High Time for Marketing Analytics
High Time for Marketing Analytics
Marketing Strategy is ripe for a gigantic change with the advances made in predictive analytics. Thus Analytics should be embedded in every organization’s marketing strategy instead of the occasional use for ad-hoc reports. Advanced Analytics can guide and support the Strategy for better customer understanding, targeting and life-cycle management. This insight can drive marketing campaigns, market share expansion, up-sell, cross-sell, churn or retention, credit and risk, and last but not least sales funnel management.
Using internal, external and social media data, the multidimensional segmentation of clients can be enriched and segmented along many profiles. Such a segmentation could be based in Value, Behavior, Propensity, Loyalty, Socio-Economic, Demographics, Life-Stage, and Attitudes.
By using analytics and data mining, the organization can build Best Practices strategies and develop marketing activities for each customer segment and select “individualized” approaches that might include:
• An offer for preventing churning, mainly for high-value, at-risk customers.
• A promotion for the right add-on product and a targeted cross/up/deep selling offer to desired customer segments with growth potential.
• Impose usage limitations and restrictions on customers with bad payment records and bad credit risk scores.
• The development of a new product/offering tailored to the specific characteristics of an identified segment and so on.
Some of the components that should be taken into account in the design of the Best Practices strategy are:
1 The propensity or likelihood of accepting offers as opposed to rejection or marketing offers
2 The market basket analysis across all the organization’s products
3 The current and future customer profitability and lifetime value.
4 Cost of customer acquisition across the various channels
5 The type of customer, the differentiating behavioral and demographic characteristics, the identified needs and attitudes revealed through data analysis and segmentation.
6 The growth potential as designated by relevant cross/up/deep selling models and propensities.
7 The attrition risk (churn propensity) as estimated by a churn model.
8 The payment behavior and credit score of the customer.
9 Cost-Benefit analysis that compares various
An example of how these pieces can be applied to work together in the Best Practices strategy in action, let’s consider the following example. A high-value banking customer has a high potential of accepting a mortgage loan offer but at the same time is also scored with a high probability to churn. What is the best approach for this customer, having been identified as more likely to accept an offer, and how should she be approached by the organization? Considering the high acquisition costs, a high-value, at-risk customer, the top priority is to prevent her leaving and lure her back with an offer that matches her particular profile. Therefore, instead of receiving a cross-selling offer, she should be included in a retention campaign and contacted with an offer tailored to the specific characteristics of her segment.
Anova Analytics is a Data Science partnership focused on advanced Data Driven Decision management, predictive and prescriptive analytics technology. We apply math, science and superior business expertise to build robust solutions for our clients. Our scientific expertise includes statistical algorithms, machine learning, logistic optimization, simulation, neural networks, and intelligent systems. Our scientists have a proven record in producing effective predictive models to extract hidden patterns to deliver efficient and robust decision support solutions. This is complemented by our Data Driven Analytics framework for rapid agile execution of predictive models and rules.
Our solutions that includes: Anomaly Detection, Best Practices Discovery, Dynamic Pricing, Sensory Data Analysis, Neuro-Linguistic Computation, At-Risk Identification, Market Segmentation, Advanced Customer Intelligence, Marketing Analytics, Web Analytics, Anomaly Detection, Risk Analysis, Simulation and Process Optimization.
