Understanding Model Building And Analysis In CRM
At the end of this article, readers should be able to:
- Scores are a numerical value that you attach to each customer’s data in ascertaining what they are likely to do now or in the future.
- A high value shows that an event will happen while a low value shows that it will not happen.
- Discuss the differences between a profile and a model knows how models help Organisation’s in satisfying customers needs and want.
- Know some of the dynamics of the market place.
The difference between a profile and a model is the element of time, making models more powerful predictors of behaviour. Model in a way shows the preferences that customer have for products and services of an organisation.
When talking about Analytical CRM, you should know can provide accurate, flexible for customers across multiple channels, and under different market conditions.
Modelling and analysis of customers data and behaviour can measure individual customer values and preferences for the Organisation’s product and prices. It captures the richness and differences in customer preferences. The use of model building and analysis can help the organisation to have a more in-depth understanding of their customers or potential customers, this can be achieved through research into customers behaviours.
Models can be used to capture:
- Customer loyalty and disloyalty
- Unexplored market segments.
- Distribution channels
- Customer awareness
- Advertising impact
- Technology substitution
- Potential moves from competitors
- Timing of product introduction
- Changes in customer’s needs and values as the market and competitive landscape evolve.
SUMMARY OF THIS CHAPTER
At the end of this lesson, we have been able to discover that:
- The difference between profile and model in the element of time.
- Models are a powerful predictor of behaviours.
- Analytical modelling can provide accurate, flexible forecasts for customers across multiple channels, and under different market condition.
- Models can be used to capture the dynamics of the market place such as customer loyalty, untapped market segments, distribution channels, customer awareness among others.
Organisation’s need to carry out advanced data analysis, this will help them in producing variables that they can use in predicting customer behaviours and coming up with models that can solve arrays of customer’s challenges. These are arrived at through sampled customer data which are used to examine how the majority will respond to it.
Scoring results in assigning each customer a predictive or a score, expressing, for example, the likelihood of a customer to react in a certain way to a particular offer.
Data mining builds models by using inputs from a database to predict customer behaviour. This behaviour has much to do with how customers responded after a particular campaign. For instance, the attrition of customers after a particular product was introduced by them.
NOTE: The prediction provided by a model is usually called a score.
A score which is normally a numerical value is assigned to record of the customers in the database, it shows the likelihood of such customers to exhibit certain behaviour. Take for example if you are trying to ascertain which of your customers are likely to move to your competitors’ lifecycle. A high score indicates the likelihood of the customer to leave while a low score means the opposite. Once you are able to score your customers,, you can use the numerical value to select targeted customers for appropriate marketing campaigns.
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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