Before I started my master I hadn't thought about using business analytics and machine learning techniques to conduct my digital marketing data. When I was in one of my lectures, some ideas came to my mind, so I started researching and reading many articles about digital marketing and business intelligence. Thanks to my course, I was able to build up my technical knowledge to combine my expertise.

In my newsletter series, I will try to examine the data analytics mindset step-by-step with examples and clear explanations. You can imagine this series according to HubSpot's topic cluster framework for SEO strategy. I will connect each episode with others around digital marketing and data analytics pillar, thus you can gain integrated knowledge to adapt your strategies.

Source: HubSpot

Now, let's build a data-oriented mindset together!

In today's data-rich business environment, leveraging analytics is no longer optional - it's essential for smart decision-making.

Data analytics allows companies to transform the wealth of data at their disposal into valuable, actionable insights. By applying statistical techniques and quantitative analysis to organizational data, businesses can reveal key trends, patterns, and relationships that would otherwise remain hidden.

These data-driven insights empower organisations to:

• Gain clarity and understanding of operations, customers, markets, and other aspects of the business. Analytics uncovers insights from company data that provide a deeper comprehension of the business.

• Enhance forecasting and predictive capabilities. Advanced analytics modelling and algorithms yield more accurate and precise forecasts to inform planning and preparedness.

• Make decisions even with imperfect information by quantifying risk and uncertainty mathematically. Analytics enables data-backed risk and uncertainty assessments.

• Identify optimal solutions and alternatives that may not be apparent without analytics. Sophisticated analytical optimization methods can uncover superior options.

Rather than relying on intuition or gut feeling, data analytics allows critical business decisions to be grounded in hard facts. Organisations gain an information advantage that enables evidence-based planning and strategy execution.

A Categorization of Analytical Methods

When I first tried to understand analytics methods I couldn't :) Then I learned from the image below. I believe it is a very basic but invaluable presentation. You can easily learn and build up which methods you need for your analysis.

In descriptive analysis, we review past data to cover what happened. Predictive analytics shows us what we are going to face. Lastly, prescriptive analytics puts forward what we should do to reach our goals. This is the summary of types of analytics.

🤖 Next Steps:

After we internalise these methods then we can look for models to use for our analysis. When we understand the meaning of each method, we can easily choose which model will be appropriate for us. This is why we need to clarify these analytics types.

1 - Descriptive Analytics Models:

Descriptive analytics reviews historical data to understand past performance. It identifies trends and relationships in current and historical data to summarise key learnings and diagnostics. This goes deeper than surface-level analytics to tell the story of your business. Common uses include analysing campaign results over time, evaluating customer retention rates, and understanding sales metrics. Essentially, descriptive analytics uses data visualisation, statistics, and other techniques to paint a clear picture of where your business has been.

While descriptive analytics doesn't predict the future, its insights are crucial for shaping strategy. By spotlighting what's worked and what hasn't, it sets the stage for more advanced analytics.

For today's data-driven marketer, implementing descriptive analytics practices is foundational. Regularly analysing your historical data diagnostics is key for informing your next moves. Combining these insights with predictive and prescriptive techniques will unlock deeper analytics capabilities. Google Analytics 4 or Big Query can be your data provider to analyse historically.

📌 Here are the examples of models to apply descriptive analytics:

- Data queries

- Reports

- Descriptive Statistics: Measures like arithmetic or geometric mean, median, mode, standard deviation, and variance to summarize and describe data

- Data visualisation: Charts, graphs, and dashboards to visualize trends, relationships, and insights from data

- Basic what-if spreadsheet models: Testing different scenarios and assumptions with historical data

- Linear Regression analysis: Model linear relationships between variables in historical data

- Correlation analysis: Measure the strength of the relationship between two variables (1 means fully correlated, -1 means negatively correlated, 0 means no correlation)

I think we are applying these several models to analyse and interpret our marketing data in the descriptive analytics context. Let's take our analysis one step further!

2- Predictive Analytics Models:

I believe the most precious part is here for data-driven digital marketers. Descriptive analytics provides insights into what's already happened. But predictive analytics looks to the future - forecasting what could happen next.

It leverages statistical modelling and machine learning techniques to turn data into actionable insights. Techniques like machine learning analyse data quantitatively to yield actionable insights on likely future results. This competitive edge allows you to optimize proactively rather than just react. For marketers, this means more intelligent planning and optimisation based on anticipating customer behaviours.

📍Predictive analytics gives you a glimpse into the future by revealing probable outcomes based on historical trends and patterns. It can help you:

- Predict trends accurately

- Segment customers intelligently

- Prioritize high-value leads

- Model campaign scenarios

- Personalize customer experiences

- Forecast churn

In our fast-moving world, intelligent forecasting is more valuable than ever. Implementing predictive analytics will unlock superior marketing performance through data-driven strategic planning.

I will write more about predictive analytics and how can we apply it in the next episodes of All About Digital Marketing & Data Analytics Newsletter. So, keep following!

👉 Here are the examples of models to apply predictive analytics:

- Regression: Model relationships between independent and dependent variables

- Decision Trees: Map out conditional sequences to predict target variables

- Time series analysis: Forecast future points in a time series based on historical trends

- Forecasting: Predict future outcomes based on historical time series data

- Data mining: Find patterns or relationships among elements of the data in a large database. For example classification and clustering.

- Text mining: Derive insights from textual data with NLP

- Sentiment analysis: Identify emotional tone/intent in text data

3- Prescriptive Analytics Models:

Lastly, prescriptive analytics uses data modelling and benchmarks to recommend optimal actions. It goes beyond descriptive and predictive analytics to actively advise the best course for capitalising on opportunities and reducing risks. By prescribing data-driven strategies, prescriptive analytics dynamically guides organizations to improved outcomes. Its actionable recommendations optimise decision-making for maximum business impact.

✨ Here are the examples of models to apply prescriptive analytics:

- Optimisation models: Determine optimal decisions under defined constraints

- Simulation optimisation: Find the best choices involving uncertainty and complexity

- Decision analysis: Develop optimal strategies with multiple options and uncertainty

- Utility theory: Quantify values of outcomes based on risk attitude.

⭐️ Bonus: You can see the whole model in summary here:

We will reveal the hidden features of predictive analytics for digital marketing processes in the next edition!