Data modeling to predict your customer’s behaviors

Data Modeling

With data modeling, it is possible to predict the behavior of your customer. Data regarding customer behavior is already existing in the various company databases, however it is not in a format that can be used to draw any conclusions from directly.

With data modeling, customer responses (eg: Revenue or Purchase) is mathematically compared to actions that the company has taken in the past and then also evaluated against the overall market conditions and then the behaviors of the customers are predicted. Then depending the conditions that are happening in the future, proactive actions can be taken to maximize company performance by understanding the customer better.

The most representative form of modeling equation is given by : $Description: y_i=\beta_0 +\beta_1 x_i +\varepsilon_i,\quad i=1,\dots,n.\!$

We help organizations understand their marketing mix, optimize spends, identify effective promotions channels, quantify impact and maximize ROI. Listed below are some of the categories of modeling.

Promotional modeling:
Promotion modeling is one of the most useful and widely used modeling applications within Marketing. It allows us to understand the precise impact of the various marketing programs on sales lift. Some of the types of promotional modeling conducted by eMpulse are;

Market mix modeling
Campaign response analytics
Customer profiling
Market share optimization
Pricing-Trade off
Promotion effectiveness
ROI modeling
Sales force analytics
Spend optimization

Behavioral modeling:
With behavioral modeling, we can understand the behaviors of the various segments of the customers and hence design marketing programs that has the highest returns and maximize net income of the company. Some of the behavioral modeling analysis conducted by eMpulse are;

Churn analytics
Channel analytics
Customer life time value analytics
Customer loyalty
Response modeling
Segmentation analysis

Fundamental modeling:
For any modeling study, some basic methodologies are used. These are then applied to the business context. Some of the examples of these generic types of modeling used are;

Regression modeling
Cohort analysis
Conjoint analysis
Demand forecasting
Lag analysis
MIS and dashboards
Predictive modeling
Sensitivity analysis

Operations modeling:
To maximize net income of the company, operations modeling can be conducted to ensure that the operations deliver to customer needs consistently and also that operations is able to protect the company against threats. Some of the operations modeling conducted by eMpulse are;

Fraud analytics
Inventory optimization
Liquidity risk analysis
Logistic optimization
Operational risk assessment
Risk analytics
Risk spillover
Supply chain analytics