THE ROAD AHEAD
Retailers and consumer goods manufacturers face a growing list of industry challenges —
supply chain volatility, inflation, labor shortages — and analytics are key to overcoming them.
The upcoming years for these businesses will likely only get harder. Weaving data-powered analytics into operations unlocks opportunities to both overcome challenges and predict future needs.
For both retailers and CGs, inventory planning and pricing made it into the top three areas of analytics focus, reflecting ongoing supply chain and economic woes. For retailers, consumer insights (26%) moved into the top five areas of focus, while personalization (16%) slid far down the list from last year, signifying retailers may be taking a step back to better understand their customers before personalizing messages to them. Nearly half of CGs named demand forecasting (48%) their top area of focus.
While retailers and consumer goods manufacturers are focused on building the foundations and capabilities necessary to succeed tomorrow, internal challenges still prevent them from fully executing their analytics initiatives.
Half of retailers (52%) cite lack of budget as a key challenge, followed by not having the right staff in place to lead analytics (45%) and a limited analytics toolset (42%). What’s worse, when they have additional analytics needs, 32% disregard this need and do not address it (see “Addressing Need for Analytic Resources” chart below).
Likewise, 48% of CG manufacturers indicate that they don’t have the right staff in place to lead their analytics strategy, but when they have additional analytics needs, 39% outsource work to vendors (see “Addressing Need for Analytic Resources” chart below).
While retailers’ top three challenges all indicate a lack of money or resources, CG manufacturers' challenges indicate company culture and strategy issues. Tied for second on the list of challenges
for CGs at 39% is “company culture resists data-driven change” and the absence of a “clearly articulated strategy.”
CG manufacturers and retailers are facing similar challenges; both groups indicate that they don’t
have the right staff in place to lead their analytics strategy. Resource constraints are widespread, as companies struggle to expand analytics in the face of tight budgets, unclear initiatives, and limited tools.
However, when we asked retailers and CGs for one word to describe the future of analytics, the results were weighted toward positivity this year.
Inventory Planning
Pricing
Demand Forecasting
Consumer Insights
Promotion Effectiveness
Social Media
Marketing Mix Optimization
Demand Forecasting
Pricing
Inventory Planning
Consumer Insights
Promotion Effectiveness
Logistics Optimization
Trade Promotions
TOP AREAS OF FOCUS
Retail
Consumer Goods
42%
35%
29%
26%
26%
23%
23%
48%
32%
32%
23%
23%
23%
23%
Budget
Right Staff Not in Place
Limited Analytics Toolset
Inability to Deliver or Prove ROI
Inability to Integrate Data from Multiple Sources
Right Staff Not in Place
Company Culture Resists Data-Driven Change
Absence of Clearly Articulated Strategy
Budget
Limited Analytics Toolset
TOP ANALYTIC CHALLENGES
Retail
Consumer Goods
52%
45%
42%
29%
48%
39%
39%
35%
35%
Percentage of Total IT budget Spent on Analytics
Retail
CG
2022 2026
<5%
5% - 9.9%
10% - 14.99%
15% - 19.99%
20% - 24.99%
>25%
48% 13% 52% 29%
23% 39% 26% 19%
23% 16% 13% 19%
6% 16% 0% 10%
0% 10% 10% 16%
0% 6% 0% 6%
2022 2026
Big Data Analytics Tools
Data Security
Data Visualization/Dashboards
Data Warehouse/Storage
Enterprise BI & Reporting Tools
In-Store Analytics
Master Data Management
Mobile Business Intelligence
Social Media Analytics
SaaS BI Tool
Web/Online Analytics
Upgrades to Existing System Change to New Supplier Add Software for First Time No Changes Planned
Software Changes in the Next 12 Months
CG
Retail
26%
39%
52%
23%
23%
39%
29%
23%
19%
23%
35%
39%
58%
45%
42%
55%
32%
39%
39%
29%
32%
42%
CG
Retail
6%
10%
0%
3%
10%
0%
6%
3%
10%
3%
6%
6%
3%
6%
3%
10%
13%
13%
0%
16%
6%
10%
CG
Retail
6%
3%
13%
13%
13%
6%
6%
6%
16%
10%
6%
10%
6%
13%
16%
6%
3%
13%
6%
3%
13%
6%
CG
Retail
61%
48%
35%
61%
55%
55%
58%
68%
55%
65%
52%
45%
32%
35%
39%
29%
52%
35%
55%
52%
48%
42%
Evolving
Imperative
Potential
Existential
AI
Must-have
Automation
Necessary
Growing
Expanding
Uncertain
Complex
Exciting
Dynamic
Progress
Value Chain
Growth
Transformation
Change
Retail
Consumer Goods
click below buttons to shuffle
Scary
Predictive
Declining
Important
For CGs the words fell into the categories of growth: “evolving,” “transformation,” “imperative,” and “potential.” Retailers found analytics to be “critical” and recognize the impact of “artificial intelligence” on analytics’ future.
