Machine Learning (ML) and Artificial Intelligence (AI) are, without a doubt, making us far more efficient, wouldn’t you agree?
Let’s take the analogy of Horsepower. From the time when James Watt (also called the father of the industrial revolution) figured a mathematical way to equate one Horsepower into an engine power in the 18th century to when engineering advances got us to building cars with up to 5000-HP capable of reaching speeds of over 300 mph (482 km/h).
Similarly, subsequent information revolutions got us to the current stage where I and ML take just one brain against thousands of brains working simultaneously to solve complex problems. As they say, new-age self-driving cars do not run-on gas but on data!
Though there are numerous use cases of AI and ML in almost every walk of life, this article explores the ways of applying AI and ML in the HR-TechStack. Specifically, ones that can work wonders for Employer Branding (as AI/ML already has in the fields of sales and Marketing).
We understand and agree with the top Global HR folks that mid-large firms are still scraping the surface when it comes to Employer Branding, but no one can dispute its importance in the context of the talent war businesses are battling through for growth. Indeed, no CEO, CIO, CXO, or Delivery Head has any doubts about why employer branding is so imperative now, especially in light of the Great Resignation, Great Reshuffle, Quiet Quitting, and the likes trending across workplaces. Though some fundamental marketing concepts are yet to be embraced, in this article, we are taking it to the next level to see if the #TalentIntelligence advocates would agree.
From the marketing perspective, the first step to integrating AI/ML into any business context is to answer questions related to the target audience. In the context of Employer Branding, and subsequently, to Resource Planning and Talent Acquisition, the audience would be the candidates (or prospective employees), and the questions for which we need answers could be bucketed as:
- What characteristics or personality traits should one segment the candidates by so they would be the best fit for the organisation’s culture and business goals, in that order? (Because your organisation’s culture is the unique recipe to deliver the service or solution in the niche you’re operating in?)
- How likely are the candidates to apply for a career opportunity listed on the organisation’s career site?
- How long would an employee be likely to stick with the organisation (Employee/Candidate Lifecycle Value)?
- Which one of the current employees is going to part ways soon?
Although AI and ML are like thousands of brains working together to find answers to these questions in real-time, some of these questions are less mature than others in ever-evolving business scenarios. Several live use cases have already addressed some of these questions, unlike those upcoming scenarios that don’t yet have significant use cases. This article addresses these cumulatively with one goal, i.e., taking your brand out to (most) desired candidates and achieving long-term business growth.
- Predictive Analytics
Essentially, it means predicting future outcomes based on current and historical data. This stream of data analytics can enable employer branding teams to predict the following:
- A prospective candidate or group of candidates (target segment) would be valuable through their lifecycle.
- A prospective candidate or group of candidates more likely to be loyal
- Whether a job application/lead is of high quality to be put into the interview process
- The resources and time the employer branding team should spend on each specific source and type of leads coming through it.
Predictive analytics is more supervised learning, with a knowledge of what one is seeking. The technique has been widely deployed in numerous business cases across industries and has provided eye-opening inferences enabling more intelligent, better, and more informed business decisions. It is also easy to implement, as it doesn’t need much data. 700-1000 records (or less) of your A-Team’s (employees) historical data are enough to yield great results.
- Clustering and Customisation
Clustering and Customization are more of unsupervised learning from the larger volumes of granular data collected from multiple sources. It throws plenty of data at the problem and asks a machine-learning algorithm to find the pattern. Why is this so crucial? Employer branding uses it to identify the main characteristics that differentiate/segment the candidate base, so they match
The organisation’s cultural values.
- Recommendation Engines
How about mixing the two? Predictive Analytics (supervised learning) and Clustering Customization (unsupervised learning), and we get a recommendation engine, a hybrid model. It is like what we’ve seen in our OTT platforms, where they recommend what to watch next or in any e-commerce site, where they recommend which product to buy based on your previous viewing or shopping patterns. The more data we collect on user behaviour, the more critical recommendation engines are to drive candidate engagement with collaborative and content-based filtering to make it meaningful.
Relevant targeting gathers like-minded candidates, and the recommendation engine creates meaningful engagement to take the brand to the hearts of prospective candidates. Use case of this is what we already see in most of the Job Boards and ATSs (Applicant Tracking Systems). When integrated into the career website through the API, these platforms offer similar solutions where they recommend jobs similar to the search performed by the candidate.
- Natural Language Processing (NLP)
These are algorithms that understand and sometimes reproduce human language. The most common use cases are speech recognition, computer-assisted coding, clinical documentation etc.
How could we use this in Employer Branding? Organisations can use NLP in data mining to analyse the sentiment of the target audience by dissecting data (video, text, etc) to determine whether it’s negative, neutral, or positive, to – what candidates are saying about the work culture in an organisation, overall brand, or competition.
- Psychographic Persona
In the recruitment world, psychometric tests are not a novel concept. Organisations use this technique to measure cognitive ability, personality, or work behaviour, to check whether a candidate has the potential to excel in a specific position or career, predominantly after completing the first level of screening.
For employer branding, psychographic segmentation is the new kid on the block compared to demographic and behavioural segmentation. It is segmentation based on personality, interest, attitude, and behaviour. This field is yet to be explored to its fullest. Psychographic persona segmentation would allow us to understand why a candidate would choose our brand over others. It would enable us to develop the right kind of messaging for the right set of candidates.
- Image Recognition
Image recognition already has use cases in many domains, such as medicine, content moderation, agriculture, manufacturing, advertising, and retail. Let’s consider the advertising use case to establish some relevance. One can leverage image recognition to measure branded content’s prominence and regularity in photographs to further analyse brand awareness and exposure in the relevant target segment. Another use case one could have already heard of is scanning online images to find similar products. It doesn’t have a direct use case for employer branding, but it is a space to watch out.
While AI and ML can help Employer Branding take a more algorithmic approach, making work organised, efficient, bespoke and impactful, the need for “humans” in Human Resources stays at the core. Today, AI/ML is leveraged by brands that are perhaps on top of the game already, as most are yet to implement marketing basics into their employer branding strategies.
With AI/ML, and the apparent benefits that come along, employer branding can be enhanced, help deliver a superior and bespoke employee experience that is most relevant to the unique culture of the organisation. With the candidate pool expanding to the Gen Z hires who leave more footprints digitally than in real life, how long should organisations wait to leverage the plethora of insights waiting to be tapped through AI/ML?
About the Author
As SVP, Talent Strategy, Rajiv Srinivas (RS) directs TA strategies and leads the tactical execution approaches that ultimately create Maveric’s globally competent and battle-ready talent supply chain. Addressing new-age businesses’ unique talent needs, RS’s role brings muscle to Maveric’s aspiration to be a top 3 niche, domain-led Bank-Tech solutions specialist.
As Senior Manager, Employer Branding, Rakesh is taking the story of how we create Mavericks out of talented professionals from the BankTech space. Working the magic of bringing together the Marketing, HR and TA to create Employer Brand impact through the candidate and employee lifecycle – aligned with Maveric 4.0 vision
Originally Published On ET HR World