Unknown Analytical CRM Application Areas in CRM
In my previous article, I have talked about all that you need to know about data mining in CRM. In this article, I want to look at some of the analytical CRM application areas in CRM> There are some key areas that analytical CRM can be used in any organisation, some of these areas include:
- Loyalty Analysis
- Customer segmentation
- customer Profitability
- Predictive Modeling
- Event Monitoring
- Channel Optimization
- Loyalty Analysis: Analytical CRM can help the organisation to know the rate at which customers are living their channel to join the competitors, life cycle. They will be able to know their defection rate. It will also portray their customer segment. This will also expose behaviours that are common among customers, the ratio of acquisition to defection among others. This will also chaw the effectiveness of defection prevention, strategies embarked upon by an organisation.
The package will allow the organization to know the rate at which customers are leaving and why they are leaving and where are they going? You will have a competitive advantage if you have this information. It will allow you to beat your competitors at their game.
As I said when I was discussing customer loyalty in series one, I said loyalty analysis is based on REM Analysis. This has to do with recently, frequency and monitory commitment of the customer to your products and services.
Customer segmentation: Analytical CRM also gives room for customer segmentation. Under segmentation, you try to break down your customer base into smaller groups based on certain characteristics that they shared together. Customers in the same customer segment are always similar but they are different compared to customers in other segments. Here, the organisation tries to segment their customers based on the value they add to a business. You can read more about customer segmentation in series one of this book.
- Customer Profitability: This has much to do with identifying the historical, current and the projected value of the customers and then using it to improve segmentation and to implement customer strategies. Customer profitability analysis is one of the most important and under-appreciated components of Analytical CRM.
Under customer profitability, you try to know the value of your customers at present and what they are likely to become in the future, using the contemporary information you have about them now as a standard. In doing this at times, you will need external information.
After profitability analysis, you need to determine the lifetime value of the customer and what they are likely to worth if they are well taken care of. In order to do this, you have to perform data mining. You must be able to differentiate between their potential value and lifetime value. The differences between the actual value and potential value are simply the differences between the value the customer will bring to the company if the status is maintained and the value he or she could bring if he or she is well taken care of.
One of the best ways to increase the usefulness of strategic analyses is to perform “Understanding/Profit link Study”. This process establishes emphatically links between the primary intangible qualitative measures of customer understanding (e.g. customer satisfaction, brand perception/strength and customer loyalty) with the critical business outcomes or quantitative data (e.g. market share, profitability or lifetime customer value).
Analytical and forecasting techniques are parts and parcel of analytical CRM. It helps the organisation to determine. Customer revenue and predict future receipts. You should know that before you can accurately predict this, you must understand customers’ profile and behaviours, and know-how they will respond to some marketing and supply strategies such as cross-selling, up-selling, retention among others.
Data mining allows assessment of different, predictive models and the subsequent selection of the best fit.
- Predictive Modeling: Predictive modelling is a system that aids an entity in prediction what one of their users will do next. Multiple actions by the user are considered in determining the identical outcome.
- Event Monitoring: triggers information or action based recognition of specific patterns of data within or among application systems. Data mining can be used to identify a pattern of interest for each event. This can be as a result of customer behaviour or other processes.
In monitoring events, the Organisation’s can activate automated e-mails or submission of actions on the event to a customer service centre. It is very useful in selling and retention services of the organisation.
Event modelling on the other hand enables the organisation to carry out successful campaigns and retention strategies. It will enable them to minor customers behaviour and predict how they are going to respond on different occasions. They will also be able to target prospects for future promotional offers. Event modelling will also enable the organisation to know which event is most important to a particular customer and they can use it to the organizational advantage.
Event modelling seeks ways to reduce the number of promotions, manage the costs of business strategies, and increase the percentage response rate to promotions – all of which ultimately boost profit.
- Channel Optimization: The purpose of channel optimization is to find ways of driving sales and services to the lowest cost channels that can bring about the desire value proposition.
The main purpose of channel optimization is to ensure that customers desire maximum satisfaction irrespective of the channel they have chosen to relate with the organisation.
