MikMak is expanding its commerce intelligence platform with new capabilities that connect digital marketing activity, AI-driven product discovery and physical-store sales, giving consumer brands a more unified view of the path from media exposure to purchase.

The enhancements, announced September 15, build on MikMak 4.0, introduced earlier this year following SPINS' acquisition of MikMak in January. The latest release brings SPINS point-of-sale (POS) and inventory data deeper into MikMak's measurement and planning tools while expanding integrations with AI assistants and agentic workflows.

The development reflects a broader shift in commerce technology: marketers increasingly need to understand not only what consumers click online, but how digital and AI interactions translate into purchases across retailers and physical stores.

Connecting Media Spend to Store-Level Sales

One of the most significant additions is an expanded version of MikMak Aura, which combines SPINS POS data with MikMak's first-party signals covering more than 1.5 billion shoppers across 8,000-plus retailers. The platform is designed to connect full-funnel media investment with verified retail sales and provide predictive "what-if" planning.

That gives CPG marketers a potential alternative to measurement approaches that depend heavily on sampled or backward-looking data. Instead of evaluating media effectiveness only after a campaign, brands can use current sales signals to identify changes in demand and adjust investment.

The integration has particular significance because SPINS brings data from physical retail into a platform historically focused heavily on digital commerce and activation. The companies' January combination was explicitly positioned around connecting online engagement, retail availability and offline purchasing behavior.

MikMak is also adding SPINS Journey Feed - Datasembly, providing store-level inventory intelligence based on more than 12 billion weekly observations, according to the company. Daily availability information can help brands identify where products are in stock and potentially reduce lost conversions caused by unavailable inventory.

AI Moves From Analytics to Execution

The release also expands MikMak's AI infrastructure. MikMak Insights MCP provides a bridge between commerce intelligence and AI environments including OpenAI, Claude and Microsoft Copilot. Teams can query performance information using natural language rather than manually navigating analytics interfaces.

Meanwhile, Build with Mak and Analyze with Mak extend the platform's agentic capabilities. Build with Mak can configure commerce experiences from conversational instructions, while Analyze with Mak supports multi-turn analysis, executive summaries and report generation.

The strategy places MikMak alongside a growing class of marketing platforms attempting to make AI agents operational rather than merely assistive. The distinction matters as marketers increasingly encounter AI-mediated shopping journeys.

Gartner reported in May 2026 that only 11% of surveyed U.S. consumers were willing to let AI make purchase decisions, while 31% were willing to use AI to narrow choices for household products. That suggests brands still need accurate product, availability and performance information even when AI assists discovery rather than completing the purchase.

Market Landscape

MikMak's expansion reflects the convergence of commerce intelligence, retail media, marketing measurement and agentic AI.

Competitors across commerce media and marketing analytics have traditionally addressed individual parts of this workflow: retail data providers measure sales and availability, advertising platforms optimize media, and customer intelligence systems analyze digital behavior. MikMak's strategy is to connect those signals within one orchestration layer.

SPINS' ownership strengthens that proposition by bringing proprietary POS and shopper data into MikMak's existing retailer and commerce infrastructure. The result is intended to give CPG and retail marketers a common intelligence layer spanning digital media, retailer sites, AI interfaces and physical stores.

For enterprise marketing teams, the challenge will be turning that breadth into measurable incremental growth. More data and AI-driven recommendations do not automatically produce better decisions; marketers still need reliable attribution, accurate inventory information and governance around automated actions.

The larger direction is clear: commerce platforms are increasingly being designed for a world in which consumers discover products across Google, social platforms, retailer websites and AI assistants, rather than following a linear brand-to-store journey. Gartner has similarly identified generative AI and answer engines as forces changing B2C purchase behavior.

Top Insights

• MikMak is combining SPINS POS data with digital commerce intelligence, giving CPG marketers greater visibility into how media activity translates into physical retail sales.
• MikMak Insights MCP brings commerce analytics into OpenAI, Claude and Microsoft Copilot, reducing friction between AI-assisted analysis and enterprise marketing data.
• Aura's predictive capabilities shift measurement toward real-time planning, allowing brands to model potential media and sales outcomes instead of relying only on historical reporting.
• Datasembly inventory signals connect marketing activity with store-level product availability, addressing a conversion problem when consumers encounter products that cannot be purchased.
• MikMak's agentic strategy moves beyond analytics toward campaign configuration and execution, positioning AI as an operational layer within commerce marketing.

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