Employer Branding: Counting Candidates vs. Measuring Silence
15/09/2026
The Hidden Cost of Focusing Solely on Candidate Volume
In the past, when asked about recruitment effectiveness, most HR departments in Vietnam would provide a single metric: the number of CVs received. This figure may sound impressive, but it masks a concerning reality. We are spending money on advertising channels and paying for recruitment platforms, yet we do not know if the applicants are actually a good fit or simply "spraying" resumes in hopes of a lucky break. This is the trap of raw data. You see a steady inflow of candidates, but the outflow (those who fail to reach interviews or reject offers) is chaotic and difficult to explain.
New approaches are changing this landscape. Instead of headcounting, advanced enterprises are beginning to measure the "responsiveness" of the labor market to their employer brand. This is not just theory. When a candidate rejects an interview after passing the screening stage, it is not just an absence. It is a data signal. It indicates that the company image you have built on job boards is out of sync with reality, or that your application process creates a worse experience than your competitors. The hidden cost here is not in the marketing budget, but in the time spent by the recruitment team and the waste of resources on candidates who never intended to stay long-term.
The core issue is that we are managing talent based on a gut feeling about the market's "heat" rather than specific behavioral data. Without a system to track candidate reactions in real time, every talent attraction campaign is like firing a cannon into a crowd. You shoot, there is noise and smoke, but no one knows if the bullet hit the target or was blown away by the wind. Shifting from counting volume to measuring interaction quality is a necessary leap, even if it requires facing the uncomfortable truth about your own brand.
The Fork in the Road: Choosing Data Sources
When starting to implement recruitment data analytics to evaluate your brand, you will immediately face a first fork in the road: choosing between qualitative and quantitative data. Many businesses still believe that in-depth interviews with a few rejected candidates are sufficient. But in reality, a sample size that is too small cannot represent the entire labor market. If you only hear one person say your company is "too rigid," you will not know if that is due to the general culture or just that person's individual experience with a specific interviewer. To get the full picture, you need quantitative data: the time candidates spend before abandoning applications, email response rates, and the number of candidates comparing your company with competitors on recruitment forums.
However, quantitative data has a clear limitation: it only tells you "what" is happening, not "why." A candidate might abandon an application after 30 seconds, but the reason could be that the registration form is too long, or they could not find information about benefits. This is where qualitative data comes into play. The second fork in the road is: do you dare to ask candidates? Many HR professionals hesitate to ask because they fear causing inconvenience or receiving difficult answers. But if you do not ask, you will never fix the small errors that cause significant losses. Combining both data sources is mandatory, but this requires a standardized data collection process from the start, rather than patching things up when problems occur.
In international markets like Thailand or the Philippines, multinational corporations have started applying this approach a few years ago. They do not just look at official recruitment channels but also listen to candidate groups on social media. In Vietnam, this trend has only just begun to take root in large technology and FDI enterprises. For domestic companies, especially in manufacturing or distribution, this is still uncharted territory. Those who move first will have the advantage of understanding candidate psychology and adjusting brand messaging in time before competitors follow suit.
Quick Answers
How do I know if my recruitment data is "dirty"?
Data is considered dirty when it is inconsistent in format, lacks important information fields (such as rejection reasons), or contains many duplicate records. If you cannot export a report to compare conversion rates between two different recruitment channels due to missing data, your data needs to be cleaned before analysis. This sounds simple but often takes up about one-third of the implementation time.
How much data do I need to start evaluating brand effectiveness?
You do not need millions of data points to start. With a sample size of a few hundred candidates in a quarter, you can already see clear behavioral patterns. More important than volume is continuity. You need data collected using the same criteria over several months to compare before and after strategy adjustments. One-time data says nothing; only time-series data creates knowledge.
Should I automate the entire process of collecting candidate reactions?
Yes, but not blindly. Automating steps such as sending follow-up emails after interviews or reminding candidates to complete applications is very effective for increasing response rates. However, important interactions like explaining rejection reasons or discussing salary expectations should be handled by humans. Machines are good at volume, but humans create connection and brand. The balance between automation and humanity is the key to making candidates feel respected.
Strategy Adjustment: From Reaction to Action
Once you have the data, the final and most important fork in the road is: what will you do with those numbers? Many businesses fall into the trap of "analysis for the sake of it." They have thick reports and beautiful charts, but no one knows what to change in the actual recruitment process. This happens because data is not linked to specific responsibilities of each department. If data shows that candidates reject offers due to benefits, but the HR department only knows how to make reports and does not have the authority to renegotiate the benefits package with upper management, that data becomes meaningless.
Effective talent management strategy must be a closed loop. You collect data, you analyze it, you adjust a specific factor (for example: shortening the interview process from 3 rounds to 2), and you measure again. This process cannot be done once and for all. The labor market changes quickly, especially in technology and service industries. A talent attraction strategy that was effective last year may be outdated this year. Real-time adjustment does not mean changing everything every week, but being ready to change when data sends a clear warning signal.
This is where flexibility becomes more important than rigid processes. In markets like Mexico, where the shortage of skilled labor is becoming increasingly severe, businesses are forced to adapt faster. They cannot wait until the end of the quarter to review recruitment effectiveness. They need to see candidate reactions this week and adjust messaging tomorrow. In Vietnam, we have seen a similar change when working with partners like Masan or corporations in the lubricant industry. They are not just interested in finding people, but in understanding why top talent chooses competitors over them. The answer often lies in small details in the candidate experience that only data can reveal.
There is a risk to note: data can be biased if you focus only on a specific group of candidates. For example, if you only analyze the reactions of candidates for senior positions, you will not understand the psychology of junior candidates. Ensure that your data covers enough levels and different recruitment channels to avoid making wrong decisions based on a narrow perspective.
The shift from intuition to data in employer branding is not purely a technology project. It is a revolution in talent management thinking. It requires the courage to accept negative feedback and the patience to see results after multiple loops. But if you do not do this, you will continue to pay the price in time and resources for ineffective recruitment campaigns. Data is not a magic wand, but it is the necessary compass to avoid getting lost in a volatile labor market.
Every company hits this differently, and the hard part is usually the data rather than the model. To pressure-test your case quickly, talk to AIVISION - or first see how we deploy and what we have written before.