Machine Learning (ML) and Artificial Intelligence (AI) are without a doubt making us far more efficient, wouldn’t you agree?
Taking the analogy of Horsepower, from the time when James Watt, also called the father of the industrial revolution, figured a mathematical way to equate 1 horsepower into an engine power in the 18th century to where engineering advances got us to building cars with upto 5000-HP capable of reaching speeds of over 300 mph (482 km/h). Similarly, subsequent information revolutions got us to the current stage where AI and ML are taking 1 brain to thousands of brains working simultaneously solving complex problems. As they say, new age self-driving cars do not run-on gas but on data!
Although there are innumerable use cases of AI and ML today in almost every walk of life, in this article, let’s explore the ways of applying AI and ML in the HR-TechStack that can work wonders for Employer Branding (as AI/ML already has in the field 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, that businesses are battling through for growth. Hope no CEO, CIO, CXO or Delivery Head has any doubts about why employer branding is so imperative now, especially in the light of the Great Resignation or Great Reshuffle we have been experiencing now. Though there are some basic marketing concepts yet to be embraced, in this article I’m taking it to the next level to see if the #TalentIntelligence advocates (especially the folks who follow my favorite, Mr. Josh Bersin) would agree.
The infoarticle is going to be in the order of:
- The indispensables
- The essentials, and
- The good-to-haves
of AI and ML in Employer Branding to achieve business growth.
From the marketing perspective (as I am a digital marketer by DNA ), typically, 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 (/prospective employees) and the questions we need answers for could be:
- What characteristics or personality traits should I segment the candidates by so that they would be best fit for my organization’s culture and business goals, in that order? (Coz your org’s culture is the only 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 my career site?
- How long would an employee be likely to stick with the organization (Employee/Candidate Lifecycle Value)?
- Which one of the current employees are going to part ways soon?
Now 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. In other words, some of these questions have already been addressed and already have many use cases, unlike those that are upcoming and don’t yet have many use cases. So, the way I’m intending to attend to all of these is by mapping out all against one goal, i.e., taking your brand out to (most) desired candidates and achieving long term business growth.
LET’S START WITH INDISPENSABLES:
- PREDICTIVE ANALYTICS
Essentially, it means predicting future outcomes based on current and historical data. This stream of data analytics enables employer branding teams to predict:
- 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
- how much resources and time should the employer branding team spend on each specific source and type of leads coming through it
Predictive analytics is more of supervised learning, knowing what you’re looking for. The technique has been widely deployed in numerous business cases across industries and has provided eye- opening inferences on which to make smarter, better and more informed business decisions. It’s also pretty easy to implement as it doesn’t need a huge amount of data. 700-1000 records (or less) of your A-Team’s (employees) historical data is enough to yield some great results.
2. CLUSTERING AND CUSTOMIZATION
Clustering and Customization is more of unsupervised learning from the larger volumes of granular data collected from a variety of sources. It’s throwing a lot of data at the problem and asking 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 organization’s cultural values.
MOVING TO THE ESSENTIALS:
3. RECOMMENDATION ENGINES
How about mixing the two – Predictive Analytics (supervised learning) and Clustering Customization (unsupervised learning), and what we got is a recommendation engine, a hybrid model. It’s similar to what you’ve seen in our OTT platforms or ecommerce sites where they recommend what to watch next or what to buy based on your previous viewing or shopping patterns. The more data we collect on user behavior, the more critical recommendation engines become to drive candidate engagement with collaborative and content-based filtering, to make it meaningful to them. Relevant targeting gathers like-minded candidates, and the recommendation engine creates meaningful engagement to take the brand to the hearts of prospective candidates. Most of the Job Boards and ATSs (Applicant Tracking Systems) when integrated to career website through an API already offer similar solutions where they recommend similar jobs based on the search performed by the candidate.
4. 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? NLP can be used in data mining to analyze 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 organization, overall brand, or competition.
FINALLY, THE GOOD-TO-HAVEs:
5. PSYCHOGRAPHIC PERSONA
In the recruitment world psychometric tests are not a novel thing. The technique is used to measure cognitive ability, personality, or work behaviour, to check whether a candidate has potential to excel in a specific position or career. But that’s mostly after the first screening is done. For employer branding, psychographic segmentation is the new kid on the block when compared to demographic and behavioural segmentation. It is nothing but the 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. This would enable us to develop the right kind of messaging for the right set of candidates.
6. IMAGE RECOGNITION
Image recognition has use cases in many domains already, such as medicine, content moderation, agriculture, manufacturing, advertising, and retail. Let’s consider the advertising use case to establish some relevance. Image recognition can be used to measure branded content’s prominence and regularity in photographs, to further analyze brand awareness and exposure. Another use case that you’d have already heard of is scanning online images to find similar products. This doesn’t have a direct use case for employer branding, but it is the space to watch out for.
CONCLUSION:
While AI and ML can help Employer Branding take a more algorithmic approach, making work organized, 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 for the unique culture of the organization. With the candidate pool expanding to the Gen Z hires who leave more footprints digitally than in real life, how long should organizations wait to leverage the plethora of insights waiting to be tapped through AI/ ML? Learn how to implement the basics of marketing into their employer branding strategies (stated in my earlier article – ReThinking Employer Branding).
