3-5% Sales uplift over 6–18 months for grocery, drug, and do-it-yourself retailers who commit to building capabilities that act on insights.
Over the past five years, traditional large retailers—such as supermarket chains, drugstores, and big-box specialty retailers—have found growth elusive. In most major markets they are facing intensified competition, particularly from discounters, as recession-era shopping habits have become entrenched. Opening new stores is no longer a surefire way to grow, in light of market saturation and the boom in e-commerce. Same-store sales growth, or “like for like” growth, has been flat or declining for most large players across all major European markets, and margins are under pressure. By embedding consumer insights into their merchandising processes, retailers can boost both like-for-like sales and profitability while creating smarter merchants.
Amid this punishing environment, how have a handful of retailers outperformed the competition and achieved substantial like-for-like sales growth? In our experience, they have succeeded primarily by developing a deeper understanding of consumer and shopper behavior and embedding these insights into the way they manage every product category. In other words, they have implemented an insight-driven sales transformation.
Large retailers are facing intensified competition, particularly from discounters, as recession-era shopping habits have become entrenched.
In this article, we describe an approach that has helped leading retailers kick-start such a transformation. We call it the “category accelerator”: it is simultaneously a thorough, data-driven category-planning process and an intensive capability-building program for category managers. Retailers in the grocery, drug, and do-it-yourself sectors that have used the approach have achieved a sales uplift of 3 to 5 percent and a net margin improvement of one to four percentage points in 6 to 18 months.
Three steps to transformation
As they seek to increase like-for-like sales, retailers encounter a number of common challenges. One is wide variability in performance and execution among product categories, in part because each category manager does his or her job independently of and differently from others. They use different tools and techniques, and some rely on data and insights more than others. Another common challenge is a lack of coordination of improvement initiatives; pricing actions, for example, are often disconnected from visual merchandising changes. In such cases, retailers miss out on capturing the full potential of an integrated category-wide (not to mention store-wide) transformation.
The category accelerator addresses all these problems in a systematic, sustainable fashion. In a nutshell, it is a program for creating insight-driven category plans for all of a retailer’s product categories, using a standardized process supported by a dedicated team of experts. The three main steps of the approach involve building the core team, creating best-practice content, and developing insight-driven category plans.
Set up a cross-functional team of ‘navigators’and analysts
The first step is to establish a cross-functional core team focused on delivering quick wins. The team should combine category-management expertise (in the form of high-profile, experienced merchants) and analytics expertise (data analysts, often hired through targeted external-recruitment efforts). Retail leaders may initially balk at the idea of pulling top merchants from their day-to-day tasks, but it is an essential sacrifice for both perception and impact. The team, which initially will have approximately four to eight members, should be situated in a dedicated space—an environment designed to encourage new thinking, foster creativity, and facilitate rapid implementation. Having a separate room for the team may seem trivial, but it is a fundamental success factor. It helps the team get away from a business-as-usual mind-set.
A common challenge to like-for-like sales growth is the variability in performance and execution among product categories.
The merchants play the role of navigators who coach and challenge category managers throughout the process, while the analysts are responsible for mining transaction and loyalty-card data and translating those data into useful insights for category managers (see sidebar, “A sampling of opportunities in big data”). This arrangement sidesteps a common pitfall of sales transformations: having an analytics team that works in isolation from the commercial team and thus generates unusable or irrelevant insights. Instead, the analysts work with category managers to make sure that decision-support tools are intuitive and accepted by end users, and that the insights are accessible to everyone who needs them— not just to a select group of “superusers.”
Retailers should resist the temptation to incorporate the team back into the business. Once it has built buy-in and momentum through quick wins, the team should broaden its focus, bring in more navigators and analysts, and become a permanent unit. For a large grocery retailer, this core team would typically consist of 10 to 20 people, split evenly between navigators and analysts.
Create a comprehensive series of modules
Among the core team’s initial responsibilities is to develop a series of modules, covering all commercial levers, to serve as the main content for sessions with category managers (Exhibit 1). The integration of levers—in contrast to the typical siloed approach whereby each initiative is managed independently of others—is part of what makes the category accelerator a powerful force.
Each module should contain standardized, best-in-class tools and methods that will help category managers perform consistently high-quality analyses of commercial decisions, manuals that explain how to use the tools, and sample outputs and templates. The materials should make clear the overall objective of each session, actions to be completed for each session, and core concepts and terminology definitions. Crucially, each module should incorporate consumer and shopper insights, generated primarily through analysis of transaction and loyalty-card data.
