It’s not surprising that inventory planning and pricing once again topped the list of analytics focus for retailers this year. What has changed is that employee performance muscled its way into the top three — likely indicative of the myriad labor challenges retailers continue to navigate.
It was a bit of a different story for manufacturers, which shook things up this year by having consumer insights leap to the top spot. Given the disappearance of third-party cookies, CGs are clearly prioritizing having consumer understanding capabilities safely within their own walls.
When looking at the challenges preventing them from realizing their grand analytics ambitions, a lack of budget is once again holding back retailers (this may be a perennial challenge). Retailers, however, indicated more confidence about the staff they have in place this year vs. last year.
CG challenges were a bit more technical than cultural this year. While they remain hampered by the lack of a clear strategy, they also were critical about the tools they have available — and the budget to leverage/acquire them.
Inventory planning
Pricing
Demand forecasting
Employee performance
Promotion effectiveness
Consumer insights
Logistics optimization
Social media
Consumer insights
Demand forecasting
Promotion effectiveness
New product development
Inventory planning
Logistics optimization
Marketing mix optimization
Pricing
TOP AREAS OF FOCUS
Retail
Consumer Goods
40%
38%
28%
28%
25%
20%
18%
18%
FOCUS
Coming into
Lack of budget
Inability to integrate data from multiple sources
Limited analytics toolset
Inability to deliver or prove ROI
Don't have the right staff in place to lead analytic strategy
Inability to deliver insights to the right resource at the right time
Culture of the company resists data-driven change
Absence of clearly articulated analytics strategy
Poor data quality - business leaders don't trust the data
Lack of single owner for analytics
Absence of clearly articulated analytics strategy
Limited analytics toolset
Don't have the right staff in place to lead analytic strategy
Lack of budget
Culture of the company resists data-driven change
Inability to integrate data from multiple sources
Lack of single owner for analytics
Inability to deliver insights to the right resource at the right time
Poor data quality - business leaders don't trust the data
Inability to deliver or prove ROI
TOP ANALYTIC CHALLENGES
Retail
Consumer Goods
48%
40%
35%
33%
28%
28%
25%
23%
20%
18%
41%
41%
38%
38%
29%
26%
26%
24%
21%
15%
Percentage of Total IT budget Spent on Analytics
Retail
CG
2023 2027
<5%
5% - 9.9%
10% - 14.99%
15% - 19.99%
20% - 24.99%
25%+
38% 20% 38% 6%
28% 23% 29% 29%
15% 28% 18% 21%
18% 8% 6% 24%
0% 13% 3% 9%
3% 10% 6% 12%
2023 2027
Retail
Consumer Goods
click left tabs to shuffle
Coming into
47%
44%
32%
29%
24%
24%
24%
18%
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 tools
Web/online analytics
Upgrades to Existing System Change to New Supplier Add Software for First Time No Changes Planned
MANUFACTURER Software PLANS FOR the Next 12 Months
CG
Retail
33%
45%
43%
40%
33%
30%
45%
23%
30%
18%
35%
18%
26%
32%
12%
35%
9%
21%
21%
24%
18%
24%
CG
Retail
10%
13%
8%
8%
8%
5%
5%
10%
13%
5%
8%
26%
15%
15%
9%
9%
18%
9%
15%
12%
12%
12%
CG
Retail
0%
0%
0%
0%
0%
3%
0%
0%
0%
0%
0%
9%
0%
0%
6%
6%
9%
15%
3%
6%
6%
9%
CG
Retail
58%
43%
50%
53%
60%
63%
50%
68%
58%
78%
58%
47%
59%
53%
74%
50%
65%
56%
62%
59%
65%
56%
Ask analytics leaders about the future of analytics, and you’re presented with an overwhelmingly optimistic picture. This sentiment tracks with previous years, with positive sentiment and growth terms leading the chorus, while just a few negative nay-sayers pipe up. (Artificial intelligence, unsurprisingly, was once again a commonly cited response.)
As to where they plan to allocate their analytics upgrades this year, retailers cited data security (45%), master data management (45%), and data visualizations/dashboards (43%) as their winning trifecta, while CGs were more likely to prioritize enterprise BI reporting tools (35%) before data visualization/dashboards (32%) and data security (26%).
