We support Marketing and Sales with tailored Marketing Analytics for Segmentation, RFM analysis and Churn Prediction. At Auriscon we integrate multiple model building techniques with programming in R to ensure our customers have access to a tailored solution. We have capacity to consult and assist our clients' Marketing and Sales teams to ensure comfort is taken in bespoke analytical approaches, aiming for less churn, higher retention and increasing sales through decoded customer behaviour patterns.
Browse through an outline of how we can support your Sales and Marking with tools and concepts rooted in modern Marketing Analytics.
Don't hesitate to contact us for inquiries.
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Disclaimer: Data, charts and commentary displayed herein are for information purposes only and do not provide any consulting advice. No information provided in this documentation shall give rise to any liability of Auriscon HK Ltd and Auriscon Ltd.
Our Approach
- Tailored solutions facilitate success in Sales and Marketing.
- The outcome obtained from analytics provides valuable insights in customer behaviour.
- Application of our analytical methods guides interpretation, strategies and valuation of customers.
Tip: Did you know, alternative approaches to marketing analytics help to unlock potential for successful sales and marketing.
Our Services in Marketing Analytics
Segmentation and Classification
Identifying and valuing profitable customer and business segments to support marketing and sales initiatives.
Tip: Did you know, on average 80% of your sales come from 20% of your customers!
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Churn Prediction
Preserving customer relationships supported by Churn Prediction Models obtains timely information about drivers of customer churn. Through Churn Prediction, identification of likely churners and gives your company time to act. Turn your customer retention strategy in actions with hands-on support and guidance from Auriscon. Secure our assistance before others do.
Tip: Customer Retention achieved by lowering the churn rates is important for increasing profits.
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CLV Analysis
Model concepts and developments covering
Under Construction...
Illustration of our Support in Marketing Analytics
At Auriscon we can support on-site or by working remotely based on flexible allocations. We assist teams and stakeholders in attaining the marketing goal. A few examples of how Auriscon can support your marketing initiatives are shown below for illustration.
Analyzing Customer Preferences and Behaviours
Analyzing customer transactional data and identifying customer preferences.
Assigning RFM scores to establish insights into valuable customer behaviour.
Predictive scoring and classification methods to promote additional insights into customer behaviour.
Dependencies between Factors
Examing Customer Purchases In-Store versus Online
Identifying and Examining Customer Segmentation
Targeting customers groups for marketing and acquisition.
Enhancing customer relationships for identified customer segments.
Identifying customer segments at risk, e.g. higher churn rate.
Large-scale clustering with suitable number of segments.
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Customer Groups based on Attributes
Churn Rate Analysis and Churn Prediction
Identifying root causes of customer churn from in-house survey and transactional data.
Using variables most predictive of customer churn to inform strategies for customer retention.
Prediction of customer churn using modern AI-ML method approaches.
Interpreting outcome of churn prediction for augmentation of targeted retention strategies.
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Examining Customer Profiles
Predicting Outcome
Forecasting who will churn
Under Construction: CLV Boosts
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Scenario Simulation and Path Analysis
Anticipating sales dynamics supported by simulaton of consumer behavioural and market forces.
Identifying latent variables and estimating cause-effect paths across multiple factors, to augment strategies for Sales and Marketing.
Approach
- Simulating Cause-Effect relationships in sales dynamics through System Dynamics.
- Path modeling of repercussions in marketing variables driven by latent factors.
- Identifying causal drivers via PLS-SEM.