The first step is to establish a cross-functional core team focused on delivering quick wins.
If a retailer has some category-management teams that are consistently high performing, it can build the modules simply by identifying and codifying internal best practices—an exercise that usually takes a few weeks. Another option is to assemble external best practices and tools, customize them to the company, pilot them for a subset of categories and suppliers, and then refine and codify them. This option obviously takes more time: two weeks to three months, depending on the starting point and the topic.
Develop insight-driven category plans
With the core team in place and the content ready, sessions with category managers can begin. A retailer typically starts by having two to four category managers go through the sessions over a two-week cycle. Each category manager runs through the entire set of modules with the core team, spending one or two days on each module. Relevant specialists participate as appropriate—a pricing specialist for the pricing module or a space planner for the visual-merchandising module. In each session, the analysts provide a fact base for the navigators to use as a basis for challenging the category managers’ conventional assumptions and for pushing them to develop ambitious category plans. The goal is to create uniformly high-quality category plans powered by consumer insights. As a category manager at a large South African retailer said, “For the first time, we built integrated category plans covering all levers, and we made bold moves that went beyond the typical knee-jerk pricing and promotions actions.” She and her colleagues set—and met—ambitious targets equivalent to 3 percent of sales and two percentage points of margin.
If a retailer has consistently high performing category-management teams, it can build modules simply by identifying and codifying internal best practices.
After working out any glitches in the first few cycles, the accelerator should be able to accommodate ten categories per cycle. A rigorous follow-up calendar—with quarterly or biannual check-ins—ensures that decisions are executed, that progress is measured, and that errors are corrected.
A large grocery retailer built a team of 25 navigators and ran all 300 of its product categories through the category accelerator over a two-year period. In one category, for example, it captured a 2 percent incremental sales increase within six months by making a series of pricing changes and expanding the distribution of select regional product lines.
How to make it stick
The approach might not appear complicated, but in practice it can be rife with pitfalls. To capture the full potential, retailers must adhere to the following success factors.
Start with targeted commercial changes that drive rapid impact
Retailers must pick their battles along the sales transformation journey; they should initially focus on a carefully chosen set of two or three improvements in core commercial processes. These should be initiatives that will pay off right away, which will build buy-in and momentum for the broader transformation.
A sampling of opportunities in big data
Big data and advanced analytics can benefit retailers in almost all areas of the business. Examples include the following.
Optimizing assortments.
Loyalty analysis—for instance, measuring purchase frequency or penetration among high-priority customer segments—allows retailers to understand product categories from a customer perspective. By measuring customer “switching” behavior, retailers can also identify which SKUs play a unique role and which are redundant. Such analyses helped a European retailer reduce its assortment by 10 percent across 100 categories while improving margin by one percentage point.
Loyalty analysis—for instance, measuring purchase frequency or penetration among high-priority customer segments—allows retailers to understand product categories from a customer perspective. By measuring customer “switching” behavior, retailers can also identify which SKUs play a unique role and which are redundant. Such analyses helped a European retailer reduce its assortment by 10 percent across 100 categories while improving margin by one percentage point.
Improving pricing and promotions.
Using market-basket analysis, retailers can measure price elasticity and identify key value items by customer segment. They can thus set prices based on consumer demand and competitor moves. In addition, by analyzing the impact of past promotions and linking it to current customer behavior, retailers can reliably estimate the success of planned promotions. A European retailer was able to increase returns on promoted sales by 3 to 5 percent after analyzing its historical promotions across marketing vehicles.
Using market-basket analysis, retailers can measure price elasticity and identify key value items by customer segment. They can thus set prices based on consumer demand and competitor moves. In addition, by analyzing the impact of past promotions and linking it to current customer behavior, retailers can reliably estimate the success of planned promotions. A European retailer was able to increase returns on promoted sales by 3 to 5 percent after analyzing its historical promotions across marketing vehicles.
Customizing marketing offers and activating the online customer base.
Retailers can tailor offers and promotions to customers based on their past behaviors, thereby increasing spending and loyalty. Big data also enables retailers to activate their online base with targeted content and offers. An Asian retailer used big data to send customized coupons to millions of customers based on their profile (taking into account metrics such as total spending by category). This effort helped the retailer reduce its reliance on the above-the-line couponing that made it easy for competitors to quickly duplicate the offers. The result: a three-percentage-point lift in same-store sales.
