Once found only in isolated pockets scattered throughout a retail organization, analytics are now playing an increasingly significant role as a key driver of overall performance and competitive advantage. However, as the business importance of analytics expands, many retailers struggle to unlock their full value and turn their data into a key strategic asset.
A recent Economist Intelligence Unit survey of more than 600 executives revealed that 85% thought the biggest hurdle to unlocking value from analytics was not grappling with the sheer volume of data, but analyzing and acting on it. The majority of respondents said data was not viewed strategically nor incorporated into their organization’s culture, hampering their ability to identify and act on the insights embedded within it.
But several leading retailers like Walmart, Macy’s and CVS are overcoming the challenges and are successfully integrating analytics into their business practices and culture. In the process, they have improved business performance through better decision-making.
If the age-old retail challenge is getting the right item, at the right place, at the right time, at the right price, then analytics are providing a dramatically better way to achieve this goal. Retailers who have aggressively pursued an analytics-driven business model have seen a significant lift in performance. A recent analysis of 179 public companies conducted by the MIT Sloan School of Management found that firms that emphasize decision-making based on data and analytics performed5% to 6% better than their peers.
For example, a multibillion-dollar national supermarket chain realized that their one-size-fits-all approach to merchandising their stores left them vulnerable to more nimble competitors. Analyzing store-level data on customers, augmented with external data, they were able to develop localized assortments relevant to customers who shopped a particular store. Kurt Salmon then assisted them in integrating this new approach into their category and space planning processes. The new approach resulted in comp-store sales improvements of 2% to 7%.
Another Kurt Salmon client, a specialty retailer, is seeing a 10% to 15% reduction in total inventory and a 60 to 90 basis point increase in sales after we helped them strengthen their inventory allocation analytics and processes.
Through Kurt Salmon’s work with retailers at all stages of analytical maturity, we have identified five steps that leading analytical retailers have taken:
1. Focus on business outcomes.
Many organizations begin, and unfortunately end, their foray into analytics by hiring a group of Ph.D.s and just setting them loose on their data. Like the “build it and they will come” mentality that continues to plague IT implementations, just setting up an analytics team is woefully insufficient. Without clear business objectives in mind and the organizational mechanisms necessary to translate analytics output into business outcomes, the organization will quickly start to question the value of analytics. Even worse, the analytics team will be perceived as a siloed group of intellectuals, divorced from the organization and unable to effectively contribute to the business’s larger goals. Soon after, questions about the cost of analytics will follow, with some companies going so far as to consider reducing or eliminating the analytics team.
Instead, successful analytically driven organizations start with a laser focus on the end business objectives and then employ analytics to achieve those results. This may entail establishing or growing an analytics organization, but the analytics team—and the organization as a whole—now has much greater clarity on how analytics create business value.
At the end of the day, it is critical to understand that analytics are the means to an end, not the end itself. Working with a large European department store chain, Kurt Salmon helped them establish just such an analytics focus. They needed to improve their historically inaccurate buying and sizing processes, which resulted in both significant lost sales and large quantities of unproductive merchandise. Through our efforts, the retailer was able to use data on customer purchase patterns to better predict future demand and realize an average increase in profits of €260 thousand per store for the categories addressed.
2. Transform the culture.
As nice as it would be to believe that every retail organization is poised to become analytically driven, almost the exact opposite is true. With but a few exceptions like Tesco, using data, analytics and insights to make decisions isn’t in the DNA of most retail organizations. As such, resistance can be severe and the objections to doing things differently numerous. As one senior executive at a retailer once said, “If I believed what the data are telling me, I’d have to change what I’m doing.”
To overcome this organizational inertia, the leadership team must lead the charge and actively campaign on behalf of a data-driven culture. In our experience, it’s not atypical that a company will spend vastly more time supporting cultural and business practice changes than generating analytics insights.
