Marketing efficiency and effectiveness: that’s what 4orange stands for. We combine customer information and marketing infrastructure.
- To gain a maximum return on (marketing) investment among (existing) customers, we support our relations in making excellent use of data.
- By sharing our (consumer oriented) knowledge and providing a marketing infrastructure that fits your company, we are able to provide our customers with hot leads. An important part of this process are analysis.
To increase your marketing efficiency and effectiveness, it is important to understand your companies current customers, the campaigns that are launched and the outcomes. If you want to improve your results, then have the current data analysed! It’s all about translating your data into useful information. That will boost your current results!
A list of several analyses:
- Data Analysis
- Profile Analysis
- Response Analysis
- Inflow and outflow analysis
- Recruitment Model
- Intrinsically customer value model – Pareto analysis
- Churn models or switch models
- CHAID analysis
- Regressie analysis
- Discriminant analysis
Data Analysis :
With data analysis , it is possible to fully analyze a set of data. For the analysis groups, comparisons and calculations can be used on the given data. Depending on the chosen data analysis, tools for analyzing scripts can be automated and the activities and results are logged. This facilitates the verification of the analyzes carried out and reduces the risk of errors. The interpretation of these data is the translation of data into information .
Profile Analysis :
A profile analysis is made to get a good picture of a group of households. These could be customers or prospects. Although the group is very similar ( eg all are golfers ) , there are still discoveries. By having access to these segments it is possible to get insight in a group’s motivation. This knowledge is used to make the (marketing/communication)message fit on the right group on the right time.
Response Analysis :
The response analysis is primarily considered whether the business case of a campaign is actually achieved . It is an important part of the evaluation of a campaign and provides input to the business case for future campaigns . By using , for example by means of a standard reporting, response analyzes in a uniform way to determine which campaigns are doing better than others . In addition, insightful than others have achieved . Which channel , communicative expression , or segment better results within a campaign This analysis provides insights into what actions can best be used to enhance the market share and what action is the most financially profitable.
Inflow and outflow analysis :
Inflow and outflow analyzes provide information on the duration and the proceeds of the relationship with customers . Specifically, a recruitment campaign the inflow channel or customer group mapped when someone customer. Then considers how many customers a period after the date intake flow again . On the basis of this analysis it can be examined at any given time after the inflow outflow is expected. This analysis provides a basis for a campaign at that time. In addition, the average duration of respondents on customer campaigns , segments or inflow channels can be calculated. On the basis of this analysis, On the basis of the average customer duration can be determined how much money should be spent on similar campaigns for the recruitment of new customers .
Recruitment Model :
with a recruitment model is predicted which prospects are most likely to be a customer conversion. This will predict which people are more likely to become a customer than others (in the same group or selection) . The recruitment model shows , the probability that someone will respond to a communicative expression . The selection (and campaign ) to focus on the people with the highest probability is the response rate of the group higher than when random is selected . This results in an improvement of the CPO , and ultimately the RO ( M) I on .
Intrinsically customer value model – Pareto analysis :
is a commonly used method to determine which topics the costs are highest. Often here Paret About Sharing from , the so-called 80-20 rule . Under this method, the costs per cost center and by product ( category ) the aggregated revenues . Because these costs and revenues for each aggregate and the difference between them creates a customer is an intrinsic customer value dollar amount . Then all customers can be divided into classes on the customer value .
Churn models or switch models :
Churn models or switch models can be addressed and interpreted in different ways . On the one hand with a churn model by means of a statistical method to predict which customers are most likely to go away . This applies in particular to a prediction of the switching behavior of the own customers in the future . Alternatively, a churn model used to explain . Actual switching behavior ( already left customers) By knowing in advance which customers an increased chance to migrate can be determined whether or not to get yet remain those customers. On the basis of internal features of the marketing database in combination with external ( socio-demographic ) data can be calculated which features a predictor for switchers . The total of these predictors gives the probability that someone switch and is calculated using a prediction model ( eg CHAID analysis , regression analysis or discriminant analysis) .
CHAID analysis :
Decision tree . CHAID analysis extends the decision tree can be out until . No significant different groups found more It is a combination of different cross- tables. By making each subgroup found within a crosstab , the tree deeper and are more and more detailed customer groups .
Regression analysis :
Regression analysis indicates how large the influence of a variable or multiple variables . Regression analysis is used to make predictions .
Discriminant analysis :
Is a data analysis technique in which the classification of information in groups made on the basis of characteristics to be the most decisive , the grouping is hereby established . The database will be created directly scores to predict the relationships in the database and can be divided into groups .