Retail and Consumer Goods Analytics Study 2018: Retailers
“Analytics success will be one of the greatest determinants of future success,” says Robert Hetu, research vice president and agenda manager for retail at Gartner.
But analytics success is proving an elusive target for retailers, who face challenges not only from online and omnichannel competitors, but also from retailers in adjacent markets expanding their assortments and supplier partners directly targeting consumers. The race is on to see who can adopt the latest tools to best understand their target customers and deliver personalized experiences that flow seamlessly across channels — matched with fast, flexible fulfillment — to gain an analytics-fueled advantage.
If analytics is an arms race, then retailers feel they are prepared for battle — at least compared with competitors. They have made strides over the past year in developing analytics skills/people: nearly half say they’re better or significantly better than competitors in this area, up from just 26% last year (Figure 6). They’ve also beefed up their analytics tools, with 36% now besting competitors, compared with 26% in 2017. Those are gains of which to be proud.
But comparisons to market leaders paint a different story. Only a handful of respondents feel they’re doing better than leaders in any aspect of analytics. The biggest differential comes in data management, where 27% feel they lag competitors but 67% say they trail the leaders.
“You always want to more closely emulate the leaders, but that might not be realistic,” says Greg Buzek, president of IHL Group. “Peers are a mix of both. The issue is level of resources and limitations on the analytics tools. Most retailers under $1 billion in size don’t have the size or ability to hire an army of data architects and data scientists.” But there is another path. As the fusion of AI with analytics systems improves, solutions will not only provide analytics, but also make expert recommendations that simply still must be confirmed by people, Buzek says. “Leaders are doing this with people right now. Tiers two and three simply cannot do that.”
Advancing Analytics Maturity
Despite limited resources and a preponderance of basic reporting and analytics capabilities, retailers have made progress in some aspects in the past year.
In supply chain analytics, retailers last year said they were prioritizing inventory management. But it was in replenishment analytics where they’ve been most successful, with the ranks of those using predictive analytics rising from 14% in 2017 to 21% this year. Another 10% are even more advanced, applying prescriptive analytics to replenishment (Figure 7).
Among customer-centric capabilities, retailers made the greatest maturity gains with in-store analytics, moving from just 2% using predictive analytics last year to 21% (Figure 8). They also increased the use of predictive and prescriptive analytics by at least 16 percentage points in several other marketing areas: marketing spend, pricing and promotions, and promotion effectiveness.
These gains, together with past investments, mean retailers now have the most advanced analytics capabilities in replenishment and in-store analytics, followed by pricing/promotions, demand forecasting, marketing spend and promotion effectiveness (all tied). Are these the areas that will best position them to compete?
“While advanced analytics are valuable for almost all areas of retail, they are most critical for replenishment and customer personalization,” says Ken Morris, principal at BRP Consulting. “Advanced analytics are necessary to predict inventory levels across channels that are complicated by omnichannel fulfillment.”
Unfortunately, many retailers have considerable ground to make up when it comes to personalization, with 75% still capable of only basic reporting or analytics.
Where the Dollars Are Going
Aware of these deficits, retailers are turning their attention squarely toward the customer. Three of the top six areas of analytics focus are related to measuring and analyzing customer behavior: social media, web/online and data warehouse/storage. (Figure 9).
Although analytics is important to every aspect of retailing, “You can’t over-emphasize the customer aspect of analytics, as it is imperative,” says Morris. “Understanding what loyal customers like and what makes former loyal customers leave will tell you where you need to focus.”
Moving to Next-Gen
As retailers work to move up the maturity curve with existing analytics technologies, they’re also eyeing next-gen advancement. Some of these offer the potential to help retailers overcome constraints such as limited analytics skill sets, while others promise to deliver new capabilities and, in turn, deeper insights.
Consistent with today’s laser focus on tailoring the brand experience, retailers are putting personalization at the top of the list, just before three key enabling technologies that can deliver it: cloud infrastructure, big data analytics and artificial intelligence (Figure 10).
“Retailers are recognizing the value of centralizing applications in the cloud: speed of deployment, faster software updates, lower software, hardware and maintenance costs, and a real-time, single version of the truth,” says Morris. “Real-time visibility and access to product and customer information is critical to effectively executing cross-channel fulfillment services. Without real-time data, information provided internally and externally is out-of-date and, therefore, risks being inaccurate and out of context.”
Retailers see particular promise in AI and machine learning to help them tackle the big data problems that have long challenged existing systems and skill sets. Gartner has predicted that AI solutions employed by retail companies may come to autonomously manage as much as 85% of all customer interactions.
The potential that retailers see in AI and machine learning is clear in the wide array of application areas where they’re testing the technologies. But three rise to the top: customer relationship management, merchandise planning and execution, and assortment planning/ category management (Figure 11).
“These are the exact areas that Gartner recommends,” says Hetu. “They’re a gold mine of opportunity to automate inefficient, Excel-based tasks, freeing resources for higher-value activities. With large numbers of relatively high-paid associates who are responsible for making big-dollar decisions that affect every aspect of the business, headquarters are also prime targets for reduction and elimination through intelligent automation services.”
Retailers know that bolstering analytics capabilities is essential to making the most of their data. But even as they move the needle, the rapidly evolving marketplace and ever-changing landscape of competitors continuously ups the pressure. Many are pinning their hopes on emerging technologies like AI and machine learning to turbocharge their ability to gain fresh, timely insights that can set their brands apart. The race is on to leverage these new capabilities.
To read the study, click on the links below:
Editor's Note: Hare Today, or Gone Tomorrow
Overview: Analytics Maturity Is a Moving Target
Retailers: Wielding Analytics in the Battle for Customers
Consumer Goods: Sharpening the Analytics Toolset
To download the full study, click on the attachment below.