This blog post was published on Hortonworks.com before the merger with Cloudera. Some links, resources, or references may no longer be accurate.
As a former merchandising executive working closely with data-driven leaders across retail organizations of all types and sizes, it occurs to me that some execs may be underestimating, and underinvesting in, the need for merchandising innovation in this once-dominant retail business area.
The recent, and necessary, shift in traditional retailer focus on digital marketing and ecommerce capabilities has been driven by the demands of today’s shoppers—by how they want to shop, by their expectations of personalized experiences wherever they are, and an overall sense of relevant engagement. As a result, there is no lack of excitement and enthusiasm among retailers across the globe—keeping me bullish on their prospects for continuing to compete effectively in this consumer-driven world.
Indeed, many retailers are justifiably proud to share their new analytic capabilities and their experimental use of data, analytics, and technology, with exciting use cases almost weekly. Many of these demonstrate a fairly new phenomenon: measured risk taking. This has been reflected in the pace at which retailers are recruiting new skill sets, striking new partnerships, and even acquiring tech start-ups with new IoT and big data analytic capabilities, especially in the area of retail merchandising analytics, at their core.
For some, this has even manifested in uncharacteristic public statements of executive commitment, in a desire to “go faster” and distance themselves from traditional competitors, to keep pace with Amazon’s analytic capabilities, to blunt emerging pure play digital disruptors, and maintain a trajectory of transformation required to remain relevant within rapidly evolving consumer expectations. Nevertheless, the merchandising formula that made retailers successful in the past is no longer what will fuel growth today and in the future.
Data-Driven Merchandising Decisions More Important Now Than Ever
The trade-off in focus and investment in marketing and digital capabilities could be leaving some traditional merchandising teams stuck with the same siloed-data issues and analytic tools from years ago.
In fact, I’m wondering to what extent merchandising is being left behind or de-prioritized for the sake of “catching up” with digital marketing and ecommerce capabilities during this retail transformation?
After all, the essence of how traditional retailers can sustain and grow a truly differentiated customer experience—and perpetuate their brand loyalty in a rapidly “commoditized, transparent, want-it-now” omni-channel world—depends equally (if not more so) on best-in-class merchandising capabilities.
The merchandising teams that are ultimately responsible for assortment, pricing, promotions, sourcing, replenishment and in-store planning and execution—you know, where 85%+ of retail sales are still happening today—remain critically important to retailers striving to compete and differentiate.
Fortunately for some, the IT teams responsible for enabling new retail merchandising analytics capabilities for digital marketing and ecommerce also considered the broader enterprise opportunity, implementing “connected data” platforms to handle both data-at-rest and data-in-motion—democratizing new multi-structured and real-time data and customer insights that merchants can now access and leverage.
Merchandising Leadership ‘Generational Gap’ Seemingly Persists
A reality that today’s merchants are grappling with is that consumers are now in control: they are better informed, more interactive, harder to influence, value seeking—and ultimately redefining “loyalty.” Furthermore, for merchants who once viewed organics, natural ingredients, ethical sourcing, and environmental and social responsibility causes as “fads,” the generational shift that is driving this trend will be the majority (53%) of the U.S. population (Millennials and Gen Z) by 2020, according to the US Bureau of Labor Statistics.
While retailers were transforming their digital marketing and ecommerce capabilities, most were also adding new talent and skill sets capable of leveraging new investments in technology, data collection, and advanced analytics in these business areas—starting at the top, with the rise of the CMO in traditional retail. Is today’s merchandising leadership equally prepared to pivot to more data-driven decision making?
According to recent survey research from RSR Research, it appears that retailers themselves recognize that an improvement opportunity exists: “…it seems that retailers are getting out of touch with their core customers; with only 20% of respondents cite ‘Millennials’ (15-35) as their core end-consumer and (yet) their Merchandising leadership reflects their Gen X (35-55) and Baby Boomer (56-65) focus.”
Based upon recent discussions with retail leaders—those that have recognized the business improvement opportunity and are actively investing in the people, processes, and technologies to support merchandising teams today—there are three data-driven use cases that inevitably rise to the top:
Merchandising Use Cases Worthy of (More) Investment
1.Distinctive Products & Services – No longer are retail merchants or traditional consumer brands the arbiters of taste—today’s shoppers are actively seeking customized, exclusive, personalized products and services.
For retailers with aisles of commodity-based product offerings, the wreckage of failed retail formats with the same is not favorable. As noted by various pundits and case studies, beyond “operational excellence” and “customer intimacy,” market leaders are also defined by being keenly focused on “product leadership.”
