Thailand’s PDPA Law Highlights

Thailand PDPA Law was enacted in May 2019 and is going to be effective on 27 May 2020. Here are some key highlights from the EDTA workshop.

Essential Features of Thailand’s PDPA Law

1) It has comprehensive protection of both public and private entities. This is unlike some countries’ law which only applies to private companies. The approach is sanction based or mainly fines (but there are also jail term as well)

2) PDPA law is adopted from European’s GDPR law. This means that if you are GDPR compliance, high chance that you will be PDPA compliance as well.

3) This law is extraterritorial reach. It also applies to companies or organizations that located outside Thailand but the data subjects (the people you are collecting data) are in Thailand.

4) The law is “Risked-based approach”. Bigger organization has more responsibility than smaller organizations.



What’s Personal Data Covered?

There are 2 levels of personal data covered: 

  • Personal Data – the data that could identify a person either directly or indirectly such as name, address, phone, location, etc.
  • Sensitive Personal Data – the data that are more sensitive to collect and process such as nationality, race, religious, etc.

The law highlights more importance on sensitive personal data than personal data.


Key Parties Involved in PDPA


Information Source from

Contact us

What is Dashboard in Business? And why we should do it?

So-called ‘Dashboards‘ are heard in our analytics driven world but what exactly is dashboard and why we should do it?

Regardless of the business functions, chances are that you are keep repeating the manual process of data to report generation.

The process you use to create your reports might look like this.

1) You extract the data from databases such as SQL or SAP or whatever the company system is.

2) You transform and summarize data in Microsoft Excel with the commonly used function such as Pivot Table or Vlookup. You might need to join the data from different sources into one source as well.

3) You use the data from your Microsoft Excel to generate graphs and reports in Powerpoint. 

4) You present the report to the management.

However you might face these key challenges

  • I need to redo all the steps in all rounds of report
  • Lots of efforts spent doing repeating works
  • I need to redo some steps to drill down data
  • I need to cleanse all the data for everyone who consume my report

With the new Dashboard concept, you can use BI tools such as Power BI or Tableau to create report only once

  • The report can be automatically updated when the data is updated
  • Data from different data sources could be joined
  • The dashboard is interactive for drilling down into details 

These are some sample of the dashboards that could be seen below.

This image has an empty alt attribute; its file name is mkt-res.jpg

This image has an empty alt attribute; its file name is data-team.jpg

Creating the dashboard utilizes the data and could unlock the potential value of data at different levels and departments that is easy to understand for everyone. 

This image has an empty alt attribute; its file name is power-viz.jpg

Interested in transforming your reports into dashboards? 

Contact us

Data Visualization Techniques

The easiest way to explain your data to someone else is actually to visualize it. Data Visualization could be considered as story-telling or how to tell your story to your audiences or as analysis tool to make you understand your data better.

To be able to visualize data, there is a very useful knowledge called ‘Semiology of Graphic’ which many seems to overlook and go to what chart I should use. Semiology of Graphic deals with how we can actually play with different aspects of the chart to reflex the differences in data.

(Image Source:

One of the tools that really works well on this Semiology of Graphic is actually Tableau.

I’ve provided some samples for common visualization practices below.

1. Conditional Formatting is a lazy way to quickly understand data.  I’d say this is the most overlooked visualization. By just adding conditional formatting, it was very clear which cell is outstanding in good or bad way. It is also very easy to implement just 1-click in excel and you already can get this table.
conditional_format.JPG(Data from Passport: Digital Consumer Index)

2. Line Chart / Bar Chart is good when you want to see the trend or magnitude of each value. It is much easier to see when you visualize it out as Line Chart (on the right) than just table (on the left). Line chart is really good for continuous data, especially changes over time.
However, I’ve one thing that I’d forbid everyone I know to do. Do not use Line Chart for categorical data.  The example below show how you cannot put Line Chart for comparison among male / female as the between  you doesn’t have ‘ladyboy’. Thus, you should use bar chart in this case instead.

3. Tree Map / Pie Chart is good for plotting ‘part-whole’ relationship or how big is the specific portion is as you can see from below chart for SEA population. Tree Map could also show hierarchical data (such as countries > cities) but Pie Chart can reflect ‘part-whole’ relationship better.  Nonetheless, both are not suitable for comparison.


4. Stacked Bar Chart to show composition (which could be combined to 100% or just the composition). The example below from  Consumer Barometer showed both comparison and proportion of each.


5. Scatter Plot is your best friend to see 2-dimensional data. The chart below from the data on the left made it clear that Singapore is lowest in both App and Web.

