The Big Challenge: Balancing Maturity with Scope
In a fast-evolving, increasingly data-driven marketplace, consumer goods companies and retailers must progress along the maturity curve in traditional analytics practices even as the number of business areas that require analytics expertise continues to explode.
That’s a tough challenge, particularly when there are limited resources to spread around. Companies must carefully consider their strategies to determine how to leverage their resources for maximum benefit. And the priorities continue to shift in the wake of technology advancements, shifting competitive forces and changing customer priorities.
“Disruptive technology is widely available at this point, and accessible to all almost instantly via the cloud. How to fit this into a sustainable, repeatable business process to scale, however, is another question,” says CGT Executive Advisory Council member Kerry Farrell, senior vice president of sales and customer success at Eversight. “This often gets short-handed as ‘culture,’ but candidly I think it’s more than that. It’s about the human processes and flows, and about reimagining decision-making in a world with access to transformational technology.”
Traditionally one step removed from intimacy with the consumer, consumer goods companies have long relied on data and analytics to run their businesses. They invested early and have adopted newer analytics technologies as they have emerged. Retailers were often the recipients of that research, but their own analytics investments began much more recently. That pattern continues to play out in terms of budgeting across the industry.
The percentage of IT budgets that CG companies allocate to business intelligence and analytics already outstrips retailer budgets by seven percentage points: 26% vs 19% for retailers. In 2020, retailers and CG companies will both boost their spending, but the ratio will stay the same, 30% to 23%.
CGs might still be spending more, but “in terms of rate of knowledge gain, I think we’re seeing the tide turn toward retailers being more in the driver’s seat,” says Farrell. “With the rise of the cloud, advanced analytics, increasing experimentation capabilities and shopper-specific data driven by digitally connected commerce, we’re seeing retailers have access to far more information about their customers.”
Investing in Analytics Maturity
Investments in BI are fanning out across an ever-widening range of application areas. The two areas where both retailers and CGs are most advanced are demand forecasting, where 40% of the former and 24% of the latter are using predictive or prescriptive analytics, and replenishment, where the percentages are 35% and 24% (see Figure 11). Only category management (at 26%) has reached the predictive/prescriptive analytics level at CGs (see Figure 12).
On the other end of the spectrum, relatively recent tech-driven capabilities are the least mature when it comes to applying analytics: personalization, social media influence, social media presence and omnichannel/digital communications. CG companies lag, understandably, for in-store analytics, while most retailers continue to do only basic reporting and analytics in marketing.
Basic reporting and analytics capabilities are still the reality for large segments of both sides. Moving up the maturity scale is not just a matter of increasing investment; data pools also must be made ready for analytics.
“The analytics are way ahead of the data quality and cleanliness for most retailers,” says Greg Buzek, president of IHL Group. “The biggest gaps are in two areas specifically. One, when it comes to anything inventory related, retailer data is woefully inaccurate — it’s off as much as 25%. So the analytics, no matter how good, is running on bad data for most retailers in this area.”
Artificial intelligence can help, both here and elsewhere. “On the analytics side, the packages often fall short of what is truly needed, which is the connection with AI,” Buzek says. The output of that should ask the user to choose what the next step should be, offering a button to approve or not approve the suggested action, Buzek explains. “This requires deep integration with the solutions.”
Even companies that have advanced past the basic level must consider whether the organization has the analytics talent in place to make optimal use of investments in more sophisticated applications. “The biggest obstacle is the availability of experienced analytical people within the sales and marketing teams,” says Jon Harding, global chief information officer at Conair Corp. “IT teams can deliver the most advanced analytics technologies, but the value is not realized if there are not enough people within sales and marketing who can understand, use and act on the analytics delivered.”
Where the Dollars Will Go
Both CG companies and retailers know well where maturity levels lag their needs. Moving forward, those increased BI/analytics budgets will be funneled toward a broad array of systems updates (see Figure 13).
The top areas of focus for application upgrades among retailers are security (65%), data visualization/dashboards (57%) and web/online analytics (57%). All three areas led the list for planned changes in our 2018 survey. This year, in keeping with an industrywide zeal to enhance the consumer experience, personalization (54%) joins the list of planned upgrades, while an additional 19% of retailers are investing in personalization for the first time. Mobile BI capabilities are also seeing a surge in new software investment (22%).
For consumer goods companies, upgrades are more about the data platform. Nearly half (48%) will upgrade their master data management (MDM) tools, and 46% will upgrade data warehousing and storage and enterprise BI and reporting tools. CGs are also most likely to be adding software for the first time to address data visualization/dashboards and analytics tools.
