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Sure, you can acquire big data technology, but without understanding and hypothesizing how previously hidden data can be harvested and applied to business processes, challenges or opportunities, big data becomes another shelfware solution with a disappointing payback and short lifespan.
In my experience, successfully deploying a big data solution begins by identifying use cases and business decisions which benefit from new information.
Retail thought leader Gary Hawkins suggests that big data may actually create a retail oligopoly.
Writing in the Harvard Business Review, Hawkins poses the likelihood that big data may "kill all but the biggest retailers." He suggests that large retailers, with their larger IT budgets and resources, can capitalize on the big data opportunity, increase market dominance and essentially relegate smaller retailers to "the role of convenience stores." Notwithstanding Hawkins well supported argument as well as big data's very real opportunity to improve marketing, product availability or the customer experience, and thereby outperform retail competitors, it's my strong belief that the new retail pecking order will be less determined by the size of the retailer's IT budget and more by the retailer's propensity toward innovation and agility.
) and 14 percent "under a coat." Twelve percent coupled in the VIP lounge, which at least makes some sense; 17 percent claim to have been caught in the act by airport staff. Jetcost.com, which tallied the results from 4,915 Americans who were 18 or older and had flown at least once in the past two years.
Three-quarters of those responded that they had some free time in the airport, and so the website followed up to learn how that down time was used.
Retailers that leverage big data will design products that are more embraced by consumers, better anticipate and respond to market shifts, and engage consumers with predictable results.
This means fewer stockouts, higher visit to buy ratios, bigger basket sizes and other performance measures that can be improved with better data.
As Darwin taught us "It is not the strongest of the species that survives, nor the most intelligent that survives.
These retail big data examples can be extrapolated in many ways — from using weather patterns to predict in-store sales to combining data from web search trends, website browsing patterns, social networks and industry forecasts to predict product trends, forecast demand, pinpoint customers and optimize pricing and promotions.
Understanding the correlation between your product sales and otherwise undetected factors such as the weather, pop culture, social media trending, your competitors and consumer sentiment can allow you to tap into these environmental events with specific actions that lead to improved financial performance.
We are now seeing this in the real estate industry which for years allowed realtors to guard home sales in locked down MLS systems.
Now sites like Zillow and Trulia unlock this data and bring it to our mobile devices. We did a survey which found 78% of retailers are collecting and storing location-based information on their shoppers, and 64% are gathering and studying data generated by social media.
Pw C’s Strategy& is a global team of practical strategists, committed to helping you seize essential advantage by working alongside you to solve your toughest problems, and capture your greatest opportunities.