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New Retail Technology in 2017 Adds Value through Analytics

New retail technology has evolved exponentially over the past few decades, especially when it comes to analytics. Originally, descriptive analytics was the primary investigative tool used across our industry. These solutions provided only a snapshot of information about past events, often in the form of reports. When the reports were opened, they still required a level of interpretation to determine what—if anything—should be done. The reports did not help to determine the root cause or identify how to solve the finding.

Diagnostic analytics came about to answer the “why” for analysts and investigators, using ideas like data mining and data discovery to find correlations in events. From diagnostic, both predictive and prescriptive analytics were born.

Predictive analytics used information gathered through diagnostic techniques to make predictions about future outcomes, helping organizations accommodate for these potential events.

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Finally, the most recent solution—prescriptive analytics—combines all these ideas to identify opportunities for improvement, and takes the next step, making suggestions designed to remedy these opportunities and capitalize on them. Ensuring corrective action is taken to address the identified problem is what makes prescriptive analytics more effective than a traditional data reporting solution. By automatically turning data into understandable insights and placing them directly into the hands of those who can impact your business, this solution will dramatically help you optimize results.

New Retail Technology is Opening Doors

Until recently, asset protection teams would put exception-based reporting into the hands of a limited number of people within their own department, making their investment one of the best-kept secrets in the company, thus missing out on the true potential to maximize the return on their investment. Furthermore, their data mining or exception-based reporting solution was used in a very limited capacity to identify dishonest behavior.

The problem with this strategy is that it virtually eliminates the chance for loss prevention technology solutions to add value in other departments. To combat this, we need to think differently about how we use our loss prevention technology investments, searching for new ways to use old ideas.

At a time when the face of retail is changing so rapidly and many companies are cutting expenses in order to either stay competitive or simply survive, it has become critical for every asset protection team to add value throughout the entire organization. I would also stress that senior-level executives do not perceive big organized retail crime (ORC) cases or internal theft investigations as value. Although they may see these results as commendable and even necessary, they don’t see them as adding value. And when the time comes for budget cuts, the number of cases you resolved will hardly be a consideration.

In today’s environment, it is critical to offer services the company simply cannot live without. The best way to achieve this is to embed your culture, knowledge, services and technology throughout the entire enterprise. It’s no longer enough to protect profits; it’s time to start thinking about how you can expand them.

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Increasing Value

In my previous role, I used a prescriptive analytics solution. We partnered with that particular vendor because they had a progressive view on how data could be used to expand a company’s profitability rather than just catch dishonest associates. In fact, they were also instrumental in helping us extend our technology throughout the entire enterprise. We trained upwards of 700 employees, across all departments, on how to use the solution to enhance the daily functions that fell within their current area of responsibility.

Basically, everyone was using the same tool to meet their specific needs. Each user was looking at something completely different than the next. By sharing our loss prevention technology and our expertise, the solution’s value increased exponentially. In addition, the company looked at the department in a different way because we were able to assist them in creating a broad range of successes by targeting their individual requirements and unique challenges.

Creative Use

Don’t limit yourself by using only POS data. The more data you push through your analytics software, the more uses you will discover. POS data is limited, however, when you add billing, invoicing and inventory data, you have dramatically increased the variety of service you can provide to other departments. Once that has occurred, you can really start to add value that your partners cannot live without. From an investigative standpoint, you will add multiple layers of clarity to what used to be a directional starting point at best.

For example, using this combination of data feeds; we offered our analytics to the entire front-end department. The traditional use would be to monitor cashier performance for training concerns as well as dishonesty. By sharing the expanded data, we were also able to help them monitor critical compliance issues and improve customer service. We then offered the loss prevention technology to our merchandising department in order to provide enhanced data designed to maximize shelf space allocation, monitor product specific performance, quickly identify quality issues and sell more merchandise at a profit. Triggers were also established to point out profit erosion and missed sales opportunities.

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All too often, these decisions are based on emotion rather than factual information. Imagine how the senior executives will perceive your department when you can say your team helped increase sales and that you have processes in place to monitor for any missed opportunities that occur in the future.

Your pricing department can also benefit. Automated patterns can quickly identify pricing errors such as product selling below the intended retail or even items selling below cost. You can also help illustrate which promotional strategy was most profitable for a specific item. And why not give the technology to your operations counterparts so they can pinpoint execution deficiencies and then create a more impactful and targeted store visit strategy?

Finally, place the software in the hands of your store-level managers. This will allow them to identify areas of opportunity and adjust as their market demands. These are just a few examples, as the possibilities for expansion are limitless.

Build a Strong Plan

Before you begin, you will need to educate yourself on which new retail technology is right for you and your organization. What old technology do you currently have, and how can you use it in a different way? What new retail technology will your company benefit from?

Selecting your solution provider is a critical step in this process. Failed technology and long implementations can kill your ideas and your reputation. It is important that you select your solution providers carefully. They can make or break a program implementation. I see a lot of buzzwords, such as “prescriptive analytics” and “artificial intelligence” being used across the industry. Although a solution provider might enjoy using the latest and greatest terminology, it is your responsibility to validate that they actually offer the specific services they advertise.

Finally, embedding your technology into other departments is not as easy as it may sound. Be aware of the fact that you may be perceived as overstepping your bounds. With that said, take the time to share your information in a manner that promotes partnership and teamwork.

As loss prevention technology becomes increasingly useful across all departments within an organization, the opportunity to elevate the importance of your department grows. By capitalizing on basic data that is readily available and using it for purposes outside of the traditional asset protection wheelhouse, you will drive improved results and increase efficiencies in other departments, thus earning respect and adding value across the entire enterprise.

This post was originally published in 2016 and was updated August 8, 2017. 

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