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All you need to know about predictive analytics

31st May 2017
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One of the major goals of every business is to grow. This growth can only be made possible by attracting and keeping more customers. This makes it important for the business to study and understand the buying behaviour of the target audience. Predictive analysis refers to a series of strategies used to analyze and customer behaviour. In digital marketing, predictive analysis has become an important tool used to support the marketing strategies for a successful marketing campaign.

 Predictive analytics has been commonly deployed during marketing campaigns for many reasons. The methods stand out as effective ways to sort out data and feedback, retrieving only the relevant information that is needed for successful marketing campaign. The use of predictive analysis simplifies the manipulation and extraction of relevant information from complex data generated from advanced processes like web analytics and multi-channel analytics. The use of predictive analysis also provides useful information that will be used by the business owner to make the best investment decisions.

 What are the basic requirements for a comprehensive predictive model to be developed?

 Some important factors should be considered before a reliable predictive model is developed and used during a marketing campaign.

Properly identified difficulties

The chances of making an error while creating a predictive model will be higher if the challenges that it will be used to address have not been clearly defined. Before a predictive model is created, it is important that an analysis of the challenges being faced by the business is carried out.

 To get the best results from a predictive model, the business owner must have a tangible reason for creating the predictive model and there must be an action plan to utilize the information obtained from the predictive model for the benefit of the business.

 As we have stated earlier, it is very easy to make errors when creating a predictive model. All efforts must be made to ensure that a good model is created which will provide useful results. The results will determine the effects of this strategy on your business and all the people involved.

Data Generation

 What your business needs to progress is the right data which can be effectively generated with a good predictive model. The data generated will also be dependent on the quality of information put into the predictive model. The results from a good model will end up being invalid if the wrong or inadequately coded information is put into the model. The consequence of this error will be negative for the business.  A standard system will use a list of most likely parameters for prediction like the following- firmographic, demographic, transactional, web data, pricing etc. for analysts who can source external information in areas like industry, macroeconomic, competitive, demographics etc. it would be beneficial to include these in the model, but the most important point is that it should increase the value of your potential results.


 Making a choice of the right methodology will be easier once the business owner has a clear idea of the kind of results that should be generated for the benefit of the business. There are many options when it comes to choosing a methodology for your predictive model. The most commonly used methodologies are a regression, association analysis, decision trees and clustering which is applied in the cases of advanced models used for forecasting, linkage analysis, and survival modelling. The best choice is ultimately dependent on your prediction goals, the information you need and your plan to maximize the use of the results.

Predictive Modelling Tools

There are very many tools available to support your predictive model. Several factors will determine your choice of tools; also, the nature of your business will also be a determinant and your budget. Other factors include the extent of their use and the conditions in which these tools will be most effective.

 However, it is not every company that can put in the required effort and resources to create and maintain a personalized predictive analytic model. Companies in this situation are left with the option of outsourcing this task. BPO solutions provide introduced, validated and standardized technologies that are perfectly suited to your company’s needs. How can I maximize the benefits of predictive analysis for my business?

 There are many advantages when predictive models are applied in business operations and marketing campaigns. We will discuss some of these benefits in the following segments.

Response modelling

Response models are specially created to determine the extent of response from an identified target audience to a specific promotion. Companies deploy response models as a means of saving costs and avoiding the waste of resources. The results will also determine if more efforts should be applied in the campaign to get a better response if the results from the model are not impressive. These models are used to process already existing customer data in the business.

Up-sell and cross- sell modelling

Another advantage of predictive models is the provision of a better insight into customer behavior that can yield useful information with which the business could accurately predict other products the customers might need asides the main products highlighted during sale promotion. This system is already being used by big organizations like Amazon, where other products are suggested and recommended to the customer when they check related products on the website.

Risk assessment modelling

 This form of modelling exposes the potential risks and estimated losses that could be experienced when a promotional campaign or marketing strategy is used for a business. Many companies have benefited from risk models because they were able to get useful information to guide their investment decisions. Businesses can stay ahead of their competition by deploying risk modeling in their operations to determine if a strategy is worth it, a situation where the gains outweigh the risk of a loss by a big margin is a good gamble for the business.

Retention Model

A retention model is used to study customer behavior with the aim of identifying the reason why a customer will want to remain loyal to the brand or move on to try another product in the market. With this information, the company can increase their efforts to impress every customer who uses their products or services to increase customer satisfaction. A satisfied customer will most likely become a loyal customer.


One of the main reasons why companies use segmentation is to understand how customers purchase goods, the prevalent spending patterns and also to identify the actual reasons for certain decisions made by the customers. It is also known that companies prefer to create segments by using predictive models which use clustering. This is because clustering provides more insightful information about the normal behaviour of the customers in different segments.

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