7 hidden costs of in-house data ...
November 15, 2023
How forward-thinking CPG sales teams utilize AI-powered data foundations for the perfect pitch, leveraging real-time insights, seamless answers, and predictive modeling to keep sales expanding.
Successful CPG growth today depends on becoming an indispensable retail partner. The right structured data foundation enables suppliers to deliver the strategic insights and collaborative solutions that merchants value most. When these insights are built on the retailer’s data, with alignment by revenue growth management (RGM), finance, and supply chain teams, it creates a unified, win-win approach that merchants can trust.
This trust becomes essential as crucial merchant meetings can make or break expansion opportunities fast. A misalignment between what merchants need and suppliers provide can derail meetings and shut down expansion opportunities. For that reason, having responsive data that can shift from POS proof points to accurate strategic analysis and incremental ROI estimates serves multiple purposes: it secures opportunities, presents compelling new paths, and solidifies your team’s position as a collaborative, technology-enabled partner with the means to deliver on promises.
A modern CPG data tech stack makes much of this possible, but AI significantly enhances these capabilities. AI’s key advantage lies in its ability to process exponentially more information than human teams alone, analyzing immense retail data sets and revealing patterns and connections that would otherwise remain hidden in complexity. AI can formulate strategic recommendations based on granular data, with modeled ROI estimations built on your retailer’s data, that powerfully demonstrate how investments in your brand will drive incremental revenue, giving buyers the confidence they need to expand your distribution.
CPG sales teams enabled with a structured retail data foundation and seamless AI integrations can maximize the success of buyer meetings, supporting joint business planning (JBP) in real-time, and turning data-driven success into greater opportunities that drive mutual growth.
1. Unite teams on a singular, scalable data foundation
McKinsey analysis across 140 use cases reveals that customer insights, demand shaping, and innovative channel management offer the highest ROI for CPG AI investments. Yet AI adoption in CPG significantly lags behind other industries, with many organizations lacking the properly structured data formats for AI to process expansive retail data. There’s also a data deviation problem: where CPG HQ teams, such as revenue growth (RGM), business intelligence (BI), and supply chain continue to operate on syndicated, delayed market reports, which present a big data mismatch from the granular, retail-specific POS insights sales teams need to stay agile and support their merchants.
When CPG organizations lack a unifying retail data foundation – let alone when they still rely on manual reporting or even just syndicated and outdated market reports – it can be hard to devise winning pitches with full company buy-in and transparency. When data connections between sources become uncertain, it can introduce doubt in your merchant’s mind, making them less willing to take risks on your brand and less likely to recognize the growth opportunities you’re capable of.
An automated retail data solution harmonizes disjointed information from all retail sources, formatting everything into a consistent structure with flexible access to real-time POS data across SKUs, stores, DCs, and more. Instead of dealing with data deficiencies in merchant meetings, teams are supported by one clean, comprehensive data resource that updates in real-time, automatically. This creates a single source of truth, internally and externally, while laying the foundation for all subsequent AI applications that can power new levels of growth and optimization for your business.
Retail data pipelines can cost $100K each to build, and $500K a year to maintain, underscoring the value of a reliable automated solution.
Read: The 7 hidden costs of in-house data automation
2. AI-powered MDM: Speaking the same language
Successful merchant conversations hinge on mutual understanding, but inconsistent product and offering attributes can create confusion, more than build trust. CPG companies that supply many partners tend to face a taxonomy conflict, where product identifiers and information, and categories and sub-categories, along with PDQs, retailer-specific, and seasonal offerings can vastly differ across accounts and in data.
Advancements in leveraging AI for master data management (MDM) mean teams can create a new layer of clean structure to disparate retail data that unifies teams on common organization and nomenclature. CPG HQ can view POS data in the internal hierarchy they care about, which can also include detailed supply chain attributes like ‘active ingredient’, or ‘material’. And sales teams can present insights in the format their buyer recognizes. AI-powered MDM ensures a streamlined structure, that can be personalized to each organization and its needs.
A master data management and product attribution solution saves teams hours, or even months, on what’s traditionally been highly manual work, and yet a critical component of a strong data foundation. AI projects stagger when data is disconnected. Having a robust, streamlined structure opens pathways of opportunities with LLMs like Copilot and ChatGPT.
“Aligning the UNFI data with our product hierarchy is one of the greatest enhancements we’ve made recently. It’s really set our team up for success.”
Hugo Lopez, UNFI, Nestlé USA | Read the case study
3. Drive conversations with AI-powered insights
What was the ROI of our last seasonal promotion in the Southeast? By store format and cluster? Against competitive benchmarks?
Curveball questions in merchant meetings can become opportunities to showcase success when real-time POS sales and inventory data is activated for use in a LLM. With AI-powered analysis behind the scenes, sales members can query all of their live insights as inquiries arise.
Answering questions in the flow of a meeting provides merchant a more dynamic, collaborative, and productive experience, and the best part is that it keeps momentum building. Where information gaps and uncertainty can lead to skepticism, conversational insights offer responsiveness, transparency, and a strategic path forward.
To implement an LLM effectively, CPG organizations can look to Microsoft CoPilot, or other solutions like Snowflake Cortex or Databricks Genie, designed for scale and equipped to process the vast data CPGs manage.
Read: What to know about AI in retail analytics
4. Eliminate out-of-stocks, unlock expansion
Before brands can sell new products or expand distribution, they must excel at serving their existing placements. Ensuring high in-stock rates is table stakes for any merchant conversation about growth.
A flow of real-time inventory intelligence at the store and distribution-center levels is a CPG salesperson’s ticket to prove greater order quantity needs, preventing costly out-of-stocks and missed sales opportunities.
AI takes this intelligence further with data science-ready AI Blueprints like On-Shelf Availability. AI Blueprints are templatized, deep-learning algorithms that sales teams can leverage seamlessly with their retail data. The On-Shelf Availability (OSA) Blueprint aggregates and calculates multiple data points to detect issues like phantom inventory and zero sales patterns across store clusters, monitoring stock levels to ensure optimal inventory and reduce lost revenue.
When it’s the same data foundation and AI Blueprints that supply chain teams are also working from, it becomes seamless to make a case for and fulfill every data-rooted sales opportunity.
5. ROI models that get buyers excited
When merchant can’t completely trust the data they’re presented with, they can’t completely trust the ROI of a brand expansion, or why ordering more cases equates to greater sales and not overstocked markdowns. Demand projections and sales forecasts based on aggregated market reports, instead of retailer-specific trends, are no longer enough to win shelf space.
An AI-powered Distribution Expansion model, offered as a data science-ready AI Blueprint, transforms this dynamic by analyzing gaps between category sales velocity and a brand’s own POS data to identify high-potential stores and regions for expansion. Many distribution growth strategies rely on broad channel trends rather than granular retail insights, leading to less than fruitful results. The Distribution Expansion Blueprint systematically detects stores where a category is performing well but your brand is underrepresented or absent, arming sales teams with high-confidence expansion targets and projected ROI as a result.
More scenarios that sales teams can present quickly and accurately with clean retail data and AI-powered insights include:
- How expansion drives incremental growth without cannibalizing
- Which store clusters would benefit most from new SKUs
- Optimal shelf placement and facings with projections based on performance data
- Projected inventory requirements across regions and clusters
- Expected promotional lifts
- And much more
Agility, accuracy, and execution – these are what buyers require and what AI can assist in delivering, when paired with a powerful retail data foundation. Respond to market changes in real-time, present data-backed opportunities, and foster stronger retail partnerships built on trust and mutual success. Unlock AI-ready CPG intelligence to maximize sales meetings. Book a demo with Crisp today.