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The importance of growth for small and medium-sized enterprises (SMEs) in the retail sector is clear. One of the primary drivers of this continuous growth is the ability to interpret daily operational data swiftly and accurately. In a fast-changing market, it's crucial to adapt strategies like product launches and customer management based on insights from metrics. Understanding the data's value promptly ensures timely decision-making, whether it's front-line salespeople or the management team.
Retail analytics involves collecting and analyzing data from various retail channels, both offline and online. This data provides insights into customer behavior, inventory management, marketing strategies, pricing, and product distribution. By harnessing the power of data analytics, retailers can make informed decisions across various domains, enhancing the overall shopping experience.
There are four main types of retail analytics that each play an important role in providing insights into business operations.
This type of analytics helps retailers organize their data to tell a story. It works by bringing in raw data and summarizing it in a way that is easy to understand. Descriptive analytics offers retailers a snapshot of key business activities, including past transactions, inventory updates, and the success of promotions.
This type of analytics helps retailers understand why something happened. It works by analyzing data to identify patterns and relationships between different variables. Diagnostic analytics can help retailers identify the root cause of a problem and make informed decisions about how to address it
This type of analytics helps retailers predict what will happen in the future. It works by analyzing historical data to identify patterns and trends and using that information to predict future outcomes. Predictive analytics can help retailers forecast demand, optimize pricing, and improve inventory management.
This type of analytics helps retailers determine what actions to take to achieve a desired outcome. It works by analyzing data to identify the best course of action to take in a given situation. Prescriptive analytics can help retailers optimize pricing, improve inventory management, and increase customer satisfaction.
Retail analytics face several challenges, including:
Data comes from different sources. These sources include inventory systems, sales volumes, customer footfall, profit margins, stock levels, and more. This complexity makes it challenging to collect, analyze, and interpret data accurately.
Retail analytics involves dealing with large data sets, which can bring about big challenges for retailers. Retailers face challenges with big data, including collecting accurate data, following data protection laws, and keeping up with competition.
Analytical software tools are expensive to acquire and maintain, and it takes a long time to realize a return on investment. This can challenge retailers who want to implement analytics-driven retail environments.
One of the key challenges for retail is that data gathered across a business often exists in silos. Different sources store their information in distinct databases. This makes it challenging to integrate and analyze data effectively.
Retailers with an online and offline presence process all kinds of user data, including customer personal data gathered through loyalty platforms. Ensuring customer security and privacy while complying with data protection laws can challenge retailers.
Retail analytics tools are pivotal in understanding consumer behavior, optimizing store operations, and enhancing the shopping experience. There are several types of retail analytics tools available in the market, each catering to specific needs:
These are just a few examples of how AI can help retailers with retail analytics. By leveraging AI technologies, retailers can gain valuable insights, optimize operations, enhance customer experiences, and ultimately improve their overall business performance.
Consider the "Challenging Questions" retailers often grapple with:
For instance, when identifying potential customers, retailers might wonder:
2. Which marketing campaign is not working well?
3. How to reduce advertising costs by 10%?
4. How could conversion change if DAU increase by 5%?
Addressing such questions can be daunting due to the intricate nature of retail data.
"Why did online sales drop suddenly on Oct. 3rd and 4th, 2018?"Old ways of analyzing this took many steps, like modeling and calculations. This process is complex and time-consuming, often requiring a specialized data team. Moreover, traditional data analysis usually involves multiple personnel, leading to potential information distortion.
Imagine a scenario where retailers can directly interact with data and metrics, receiving real-time insights. This is the promise of Kyligence Copilot, an AI-driven data assistant built on the Kyligence Zen metrics platform. Using Azure OpenAI, Kyligence Copilot enables users to easily search metrics, analyze data deeply, and automatically create dashboards with plain language.
Now let's experience data analysis through a conversation with Kyligence Copilot:
After importing, the retail metrics are displayed as cards in the catalog. Then, click the robot logo in the top right to open the Kyligence Copilot dialog in the sidebar.
Just ask Kyligence Copilot your questions and get answers quickly! For example, we queried, "Why did sales in the East region drop sharply from Oct. 3rd to 4th, 2018?". It quickly gave a thorough analysis, showing the main reasons for the sales drop and the biggest positive and negative influences.
Kyligence Copilot's chat feature lets front-line staff and management quickly gain data insights by asking questions. This simplifies data access and comprehension for everyone. Beyond data analysis, Kyligence Copilot aids management in spotting business risks and provides actionable advice.
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