Fueling Success: The Employee Experience Connection

There is a strong link between Customer Experience (CX) and Employee Experience (EX). An optimized CX generates loyalty and additional sales. A stellar EX boosts workforce engagement, productivity, and retention. This in turn directly improves business financial performance. Most organizations have acknowledged the need to have an engaged workforce to achieve greater ROI.

Is there a tool that connects CX with EX? Yes, and that called -Success Profile (SP). A concept first introduced by DDI in 2009, holistically capture the requirements of job success—what knowledge, experience, competencies, and personal attributes are critical to drive business strategy in a job.

Every role has a success profile, every role has a set of KPIs, every role requires a set of skills and abilities to perform the given responsibilities, every responsibility is associated with activities and tasks. SP is a detailed description of the characteristics and behaviors that are required to perform the job at a high level.

Success Profile definitions for each role are central to an organization, which fosters a culture of transparency of purpose, data driven decision-making and that it has adopted adaptability and agility as its theme for business operations.

At Entomo, we go to great lengths in generating Success Profiles, which reflects the state of market dynamics that the organization belongs to and in aligning it to the KPIs and OKRs that the organization has set out for itself.

Here’s a SP for a data analyst, it’s the junior most role for future data science architects or Data Analytics advisor, working on financial modelling or an Industry analytics advisor working on Oil&Gas P&L function.

A success profile for a data analyst would typically include the following:

1.???Technical skills: proficiency in data analysis tools and programming languages such as SQL, Python, R, and Excel.

2.???Analytical skills: ability to analyse large and complex data sets, identify patterns, and draw insights.

3.???Communication skills: ability to clearly communicate data findings to non-technical stakeholders, including executives and team members.

4.???Attention to detail: ability to meticulously review data and ensure accuracy.

5.???Business acumen: understanding of business operations, goals, and strategies to be able to identify relevant data and draw insights that can support decision-making.

6.???Problem-solving skills: ability to identify problems, propose solutions, and make data-driven recommendations.

7.???Teamwork: ability to work effectively in a team environment and collaborate with others to achieve common goals.

8.???Adaptability: ability to adapt to changing priorities, requirements, and technologies.

I have excluded proprietary information which is generated by the product for each of our customers using their data.

Additionally, SP carries attributes related to soft-skills and that of expected behavior:

1.???Learning Agility and Curiosity:?

• A strong desire to expand knowledge in data analysis techniques and Statistics.
• Actively seeking out learning opportunities, online courses, certifications, or joining relevant professional communities to enhance data analysis skills.

2.???Analytical and Problem-Solving Skills:

• Ability to break down complex problems, analyze data, and derive meaningful insights.
• Demonstrating a proactive approach to problem-solving, curiosity in exploring patterns, and finding innovative solutions.
• Having an eye for detail when working with data, ensuring accuracy, and identifying data anomalies or inconsistencies.

3.???Collaboration and Communication:

• Willingness to collaborate effectively within cross-functional teams, supporting stakeholders in understanding data requirements and delivering actionable insights.
• Communication Skills: Clear and concise communication, both written and verbal, to convey findings, insights, and recommendations to non-technical stakeholders.

4.???Professional Attributes:

• Demonstrating the ability to prioritize tasks, manage deadlines, and work efficiently on multiple projects.
• Maintaining high ethical standards when handling sensitive or confidential data.

In the next blog, I will be showing how SP is used in defining the career-path of a data analyst.