What is Performance Analytics?

Performance analytics refers to the practice of analyzing key performance indicators (KPIs) in an organization to develop strategies to help reach organizational goals. The aim is to ensure continuous improvement across all organizational functions and outputs.

Performance Analytics Example

Performance analytics uses key performance indicators designed to show whether an organization achieves its set objectives within a set period. Performance analytics uses specific metrics to measure if, and how, the set objectives are being achieved. They are not an aspirational goal, but a realistic set of measures that are attainable.

When the first set of objectives are realized, the next performance targets are set to continue to meet and exceed business goals. However, when these goals are not achieved, it shows that there is a problem either in the strategies adopted or their implementation. At this point, it is critical for a thorough evaluation of the root cause to be determined so that the appropriate strategies can be put in place to trend towards more desirable results.

Why Performance Analytics are Important for Business Owners

Performance analytics acts as monitoring tools that enable the business to keep track of organizational performance at all levels. They provide a clear picture of the factors that affect the performance of an organization. By understanding the factors that affect business performance, the business can then take the appropriate actions to make the operations more efficient.

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How Performance Analytics Work

Performance analytics works through the evaluation of numerous measures that have the potential to affect the operations of a business. The analytics can range from performance indicators that businesses choose, to those that are internationally recognized. For a small business, simple performance indicators can be sufficient to measure how well the strategies that have been put in place are working. Larger businesses may use more sophisticated indicators such as the thousand point strength index (TPSI).

The thousand point strength index is a performance analytic tool that comprehensively analyses every aspect that has the potential to affect the future of an organization. Results from this analysis are particularly important to executives as they enable them to have a glimpse of the future and plan accordingly. In addition, the analysis shows what needs to be done to achieve certain goals in the future.

Examples of How Performance Analytics Is Used in Business Environments

Boosting Sales

Performance analytics can monitor sales to determine whether the volumes are consistent with projections. The analytics will show how the products are performing in different markets. If sales of the same product show a wide disparity in different markets, this may signal that marketing efforts are not targeting different markets effectively.

Developing Marketing Strategies

Different marketing strategies will have varied outcomes. Some strategies can be more effective than others in driving more sales. The use of performance analytics can enable the marketing team to determine effective strategies and improve on them. Those strategies that consistently show poor performance should be stopped and replaced by others deemed to be more effective.

Performance analytics also makes it possible for poor strategies to be identified within the shortest time possible as the system processes information in real-time. Senior managers can monitor sales data on a daily basis if they wish and determine immediately whether any interventions are required.

Performance analytics can also help assess if appropriate marketing strategies are being used in relation to the demographics. Through analysis of sales data, it is possible for the marketing team to determine the population that is interested in a particular product. From such analysis, promotion campaigns can be designed to resonate with this segment of the population as the main market for the product.

The same data can also lead to personalized marketing, especially where it is determined that the buyers are from different population age groups. Under such circumstances, there will be different marketing campaigns for the same product targeting different population segments. Such a strategy can improve sales by ensuring that the target audience relates to the message.

Performance analytics helps businesses see at a glance if goals and targets are being met on any campaign, and take action if not.

Benefits of Performance Analytics

Increasing Productivity

Performance analytics can single out areas of poor performance. In sales, for example, teams that consistently have the lowest numbers can be identified and given corrective training. Performance analytics make it easy to identify the individual, team, or department that are not meeting KPIs and management can decide the next best course of action.

Enables Data-Driven Decisions

Performance analytics enables an organization to create predictions of performance and diagnosis of problems in different aspects of its operation. This information can then be used to make future decisions to avoid negative business performance. It also ensures that past mistakes which have the potential to cause losses are avoided and replaced by choices that are data-based.

Enhanced Future Planning

Performance analytics provides data on historical events and how they were addressed. Such data can be useful in predicting the occurrence of similar events in the future and planning for them. For example, suppose historical data shows that overloading employees with work leads to attrition. In that case, the organization can plan in advance to engage additional employees whenever they anticipate that there will be more work. By doing this, it will be possible to retain valuable employees, as the mistakes that contribute to attrition will have been avoided.

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Challenges Facing Performance Analytics

Poor Design

The performance analytics tool used by an organization must be consistent with the needs of the company. The design of the tool should be able to measure all the parameters that affect the company. When there is a perfect match between the tool and the organization's needs in terms of data analytics, the results can be effective.