Retailers can tailor offers and promotions to customers based on their past behaviors, thereby increasing spending and loyalty. Big data also enables retailers to activate their online base with targeted content and offers. An Asian retailer used big data to send customized coupons to millions of customers based on their profile (taking into account metrics such as total spending by category). This effort helped the retailer reduce its reliance on the above-the-line couponing that made it easy for competitors to quickly duplicate the offers. The result: a three-percentage-point lift in same-store sales.
Conducting negotiations.
By measuring vendor-performance fundamentals (such as penetration rate and repurchase rate), retailers can develop compelling arguments to improve their bargaining power during supplier negotiations. A grocery retailer in the Asia−Pacific region trained buyers on how to use data and insights in supplier discussions—an effort that yielded $300 million in savings within the year.
By measuring vendor-performance fundamentals (such as penetration rate and repurchase rate), retailers can develop compelling arguments to improve their bargaining power during supplier negotiations. A grocery retailer in the Asia−Pacific region trained buyers on how to use data and insights in supplier discussions—an effort that yielded $300 million in savings within the year.
One retailer had seen its value perception among customers fall by more than ten points over a six-year period despite having the lowest prices in the market. Through analysis of transaction data, the retailer found that the decline in value perception was due to a large share of its baskets being more expensive than competitors’. While it took approximately a year to put in place new pricing processes, in just a few weeks, the retailer reduced prices on some of its best-selling items, consequently reducing the share of more expensive baskets while tactically increasing prices on background items. Customer value perception improved, and the retailer was able to achieve an increase in like-for-like sales from 2 to 5 percent, while also recouping one percentage point of margin.
A European grocery retailer chose supplier negotiations as one of its priority areas for quick wins. It held two-day workshops for all buyers, and its core project team wrote a one-page negotiation playbook that quantified and justified the “asks” it would make of each supplier. In only six weeks, this initiative generated a 1 percent reduction in cost of goods sold.
Invest in big data talent and systems
Retailers know that their transaction data and loyalty-card data are a treasure trove that they could mine to find new pockets of growth. The most sophisticated retailers also use big data and advanced analytics beyond commercial applications—for example, instead of relying exclusively on traditional sales and margin indicators, they use more data and analytics (such as household-penetration metrics or insightful and nuanced performance evaluations of category managers.
The goal is to create uniformly high-quality category plans powered by consumer insights.
There are a number of reasons that retailers fail to embed insights from big data into their daily decision making. One is a lack of technical capabilities. Indeed, the category accelerator won’t work unless skilled data analysts are a core part of the team, collaborating closely with category managers. Another reason is poor systems and infrastructure. Investment in the right data infrastructure is a key enabler for delivering insights in a timely manner. One retailer, by changing its data middleware, accelerated its insight-generation process from days to minutes.
A North American nonfood specialty retailer used a heat map to assess its strengths and weaknesses in using big data across all functional areas (Exhibit 2). The heat map helped the company identify and prioritize opportunities for investment. The resulting initiatives included targeted efforts to improve data quality and management, technology and software updates, and the introduction of a new pricing model.
Use multiple levers to shift mind-sets across the organization
Making any change stick beyond the specific project or intervention requires the use of several levers, one of the most important being highly visible role modeling by senior leaders. For instance, top management should serve as faculty and coaches for some of the modules.
Performance management is another important lever. Handing out “category manager of the month” awards or special prizes for the “best negotiation team” can be surprisingly effective in spurring performance.
Investment in the right data infrastructure is a key enabler for delivering insights in a timely manner.
And to make sure that the new ways of working stay embedded in the organization, companies should choose the two or three capabilities that will make the most difference and invest in those capabilities, either through additional training or new hires. The category accelerator gives retailers a clear path for developing and honing their category-management and merchandising skills; it serves as a training ground for future commercial directors and buyers. But hiring new people, particularly data analysts or customer-insights managers, is often also necessary. Retailers should try to upgrade existing capabilities—for example, by reassigning employees to new roles or by providing training—but such moves are typically not enough to make a difference.
A retailer that chose pricing as its priority battle put in place a new offshore team tasked with analyzing competitive pricing data on a weekly basis, working hand in hand with the onshore category-management team. The new global pricing team delivered one percentage point of margin uplift, with very limited additional overhead.
As retailers strive to boost like-for-like sales, an insight-driven approach can increase their chances of success tremendously. The category accelerator’s distinctive elements— particularly the combination of quick wins with longer-term capability building and the translation of consumer data into actionable commercial insights—have helped large retailers across the globe capture growth in spite of fierce competition.
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