Critical
Overwhelming
Opportunity
Unclear
Change
Increasing
Dying
Awesome
Promising
Necessary
Must
Helpful
Uncertain
Essential
Useful
Paramount
Dynamic
Growing
Current
Evolving
Success
Exciting
Artificial Intelligence
Bright
Excellent
Explosive
On both sides of the coin, around half of respondents spent less than 5% of the IT budget on
analytics in 2022, but looking to the future, half expect to spend 10% or more of the budget on
analytics in 2026.
To help bolster their analytics capabilities moving forward, both retailers and CGs are planning changes to their analytics software over the next 12 months. CG manufacturers plan to focus upgrades on data security (58%) and reporting tools (55%); retailers plan to focus upgrades on data visualization (52%), security (39%), and in-store analytics (39%).
For CGs, this most commonly takes the form of a shared analytics department or a center for excellence (35%). When we asked survey takers how the use of analytics resources has changed at their organization in the last year, one CG respondent explained the need for a clear strategy: “There is a central analytics team, but the agility to deploy the new, planned data and analytics platform has been lacking. This is largely due to a lack of clear strategy and strong leadership in this internal organization.”
For retailers, the most common structure responsible for executing is the IT/technology department (29%).
When asked the same question, some retail respondents make mention of moving to centralize data. One
noted that a single version of truth has helped connect teams, with another emphasizing the need to unify
data: “We are starting to compile data in several different software systems and dashboards. Now we need
to unify this information rather than have it in several different places.”
Building an
Analytics Dream Team
Having a clear analytics strategy starts with having the right staff in place and continues with that team being able to lead a well-defined plan.
Retailers
Consumer
Goods
“It is top of agendas”
“We are adding data to be able to both qualify and quantify our analyses”
“Smarter merchandising decisions”
“Improved yield, sell-through, and profitability”
“Spending more time actually studying data”
“We're now recognizing the need for analytics at the highest level so we're getting budgets and resources allocated.”
“Went from very basic affinity reporting to some more advanced data models”
“Recognition of the importance to improve operational performance”
“Increased awareness of capabilities available and now making changes to capture the ROI potential.”
“We are just starting to tip our toes in, but understand it's a huge need to continue growth.”
“Improved, but still lots of room for continued growth and maturity.”
“More focus on analytics related to demand planning and supply chain management”
“Ability to make decisions faster”
“Better understanding of customers, markets, and channels”
“Able to forecast better and turn product quickly”
“Tons of wins at retail, created S&OP process”
“Improved efficiency throughout the company”
How has the use of analytics resources changed at your organization in the last year?
IT/Tech Dept
Managed By Each Dept
Shared Analytics Dept/
Center of Excellence
Other
Hub & Spoke Structure
Strategy Dept
29%
26%
23%
13%
10%
0%
35%
13%
3%
13%
13%
23%
Who IS responsible?
Who SHOULD BE responsible?
26%
29%
23%
10%
6%
6%
42%
3%
29%
13%
3%
10%
business analytics
AddressING NEED FOR Analytic ResourceS
Not Addressing this Need
Adopting AI/ML tools
Organization Has No Additional Needs
Outsourcing Some Work to Vendors
Hiring Internal Analytics Personnel
26%
13%
3%
39%
19%
32%
23%
19%
16%
10%
CG
Retail
CG
Retail
CG
Retail
74%
Retail
Internal Employees Dedicated to Business Analytics
Less than 10 people
Developing an analytics team is important to a company’s analytics success, yet half of CG manufacturers and nearly three-quarters of retailers have fewer than 10 internal employees dedicated to business analytics (however, some of this is likely attributable to company size).
When asked about addressing the needs for additional analytics resources, a whopping 32% of retailers said their tactic is to simply not address these needs, which certainly isn’t solving any problems for overwhelmed IT staff. Meanwhile, 39% of consumer goods manufacturers say they solve this problem by outsourcing some work to vendors, but another 26% agree with retailers that they are not addressing these needs.
The one area where retailers beat out their CG counterparts is when it comes to artificial intelligence (AI) and machine learning (ML) tools. Retailers (23%) are quicker to adopt AI/ML to address their additional analytics needs than CGs (13%).
While another CG respondent noted their company has restructured to put the right resources (e.g., people) in the right place to help with the bandwidth, “there’s still plenty more to do.”