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
15%
8%
23%
25%
20%
5%
15%
30%
28%
3%
10%
10%
23%
20%
10%
15%
13%
15%
21%
15%
12%
26%
24%
26%
35%
21%
24%
21%
18%
15%
18%
9%
26%
24%
18%
35%
CG
Retail
53%
50%
45%
48%
45%
55%
48%
53%
28%
45%
50%
45%
45%
43%
48%
55%
58%
50%
38%
38%
44%
32%
47%
26%
29%
35%
47%
32%
44%
41%
47%
50%
47%
29%
26%
35%
CG
Retail
25%
38%
25%
25%
35%
38%
30%
13%
40%
45%
35%
45%
28%
35%
38%
28%
28%
30%
38%
38%
38%
38%
26%
41%
29%
38%
26%
38%
32%
41%
29%
26%
24%
38%
47%
21%
CG
Retail
8%
5%
8%
3%
0%
3%
8%
5%
5%
8%
5%
0%
5%
3%
5%
3%
3%
5%
3%
9%
6%
3%
3%
6%
6%
6%
3%
9%
6%
3%
6%
15%
3%
9%
9%
9%
Leaning into analytics to gain a deeper understanding of the business and the buyer is a mission that both retailers and consumer goods manufacturers are united in. Properly building a foundation that’s able to unlock the true potential of analytics is another story — one fraught with disagreements about ideal resource allocation.
Exciting
Evolving
Pervasive
Visibility
Effective
Personalization
Necessity
Enlightening
Innovative
Knowledge
Useful
Connected
Necessary
Challenging
Murky
Scalability
Table Stakes
Mandatory
Promising
AI
Opportunity
Dynamic
Transformational
Evolving
Advanced
Progressive
Smarter
Promising
Evolution
Safe
Expanding
Evolutionary
Success
Faster
Necessary
Challenging
Unknown
Transformative
Essential
Important
Exciting
Innovative
Rapidly Changing
Huge
Engagement
Optimized
Bright
Marvelous
Potential
Helpful
Great Opportunities
Unique
AI
Complicated
Accurate/Precise
Data
Data-driven
While half of manufacturers would like a shared department running the analytics show, just 18% could say this was currently the case. Retailers were also more likely to prefer a shared or centralized department, but they were more eager than CGs to shift away from having their IT or tech department be the designated stakeholders for business analytics.
FUTURE
Building Data Teams
for the
Building Data Teams
for the
Managed By Each Dept
IT/Tech Dept
Shared Analytics Dept/
Center of Excellence
Strategy Dept
Hub & Spoke Structure
Other
18%
28%
20%
13%
10%
13%
18%
6%
6%
6%
41%
24%
Who IS responsible?
Who SHOULD BE responsible?
15%
13%
33%
13%
20%
8%
50%
21%
9%
3%
12%
6%
business analytics
HOW THE USE OF Analytic ResourceS HAS CHANGED IN THE LAST 12 MONTHS
More / better insights
Early stages / room to grow
Better data integration
Faster insights
Marketing improvements
Growth in sales
Experiencing challenges
More investments
No changes
35%
18%
9%
9%
9%
6%
6%
6%
3%
18%
18%
10%
8%
5%
5%
5%
3%
3%
38%
CG
Retail
CG
Retail
CG
Retail
More / better insights
More resources added
Marketing improvements
More investments
Experiencing challenges
Early stages / room to grow
Faster insights
Better data integration
Improved operations
No changes
Putting analytics ambitions into motion requires building the right data teams — something industry leaders have repeatedly shared with CGT as one of their key priorities. In looking at this year’s survey findings, about one-half of both manufacturers and retailers count fewer than 10 internal employees dedicated to business analytics. (It’s worth noting, however, that this is partially explained by the company size of the survey respondents.)
When it comes to comparing themselves against their competitors, CGs reported more confidence in their performance this year than last — though that drops when they compare themselves with industry leaders like Procter & Gamble and Unilever. Just over a third of retailers feel good about their performance vs. all competitors, and they also report more confidence vs. industry leaders than they did last year.
It’s hard to believe, but generative AI was barely a blip on the mainstream radar when we fielded the
2023 survey. Not wanting to miss an opportunity to learn where retailers and manufacturers currently stand, we quizzed them on both their planned use cases and how likely they are to use or explore generative AI.
Manufacturers are a bit more mature in this space than retailers, with half of them currently
using generative AI or planning to use it within the next 12 months. Forty-three percent
of retailers, meanwhile, reported they’re not using or exploring the use of generative AI.
HYPE CYCLE
Clarifying the
Clarifying the
Self-Evaluation Versus THE Competition
Retail
Data Management
ANALYTICS TOOLS
DATA QUALITY
Analytics Strategy
Analytics SKILLS/PERSONNEL
45%
23%
43%
25%
33%
43%
23%
35%
33%
45%
23%
33%
45%
33%
23%
35%
28%
28%
30%
43%
33%
30%
38%
38%
25%
38%
38%
30%
35%
35%
DIRECT COMPETITION
INDUSTRY LEADERS
Significantly
Lagging/Lagging
At Par
Better/
Significantly Better
Consumer Goods
65%
15%
53%
38%
9%
65%
24%
12%
21%
50%
26%
24%
62%
15%
24%
38%
15%
44%
18%
38%
26%
44%
29%
47%
35%
38%
26%
38%
24%
38%
analytics tools
Data management
Analytics Skills/Personnel
data quality
Analytics strategy
Click above tabs to shuffle
Click above tabs to shuffle
DIRECT COMPETITION
INDUSTRY LEADERS
Significantly
Lagging/Lagging
At Par
Better/
Significantly Better
Content creation and marketing topped the list for the planned use cases, followed by analytics. One manufacturer noted that generative AI is empowering them to analyze vast datasets and consumer behavior patterns, “enabling us to tailor our sales and marketing approaches for maximum impact and customer engagement.”