For traditional retailers who have successfully integrated external data (social sentiment, product trends) with internal data (stores, website, mobile), the opportunity to accelerate competitive advantage through distinctive products may be closer than many realize.
Of course, turning all this data into actionable intelligence is key, but one need look no further than the recent moves by Amazon (yes, a laggard in private brands), which has added 20 new GMA private label brands in the past two years, driving $4B in incremental revenues ($700M from Whole Foods alone). They see the opportunity gap, and are able to harvest rich customer data sets, apply data-driven decisions, and move on it.
Most retailers recognize that private label is no longer relegated to “me too” commodities for the financially strapped. According to a recent IRI study, many in North America seem to be stuck with a 23% private label penetration rate, while European retail peers are pushing above 40% —a sign that product innovation in North America has been languishing.
At the end of the day, improving digital marketing and ecommerce capabilities alone is not enough—the demand for distinctive, unique, exclusive products and services from today’s consumer is high—and traditional retailer response is necessary.
2.Dynamic Pricing – One of the realities of the digital age for retailers is price transparency. According to McKinsey, 89% of consumers conduct product research before purchasing. The ecommerce necessity of providing your product catalog (including pricing) online has, in turn, provided all your competitors with the opportunity to “scrape” competitive pricing in an automated fashion.
The prowess of dynamic pricing and real-time recommendations by digital giants is well known. Armed with this information and the ability to change prices across hundreds of thousands of items daily, or even intra-day, based on competitive advantage—plus individual customer browsing history and purchase behavior—is said to add 24% to Amazon’s gross profits alone.
For retailers having already adopted dynamic online pricing capabilities, what’s especially interesting—and perhaps a differentiator—is the application of dynamic pricing in FMCG. A few innovative retailers like Kroger, Walmart, and others are already experimenting with this—perhaps giving ESLs (electronic shelf labels) a legitimate comeback opportunity. Alas, the business case of this early-generation technology has certainly been enhanced.
For fashion retailers, the focus tends to be more on markdown optimization—pricing goods right from the start—effectively allocating product according to anticipated demand and minimizing potential losses when overstocked. This is where we see retail leaders willing to experiment with emerging tech and social media platforms (e.g., partnering with Pinterest in proximity marketing, or bringing social media Facebook “likes” to hangers in-store). The application for dynamic pricing here tends to be more promotional and interactive in nature, providing “nudges” to improve sell-through. And the possibilities here seem limitless.
Regardless, for traditional retailers still gathering local competitive pricing data manually or from third-party providers, inputting this data into legacy price “optimization” applications to adjust prices online and/or offline at local store locations only once a week is simply not good enough.
It should also be noted that it is not advisable to post entire product catalogs online, as this could effectively be providing savvy competitors with a roadmap to your entire P&L. This is yet another benefit of providing a personalized customer experience and underlines the need for advanced online search capabilities.
3.Hyper-Localized (In-Store) Assortment – Perhaps it goes without saying that highly personalized products and services have long been easier for—and leveraged to greater advantage by—smaller, local, independent retailers. National chain retailers have always wanted to get local and personal with their consumers, as they understand that personalization from a very local market perspective and their customers’ preferences differ.
What’s different today is that broader customer insights and advanced analytics can now provide a national retail chain with information that becomes meaningful in new ways. It’s not just about stocking more locally produced goods, but more locally desired goods—as voiced by consumers above and beyond in-store transactional data.
A few years back I had the privilege of working with a forward-thinking national grocery chain that recognized an opportunity to better align their in-store assortment with evolving customer tastes and preferences through advanced analytics. Leveraging transactional, market, and customer segments, and store real-estate data, we were able to develop new store cluster segmentation schemes and macro space optimization recommendations reflective of how local customers shopped the stores with estimated store-level revenue improvement between 2.5% and 4.7%, with most existing stocking sections either expanding or contracting.
There’s still a place in retail for creative merchants—those savvy individuals who can spot a trend coming and get out ahead of it. Now, armed with more data points from online, in-store, and external sources, new analytics and insights can be applied to help—not hurt—that creativity.
My belief is that retailer leaders will continue to harness big data and use advanced analytics and cognitive and machine-learning capabilities to enable automation and precision at scale—really, it is no longer optional.
Big data is providing actionable customer insights that will direct merchants’ pricing decisions, new product development, and curation of localized merchandise selections reflective of what today’s consumers want to buy.
The future looks bright for those who recognize, invest, and act now on data-driven merchandising insights and capabilities. For others, the “uphill battle” is just getting steeper.