Different organizations have varied metrics that are used in measuring performance. The metrics that work for one organization may not particularly be effective for another. In addition, companies that operate in different sectors may not have the same operating models, which means that their performance cannot be measured using the same tools.

Even organizations in the same industry may differ in how they approach their business. The strategies that they adopt may also be different. Due to this difference, the performance metrics that used to measure the operations of these two companies are different. The implication here is that companies in the same sector should not adopt the same analytics just because they have been effective in a similar organization, as this could lead to failure.

The analytics needs to be created for the specific organization. Each business has different metrics and systems and employees, and this needs to be reflected in the personalization of the analytics software.

The first thing to be considered when adopting a tool for conducting performance analytics is its compatibility with the organization's needs. The first step should be to identify the needs of the organization. These needs will help to inform an organization’s decision on selecting the appropriate tool to be used in order to achieve the desired results. The analytics tool adopted should measure whatever challenge the business has decided to address.

Since the same tool cannot meet the analytical needs of all the companies, it should be customized to address the specific requirements for the organization to offer solutions to the problems that the company could be facing.

Inadequate Knowledge on the Implementation of Performance Analytics

Some business leaders may not be familiar with the use of performance analytics tools and methods to increase those tools’ effectiveness. As a result, the implementation or success of performance analytics is affected.

There are also leaders who insist on using more traditional performance analysis techniques such as the use of appraisals in human resource management. The effectiveness of these two methods cannot be compared since performance analytics is based on actual data. At the same time, the appraisal is dependent on the appraiser's perception towards the employee, which creates room for bias.

To resolve this, there should be consensus from senior management from the start. The organization must employ experts to manage the design and implementation of the performance analytics system.

Organizations, through senior management, should embrace the opportunities that are brought about by technological development. There should be adequate sensitization on the need to have a more effective performance analysis tool that will enable the organization to monitor its operations effectively and in real-time. Such a milestone can be achieved by subjecting senior managers to training and development programs that will enable them to understand the new system better.

Resistance to Change

Humans are by nature resistant to change. Given a choice, people prefer to maintain the status quo, irrespective of the benefits that the new change is likely to bring. As such, some managers may resist the use of modern performance analytics tools based on recent technology in favor of outdated systems. Their resistance usually stems from the fear of the unknown as they do not know what to expect from the new system. The consequences of this resistance are that the organization will miss out on the opportunity bringing immense positive changes to the organization.

Once the senior management is on board with the use of analytics, they need to drive change from the top. To get buy-in, the time saving aspects of use and the huge benefits to teams and individuals needs to be communicated.

All the parties involved in implementing the change should help to minimize resistance and persuade employees to accept the change. The engagement should be aimed at addressing issues of concern from employees and, in particular, leaders who are in the position of initiating change. The benefits of the change compared to the status quo should be highlighted to help them make a decision.

Using Old Technology

Some organizations adopted the use of enterprise resource planning (ERP) systems when they first entered the market and are still deeply embedded in them despite the emergence of more advanced systems. Although the old system offered some levels of analysis, its capabilities cannot be compared to that of modern systems. Many on-site systems that have been in use for decades are increasingly becoming obsolete.

A more advanced cloud-based system has replaced the on-site enterprise resource planning systems. However, not all organizations have embraced this change.

The only solution to this problem is upgrading to a more advanced cloud-based system that offers more capabilities in terms of data analytics. Leaders who have been using the old technology must adopt the newer system. It is only by moving to the new technology that businesses can adopt performance analytics and truly reap the rewards they can offer.

Performance Analytics or Performance Appraisals?

These two concepts are closely related and are used by managers in their attempt to improve outcomes. The major difference between the two lies in the objective of the tool.

Performance appraisals aim to determine the actual performance of the employee. They answer the question whether the employee is performing in accordance with expectations. A performance appraisal does not seek to identify why the employee performed the way they did, or how they can improve their outcomes.

On the other hand, performance analytics goes a step further into developing strategies that will help improve the productivity of the employee. It seeks to determine how an employee performed with the aim of identifying areas where they can be assisted to improve their productivity.

The second difference is that performance appraisal is subjective as it can be affected by bias from the appraiser. As a result, the outcome may not be a true reflection of the employee’s productivity.

In comparison, performance analytics is based on data that has been collected over time, and therefore it is objective. The data provides the actual trends and is, therefore, more objective. This is a significant step towards removing obstacles to fair and unbiased management of employees.

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