When it comes to the challenges of generative AI, however, retailers noted that it “takes a lot of fine-tuning” and that there’s a “lack of cohesion with franchiser and inability to regionalize data for marketing purposes.” They also noted that there’s a “lack of acceptance.”
When asked how they’re using any type of AI, including traditional and generative, and machine learning, retailers and manufacturers had fairly different results. Consumer relationship management and marketing/promotion campaign planning were tied for top billing with CGs, cited by 32% of respondents, while new product development was close on their heels (29%). For retailers, social media was the No. 1 use case (30%). There was then a bit of drop-off, with the second choice (inventory planning) cited by just 20% of respondents.
Allocation
Assortment planning/category mgmt.
Consumer relationship mgmt.
Consumer-facing service/interaction
Demand planning & forecasting
Inventory planning
Logistics optimization
Mktg./promotion campaign planning
Merchandise planning and execution
New product development
Personalization
Pricing
Retail media
Social media
Supply chain planning and execution
Trade promotions
Warehouse mgmt.
Workforce
Other
None of the above
USE OF ANY TYPE OF AI
CG
Retail
5%
5%
8%
13%
15%
20%
10%
18%
13%
3%
15%
18%
15%
30%
13%
8%
5%
0%
5%
38%
3%
9%
32%
15%
15%
9%
15%
32%
9%
29%
24%
21%
12%
18%
12%
15%
6%
9%
0%
26%
Quality of AI-generated output
Optimizing the use of AI-generated output
Having the right talent
Data privacy
Cost
Ethical considerations
Other
Top CHALLENGES FOR RETAILERS and CGs USING GENERATIVE AI
USE OF GENERATIVE AI
CG
Retail
EVOLVES
Data Sharing
While data sharing — and especially the lack thereof — was once the primary focus of this report, the issue is not quite the bone of contention it once was. Although CGs still press for retailers to share more data (and retailers remain less than enthused about doing so), the conversation has evolved. For one thing, manufacturers have been supplementing insights with their own stores of first-party data through increased direct-to-consumer efforts. (The rocketship growth of retail media networks, however, also means that there’s a new tool in retailers' data toolboxes that CGs are now grumbling they're not sharing.)
When looking at the types of data, CGs are more likely to receive online sales data from their retailer partner at least monthly vs. last year, but less likely for all other data types. The retailers told a different story; they’re more likely to report sharing all data types at least once a month compared to last year.
Retailers Say They're Sharing Data With CGs
Data Sharing
CGs Say They're Receiving Data from Retailers
Customer Behavior Data
Inventory Data
Loyalty or CRM data
On-Shelf Availability
Online Sales Data
POS Transaction data
Pricing Data
Promotions Performance
26%
35%
26%
9%
3%
47%
12%
21%
15%
6%
15%
15%
12%
32%
26%
18%
9%
12%
29%
32%
21%
29%
15%
24%
12%
9%
32%
21%
15%
24%
18%
32%
24%
12%
15%
26%
29%
21%
12%
12%
48%
10%
18%
20%
43%
10%
15%
28%
5%
18%
18%
18%
40%
8%
15%
13%
18%
48%
8%
30%
13%
13%
33%
13%
28%
15%
10%
33%
15%
23%
18%
18%
35%
8%
38%
15%
15%
28%
Don't Share This Data
No Set Cadence (Ad-Hoc)
Yearly/
Quarterly
Monthly/
Weekly
Daily or More Often
Don't Share This Data
No Set Cadence (Ad-Hoc)
Yearly/
Quarterly
Monthly/
Weekly
Daily or More Often
CHARGING FOR DATA
What Retailers Say
What CGS Say
63%
SURVEY
Consumer Goods Technology commissioned the annual Analytics Study to benchmark the state of analytics across the consumer goods and retail industries, highlighting current and future investment plans. The survey was conducted in partnership with parent company EnsembleIQ’s research department, with the resulting findings prepared by the research team. The survey was fielded from February 9 - March 5, 2024. It was sent electronically to the CGT and a segment of the Chain Store Age readership, and qualified respondents had to be consumer goods manufacturers or retailers.