Less than 10 people
51%
CG
We asked survey takers to rank their analytics capabilities against both their direct competition and the industry’s most analytically advanced. For retailers, the examples of the gold standard of analytics that we provided respondents with were Amazon and Kroger, while examples of the top analytics leaders in consumer goods were Unilever and Procter
& Gamble.
Against their direct competition, 35% of retailers rank themselves as lagging or significantly lagging in analytics tools, indicating that they see their adversaries as having the superior solutions. Nearly 40% believe the same about their analytics skills/personnel. When it comes to their data quality, however, retailers remain more optimistic, with 61% believing they’re at par with all competitors.
When self-ranked against the industry’s best, however, things worsen. The majority — 71% — position themselves as lagging in analytics tools, while a near-equal amount (68%) feel they’re falling behind in analytics strategy. Once again, data quality is the brightest bulb in the box, with more than half (52%) of retailers reporting that they’re at par with industry-leading retailers.
It’s a fairly similar story for CGs. When comparing themselves with all competitors, just 6% feel they’re doing better or significantly better with data management. Forty-five percent say they’re lagging in analytics tools, while more than a third feel they’re lagging in both analytics skills/personnel and strategy.
The picture continues to darken when placed against the leaders of the pack: When comparing themselves with industry-leading manufacturers, nearly three-quarters of CGs believe they’re lagging for data management, while more than two-thirds say the same for data quality, analytics tools, and analytics strategy.
Going Head-to-Head
Pricing
Social media
Inventory planning
Marketing / promotion campaign planning
Demand planning & forecasting
Demand planning & forecasting
Pricing
Marketing/promotion campaign planning
Inventory planning
Trade promotions
Top 5 AI/ML Use Cases
26%
26%
23%
23%
23%
39%
29%
26%
23%
23%
Retail
CG
Self-Evaluation Versus THE Competition
Retail
Data Management
Data Quality
Analytics Skills/Personnel
Analytics Strategy
Analytics Tools
DIRECT COMPETITION
INDUSTRY LEADERS
Significantly
Lagging/Lagging
At Par
Better/
Significantly Better
Consumer Goods
Click above tabs to shuffle
Click above tabs to shuffle
DIRECT COMPETITION
INDUSTRY LEADERS
As in years past, we also asked retailers and CGs to rank their analytic maturity on a four-tiered scale ranging from lagging to transformational. Retailers report they’ve made the greatest gains in both inventory management and space planning, with more than half describing their maturity as advanced or transformational. Omnichannel/digital commerce was among the areas of the greatest need of improvement, with more than a third (35%) reporting their maturity as lagging.
Perhaps unsurprisingly, CGs were much more bullish on their maturity. They report that they’re the most analytically advanced in marketing spend, pricing, and promotions. Notably, however, very few CGs report that they’re transformational in anything.
Assortment planning
Category management
Consumer insights
Demand forecasting
In-store analytics
Inventory management
Marketing spend
Omnichannel/digital commerce
Personalization
Pricing & promotions
Promotion effectiveness
Replenishment
Retail media
Social media
Space planning
Transportation/logistics
Warehouse management
Workforce
Lagging Rudimentary Advanced Transformational
AnalyticS Maturity
CG
Retail
16%
10%
16%
16%
16%
6%
19%
35%
19%
16%
19%
10%
16%
19%
6%
16%
23%
16%
29%
13%
13%
19%
19%
6%
6%
13%
26%
10%
19%
19%
6%
10%
16%
13%
16%
19%
CG
Retail
48%
52%
65%
58%
61%
42%
39%
32%
42%
42%
45%
48%
55%
45%
42%
55%
45%
48%
35%
45%
55%
45%
58%
58%
42%
58%
58%
45%
52%
52%
71%
48%
55%
45%
42%
52%
CG
Retail
29%
39%
16%
23%
23%
48%
32%
16%
35%
42%
35%
39%
23%
26%
52%
29%
29%
29%
29%
39%
29%
32%
19%
35%
48%
26%
16%
42%
29%
29%
23%
39%
29%
35%
42%
26%
CG
Retail
6%
0%
3%
3%
0%
3%
10%
16%
3%
0%
0%
3%
6%
10%
0%
0%
3%
6%
6%
3%
3%
3%
3%
0%
3%
3%
0%
3%
0%
0%
0%
3%
0%
6%
0%
3%
We once again asked respondents to share their top use for AI and machine learning, and it’s interesting to see what has changed — and what stayed the same. Last year, just retailers listed pricing as their No. 1 use, while both groups said the same this year. For CGs, demand planning also once again took second billing, while social media climbed its way up from the No. 4 spot into second place.
