soon-expiring food. This helps them to reduce waste and appeals to budget shoppers. How? By taking into account information about the current stock, historical sales of the product, expected remaining time to sell the product, and other parameters. Then they advertise discounted items to price-sensitive consumers. Making it a win-win for both parties.
  2. Pincho Nation: By testing different price displays in their app in Keboola, they found a way that adds an extra 0.42€ to each order. Over a year, this translates into a 6-figure revenue boost.  

6. Customer Lifetime Value (CLV)

Some metrics don’t just inform decisions, they reshape marketing strategies. CLV, also known as CLTV, stands out as one of them.  

CLV calculates the total worth of a customer during their engagement with a brand. It considers everything from the first purchase and repeat buys to potential referrals. 

How can CLV help marketers and businesses?

  1. Optimize budgets. By knowing how much revenue a customer will bring in their lifetime, you can calculate the marketing ROI. For example, if a customer segment brings in $10.000 in their lifetime, it’s worth spending $100 for their acquisition.
  2. Identify opportunities for repeated purchases. Whenever a customer’s lifetime value is below their current overall transaction expenditure, you get a clear signal that you should advertise and upsell or resell to this customer. 
  3. Quantify your marketing needs. CLV provides you with a tool to evaluate your marketing needs. If you can showcase to your CMO that a customer segment is potentially worth catering to (develop specialized campaigns, fancy visuals, hire another copywriter, etc.) and have the marketing data to back it up, it makes your argument more convincing.  

Want to dig deeper?

  • Although it might seem dated (published in 2016), the venture capital investor David Skok wrote the best guide to calculate CLV.
  • If you’re familiar with coding in Python, the Lifetimes library automates a lot of the data engineering and mathematical heavy lifting behind the computation.

How to implement advanced marketing analytics?

There are many tools that help you implement advanced marketing analytics, including business intelligence tools and data platforms specializing in data management, visualization, and analytics. 

In this specific example, we’ll show you how to do it with our own tool - Keboola - an all-in-one data operations platform that helps you connect, manage, and analyze data in one place.  

The first thing you want to do is set up ETL pipelines to get good-quality data. 

You start by arranging components within the Visual Flow Builder. In the Flow Builder, you drag and drop one of the 250+ pre-built integrations that help you collect or load data.

You arrange the components into a flow that represents your ETL pipeline. 

Next, you click “run” to get analysis-ready data.

If you want to try it yourself, follow this step-by-step guide that helps you build and automate a digital marketing report.

“But I also need to compute new metrics and build advanced machine learning algorithms to predict customer churn!”

Worry not. You can build new metrics with no-code transformations. You just select the transformation you want and Keboola performs it for you.

And if you’re familiar with coding (Python, R, Julia, SQL, pick your poison), you can head over to the data science toolbox and start building ML models.

“What if I need to build a data app?” You can either use the integrated Streamlit and Snowflake to streamline your data product development. Or launch pre-build advanced marketing apps in a few clicks that analyze Google Analytics, Hubspot, and Shopify data (learn how).

Keboola will help you launch advanced marketing analytics initiatives faster, cheaper, and more reliably.

Automate your marketing analytics and reporting with Keboola

More useful resources and courses on advanced marketing analytics

Looking for more resources to get the know-how before you start deploying your new use cases? We’ve got you covered!

Keboola Data Academy: You’ll learn how to develop ETL pipelines, run machine learning models, and launch many of the advanced analytic use cases that propelled our customers ahead of the competition. This is best for data engineers and data analysts who want to learn about workflows in Keboola. 

DataCamp’s Marketing Analytics for Business: You’ll learn how to compute Customer Lifetime Value, forecasting models, and attribution models that can serve as a basis for your advanced marketing analytics. The course is beginner-friendly with a lot of background and no coding needed. The first chapter is free, then €11.35/month.

Google Data Analytics Professional Certificate: You’ll learn how to code in R and SQL and use the coding skillset to prepare the data for analysis. This course is best for beginners looking for a starting point on their data analyst path.

Meta Marketing Analytics Professional Certificate: This certificate focuses heavily on marketing examples and advanced statistical approaches. It’s best for beginners looking for a starting point on their data analyst path.

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