Who Took the
Data Sharing
5%
5%
33%
0%
5%
ALL
MOST
SOME
NONE
18%
71%
9%
3%
ALL
MOST
SOME
NONE
FINDINGS
Key
Key
Who Took the
JOB TITLE
Retail
5%
30%
23%
28%
Manager
Data Scientist/Data Engineer/
Data Architect
Director
CG
3%
24%
18%
ANNUAL REVENUE
Retail
CG
48%
8%
15%
10%
24%
21%
35%
21%
<$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
$1 billion to $4.999 billion
$5 billion+
Primary BUSINESS FOCUS
Retail
CG
8%
5%
25%
18%
6%
9%
21%
5%
Other
Mass Merchant
Department Store
Apparel & Accessories
Grocery
Convenience/Gas
CORE BUSINESS FUNCTION
Other
Manager
Data Scientist/Data Engineer/
Data Architect
Director
Consumer Goods
Retail
Retail
CG
13%
38%
18%
8%
8%
13%
21%
9%
18%
12%
12%
9%
15%
IT/Technology
Marketing
Insights/Analytics
Supply Chain
Customer Mngt./Sales
Merchandise Mngt.
Omnichannel/Digital Commerce
Store Operations
3%
13%
VP
VP
President/CEO/Owner
C-Suite
President/CEO/Owner
C-Suite
3%
6%
29%
18%
10%
10%
Don't Know/Not Sure
Pharmacy/Drug Store
Specialty Retail
5%
18%
18%
Other
Wine & Spirits
OTC Pharmaceuticals
Specialty Retail Goods
Apparel/Footwear/Accessories
Housewares/Appliances
Health & Beauty Aids
Household Goods
Food & Beverage Products
3%
3%
9%
9%
3%
38%
IT/Technology
Marketing
Insights/Analytics
Supply Chain
Customer Mngt./Sales
Merchandise Mngt.
Omnichannel/Digital Commerce
Other
Trade Marketing/Category Mngt.
3%
3%
3%
3%
Manufacturers would ideally like for business analytics to be executed by a shared analytics department, while retailers believe a shared analytics or centralized department should hold the keys.
Retailers are farther along than manufacturers at sharing the same data source company-wide (38%), with another 38% working toward a shared data model.
Top analytic upgrades for manufacturers will focus on enterprise BI and reporting tools and dashboards, and retailers will focus on data security, master data management, and dashboards.
1.
2.
3.
4.
5.
6.
Manufacturers feel constrained by a limited toolset, vague analytics strategy, tight budgets, and lack of leadership. Retailers are crunched by tight budgets and data integration issues.
Outlining a comprehensive strategy with clear directives and goals will allow manufacturers to maximize resources and measure performance for iterative improvements.
Assessing opportunities for generative AI and how it could help retailers advance analytically may uncover solid use cases.
Denodo is a leader in data management. The award-winning Denodo Platform is the leading data integration, management, and delivery platform using a logical approach to enable self-service BI, advanced analytics, hybrid/multi-cloud integration, and enterprise data services. Retailers can create a complete view of the customer, product, or supplier in weeks.
2024 ANALYTICS STUDY SPONSORS
PRESENTED BY
A leader in data-based solutions for the consumer goods industry, MSA helps customers identify innovative uses of their data. With expertise in enterprise data warehousing; sales execution and trade program managements; and market/consumer analytics, MSA provides actionable predictive and prescriptive insights to grow our clients’ business.
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13%
15%
20%
43%
10%
21%
29%
21%
21%
9%
Currently using
Planning to use in next 12 months
Exploring, but no plans to use
Not using
Don't know/not sure
Quality of AI-generated output
Optimizing the use of AI-generated output
Having the right talent
Data privacy
Cost
Ethical considerations
Other
74%
42%
42%
42%
42%
21%
5%
54%
54%
46%
42%
29%
25%
0%
Top CHALLENGES USING GENERATIVE AI
1.
2.
3.
4.
5.
6.
7.
Lagging Rudimentary Advanced Transformational
Lagging Rudimentary Advanced Transformational
Lagging Rudimentary Advanced Transformational
Upgrades to Existing System Change to New Supplier Add Software for First Time No Changes Planned
Upgrades to Existing System Change to New Supplier Add Software for First Time No Changes Planned
Upgrades to Existing System Change to New Supplier Add Software for First Time No Changes Planned
Customer Behavior Data
Inventory Data
Loyalty or CRM data
On-Shelf Availability
Online Sales Data
POS Transaction data
Pricing Data
Promotions Performance
JOB TITLE
ANNUAL REVENUE
Primary BUSINESS FOCUS
CORE BUSINESS FUNCTION
