The Big Data Hype is Over: Now’s the Time for Retailers to Use It
Two years ago, big data generated the most buzz among technology and business gurus, seen as the next leading company’s secret weapon. Yet, despite the alleged potential of big data, most companies built on traditional business models encountered problems utilizing and implementing big data, the information that would give unparalleled insight to consumer behavior. Other than high-tech and highly data-driven sectors, most industries have been slow to adapt to big data.
Like most emerging technologies, big data follows a certain hype cycle, charted by Gartner, in which technologies move eventually from “inflated expectations” to the desired “plateau of productivity.” In Gartner’s analysis, big data is currently transitioning from expectations to the “trough of disillusionment” period.
While multiple industries are struggling to reap the promised benefits of big data implementation, retail especially has lagged behind in adopting widespread usage. According to IDC Retail Insights, only about 50% of retailers were aiming to use big data for pricing strategies in 2013, and most indicated they would look into it in the next 2-3 years. In a recent retail survey, a majority (73%) considered big data to significantly impact competitiveness but only 18% believed leading companies were currently capitalizing it to gain advantage in the market. To most companies, actual capitalization would occur in the next five years.
It’s no secret that Amazon has effectively utilized big data in determining pricing, predicting trends, and understanding consumer behavior. Traditional retailers have typically approached predictive analysis from a macro perspective, focusing mostly on overall supply and demand. Amazon takes a different route, and its personalized predictive analytics has paid off: it landed in retails’ top ten companies for the first time in 2014, following longstanding leaders like Wal-Mart, Kroger, and Costco.
Increasingly, executives and board members recognize the impending necessity of big data implementation. As a leading disruptive technology, when used correctly, big data ensures high-level competitiveness through the enhancement of predictions, pricing models, marketing, product development, and supplier and customer relationships.
The Biggest Benefits Big Data Offers to Retailers
Appropriately titled the “trough of disillusionment,” companies often find big data intimidating or discouraging because the term has been labeled as a panacea for all business problems and shortcomings. Unfortunately, big data, like any other technological innovation, requires sufficient research, resources, and management to work. To minimize inputs and increase efficiency of implementation, companies have determined the areas that benefited the most from big data through the trial-and-error stage. In particular, big data has had the most impact on offers and promotions, pricing and demand forecasting, customer-centric merchandising, and real-time personalization.
Capitalizing on Promotions and Offers
Promotions are a standard within retail to attract new customers, yet they’re often much trickier to successfully implement than expected. With confusion regarding whether the vendor or the retailer handles promotions (and therefore is responsible for customer research) as well as the undesired and often unavoidable discounting, 30-50% of promotions have no positive impact on sales.
In order to prevent this problem, companies need to utilize stronger data analysis of the operational implications of promotions, including marketing, supply-chain, and store-labor costs. The multi-various data needed to not only measure current promotional effectiveness, but also external effects is highly complex. Yet, the long-term benefits of big data promotional analysis really advance a company’s profitability in the sense that companies much more effectively segment brands and their most suitable promotions.
Advancing Pricing Models
Big data still offers some of the greatest advantages in pricing models, but the correct usage is rather difficult with increasing diversity in demographics, geographies, and consumer preferences. Additionally, the increased number of distributional channels makes it harder to establish and target a loyal customer base. Price intelligence is one of the strongest assets of big data analysis for it determines optimal pricing zones scaled locally and nationally, based on individualized shopping habits.
Amazon effectively navigates competitive pricing ‘games,’ and can offer the most ‘competitively attractive’ price because it understands its merchandising through complex and advanced big data analyses. As demonstrated by its performance in 2013’s Black Friday sales, Amazon hasn’t always been a price leader: instead, it engaged dynamic pricing to continually capture customers from all angles. Yet, brick-and-mortar retailers have opportunities to beat Amazon. Retailers with strong omni-distributional channels can attain the most competitive prices and this edge will only strengthen with the full integration of big data.
Looking towards future development of price intelligence, more elements and practical measures will factor into big data analysis. In particular, competitor tactics and market conditions will influence pricing to give greater insight to marketing assortments and buying and product development. For 53% of companies surveyed in 1010 data, merchandising (category management, buying, planning, allocation) was the function that was seen to benefit most from big data.
Current Challenges to Big Data Implementation
Despite the endured buzz surrounding big data, most retailers have not successfully integrated big data analysis and continue to struggle to not only to obtain the necessary data, but also to utilize it. Gaining a clear, unified interpretation of data was a leading obstacle for 41% of retailers, according to company executives. 38% indicated the inability to analyze data at a low enough level of detail and for 34%, the biggest struggle remained in accessing and integrating enterprise / 3rd-party data users they wanted to analyze.
Furthermore, most companies resist big data because they haven’t determined how it can solve actual business problems, and they lack the resources and knowledge to handle big data. Talent is a huge factor in being able to process big data, but with increased technological innovations, more and more people are entering IT and statistics professions. Gaining access to personal information challenged many companies initially, but as industries continue to become more customer-centric, collecting larger amounts of personal data will get easier.
The Next Steps to Embracing Big Data
Rather than exclusively searching for one overarching solution, companies should focus on smaller areas within their business models that they’re most capable of improving through big data analysis. This translates to only using the data that the company needs, which can be difficult to specify with the enormous amount of data that is readily available.
In order to “know what not to look at,” in the words Phillip Clark of Clark & Associates, executives and board members need to rethink company structure to accommodate big data. Although talent is a concern, big data analysis should not be isolated to the responsibilities of a few: the entire organization should be informed of big data objectives. Every department can play a role in collecting, analyzing, and integrating big data because it ultimately affects both day-to-day and long-term decision-making.
A critical component of successful access to customer data is trust. Recent breaches in privacy and security have led to campaign efforts to protect customer information by way of government policies. However, the implementation of these efforts generally takes a longer amount of time than if companies took the initiative. It’s essential that executives and board members understand governance and compliance when using personal data. That stewardship greatly enhances the relationship between retailer and customer, and will undoubtedly help retailers in the long run.
Ultimately, there is no ‘right’ way to implement big data analysis. Depending on the brand and the products, retailers will use big data very differently. Successful big data analysis does depend largely on leadership and management, but the allocation of those duties varies across retailers. At the core, executives need to recognize company values in order to determine what data is indispensable to advancing those values (and the company’s vision) and what departments need re-organization to make big data integration possible.
