Using statistical models to predict future sales

Statistical Analysis and Modeling Demo


Modeling techniques include nonlinear multiple regression, binary or multinomial logistic regression, and canonical analysis.


Exploratory statistical modeling is used to discover which variables are associated with sales


Statistical Modeling and Business
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Weather, Sales Trend demand forecasting
Identifying and Quantifying time-related, psychological, and environmental factors that predictably influence sales lets us build statistical models that:
  • Provide insight into the decision-making process and behavior of customers
  • Identify general, time-related, and seasonal sales trends
  • Accurately estimate short-term, future demand
  • Allow us to properly answer questions such as, "How effective was the Marketing Campaign?"

To stay competitive, companies must frequently assess different advertising strategies in order to determine the best way to invest their marketing dollars. In order to directly compare the effectiveness of diverse advertising approaches, many companies use a measure known as return-on-investment (ROI).  The measure indicates how many dollars were returned via increased sales for each dollar spent on advertising. To figure out a ROI for marketing dollars spent, it is necessary to estimate how many additional items were sold because of the marketing campaign's influence on customer behavior.

Attempts to estimate the impact of a marketing campaign on sales by comparing the number of sales made before and after introduction of the campaign ignores other important factors that may also have also influenced sales, and frequently leads to faulty assessment of the campaign's effectiveness.  Therefore, if we want to assess the direct impact of a marketing campaign, we need to isolate and remove other predictable factors that concurrently influenced sales over the duration of the marketing campaign. To do this, we must establish a direct link between Marketing and Sales.

The figure to the right illustrates concurrent influences on sales: seasonality, current market trends, weather and environmental influences, and marketing.  Each one of these may influence customer preference and behavior (and may be influenced by customer preference and behavior) through different paths, as illustrated.




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