Such investments are critical for overcoming obstacles to advancement. “Data normalization, or the elimination of data redundancy across multiple systems, requires MDM to bring together the disparate islands of customer, product, sales and inventory information within the organization,” says Ken Morris, principal at BRP Consulting. For retailers, the the best-of-breed approach many have taken has created multiple data elements and redundancy that must be overcome, he says. “Retailers need to bring all the information in their enterprise together in a real-time environment. They need to replace antiquated store and forward retail technology with systems interconnected in real-time to reduce latency.”
However, companies shouldn’t let a lack of complete data alignment impede their adoption of tools that can drive specific business wins. “We’ll still be talking about master data accuracy in 50 years. To the extent that specific business needs require systems to better share data, by all means pursue shared data sources,” says Farrell. “[But] it’s not a prerequisite to being able to make a large impact immediately. One single shared data source [doesn’t facilitate] a customer getting an order faster, a price being more competitive, a new product innovation being awesome, a marketing campaign being better targeted, or food being safer.”
Tools on the Priority List
Big data analysis and cloud infrastructure for uses such as data storage and management are getting the lion’s share of analytics investment by both CGs and retailers over the next 12 months, and at even higher planned numbers than in 2018 (see Figure 14).
CG companies are also moving more aggressively into AI and machine learning than retailers with investment plans that have accelerated compared to last year’s survey. Retailers, on the other hand, are shifting a bit away from AI and machine learning in favor of Internet of Things-related tools.
These upgrades promise multiple benefits. One is supporting improvements to data sharing capabilities. Both retailers (35%) and CGs call big data analytics the most impactful on data sharing (Figure 14). CGs also consider cloud infrastructure and AI/machine learning as important. After big data analytics, retailers are most likely to cite IoT as having the greatest impact.
“In terms of data sharing between retailers and CGs, the availability of ‘best-of-breed’ analytical solutions delivered in a ‘software-as-a-service’ (cloud) model is a critical success factor,” says Conair’s Harding. “The use of SaaS solutions makes the initial investment easier and switching solutions within a few years (as the original ones age out) so much easier, both in terms of cost and ease of changeover.”
Aligning Internally
There also are internal benefits, of course. Retailers (at 54%) and CG companies (50%) both cite big data analysis as delivering the most assistance for internal data alignment (see Figure 14). Retailers increasingly see AI/machine learning as helping with internal alignment as well: 22%, up from 12.5% last year.
Retailers in particular need tools that assist with internal alignment. Nearly two in five (38%) say they “still have a long way to go” when it comes to migrating to a single, shared database across the organization. But the good news is that 24% have achieved that milestone and 22% are making progress toward it (see Figure 7b).
“The need for a single, shared view of data is crucial,” says Amanda Astrologo, associate partner for Parker Avery. “The consumer does not know boundaries, they only know that they’re interacting with a brand. So a retailer needs to adeptly leverage analytics to understand the demand source, as well as consumer shopping behavior and drivers, and be able to react quickly. The time and resources spent piecing together multiple sources and trying to make sense of them represents missed opportunities.”
Consumer goods companies are a little further along. While only 10% of CG respondents are sharing one database across the entire organization, a significant 48% are making progress toward a shared model (Figure 7b).
The Rise of AI
Data sharing is not the only place companies see the potential for AI. CG companies continue to consider the greatest promise in demand forecasting/planning (34%) and supply chain planning/execution (32%), and despite a long list of application areas to choose from, also suggested there are other application areas where they see promise. Pricing and promotion also hold promise (see Figure 15).
For retailers, AI is most commonly being explored in marketing/promotion planning/ execution (46%), followed by inventory planning (41%) and personalized marketing (38%). Last year, as a comparison, retailers were more likely to be exploring or using AI in merchandise planning/execution and customer relationship management (39%).
The Data-Driven Focus
Consumers face a dizzying array of choices for when, where and from whom to buy a product — and then the best way to receive it. Consumer goods companies and retailers face a similarly overwhelming array of choices when it comes to the best way to tease apart the choices consumers are making. Even as analytics budgets increase, there are more and more things that demand analysis, as well as an ever-increasing amount of data waiting to be analyzed.
And that is only intensifying the need for more sophisticated methods of predicting and prescribing consumer behavior.
To read the rest of the study, click on the links below:
Editor's Note: Companies Align on Data, Inside & Out
Data Sharing in the Age of Analytics
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