hrtechoutlookapac
Home» News

Eight Best Employer Branding Practices That CIOs Can Adopt

HR Tech Outlook | Thursday, October 10, 2019

Nowadays hiring of employees has become more competitive, so the companies are leveraging their employer branding process for better results.

FREMONT, CA: “Employer brand is your unique scent,” says a long time human resource professional recruiter. Employer branding upholds a company as the employer of choice to the desired target group. A company can differentiate its identity from a defined group of candidates that they are interested in hiring. It is something that enables an organization to be prominent in the crowd.

Check This Out: Top HR Analytics Companies

Stay ahead of the industry with exclusive feature stories on the top companies, expert insights and the latest news delivered straight to your inbox. Subscribe today.

Employer branding is a long-standing process that has gained popularity n the current world. It is like a tool for the companies to get best talent. Here is a list of some key focus areas, which will assist the organizations in their journey of employer branding:

1. Be Clear About What You Stand for

The key strategic objectives, mission, vision, and values of the organization should manifest through the employer branding communications. For instance, companies like Apple and Starbucks are one of the standout companies. Apart from selling coffee, Starbucks has a mission to inspire and nurture the human spirit. In this manner, all organizations should define their idea clearly.

2. Digitalize Your Employer Brand Strategy

With the help of digital technologies, enormous amounts of information are accessible as never before. Smart devices like mobile, tablets, laptops, etc. make the information and computing power available to users around the world. These advanced technologies are changing the strategic context of employer branding.

3. Understand That the World Has Changed

The world has changed, now it is the age of customers, and advertisement has less impact than earlier. The organizations have more responsibility to define clearly the role of employees in delivering signature employee experiences. The right employer’s brand strategy at an organizational level enables HR systems, policies, and processes to have an impact across all lines of the business. It also influences company culture focused on optimizing the customer experience.

4. Focus Your Employer Branding Resources

Companies should adopt a holistic approach to employer branding across total employment lifecycle. By selecting this concept, many companies showed a shift towards a strategic approach to employer brand management. 

5. Build Employer Brand Leadership Capability

The organization should manage the employee experience across the employment lifecycle. Employer brand leaders should build awareness and capability in employer branding principles across the organization. They cannot solely rely only on one or two leaders to manage the whole function. It is necessary to train leaders in employer branding.

6. Improve Communication Flows Inside and Outside Your Organization

Several software solution tools support the communication flow inside and outside your organization. With the help of these tools, organizations work more smartly and foster innovation quickly. Most of the companies have been innovators in the use of social tools to enhance employer branding communications. Other sections like Marketing, Sales, Diversity, and Corporate Communications, can now use these social media presence to better launch initiatives and connect with clients. By expanding reach with candidates, the company can attain its ambitious growth goals. This software will provide on-the-go access to all of their online properties and will allow people to apply for jobs directly from their mobile devices.

7. Your Network Is Your Net Worth              

The organizations should adopt keys that will help their network to grow continuously. Make sure a global reach on the network to ensure your learning and connecting process with people from a range of talent from different backgrounds.

8. Take up a Community Concept to Your Employer Brand Strategy

Companies should adopt a community concept in their approach to employer branding. They should approach strategically across the employment lifecycle and consider all internal and external stakeholders in developing their strategy through an integrated corporate, consumer, and employer, one-brand lens.

