How to extract the most value from your retail data
- Nowadays, companies have a countless amount of data to process, both internal and external. The secret lies in analyzing the data correctly and making the right business decisions.
- However, there are companies that fail in the analysis of their information because they do not have the right tools, technical expertise, time or resources required for the development of data science.
- Also, there is a concern and resistance to acquire external solutions, mainly due to the fear of sharing confidential information with third parties.
- As far as data science teams and optimization tools, they are on every company's investment “wishlist” and roadmap. Today, we are witnessing a similar situation to what happened some time ago when chose the path of in-house development of ERP or CRM systems. Later on, they realized that it is more efficient and less expensive to invest in external solutions created by companies that are specialized in the field, allowing them to focus on improving their core business.
- On the other hand, there are companies that do not have powerful analytical tools and therefore find it difficult to obtain more value from their data. Or are used to working with popular “low cost” tools such as Excel and are resistant to change.
- Here are three scenarios for data science management we will be reviewing:
Inefficient collaboration.
- There are companies that decide to create their own in house Data Science team to generate predictions while they maintain full "control" of the information. Likewise, another department or business category operating as an independent unit may decide to acquire an external solution that analyzes data and makes predictions, suggestions and generates reports to enable decision making.
- In this first scenario, the two parties work separately although they share a common objective. That is, both parties perform the data collection and analysis, but fail to strengthen their skills by leveraging joint efforts. In this scenario, the external solution fails to enhance the data scientist's tasks to a 100%.
Outsourced.
- In this case, companies choose to acquire a solution dedicated to the optimization of key retail variables to generate predictions and suggestions based on internal and external data.
- It is an excellent alternative for companies with limited resources. The outsourced services and tools are specialists in their field, and are constantly analyzing and improving their solutions to offer the latest technology. Delivery and implementation times are usually around 3 to 6 months, which allows companies to start working quickly. In turn, the company will be able to dedicate their resources to the core business and avoid losing their strategic focus.
Integration.
- In the third scenario, the company has an area dedicated to data analysis but also acquires a solution that works with them to strengthen the role of Data Scientists.
- The Data Science team can focus on data collection while the external solution performs an analysis of the business and its context. Together they work on developing the best algorithms and practices for the business.
- Companies leverage the predictions and suggestions generated by the optimization solution while having an overall perspective of the business.
- With the right data analysis, companies will be able to understand their clients and acquire new customers, while improving operational efficiency, reducing costs and optimizing profitability. Artificial intelligence can behave as a central nervous system, reading signals, reacting to events and anticipating future outcomes. For this purpose, it is ideal to have an in house or outsourced Data Science team integrated with an optimization solution.
- Prisma is an omnichannel solution that analyzes, predicts and suggests actions that increase your profitability through the combined optimization of Prices, Assortments, Promotions, Spaces and Inventories. It integrates the data collected from internal and external sources, and works closely with the Data Science Teams to develop new algorithms/practices focused on retail optimization.
- Our business experience tells us that having an in house or outsourced data science team that receives insights and suggestions from an external solution - is the best combination for business optimization.
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