Nearly a third of both groups — 32% of CGs and 29% of retailers — reported that they’re not using AI/ML for any of these use cases. As the potential of generative AI grows rapidly and becomes more widely adopted, we can’t wait to see what next year's top use cases look like.
Data Management
Data Quality
Analytics Skills/Personnel
Analytics Strategy
Analytics Tools
29%
23%
39%
39%
23%
39%
42%
19%
48%
45%
35%
19%
35%
58%
6%
16%
23%
39%
29%
32%
35%
42%
23%
61%
35%
42%
23%
29%
45%
26%
32%
16%
58%
26%
16%
68%
16%
16%
52%
71%
13%
58%
29%
13%
16%
Significantly
Lagging/Lagging
At Par
Better/
Significantly Better
65%
6%
55%
29%
16%
68%
23%
10%
29%
65%
23%
13%
74%
3%
23%
Share and Share Alike
Retailers Say They're Sharing Data With CGs
Customer Behavior Data
Inventory Data
Loyalty or CRM data
On-Shelf Availability
Online Sales Data
POS Transaction data
Pricing Data
Promotions Performance
26%
32%
16%
19%
6%
16%
23%
9%
35%
16%
32%
26%
16%
19%
6%
16%
26%
10%
39%
10%
16%
23%
6%
35%
19%
6%
26%
6%
42%
19%
13%
29%
13%
35%
10%
13%
32%
16%
32%
6%
45%
23%
19%
13%
16%
13%
32%
26%
13%
32%
13%
13%
32%
10%
26%
16%
26%
29%
3%
35%
19%
16%
23%
6%
26%
13%
16%
26%
19%
23%
10%
16%
29%
23%
42%
23%
13%
23%
CGs Say They're Receiving Data from Retailers
Don't Share This Data
No Set Cadence (Ad Hoc)
Monthly/Weekly
Daily or More Often
Yearly/Quarterly
Don't Share This Data
No Set Cadence (Ad Hoc)
Yearly/Quarterly
Monthly/Weekly
Daily or More Often
Who Took the Survey?
Consumer Goods Technology and RIS News commissioned our annual Analytics Study to benchmark the state of analytics across the consumer goods and retail industries, highlighting current and future investment plans. It was conducted in partnership with parent company EnsembleIQ’s research department.
The survey was fielded from February 8 to March 14 of this year. It was sent electronically to the CGT and RIS readership, and qualified respondents had to be a consumer goods manufacturer or retailer with a director or above job title.
Data sharing — or lack thereof — is an ongoing disagreement between retailers and
consumer goods manufacturers. It was a core component of this study since its founding,
and we once again took the pulse on this topic.
Manufacturers and retailers are, for the most part, aligned on the type of data shared, with both sides reporting that POS transaction data is among the most likely to be shared on a regular cadence. (It is slightly bested by on-shelf availability data, according to retailers.) Loyalty or CRM data and customer behavior data topped both groups’ lists of the data that was least likely to be shared.
In a bit of a win for both sides; nearly all types of data increased from last year in being shared
at least monthly. For example, 61% of CGs say that retailers are sharing POS transaction data
at least monthly, up from 56% last year, while 54% say the same for online sales data, up
from 40% in 2022. Fifty-two percent of retailers, meanwhile, say that they're sharing
on-shelf availability data at least monthly, more than double last year's 24%.
The lone exception was, again, customer behavior data, with 14%
of retailers saying they were sharing it monthly in 2022, but just
13% reporting the same this year.
When examining internal data alignment within organizations, more than two-thirds of CGs are working on establishing a shared data model — although just 10% report they’re using the same data source. Retailers are farther along in the process, with 42% already sharing the same data source.
Data Sharing
What Retailers Say
What CGS Say
CHARGING FOR DATA
None
Some
Some
Most
All
Most
None
All
68%
13%
58%
16%
13%
16%
10%
6%
Sixty-four percent of consumer goods respondents hold titles of VP or higher and another 35% are directors. More than a third (35%) are employed at companies with revenue of greater than $1 billion.
Food and beverage products (45%) were the overwhelming project category. The most popular core business function is marketing (23%), tied with IT (23%).
Seventy-seven percent of retailer respondents hold VP titles or higher (61% identify as a CEO/president/owner), with 16% hailing from retailers with revenue north of $1 billion.
The majority of retail respondents are employed in specialty retail (29%), apparel and accessories (26%), and grocery and convenience (19%). Store operations (32%) is the most popular business function among retail survey takers.
