Predictive Analytics Model For Advance Sales and Marketing Strategies

Sales and marketing teams have the responsibility of adding customers in a highly competitive market. Predictive Analytics provides the necessary edge to face these marketing challenges. Most organisations are now dealing with Big Data procured from various channels and Predictive Analytics is the most effective way to take use of it. It is important to create Predictive Analytics Models which have the capability of providing accurate real-time analysis to help make better business strategies.

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Predictive Analytics Model for Effective Marketing Strategies

Predictive Analytics helps marketing divisions develop new strategy models based on the past and current customer data. Better customer targeting with the use of patterns and trends leads to a better engagement and conversions. There are different types of models created to work on the various aspects of customer relationship, broadly they are categorised as –

Acquisition Models – Deals with the targeting of the right customer segment for products and what model will suit best for a particular segment. It helps on reducing costs and gives much better efficiency. Reaching to different market segments in a way which appeals to them most is a primary marketing solution and with the help of Predictive Analytics, marketing divisions can get direct access to complex analytics which gives a clear insight without having the knowledge of statistics.

Cross-Sell and Up-Sell models – They explore the likelihood of further customer engagement by selling other products or from investing more in the same products. Additional benefits given to loyal customers leads to long standing profitable relations, but it involves higher costs and there is a need to use analytics to be selective.

Customer Attrition – Getting feedback on attrition of existing customers is very important for long term survivability. The data provides key insights into what needs to be improved in the business process.

Risk and fraud Management – This is another important point that needs to be addressed especially for large organisations to ascertain leakages in their business processes. As they lead to direct losses and cumbersome legal tangles, using Predictive Analytics for providing preventive measures gives safer methods for doing business.

Data Mining for Predictive Analytics

Data mining is a key process for Predictive Analysis by applying software analysis tools on data storage to build models, it mainly deals with pattern detection and identifying relations to give a clear picture of the business performance. Business targets can be reached by predicting future performance on the basis of these trends and new marketing and sales strategies. Mostly, two types of Data mining, Supervised and Unsupervised are used, they will be discussed later.

Sample Predictive Analytics Model for Marketing and Sales

It will be best to look at a sample case of Predictive Analytics Model Development for Marketing. There are 3 main points to cover in the whole process – Who, When and What. Who is the customer likely to purchase the products, when is the optimal time in their buying cycle to engage them and what are the products which will be of their interest. An analytics model giving insights into above questions will be the aim of this sample study.

The 3 main features of any analytics models are the Enterprise Data Warehousing (EDW), Predictive Analytics Model and an easy to understand User Interface (UI). It is important to give clear insights of the analysis through user interface because many times the marketing persons do not have statistics experience.

Enterprise Data Warehousing – It involves supplying of all the data procured from various channels for the analytics process. It consists of many internal databases and feedback data from users. It is important to keep sourcing in data constantly for real-time analysis.

Predictive Analytics Model – A complete analysis of customer profile is needed to find the customer segment most likely to respond positively to marketing efforts. The model is used for ranking of different customer groups.

User Interface – An easy to work with user interface makes understanding and implementation much simpler and faster for the marketing team. Also, it helps in giving the feedback received for the results obtained which are further used for model optimisation.

A Look At The Sample Predictive Model Development

At a primary level there needs to be an incorporation of all the channels used for gathering data. When working on the data there is a need to find new patterns which were affecting performance. There are many platforms being used to gather data including online and mobile sources at various stages of the customer lifecycle, all these channels need to be accounted for in order to come up with the best solution.

Building a Model Solution – A three-phase process can be followed for finding an appropriate model. Firstly, segmenting the data to find which data sets need to be worked upon. Secondly, devising a predictive analytics model based on the mined data. Thirdly, model optimisation through feedback of the results and avoiding static rules. Now let us look at these sub-points more closely –

1) In order to segment the data for mining, we need to populate the Enterprise Data Warehouse (EDW) with all the demographic, customer, organisation and training data available. Now it is important to see how much revenue potential is available for every customer.

2) Once the data is segmented to analyse customer profiles for their respective potential. A rank based customer profile analysis needs to be derived. There are 3 main techniques that can be used to arrive at this point –

Unsupervised Data Mining – It used for finding new patterns in data. Certain data characteristics need to be set for the customer profiles deemed ideal for doing business. After running the analysis new patterns and trends start showing, the data selected is grouped into clusters.

Supervised Data Mining – It is for the implementation of static business rules which are based on known high volume customers. Using the random forest algorithm to analyse the static business rules and unsupervised clusters obtained previously, a relationship can be made between the known and unknown high volume customers. This helps in coming up with the model engine structure for data analysis.

Ranking – After unsupervised and supervised data mining leads to high volume customer groups identification, a probability score (0 to 1) is given to every account. Accordingly all the accounts are ranked according to their probability score.

3) Model Optimisation – After the model is built there is a need to optimise it with feedback from results. As said earlier, static business rules were applied to find the right customer profiles for maximum revenue potential. This was done to find a preliminary set of accounts to work with. After feedback, the rules were further streamlined to give better customer profiling and ranking. This is a continuous process for the evolution of the model to come up with accurate insights which can be implemented.

Covering the question – ‘When the customer should be contacted’ requires more data sources and leveraging all the resources for online profile building of customers including social media. It requires more powerful analytic models for efficient calculations from Big Data Analytics. Lastly, addressing what products to sell using PA would use real-time analysis of the response of customers to various customers. As new products are released customers may opt for them more than the previous products that were in the market, therefore Analytics need to take in all the results into account and come up with an optimised model.

Building Models for different market situations will require different business rules and their implementation requires complex algorithms. Predictive Analytics is well suited for Market research and analysis for every kind of business. As more platforms are being used for customer interaction at various stages of customer lifecycle all the data procured can be effectively used through Predictive Analytics.

Keywords: Predictive Analytics, Advanced Analytics, Predictive Modelling, Sales and Marketing, Big Data Analytics, Marketing Models, Acquisition Models, Cross-Sell, Up-Sell, Customer Attrition, Risk Management, Fraud Management, Data Mining, Enterprise Data Warehousing, Customer Intelligence

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