3. Build the “right” analytics team.
We’ve said that the answer to becoming a successful analytically driven retailer isn’t to just hire a bunch of Ph.D.s. However, the importance of having high-quality data scientists as part of your analytics team can’t be overstated. These individuals need to have the technical skills and intellectual curiosity to solve complex analytics problems, coupled with the desire to achieve business impact and not just build models.
We are also seeing high-performing analytics teams begin to augment their data scientist roles with a second group of analytics liaisons. Typically, these individuals combine technical knowledge with business acumen. They function as a bridge to the larger organization and, more importantly, are responsible for ensuring that analytics drive business impact. The liaison role helps generate new opportunities for the business, works with data scientists to uncover the requisite insights and then links those insights back to the business decision where they apply.
This combination of skill sets enables the analytics team to fully integrate with the business and focus on key business needs and decisions. The team doesn’t replace the decision-making of the business manager; rather, they partner to drive smarter decisions. To borrow liberally from a dated advertising phrase, the analytics team “doesn’t make decisions, they help make better decisions.”
4. Embed analytics into day-to-day business operations.
When implemented well, analytics create impact throughout the organization by empowering better decisions. So it follows that to generate this impact, analytics need to be integrated into the key decision-making processes across an organization’s functional groups—such as merchants using customer insights to develop localized assortments, marketers adjusting the depth of a targeted promotion based on customers’ price sensitivity or inventory managers shifting product allocations to a specific store based on a weather forecast. Taking a step back, it’s easy to see that there are numerous opportunities for retailers to use analytics to improve sales and profits.
5. Invest intelligently in technology.
While Microsoft Excel continues to be the Swiss army knife of many a retail organization, the reality is that unlocking data’s full value requires the appropriate technology above and beyond Excel. The good news is that the technology is now more mature, computing and data storage costs continue to decline, and new delivery models like software as a service (SaaS) enable companies to access the technology they need much more easily. To ensure technology investments generate sufficient returns, companies should establish a collaborative partnership between the analytics team, end business users and the IT organization.
Working together, this partnership should first clearly identify the end business opportunity, the current hurdles to realizing the opportunity and the functionality necessary to clear those hurdles. Only then should they lay out the roadmap and business case for any technology investment required. In this way, the organization and, more importantly, the CEO and CFO, will have confidence that the investment is justified.
Without a doubt, investing in analytics can pay off. But to ensure it does, analytics must be integrated into the larger organization and engrained in its culture, embedded in clear business objectives, and used to drive key strategic decisions. And as more and more retailers develop advanced analytical capabilities and customers come to expect the personalization they enable, retailers who are slow to respond will quickly be left behind.
Think Analytics Aren’t for You?
Analytics aren’t just for online pure-play retailers. Many retailers across segments and channels are successfully harnessing the power of analytics.
» Macy’s. With a national footprint and seven of every 10 U.S. households shopping them each year, Macy’s faced the challenge of being relevant to each and every customer. By tapping a vast trove of customer data with the MyMacy’s program, Macy’s is able to create personalized customer experiences that manifest through both localized merchandise assortments in the store and targeted and timely customer communications through email and mobile.
» Safeway. The grocer recently announced customized promotional offers based on customer buying habits and says it may expand the program to include personalized prices tailored to individual price sensitivity.
» Sears. While many retailers typically make pricing decisions based on historical data augmented with a bit of competitive research, Sears is using a Hadoop-based analytics platform to determine the optimal price for millions of items every week—every day, in some cases.
» Amazon. Of course, no conversation about analytics would be complete without Amazon. In a recent letter to shareholders, Amazon CEO Jeff Bezos outlined his philosophy on investing in advanced analytics to drive business decisions.“Technology infuses all of our teams, all of our processes, our decision-making and our approach to innovation in each of our businesses.”As a result of that approach, Amazon has been able to use customer data to personalize search results; drive product recommendations; ward off fraud; determine which marketing channels are most effective; and support a variety of fulfillment, sourcing, capacity and inventory decisions.
11 September 2012