The right set of analytics tools and techniques for your business

You may be selling online and furiously trying to engage your customers in the digital world, you’re taking risks by discarding the fact that consumers still love to shop in stores. The brand experience is much more enjoyable when the website experience is consistent with the experience in store.

Research by Accenture found that 60% of Gen Z shoppers prefer to purchase in stores” and “46% will check in store to get more information before making an online purchase.” These numbers highlight the importance of having a cohesive physical and digital strategy. While stores continue to play a critical role in the retail journeys of modern shoppers, digital channels are increasingly becoming an important part of the mix. For this reason, it’s essential to have in-store analytics capabilities that can enable digital businesses to get to know their customers as they move from one channel to the next. In this article, we will study about some of the most actively used analytics tools in the industry and how they can be leveraged to create additional value for your business and your stakeholders.

In the digital age, there are a million things digital businesses have to stay on top of, and simply not enough time in the day. Yet, despite these challenges, some traditional retailers are managing to grow year-over-year, shredding previous sales records time and time again. The winners are doing something differently — something that not only helps them survive, but also thrive in this quickly unfolding retail apocalypse. According to McKinsey & Company, the reason some retailers are winning (while others struggle) is advanced analytics. New research says that retailers using advanced analytics outperform the competition by 68% in earnings  — and the disparity is growing exponentially. But before going to explain the types of analytics tools that you can leverage, it is crucial to understand what does digital analytics entail? What does it consist of?

Digital data analytics is the process of collecting and studying data (like sales, inventory, pricing, etc.) to discover trends, predict outcomes, and make better business decisions. Done well, data analytics allows retailers to get more insight into the performance of their stores, products, customers, and vendors — and use that insight to grow profits.

Types of Digital Analytics

1. Descriptive Analytics: The most common type of data analytics, descriptive analytics help retailers organize their data to tell a story. It works by bringing in raw data from multiple sources (POS terminals, inventory systems, OMS, ERPs, etc.) to generate valuable insights into past and present performance. Traditionally, analysts did this manually in Excel; gathering data from different sources, formatting it, charting it, etc. Today, a lot of this data gathering and reporting work can be automated with BI tools and integrations. Simply put, descriptive analytics uses data to describe “what” is happening in your business. But it doesn’t do much to answer the “why” — unless combined with other types of data analytics that can show patterns and correlations.
2. Diagnostic Analytics: The simplest form of “advanced” analytics — diagnostic analytics helps retailers use data to answer the “why” of specific business problems. Taking the same raw data used in descriptive analytics, diagnostic analytics uses statistical analysis, algorithms, and sometimes, machine learning, to drill deeper into the data and find correlations between data points. Historically, the most accomplished analysts did all of this manually. They would sift through data, apply statistical models, look for patterns, and find correlations. But in today’s data-heavy world, this is nearly impossible for a human to do. With billions of data points and increasing complexity, larger retailers can’t effectively use diagnostic analytics without machine learning and AI.
3. Predictive Analytics: The simplest form of “advanced” analytics — diagnostic analytics helps retailers use data to answer the “why” of specific business problems. Taking the same raw data used in descriptive analytics, diagnostic analytics uses statistical analysis, algorithms, and sometimes, machine learning, to drill deeper into the data and find correlations between data points. Historically, the most accomplished analysts did all of this manually. They would sift through data, apply statistical models, look for patterns, and find correlations. But in today’s data-heavy world, this is nearly impossible for a human to do. With billions of data points and increasing complexity, larger retailers can’t effectively use diagnostic analytics without machine learning and AI.
4. Prescriptive Analytics: The simplest form of “advanced” analytics — diagnostic analytics helps retailers use data to answer the “why” of specific business problems. Taking the same raw data used in descriptive analytics, diagnostic analytics uses statistical analysis, algorithms, and sometimes, machine learning, to drill deeper into the data and find correlations between data points. Historically, the most accomplished analysts did all of this manually. They would sift through data, apply statistical models, look for patterns, and find correlations. But in today’s data-heavy world, this is nearly impossible for a human to do. With billions of data points and increasing complexity, larger retailers can’t effectively use diagnostic analytics without machine learning and AI.

There are multiple approaches:

• Running simulations on a finite number of different initial conditions (different assortment, allocation, pricing, etc.) and choosing the conditions that lead to the highest profit
• Using algorithmic AI, purpose-built for retail to make recommendations that lead to the best possible mathematical outcome (profit, GMROI, etc.)
• Teaching a machine learning program to identify patterns and clusters of actions that lead to the best outcomes

Examples of Digital Analytic tools

There are many applications of using and integrating various forms of analytics techniques that we have studied before. It’s no surprise then, that there exists a massive, thriving industry for retail analytics solutions. Below, we’ll discuss some of these applications, how they work, and what benefits you could see from using them.

Business Intelligence: To effectively manage and organize their data, many businesses turn to Business Intelligence tools. Because BI tools help you structure and visualize your data, they are an example of descriptive analytics. Many retailers conduct basic BI using native features in their ERP (Enterprise Resource Planning) system, or by importing data directly into Microsoft Excel.

Slightly more sophisticated retailers will use dedicated BI software like:

• Power BI
• Tableau
• SAP
• QlikView
• Apache Spark

Sales Forecasting software: As the name suggests, sales forecasting is predictive in nature — and it is the most rudimentary type of predictive analytics used by retailers. Because businesses have been attempting to forecast sales for centuries, there are many different approaches to doing so:

• Using last year’s numbers to estimate sales for this year
• Market research (surveys, observation, etc.)
• Pundit estimates
• Statistical models in Excel
• Dedicated software (Salesforce, Pipedrive, Oracle CX Sales)

Demand Forecasting Software: Rather than attempting to predict sales using merely historical sales data, demand forecasting uses a much broader range of data to calculate the demand of each product, at each store, at specific time intervals. As always, there are multiple ways to forecast demand. In increasing order of sophistication, retailers can use:

• MS Excel with statistical models
• Generic statistical modeling / analytics software (R, SPSS, Tableau, PowerBI)
• Retail-specific analytics software with AI

In this age of connectivity, people are getting more and more connected to the internet. This leaves a major opportunity and potential in WiFi Analytics. It is becoming more and more prominent to identify, track and communicate with your customers in real time. The creative lab team at Cloudi-Fi strives everyday to create omnichannel strategies and footprints for all type of businesses based on efficient analysis of WiFi analytics data. In the age of data, it won’t be wrong to say that “Who owns the data, owns the businesses”.  It is the world of analytics and data, is your company prepared?