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Retail growth has become increasingly complex in the modern retail landscape. Consumer needs and expectations are evolving as fast as ever. As a result, data analytics is gaining massive popularity as it solves retail complexities and capitalizes on customer behavior.
To summarize, collecting and analyzing data is useful as it allows retailers to:
- Shift from product-centric to customer-centric business models.
- Shift from data siloes to integrated data analysis.
- Elevate decision-making by reducing guesswork.
- Enhance evaluation opportunities.
- Formulate predictions and mitigate risk
The shift from product-centric to customer-centric business models
Business growth now is not just as simple as selling a product or service, it is a constant struggle to retention of existing customers and acquisition of new ones. Having just a product-centric approach to selling, therefore, is deemed insufficient amidst the rapidly evolving consumer preferences patterns. With more and more options to choose from especially after the onset of the rise of e-commerce, consumers are exploring bargain-hunting opportunities and indulging in price comparisons making them more aware than ever.
Consumer behavior analysis, thus, is crucial to introduce objectivity in business operations. With high-tech solutions including people counting, POS system, and warehouse and inventory management at their disposal, retailers can now easily observe customers’ responses to their products and stores; physical or digital.
Integrated data analysis vs the siloed analysis approach
More and more data analytics tools such as inventory management systems, roster management systems, queue management systems, and footfall counting systems are crucial to integrating and analyzing multiple KPIs together, as opposed to in isolation.
Elevated decision-making and reduced guesswork
Data analytics now plays an imperative role in understanding growth opportunities in retail. Retailers can feel more confident in their decisions as they are backed by hard data. They no longer need to rely on intuition or guesswork to measure performance and set goals.
Alkaram Studio, one of the leading retail giants in Pakistan, is a good example of an organization that used hard data analytics to enhance the performance of its slowest growing region.
The role of retail analytics is not just limited to assisting decision-making, but also serves the extremely important purpose of evaluating the impact of the decisions made. Positive and negative evaluation of current decisions can help retailers understand which strategizes work best for their business models and growth targets. Problem areas can be identified through analysis which can then enable informed decision making and strategizing.
In the graph below, the higher on the y-axis and the more right on the x-axis you are, the better your campaign has performed.
Formulate predictions and mitigate risk
Not surprisingly, data analytics allows retailers to mitigate risk in both the present and the future. Data anomalies and correlation analysis based on past trends can help with detecting potential operational issues such as service outage so that timely measures can be taken to mitigate any associated risks.
Interestingly, Artificial Intelligence modeling can be used to manipulate data to forecast consumer behavior. This can go a long way in making cost saving decisions on procurement, staffing etc.