JOB TITLE
Retail
23%
6%
10%
61%
C-Suite
VP
Director
President/CEO
CG
35%
32%
16%
16%
ANNUAL REVENUE
Retail
CG
65%
3%
13%
6%
13%
29%
16%
29%
19%
6%
<$100 million
$100 million to $499 million
$500 million to $999 million
$1 billion to $4.999 billion
$5 billion +
<$100 million
$100 million to $499 million
$500 million to $999 million
$1 billion to $4.999 billion
$5 billion +
Primary Retail Segment
Retail
CG
29%
6%
19%
10%
6%
3%
6%
45%
3%
Housewares/Appliances
Other
Health & Beauty Aids
Household Goods
Apparel/Footwear/Accessories
Food & Beverage Products
Specialty Retail
Apparel & Accessories
Grocery
Convenience/Gas
Department Store
Pharmacy/Drug Store
Other
CORE BUSINESS FUNCTION
Consumer Goods
Retail
Retail
CG
3%
16%
3%
10%
32%
6%
23%
3%
23%
16%
13%
6%
6%
13%
Marketing
IT/technology
Supply chain
Customer management/sales
Insights/analysis
Merchandise management
Trade marketing/category mgmt.
Omnichannel/digital commerce
Store operations
Other
Marketing
IT/technology
Supply chain
Customer management/sales
Insights/analysis
Merchandise management
Trade marketing/category mgmt.
Omnichannel/digital commerce
Store operations
Other
3%
6%
6%
26%
C-Suite
VP
Director
President/CEO
6%
3%
10%
16%
19%
Key Findings
Having a clear analytics strategy starts with having the right staff in place and continues with that team being able to lead a well-defined plan. Moving to centralized, unified data can connect teams.
When it comes to internal data sharing, retailers are further along in the journey than CGs, with 42% reporting that they’re sharing the same data source.
While easier said than done, establishing a strong analytics leadership team is required to help address other key challenges, such as ambiguous strategies and constrained budgets.
Staying attuned to the latest analytics trends will allow business professionals to become better educated on how to manage their internal analytics pipeline.
Daasity enables consumer brands to be data-driven by supporting the varied data architecture, analytics, and reporting needs for eCommerce, Amazon, retail, and wholesale. Using Daasity, all teams get a centralized and normalized view of all their data, regardless of their tech stack or future changes in data needs.
RightSense offers automated Data Stories integrated with chat powered by large language models such as GPT-4 and delivers dynamic, actionable insights for Key Performance Indicators (KPIs). By automating the detection of outliers and anomalies, we improve organizational data literacy, productivity, and enable informed decision-making and eliminates limitations of traditional dashboards.
Treasure Data helps enterprises use all of their customer data to improve campaign performance, achieve operational efficiency, and drive business value with connected customer experiences. The Treasure Data Customer Data Cloud , our suite of customer data platform solutions integrates customer data, connects identities in unified customer profiles, applies privacy, and makes insights and predictions available for Marketing, Service, Sales, and Operations to drive personalized engagement and improve customer acquisition, sales, and retention.
2023 ANALYTICS STUDY SPONSORS
Asper unlocks revenue growth for customers through a purpose-built AI to enable interconnected decisions. We understand the challenges CPG & Retail companies face in making decisions at speed and scale. With the right mix of talent and technology, we help organizations overcome those challenges and become adaptive intelligent enterprises.
Analytics is an area of high growth, and both manufacturers and retailers must be flexible and adaptive to succeed in a fast-paced environment.
In the next year, peer CG manufacturers plan to focus software upgrades on data security and reporting tools; retailers plan to focus upgrades on data visualization/dashboards.
Retailers are quicker to adopt artificial intelligence and machine learning to address their additional analytics needs than CG manufacturers, but the technologies offer benefits to both industries. CGs should take a cue from retailers that AI/ML is necessary to unlock future success.
1.
2.
5.
6.
3.
4.
7.
PRESENTED BY
INTERNAL DATA ALIGNMENT
CG
Retail
Making Progress Toward Shared Data Model
Working In Silos
Entire Organization Shares Same Data Source
42%
29%
29%
68%
23%
10%
And as has become a common theme throughout the years of this study, retailers and manufacturers simply don’t see eye-to-eye when it comes to the question of charging for data, likely in part because of how “charging” may be defined by either side. One-third of retailers state that they are charging manufacturers for data, while 87% of manufacturers are ponying up for said data.
from Multiple Sources
29%
Upgrades to Existing System Change to New Supplier Add Software for First Time No Changes Planned
Lagging Rudimentary Advanced Transformational
Customer Behavior Data
Inventory Data
Loyalty or CRM data
On-Shelf Availability
Online Sales Data
POS Transaction data
Pricing Data
Promotions Performance