- Personalization: Personalization has to do with the act of getting to know the customer through well-informed data gathering. You are able to look at the past interaction of the customer with the organisation and use the knowledge to come up with personalized services for the customer. This might also include the provision of personalized contents to the customer irrespective of the communication channel that he has chosen to interact with the organisation.
At the end of this section, we have been able to discover that:
- Some of the application areas of analytical CRM includes loyalty analysis, customer segmentation, customer profitability, predictive modelling, event monitoring, channel optimization and personalization.
- Loyalty analysis helps Organisation’s to know the rate at which customers are defecting to competitors’ lifecycle
- Customer profitability has much to do with identifying the lifetime value of a customer through proper assessment of their past, present and future with the organisation.
- Customer segmentation has to do with breaking down of customer segments into various groups with the same characteristics
- Predictive modelling uses customers past information with the organisation to predict customers’ next move.
- Event monitoring monitors important dates in the lives of propels and customers and uses it to come up with strategies that will allow those dates to be used to the Organisation’s advantage.
- Channel optimization ensures that customers have the same experience irrespective of the channel they are using to interact with the organisation.
- Personalization allows an organisation to come up with personalized contents that help the organisation to customers for life.
We already know that CRM is in case you have forgotten to get a copy of series one of this book. But when we talk about Analytics, we talk about the analysis of data that the organisation have about their customers. This will help them in meeting their customers in the right place with the right product and services.
Benefits form CRM Analytics
One thing you should know is that Data Analysis in CRM is a continuous process. Organisations have to continue to review their customer’s information and use the information gathered in evolving a well informed and balanced decisions about their customers.
Customers information are always gathered front offices which are departments that have a direct relationship with the customers. As this information is obtained, they are systems that perform data cleansing, application rules and storing of information in the data repository.
The organisation needs to do some calculations in order to know whether they are taking the right decisions. While applying calculations, the organisation needs to analyse if the rules are consistent across multiple channels. This need to be done in order to ensure that customer is satisfied irrespective of the channel that they have chosen to interact with the organisation.
You should also know that the organisation must be able to have established ruled that will regulate customers interaction and know the number of systems that each rule will govern in general.
Now the benefits …
Customer Retention: A sophisticated customer-centric data warehouse must be able to mirror customer’s that has left the Organisation’s lifecycle and join competitors chain. It must be able to portray the reasons why they have left in order to prevent future attrition. The models must also be able to identify potential defectors among the current customers and prevent their movement.
Sales and Customer Service: In today’s business environment, you should know that it is an organisation that is able to provide superior customer service that rules the market space. If an organisation have adequate information about their customer, they will be able to provide valuable information to their sales and marketing people which they can use to corner sales. You should know that if an organisation have adequate information, it is possible for them to create software that will predict products that can be introduced to customers and prospects alike.
There are programs such as market basket analysis which analyses transactional databases to find a set of items that appear frequently together in a single purchase. This helps in increasing cross-selling ratio, product placement improvements and better layout of catalogues and web pages among others.
Marketing: Business people know that marketing cannot thrive without adequate information about the prospects. When the markers have adequate information about the prospects, they will be able to execute retention campaigns, lifetime value analysis, trending, targeted promotion among others. Indeed only by having a complete customer profile can promotion be targeted, and targeting dramatically increases response rates and that decreases campaign cost.
Direct mail costs are directly proportional to the completeness and accuracy of customer data.
Risk assessment and Fraud Detection: An accessible customer base reduces the risk of entering into undoing risk. When an organisation have a database of their customers, they will be able to know the relationships between their various customers, they will also be able to identify customer’s carrying out fraudulent activities or using different names. An insurance company will also be able to identify customers with different types of policies more than expected.
At the end of this lesson, we have been able to discover that:
- Analytical CRM has to do with a critical assessment of customer data in order to come up with facts that will allow an organisation to do business from the customers perspective.
- Some of the benefits include Customer retention, sales and customer service efficiency, marketing and risk and fraud detection among others.
I know you might agree with some of the points that I have raised in this article. You might not agree with some of the issues raised. Let me know your views about the topic discussed. We will appreciate it if you can drop your comment. Thanks in anticipation.
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