While data and analytics are two of the biggest factors that contribute to the success of retail, they’re also very easy to misinterpret. In the past five to ten years alone, and with the rise of online retail, we have been experiencing a cogent shift in the state of retail altogether. Now, more than ever, businesses are growing at lightning speed, and in many ways the task of building a retail company has proven less daunting, yet more nuanced. While launching a business with potential to scale is one feat, remaining sustainable and profitable long-term is another. It is this critical juncture that retailers must fully understand and grasp the power of metrics.
"Long-term sustainability and profitability rest on a deep and actionable understanding of data and analytics"
In other words, long-term sustainability and profitability rest on a deep and actionable understanding of data and analytics.
Beyond simple numbers alone, this comprehensive understanding gives retailers the leverage needed to set themselves apart and make their products relevant to a range of markets, albeit diverse. Take a look at department store retailers like Nordstrom or Macy’s: both sell completely different product mixes depending on store location, and they recognize differences between customers in Los Angeles and those in Chicago. Understanding the data and analytics behind these very diverse and distinct markets gives retailers like Nordstrom and Macy’s the ability to approach their customers on a smaller, more curated scale, all while maintaining a consistent level in branding and messaging throughout.
Data mining, from initial forecasting to supplementary analysis, has become an important tool for this very reason. Retailers are progressively using big data to justify strategies and analyze customers in a myriad of different ways, further consolidating their findings to spearhead efficient and sustainable growth.
Using data and analytics simply for the sake of doing so, however, can be an inefficient and dangerous exercise. Data has become so accessible that businesses, and retailers in particular, are susceptible to hyper-collecting whatever is available—a problem rampant in today’s consumer market. In addition to accepting a large workload for what could be a small return, data overload can muddy marketing strategies and customer analyses. Companies, especially those young and small, should strategize for proper data collection within targeted areas and use their findings as the backbone by which to scale their business for long-term growth and tell their unique stories to customers interested in hearing it. While it may seem simple, a properly targeted strategy is one many companies strive for but few grasp.
For large companies, acknowledging and transitioning into a data-driven structure is necessary for maintaining both relevance and development. Major Fortune 500 companies are beginning to lose visibility as their younger data-driven counterparts rise. This proves one thing: money (and experience) is not necessarily a precursor to success. Instead, success today is defined by the balance between backend and front end—that is, data and engagement. Traditional retailers that use stodgy ERP-based systems are finding it difficult to understand the modern consumer; and rising tech leaders, like Salesforce, are offering solutions by providing the apropos data necessary to scale—however small or large, young or old, the company may be.
Keeping up with data and analytics has become a prerequisite to transforming the omnichannel experience. With data mining, retailers have become cognizant of their demographics with near certainty, and they can now aggregate insight to what will and will not sell both online and in-store.
As such, big data continues to influence the business-consumer dynamic and transform retail as we know it. Traditional retail buying practices, for example, are becoming less relevant in today’s market. Because data and analytics can paint specific pictures of what product is needed along with sell-through velocity, high costs for human labor become less essential. As retailers embrace data and just-in-time strategies—whether by leveraging metrics to forecast sales, analyzing multiple variables and markets, justifying purchase orders in real-time or, even, managing liability on inventory—the demand for buyers is dwindling, trade shows are experiencing a decline in foot traffic, and the concept of overall seasonality is becoming irrelevant.
This is not to say that the role of buyers or overall human labor will become obsolete, rather, data and analytics have become so imperative to the omnichannel experience and larger retail success that the strategy behind where and when human capital is deployed has become incredibly important.When retailers strive to understand and allocate the metrics suitable to help accelerate the company as a whole, the balance between human know-how and data-driven decisions become more and more attainable. Every business can harness big data and analytics in their own respective ways. It is, at the end of the day, how each business interprets their findings, making their data and analytics their own, that will sustain long-term growth and ultimately set them apart.
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