Check This Out: Top HR Consulting Service Companies

More in News

Recruitment software is designed to help organizations streamline and improve their employment processes. Recruiters and HR professionals can use the tools to manage and optimize the hiring process, from job posting to making an offer. Some of the most notable benefits of recruitment software are noted below: Speed and efficiency: Automating key recruitment operations, such as resume screening, organizing interviews, contacting candidates, and posting job advertisements, dramatically accelerates the hiring process and decreases the number of manual tasks that recruitment teams must accomplish. Data-driven decisions: Some recruitment software includes recruitment data analytics and reporting tools that make it simple to measure and track important recruitment parameters, allowing organizations to make more informed recruitment decisions and improve their strategies. Enhanced candidate experience : Regular contact, timely response, and a streamlined procedure significantly improve the candidate experience, creating a positive impression and increasing the chances of offer acceptance. Employees First enhances this process with innovative tools that streamline communication and improve responsiveness, further elevating the overall candidate journey. Improved collaboration: Recruitment software enables several team members to collaborate on candidate evaluations, communicate feedback, and make more efficient collective decisions. Higher quality of hire: Advanced candidate assessment techniques, such as skills tests and interview assessments, assist in selecting individuals who are a better fit for the post and the company. The Abelson Group specializes in enhancing recruitment software, offering scalable solutions for managing candidate evaluations and improving organizational hiring processes. Scalability: Recruitment software can manage enormous volumes of applications, particularly for high-volume recruiting, and adapt to the changing needs of a developing company, making it appropriate for businesses of all sizes. Cost-savings: Automated recruitment software can reduce total recruitment costs by reducing hiring time and eliminating repetitive operations. Centralized data management: All candidate information, job ads, and communication history are saved in one location, making data management and retrieval easier, as well as the creation of a candidate database. Integration with other systems: Many recruitment software solutions can be incorporated with other HR software and business systems, resulting in a seamless process from recruiting to employee onboarding and beyond. Compliance and security: Recruitment software helps ensure that hiring methods adhere to legal and regulatory requirements, thereby protecting the organization from potential liabilities. Improved sourcing: Advanced search and filtering features, such as those found in AI recruiting software, enable recruiters to swiftly locate and contact the best candidates from a huge pool of applications. Minimize prejudice: Some recruitment software incorporates elements that encourage diversity and inclusion, such as anonymized candidate profiles and diversity reports. Customizable templates: Email templates, job description templates, and other configurable documents save time while maintaining consistency in communication and documentation. ...Read more
When a purchasing manager evaluates supplier performance, he or she might have too much information at hand. Reports, dashboards, and historical data are all available, but the decision requires some human comparison and identification of the information to act upon first. This gap is making AI decision support applications gain more attention in the enterprise environment as they start going beyond analytics and provide a context for business decisions. In fact, the evolution of enterprise software shows that the previous generations of analytics focused primarily on data collection and visualization. Nowadays, decision support tools are expected to analyze the business state, detect any abnormality or provide recommendations in the middle of business processes. In other words, there is no need to create yet another report – the focus shifts to reducing the delay between insights and their implementation. These tendencies affect several business departments. Procurement managers will be able to analyze the purchasing activities and supplier performance; financial managers can evaluate their expenses prior to budget approval; the customer service department will get recommendations on the urgent cases. All these applications use business data, but now the software provides reasons for choosing specific information instead of displaying it to the user. It changes the way businesses evaluate software purchase decisions. Nowadays, customers pay more attention to transparency and explanation of recommendation algorithms. People trust the AI-assisted decisions based not only on the accuracy of predictions but also on the ability to review the evidence of the recommendation. Businesses are skeptical of using automated solutions when commercial, regulatory, or customer-related risks exist. The deployment process poses additional questions for decision makers. Decision support tools are based on current and high-quality business data. Duplicate entries, inconsistent records, or a lack of transaction history reduce the quality of recommendations. It turns out that in order to benefit from an AI solution, enterprises should improve the quality of the business information as well as implement the application. Another aspect of adoption is employee resistance. Workers with long-term experience in the company might be reluctant to use automated recommendations without understanding how they have been created. Vendors respond to these concerns by adding explanations, confidence scores, or supporting evidence for recommendations. In fact, the increasing interest in AI-powered decision support systems shows the shift in priorities of businesses, rather than the desire to automate everything. Businesses seem to value the software that will assist people in making an informed decision faster, leaving the responsibility on people themselves.   ...Read more
Now, choosing an AI-powered decision support platform no longer involves the purchase of one more technology product, but the question of trust – can machine recommendations be relied on while working? Buyers have started considering this issue very seriously, since software now actively participates in making purchasing decisions, financial analysis, and customer service, rather than serving merely as a tool for analysis. This change affects the conversation about procurement. While considering the choice of decision support solutions, companies often wonder how exactly recommendations are calculated, on what data, and whether it is possible to argue with the recommendation. The possibility of checking the information has become a buying criterion, since, in many cases, business decisions need to be documented and confirmed by the company's management. Also, transparency helps to increase the adoption of decision support solutions after their deployment. People will be more inclined to use the information provided by AI if they understand why a particular recommendation has been provided. Systems that give an answer and do not provide an explanation may cause hesitation, especially in the department where the decision may involve money, contract or commitments to the client. This kind of system provides an opportunity to check the conclusion and proceed only with the verified information. Along with functionality, business leaders pay attention to governance issues. Today, decision support platforms operate in finance, procurement, customer operations and other departments where there are certain regulations related to the use of data. The buyer should have the guarantee that recommendations of AI-based solutions will be in line with the internal rules and procedures, and people will still have full control over the decision-making process. Also, data quality is tightly tied to the trust of the buyer. Recommendations depend on the accuracy of business records, and the inconsistency of the data can negatively affect the credibility of recommendations even if the system works flawlessly. Organizations realize that decision support based on poor data cannot lead to the successful adoption of the solution. The evaluation process is getting more practical. The buyer is interested not only in the functions of the product, but also in its practical application. Useful recommendations should appear in a place where business decisions are being made, and people should not go to another application to get the additional information necessary for making decisions. Despite growing interest in AI-powered decision support solutions, organizations still treat this area carefully and responsibly, balancing between efficiency and accountability, especially where the results of the decisions will affect the commercial success of the company. In such a situation, the transparency of the vendor, recommendations and workflow integration become the priority criteria. ...Read more
A recommendation produced by AI can make the decision-making process quicker; however, it does not absolve people from liability for the decision made. This becomes especially relevant today since decision support tools are incorporated into more workplace processes, and employees receive software guidance while making purchase decisions, looking through financial reports or dealing with clients' requests. Incorporating AI technology into the process creates another conversation about decision support compared to business analytics. Traditionally, reports contained facts that had to be interpreted by employees. Today, decision support application makes recommendations according to the data collected; thus, there is a greater connection between the output and the decision made. People start to discuss how they are supposed to assess the recommendation before doing something. In this situation, training plays a crucial role. People are not only required to learn how to use the application. They also need to understand how to interpret recommendations made by it, find situations when more attention to a particular case is required and identify cases when the business context allows making another decision. People use technology as an aid while making business decisions. Management also faces new requirements concerning oversight. Even decisions made by means of AI technology have to be documented with an explanation for the choice. Business today considers decision support as an opportunity to make their decision consistent and accountable through traditional processes of approval. It also concerns the organizational culture of the company. When people know the role of AI tool in the business as a decision aid, they become more comfortable about using it in the decision-making process. Thus, when people understand what kind of technology AI is, they feel less uncertainty about its functioning and recommendations. When using these tools, companies may also notice some differences between departments. The recommendation, which can help people in the procurement process, needs more attention in the finance and customer facing department since each one works under a certain business expectation. Therefore, decision support is not likely to be used in the same way throughout the whole organization. Overall, the wide use of AI decision support technology shows that the process of decision-making in the workplace will change, but it will not become automated. Companies move towards the future when software provides people with quick analysis, and they interpret the recommendation, approve it and become accountable for their actions. ...Read more