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Industry Sector : CPG & Retail

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Marketing Decisions:

Moving beyond historical analysis by deploying predictive models that simulate the impact of campaigns before execution.

Consolidating data from all marketing channels to enable enterprise-wide decision-making and reduce inefficiencies from decentralized actions.

Using real-time consumer, regional, and channel data to tailor campaigns for specific geographies, seasons, or buyer profiles.

Setting up dashboards that track performance mid-campaign, allowing quick pivots for underperforming tactics.

Aligning marketing goals with sales lift, category growth, and customer lifetime value to assess true campaign effectiveness.

Deploying A/B or geo-based testing to validate assumptions, refine messaging, and optimize budget allocation iteratively.

Our Market Ready Solutions

01 Marketing Mix Modeling

Marketing Mix Modeling:

Quantify Yield of Each Marketing Element: Analyzing TV, digital, promotions, and in-store activities for their individual and interactive impact using reach, frequency, and timing data. Apply Advanced Residual Modeling Techniques: Using models that isolate the effects of promotions, seasonality, and competitor actions from core drivers like distribution and pricing. Customize Data Sources by Geography: Leveraging scanner, audit, or shipment data depending on market maturity—ensuring accuracy across regions with inconsistent POS coverage. Build a Robust Fixed Effects Model: Creating and managing 200+ high-impact variables from a larger set of 1000–3000 across campaigns, controlling for overlapping influences. Ensure Rich Data Vintage & Variety: We recommend this modeling for firms with sufficient historical and activity-level data to fuel precise impact measurement. Enabling Scenario-Based Planning: Using model outputs to simulate budget shifts across media, optimize spend, and predict sales lift before campaigns are deployed.
02 Web Analytics & Advertising

Web Analytics & Advertising Effectiveness

Integrating Organic & Paid Media Measurement: Unifying performance tracking across SEO, SEM, DTC, marketplace, and social platforms to ensure cohesive measurement of organic rank, share of search, sponsored content, and influencer engagement. Boosting Digital Shelf Presence: Deploying strategies to elevate product visibility on brand websites, e-commerce marketplaces, and social platforms by optimizing content, reducing bounce rates, and enhancing page rank for top-performing category keywords. Measuring True Ad Impact with Residual Modeling: Using advanced statistical techniques to isolate advertising’s impact by removing effects of pricing, promotions, and other marketing activities—enabling realistic short- and long-term ROI estimation. Evaluating Ad Copy Effectiveness Separately from Weight: Disaggregate GRP influence by assessing creative message strength and copy quality independently from media spend to understand what truly drives performance. Optimizing Campaign Exposure Plans: Design weekly and annual GRP exposure curves that incorporate saturation, decay, adstock effects, TARP ratios, and seasonality. Use these to guide ad versioning, daypart splits, and messaging cadence for sustained category growth.
03 Promotion Effectiveness

Promotion Effectiveness:

True Lift Estimation: Measuring net lift by adjusting for pre/post-promotion cannibalization, overlapping campaigns, and external factors like seasonality and competition. Focus on incremental gains, not just peak sales. Base Price Calibration: Continuously tracking and updating base price benchmarks, especially in heavily promoted categories, to get an accurate measure of actual promotion impact. Contextual Effectiveness Modeling: Account for store coverage, SKU share, and concurrent marketing efforts in promotion analysis. Use granular data to isolate true drivers of lift. Promotion Optimization Matrix: Matching promotion type, depth, and duration to store format, cost structures, and customer segments. Consider bundling with catalogs, displays, or digital triggers for higher ROI. Execution Strategy Design: Determining ideal shelf location (FGE/BGE/wings), in-store visibility, aisle strategy, and communication type to maximize footfall and conversion during campaigns.
04 Cross-Sell & Up-Sell Optimization

Cross-Sell & Up-Sell Optimization :

Segmenting Customers by Propensity & Affinity: Using behavior, purchase patterns, and product affinities to identify cross-sell and up-sell potential within existing customer segments—prioritizing needs-based and lifecycle-based clusters for better relevance. Going Beyond Response Models: Replacing traditional response rate models with advanced value-maximization algorithms that estimate each customer's Maximum Value Threshold (MVT), improving yield and relevance per offer. Mapping New Use Cases & Adjacencies: Identifying natural category extensions and new use occasions that match current customer lifestyles. This allows expansion without acquisition costs, increasing category penetration. Time & Channel Optimization: Deploying offers at the right stage of the customer journey via preferred channels. Contextual timing drives uptake and avoids fatigue or cannibalization. Test and Scale Smart Bundles: Bundle complementary products based on performance data and predicted adoption likelihood. Run A/B tests to identify the most responsive offers before scaling.
05 Loyalty & Rewards

Loyalty & Rewards :

Deploying CRM-Integrated Behavioral Models: Build CRM-based BI tools to track customer history in real-time and predict tier movements, reward eligibility, and loyalty score variations. Integrating Loyalty with CLTV and Cross-Sell Frameworks: Align loyalty analytics with Customer Lifetime Value (CLTV) models and cross-sell/upsell initiatives to drive sustained customer value. Incorporating Household Panels and Primary Research: Use household panels and primary surveys to validate model insights, detect loyalty drivers, and uncover root causes behind loyalty swings. Enabling Real-Time Loyalty Program Adjustments: Implement systems that allow dynamic reward, tier, and engagement strategy adjustments based on evolving customer behavior signals. Tracking Loyalty Indices Over Time: Set up continuous monitoring of loyalty KPIs across segments to identify early warning signs and preempt customer churn risks. Using Predictive Loyalty Modeling for Personalization: Leverage predictive analytics to personalize rewards, communications, and experiences, ensuring relevance at every customer touchpoint.
06 Consumer Behavior Models

Consumer Behavior Models :

Analyzing Attitudinal Composition: Segment customers based on their dominant evaluation mode — cognitive (rational) vs. affective (emotional) — using surveys, behavior tracking, and psychographic profiling. Tailoring Marketing Cues: Align marketing communication to match customer attitude mode — emphasize facts and rational benefits for low-familiarity segments; focus on emotions, brand storytelling, and experiences for high-familiarity segments. Accelerating Familiarity Transition: Design experiences and messaging that move customers quickly from rational evaluation to emotional attachment, using loyalty programs, personalized content, and brand community initiatives. Leveraging Affective Strengths: Prioritize affective cues such as visuals, storytelling, and emotional triggers over purely cognitive messaging in both mass media and in-situ channels to drive deeper brand preference. Monitoring Loyalty Evolution: Track attitude shifts over time using periodic customer surveys, brand health tracking, and loyalty indices to detect transitions and intervene with reinforcement strategies if needed. Defending Against Competitive Cues: Strengthen emotional bonds early by reinforcing brand values, customer experiences, and satisfaction touchpoints to make customers less vulnerable to competitor marketing.
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