6. Divergent Bar Chart is very suitable for Likert scale where you have both positive and negative answers such as this visualization of Wiki4HE Dataset which shows clear positive answers.

divergent bar

Just to share that these are only sample on how you can visualize the data. There are much more chart and graph types that you can use such as network graph or map visualization and you can be even more creative and utilize more Graphical Semiotics!

Ps. I also found it very useful to go through this checklist for any visualization I’ve created.

At Davoy, we could provide you with Data Visualization consulting and training. Just fill in the contact form and we’ll get back to you for free first consultation!

Contact us

Segmentation by Analytics

Segmentation is to split the market into different groups – each group with similar characteristics. Why do we do this? Because, STP or Segmentation, Targeting, Positioning will help the company select the right segment to target with right positioning. You could not sell your product or services to all people on earth.  For example, not all people will buy fruit juices, only those who concerned about health will be interested.


Generally, there are many methods to perform segmentation: Geographic, Demographic, Psychographic, and Behavior. how-to-segmentation

To obtain the data, there are several methods from marketing research (focus groups, survey, etc.) to customer database (purchase data, customer profile, etc.). 


I’d discuss about few algorithm or concepts that are popular among data guys for segmentation below.

1) RFM Segmentation

RFM is to split customer based on purchase behavior

  • ‘Recency’ – how recent is the last purchase
  • ‘Frequency’ – how often the customer purchases
  • ‘Monetary Value’ – how much money the customer spend

You can see more on how to perform RFM here.

It is defined in a way that R is the most predictive of future purchases, followed by F and M. In other words, those who recently purchased the product are more likely to purchase in the future – especially if they’ve purchased many times.

Key benefits of RFM is that it only requires minimal data and no need for advance statistical modelling knowledge to run. In fact, it could be done in just Microsoft Excel.

Many retailers use RFM to understand their customer purchase pattern and how can they priority which customer to target.


2) K-means Clustering

K-means clustering asked analysis for number for clusters (or segments) and which variables to use. Then, it groups the data to be segments for us.

You can see more how the algorithm works here.

Unlike RFM segmentation, K-means clustering can take any variables as input e.g. Gender, Age, Purchase frequency, etc. Thus, it makes K-means clustering more flexible to implement. 

However, doing so requires analysts to understand the business well to understand which actually is significant variable and which type of segmentation we are doing i.e. Are we using behavior segmentation or geographic segmentation or both?  Moreover, it is quite sensitive to different scales. This means that the analyst must knows how to prepare the data before running the algorithm. 

For me, one of the most useful case for K-means clustering for segmentation is actually to run it among purchase category. For example, a retailer sells product in facial care, hair care, body care, cosmetics and miscellaneous. We can calculate % of of purchase is contributed from each specific category. (Hint: This will be on the same scale as we’re doing it in term of %, not sales) Then, we run K-means algorithm on the data. This will allow the retailer to understand their segments and plan appropriate marketing strategy for each segment.

Note: Some of you may have heard of hierarchical clustering as well. The usage is similar to K-means but the way the algorithm works requires more computational resources and not suitable for big data set. So, I think it’s not a good choice for segmentation.


Even though RFM may seem different, I often found that combining RFM and K-means together could give a very interesting result. We can know that the people who come often buy products in which category and which category has lower loyalty. 


At Davoy, we’re expert in consumer analytics and we can help you with segmentation. Just fill in the contact form and we’ll get back to you for free first consultation!

Contact us

What are CLV and CAC?

With the raise of Data Analytics, the most common concept we have heard about from Marketing side of Data Analytics is ‘CLV’ and ‘CAC’. So, what are CLV and CAC?

Customer Lifetime Value
In the world of marketing analytics or ‘nontraditional way of marketing’, we can measure Customer Lifetime Value (CLV) which given the data we could collect from customers. Instead of measuring brand awareness or brand loyalty, these new era of marketers skip towards the end of the journey which is ‘Revenue’. CLV measures the lifetime revenue we could get from a specific client or consumer. Enhanced CLV by using Time Value of Money (from my Finance 101 course) to discount the value back to present value that the client is worth to us.

Customer Acquisition CostOn the other side, we can measure Customer Acquisition Cost (CAC) which measures how much the company spend to get the exact same customer. This means that we can actually compare both CLV and CAC to see how much profit or how valuable a specific customer value to the company.

In theory, this is a great concept. However, to attribute all costs and revenue to a specific customer is not so simple. For example, if the company put the efforts to run the billboard advertisement along with Facebook advertisement and get some new customers. How should we calculate the cost of acquisition (CAC) for a customer?

At Davoy, we’re expert in consumer analytics and we can help you with CLV and CAC attribution. Just fill in the contact form and we’ll get back to you 🙂


Contact us