+372 636 0828
info@cima.ee
Tallinn, Harju maakond, Estonia

Data Analysis and Modeling

Using advanced data analysis methods to develop Marketing Mix Models (MMM) and Agent-Based Modeling (ABM) for strategic decision making

MMM Models ABM Modeling Forecasting Business Analytics Market Modeling

Transform Marketing Decisions with Advanced Data Analysis

At CIMA, we use cutting-edge data analysis methods to help you move from guesswork to facts and make strategic decisions based on real data. In today's marketing world, knowing what is happening is not enough—you need to understand why and be able to accurately predict future outcomes.

Our data analysis and modeling service allows you to:

  • Determine the real contribution of each marketing channel to sales and conversions
  • Optimize budget and resource allocation between channels
  • Model and forecast consumer behavior in the market
  • Assess the impact of external factors (seasonality, competitors, economy)
  • Test different marketing strategy scenarios before investing real money

We use a combination of Marketing Mix Models (MMM) and Agent-Based Modeling (ABM) to give you a complete picture of your market at both macro and micro levels, helping you make more informed and effective decisions.

Data Analysis and Modeling

Our Revolutionary Technologies

The combination of statistical analysis and behavioral modeling delivers results unavailable in traditional analytics

Marketing Mix Modeling (MMM)

MMM is a statistical analysis using Bayesian causal inference to measure the incremental impact of various marketing tactics on sales and other business metrics.

What this provides:

  • Analysis of each channel's contribution to overall results
  • ROI measurement for each marketing channel
  • Budget optimization recommendations
  • Statistical confirmation of marketing impact
  • Accounting for long-term effects and delayed influence
  • Assessment of external factors (seasonality, competitors)

Agent-Based Modeling (ABM)

ABM is a modeling method that simulates the actions and interactions of autonomous "agents" (potential customers) to assess their impact on the entire system from the bottom up.

What this provides:

  • Modeling of individual consumer behavior
  • Analysis of audience segmentation and their reactions
  • Understanding competitive market dynamics
  • Testing "what if" scenarios without real costs
  • Identifying non-obvious patterns of consumer behavior
  • Discovering emergent effects and non-linearities in market behavior

Advantages of Our Approach

Why the combination of MMM and ABM gives a more complete picture of your marketing effectiveness

Macro + Micro Understanding

MMM provides overall channel effectiveness, while ABM models individual consumer responses, giving you a complete picture from strategic level to tactical.

Audience Heterogeneity

Our models allow you to understand how different audience segments respond differently to your marketing, going beyond what traditional analytics can show.

Emergent Effects

We identify unexpected patterns and consumer behaviors that arise from complex market interactions and cannot be detected through simple analysis.

Scenario Testing

Test marketing strategies in a virtual environment before investing real money, reducing risks and increasing confidence in decisions.

First-Hand Data

We use data directly from your website and customer touchpoints, calibrating models based on real customer behavior, not just assumptions.

Privacy Focus

Our entire analytical stack is designed with privacy in mind, allowing deep analysis without compromising customer data or regulatory compliance.

How We Find the "Why"

Our process of data analysis and model creation to obtain truly meaningful insights

1

Data Collection and Integration

We automatically collect data from all your marketing channels, website, and market conditions. We use API integrations with Google, Meta, TikTok, set up website analytics, analyze external factors (weather, economy) and competitor actions.

2

Data Preparation and Validation

We clean data, identify outliers, process missing values, and prepare data for modeling. We create unified customer profiles based on all touchpoints for a complete representation of the customer journey.

3

Marketing Mix Modeling (MMM)

We analyze historical performance to quantify the contribution of each channel. We conduct channel ROI assessment, budget optimization, sales forecasting, and statistical validation of results.

4

Agent-Based Modeling (ABM)

We create a simulation of consumer and competitor interactions in your market. We model consumer behavior, analyze competitive dynamics, forecast demand, and test "what if" scenarios.

5

Results Interpretation and Recommendations

We transform complex data into understandable, actionable recommendations. We automate analysis and reports, explain reasons for metric changes, provide optimization recommendations, and formulate a strategic action plan.

From Data to Real Insights

Examples of specific insights and recommendations you will receive

Reasons for Metric Changes

Standard Analytics:
"Facebook ROAS dropped by 23% this month"
Our Analytics:

Your Facebook ROAS dropped because:

  • Competitor X launched an aggressive promotion campaign
  • Creative fatigue increased by 45%
  • Seasonal demand shifted to other products

Recommended Actions:

Update creatives and adjust targeting to segments with high interest, which, according to our model, are still responsive (mainly the 25-34 age group from urban areas).

Budget Optimization

Standard Analytics:
"Search advertising (ROAS: 3.2) is more effective than social media (ROAS: 2.1)"
Our Analytics:

Based on MMM analysis and ABM simulations:

  • Transfer 15% of the budget from TV to digital channels to increase overall ROI by 7%
  • Increase search budget by 20% (highest incremental return)
  • Maintain overall social media budget, but redistribute within: +10% Instagram, -10% Facebook
  • Invest in retargeting users with abandoned carts (3.2 times higher conversion probability in simulation)

Detailed Analysis:

Our ABM simulation tested 32 different budget allocation options and found that this combination maximizes ROI while maintaining brand awareness metrics.

New Strategy Forecast

Standard Analytics:
"Historically, new advertising campaigns increase conversion by 8-10%"
Our Analytics:

Our analysis predicts:

  • The new strategy is likely to increase conversion rate by 12-15%
  • ABM simulation shows the strongest response among urban millennials
  • Expected competitor reaction: 2-3 weeks delay before countermeasures
  • Optimal channel mix: 40% paid social, 35% search, 25% display advertising

Recommendation:

Implement the strategy in phases, starting with high-response segments, and track competitor reactions weekly. Prepare alternative creatives for the second phase to prevent ad fatigue.

Customer Journey Analysis

Standard Analytics:
"Cart abandonment rate is 68%, which is 5% higher than the industry average"
Our Analytics:

Based on customer profile analysis and analytics:

  • 76% of cart abandonments occur on the shipping cost page
  • Customers who abandon previously visited competitor sites with free shipping
  • Mobile users experience a 2.8-second longer loading time on the payment page than desktop users
  • Abandoned cart emails have 42% open rates but only 5% conversion

Recommended Solution:

Implement multi-tier free shipping thresholds and improve checkout performance on mobile devices. Our ABM simulation predicts this will reduce abandonment rate by 31% and increase average order value by €12.40.

Stop guessing. Start understanding why.

Contact us today for a free consultation and learn how our advanced analytical tools can help you make more accurate, data-driven marketing decisions.