In a rapidly growing, data-driven world, the effective implementation of new data programs, platforms, and products is key for success. However, training is an important, but often forgotten, aspect that ensures users can fully leverage the capabilities of these tools to drive operational efficiency. This article dives into effective training for data tools in retail, exploring the key aspects of training, from design to post-training support, that lead to the successful adoption of new data tools for a retail business. 

Why We Care About Training for Data Tools 

Given the value a data product can provide to a retail business, it’s critical to invest in training to ensure user acceptance and achieve a full return on investment (ROI). Even the best-designed systems offer no value if users don’t know how to use them. 

Poor training may result in: 

• Underutilization of tools, leading to no benefits  
• Misuse, driving bad decision-making 
• User confusion and stress, delaying user acceptance 
• Possible security violations and improper sharing of personally identifiable information (PII) 

As retailers focus more on leveraging AI and data, providing comprehensive training to end-users will maximize the benefits of the new data tools. 

Understanding Training Requirements 

Successful training first comes from understanding the tool, its users, and their preferred training styles. But ultimately, a data tool’s complexities and features will determine the training needs of the retail business. Simple tools, such as customer retention or seasonal sales dashboards, may require less intensive training, where users only need to know how to use the dashboard and change filters. Conversely, more complex tools, like AI-driven omnichannel dashboards or tools that leverage customer PII, may require more intensive training such as certification programs or hands-on workshops.   

In addition to the data tool, users’ skill levels should be investigated and defined to understand their training needs. The role users are expected to have with the data tool can also dictate their training. For example, store operations or inventory management teams may need training on interpreting inventory forecasts and sales performance dashboards, while store managers might only need to know how to generate basic sales reports. When and how they will use the data tool helps determine the appropriate depth of training required.  

Knowing the preferred learning styles and the most successful implementation strategies previously used by the business can help determine the medium for training content. Presentation-style sessions are useful for initial introductions or when contextual information is necessary, whereas live demonstrations or interactive sessions can provide more engaging learning experiences.  

In addition to interactive sessions, having a ‘sandbox’ or ‘pilot’ version of the data tool allows users to practice and explore it without the fear of causing any disruption to actual data. This is a valuable way to boost confidence and ease the adoption process for users. However, if learning styles (visual, auditory, hands-on) and company culture aren’t taken into account, users may become frustrated or resistant to change, hindering the training’s effectiveness. By considering these factors, you not only enhance the training process but also build confidence in the system, fostering a more positive and accepting attitude toward the new tool. 

Training and Delivery 

Designing training content requires prioritizing key takeaways for users. The introduction to a data tool should explain its purpose and value proposition, highlighting its impact on workflows. For example, a retailer implementing an AI-assisted supply chain management tool should demonstrate to users how the tool will streamline their daily supply chain processes and reduce the risks of stockouts or overstocks. Ensuring that end-users understand the overall vision for the data tool is critical in empowering them to use it in their daily work.  

What’s equally important is selecting the right trainers. Whether the trainers are external experts or business champions who have been trained to become subject matter experts, choosing the right individuals is crucial. Business champions can be highly effective as they understand the company culture and can relate to their peers. By utilizing knowledgeable and enthusiastic trainers, you can foster a positive training environment that encourages users to embrace the new tool and integrate it into their workflows. 

Establishing a community that encourages practice and collaboration will allow users to improve their skills and become more confident with the tool’s functionalities. This can be achieved through a Microsoft Teams channel, for example, which includes spaces for discussion with trainers or other users, documentation storage, or even a shared calendar of events. Enabling these shared spaces for open discussion and problem-solving guidance will give users a sense of support and comfort if and when obstacles arise. 

Post-Training Support and Resources 

A collaborative space should not only serve as an avenue for support and guidance during training but also as the primary hub for post-training support. It’s crucial to have a plan for ensuring the continued success of a data platform’s integration into a retail business, and establishing a channel to facilitate ongoing assistance, address incidents, and communicate updates about the data tool can help to do so. 

Various aids and teams fostering support post-adoption can impact the long-term sustainability of the data platform. Investing in post-training support can take many forms, including accessible and organized documentation or having a central hub that guides users to different tutorials and resources. Having a task force to handle incidents and ensure users know who to contact for troubleshooting or questions that arise after the initial adoption is also crucial. Within this task force, there may be business champions, or super users that are heavily involved in development and testing, who can promote the tool’s use and help troubleshoot issues. They may also help identify whether problems are due to user error or actual tool malfunctions.  

Regularly tracking progress through assessments and user feedback can also help determine if the training is effective or needs adjustments. This ongoing evaluation ensures the training strategy evolves to meet the changing needs of users.  

Getting Started: Tailored Training for Data Tools in Retail

Tailored training is crucial for retailers to adopt new data products and platforms successfully. Businesses can maximize the value of data investments by designing comprehensive training programs, employing effective delivery methods, and providing robust post-training support.  

In the retail space, where seasons and trends move quickly, encouraging ongoing learning ensures sustained success, and the continuous improvement of training of data products – based on user feedback and changing retail industry needs – helps maintain the relevance and effectiveness of these tools. 

If you’re looking for guidance for a new data product or tool for your retail business, reach out to our data and analytics experts. 

Contributions from Macy Zylstra