What are the most used BI tools in the retail industry?
The retail industry is a complex field, where businesses have to focus on several critical areas for the success of their operations: starting from supplier collaboration and return policies, thorough return operations, and supply chain organization, to merchandise disposition, and analytics.
And each of those focus fields is composed of many smaller, yet important variables: stock availability, sales rates, overhead costs, marketing approach, etc.
Nowadays, retail entities often adopt business intelligence tools with the aim to become smarter, improve their decision-making process, and enhance their operational and financial performance.
This article will reveal the most used BI tools in the industry, as well as the benefits they provide to the companies they are built-in.
This first tool provides the retail organizations with multidimensional data gathered from unrelated, distinct, or even siloed sources (with the exception of transactional data). It cleanses, stores, and transforms those complex data in a logical sequence to prepare them for further on-demand analysis: whenever and wherever is needed.
The benefits, which this tool provides the organizations with, are mainly related to enhanced management processes. OLAP enables the companies to view business data from multiple points of view, evaluate "IF" cases, and contribute to smarter processes of planning, purchasing of assembly or raw materials, and project costs. What is more, it helps retail entities to prepare the budget for their next financial period, access detailed and dimensional data, and solve complex business issues in a smart way.
In fact, OLAP is the foundation of other popular BI tools, such as reporting and data mining.
BI reporting is not the same as the old school reporting we know about. In the past, the reports were structured to show us the progress status of projects and operations, as well as to give us information about the current store, stock, and sales situation.
Nonetheless, business intelligence reporting provides us not only with such basic information, but also why things happen, what is the relationship between different variable factors, and how to improve current performance. And all those insights are fully data-driven and based on past evidence.
Retail organizations take advantage of such automated, timely reports, to track tailored KPIs and receive time-bound insights about sales margins, rate of web sales to in-store purchases, cost fluctuations, depleted resourced, depreciation of assets, etc.
Data mining is an automated pattern-identification tool for acquiring high-quality BI, which has great use in the retail sector.
It examines pre-existing databases and storages and uses statistics, artificial intelligence, and machine learning basics to acquire new or undiscovered information and tendencies from the available data sets.
Data mining uses techniques such as classification, clustering, regression, and sequential patterns to recognize any hidden relationship between data, which would be valuable for the company. For retail, this tool is an efficient solution to facilitate automated trend and pattern identification.
And by taking into account the derived insights, retailers are enabled to get a better understanding of their business processes and see any established patterns in terms of order placement, an average time of customer journey, client consideration and satisfaction, efficiency of marketing campaigns, cost frameworks, and many other KPIs.
Data dashboards are the perfect tools to gather all the organizational BI data in one place and understand it.
Those tools track tailored KPIs, metrics, and objectives' progress rates and visualize the results in a comprehensible, accessible, and simple manner.
Built-in dashboards give detailed, yet not-overwhelming information about the business's focal points. Therefore, the retailers are enabled to see key performance data at a glance, such as CTR, conversion, and customer retention rates, inventory turnover, year-over-year growth, and others.
Besides, implementing real-time data tracking and visualization provides us with up-to-the-minute business intelligence and gives us the opportunity to achieve better control of the operations and capabilities within our reach.
Similarly to data mining, this tool for business intelligence examines past data to generate timely evidence-based insights. However, there is one big difference: Predictive analytics uses the derived insights to forecast and project future situations, circumstances, and risks, but not to report current trends and patterns.
For retailers, this tool is extremely convenient, and in fact, 42% of the organizations who want to acquire business intelligence prefer the adoption of predictive analytics, because they perceive it as the most efficient option for providing quick and accurate results.
This tool indeed gives precise future insights to make decision-making more reliable and data-driven. For example, excellent uses of such analytics in the retail sector are forecasting customer demand, inventory requirements, store configuration or website layout, cost fluctuations, etc.
However, the benefits don't end there. The major advantage of this BI tool is that it gives us extensive data about future customer behavior, requirements, and desires, and this way allows us to implement a more efficient and competitive marketing strategy.
On the other hand, predictive analytics helps us to detect operational and financial risks early enough so that we can avoid or mitigate them (and eventually prevent future losses.) For instance, identifying big demands for holiday-related products will stimulate the retailers to order enough safety stock for their inventories, in order to diminish the risks of stockouts and lost revenues.
Retail companies strive to become smarter and smarter in order to acquire a competitive advantage and enhance their sales rates. Some of the most used and efficient BI tools which help this process come true are OLAP, reporting, data mining, dashboards, and predictive analytics. Those help retailers to grow and support their steady development in the long-term.