In this article, we are going to explore customer analytics in five key areas:

• Customer Segmentation
• CRM Analytics: Behaviour Analytics
• Customer Intent Analytics
• Customer Journey Analysis
• Persona Definition

Let’s start with Customer Segmentation;

Customer Segmentation

Customer segmentation is the process of dividing your customers into segments based on demographics, behaviors, and characteristics: the best and the smartest way to build strategy.

By understanding that each customer is different, segmentation helps companies tailor their marketing and services to better connect with specific groups. This ensures a more personalized and effective approach.

Steps

Segmentation

• Figure out how to divide the market into different groups
• Create profiles for each group

Targeting

• Decide which groups are the most appealing
• Pick the specific groups you want to focus on

Positioning

• Create a unique image for each chosen group
• Tailor your marketing strategies for each group

Effective segmentation goes beyond just basic details like age or gender. Understanding the deep motivations and interests of the audience helps create a strategy. By using these insights, businesses can build strong connections and provide exceptional value to their customers.

There are four ways to group customers as segments, and using a combination of these segments, known as hybrid segmentation, is the best way to do this. This approach helps us organize and analyze different aspects more effectively.

• Profile Based (Demographics, Social class, Culture, Geographic)
• Psychographic Based (Personality Lifestyle Social Status, AIO (Activities, Interests, Opinions), Attitudes)
• Behavioral Based (Benefits, Usage, Price or promotional sensitivity, Buying situation, Economic)
• Value-Based (Revenue, Profit, Cost, Churn, Loyalty)

For example, we can think of customer RFM analysis as a behavioral and value-based type of segmentation. Please check for details on my GitHub profile.

For the latest trend in digital marketing data and segmentation, our strategy should focus on creating segments that are easy to measure and can be accessed in real time. Each segment should respond promptly to specific marketing messages and remain relevant in the moment.

Analytical Perspective of Segmentation — How does it work?

In data analysis, systematic processes of segmentation and data mining occur as shown in these frameworks. It starts with defining the problem and gaining an understanding of the business requirements, right through to the deployment phase.

Segmentation has 3 levels as clustering includes Rule-Based, K-means Clustering, and Hierarchical Clustering and it is a type of unsupervised learning that groups and interprets data based only on the input data (without desired outputs).

Let’s continue with CRM Analytics ;

CRM Analytics: Behaviour Analytics

CRM and Behavior Analytics combine to provide a deep understanding of customer preferences and online actions. Using these insights, companies can refine their marketing and enhance digital engagements.

Behaviour Analytics

Recency, Frequency, and Monetary (RFM) analysis is a powerful tool that evaluates multiple dimensions of customer behavior. By assessing tenure, retention, recency, frequency, monetary value, and basket size, companies gain a comprehensive understanding of customer engagement and transaction patterns.

Customer Segmentation using RFM Analysis

Recency, Frequency, and Monetary expressions are referred to as RFM metrics. Customer segmentation is done based on these metrics by related scores.

When examining RFM metrics in a given data set, it can be interpreted that lower recency, higher frequency, and higher monetary values are desirable. However, to compare RFM metrics both internally and with each other, it is necessary to convert them into RFM scores. This involves standardizing each metric to express it in a comparable format, essentially putting them on the same scale. After this standardization, the values of the RFM metrics are combined to create RFM scores. Customer segments are then created based on these RFM scores.

How can we read this score table; as we know“F” stands for Frequency and “R” for Recency.

The point where both Recency and Frequency are 5 is characterized as “Champions” since they will be both new and frequent customers that the brand will never want to lose.

Another case for customers with Recency: 1 or 2 and Frequency: 4 or 5 are segmented as customers with high frequency, i.e. customers with high purchase frequency but who have not made a purchase recently, and are represented by the term “at risk”. As an example in this context, a campaign can be created by combining the “at risk” and “can’t loose them” segments according to the number of groups in the segments.

In this study, the goal will generally be to achieve the highest efficiency with the least effort.

As a result of all these segmentations, existing customers can be segmented according to the defined classes and different sales, marketing, and communication strategies can be determined for each of them.

To better understand how to segment customers with RFM analysis, please check out my project on GitHub with realized data from the company FLO.

We will continue with the Customer Intent Analytics topic in the following article. (Customer Analytics - Part 2). Stay in touch.

Thanks for reading 😊

For Contact:

Linkedin bedirhankelez

Github BedirK

Sources:

https://www.linkedin.com/in/atillayardimci/

• Data Analyst Bootcamp
• RFM Analysis for Customer Segmentation [Comprehensive Guide]

In the ever-evolving landscape of construction projects, we’re witnessing a transformation thanks to the integration of data analytics into project controls.
In this article, we’ll examine the practical applications of data analytics in the construction and development industry, explore its benefits, and look at what the future holds.

Practical Applications in Project Controls

As project controls engineers, we deal with a constant flow of data from multiple sources — cost reports, scheduling tools, resource allocations, and more. Managing costs, schedules, and resources is a constant challenge for project controls but data analytics simplifies this complexity by providing tools to organize and interpret this information. For example, analytics tools can consolidate data from different sources, including Excel spreadsheets, SQL databases, Data Warehouses (DWH), ERP systems, and more, across different project phases, and this integration allows us to gain a comprehensive view of project performance using data visualization tools such as PowerBI.

Real-time Decision-Making

Imagine being able to make informed decisions in real-time. Data analytics tools do just that. By continuously monitoring project data, we can gain insight into current performance and identify potential problems before they escalate. For example, if a task is running behind schedule, analytics can specify the root cause and enable timely adjustments to get the project back on track. (Earned Value Analysis, SPI, CPI, etc.)

Predictive analytics

Predictive analytics is where data comes into its own. As project controls engineers, we can use historical project data to predict future trends and potential challenges. For example, by analyzing past project performance like resource allocation, material availability, workforce productivity and external factors such as weather conditions, we can predict potential delays and supply chain disruptions. With this foresight, we can proactively develop strategies to keep the project on schedule.

Improved Resource Allocation

Resource management is a critical aspect of project management and control. Data analytics helps us optimize resource allocation by analyzing past usage patterns and predicting future needs. For example, if historical data indicates a peak in resource demand during a particular phase, we can proactively allocate resources to avoid bottlenecks and ensure a smooth workflow.

Enhanced Collaboration

In the world of construction projects, collaboration is key. Data analytics facilitates collaboration by providing a common system for all stakeholders to access and understand project data through platforms such as Autodesk BIM 360, Microsoft Teams, Azure, AWS, Asana, Slack, and Sharepoint.

Whether it’s project managers, engineers, or finance teams, everyone can contribute to and benefit from a common understanding of project performance.

The future of data analytics in project control

Looking ahead, the role of data analytics in project controls will only increase. As technology advances, we can expect to see more sophisticated tools that provide even deeper insights. Artificial intelligence and machine learning, for example, could play a more significant role in predicting project outcomes and recommending optimization strategies.

Conclusion:

As project controls engineers, our journey with data analytics is just beginning. By embracing these tools and methods, we can improve our project management capabilities, make more informed decisions, and ultimately ensure the success of civil engineering projects. The future is data-driven, and as practitioners in the field, we’re at the forefront of this exciting evolution. Here’s to navigating the road ahead with the power of data analytics on our side!

Thanks for reading 😊

For Contact:

Linkedin bedirhankelez

Github BedirK

P.S. The figures in this article are from real projects with dummy figures and names.