Marketing is the backbone of any profitable business. This function contributes in understanding the customers and devising strategies beneficial for the business. This customer-facing entity in any business has the responsibility of managing competitors, stakeholders satisfaction and demands and the ROI of the business. This entity interacts with all other operational and strategic entities of a business to gather inputs and come up with a business strategy in handling the key responsibilities.
The analytics approach helps the marketing function in identifying the key market as well as customer segments, assessing the business needs and coming up with effective business strategies. Marketing analytics harnesses the capabilities of advanced techniques in analyzing customer-centric data that helps organizations identify avenues to create business value, thereby making the business profitable.
Marketing analytics and campaign analytics are considered synonymous by service provider organizations. Marketing is the key entity in a service provider organization acting as an interface between customers and an organizations other internal entities.
Marketing Analytics in a Nutshell
The business value of any service provider organization is highly dependent on its valuable customer base. With growing competition and customer demand, globalization and mergers and acquisitions, the service provider needs a high-value persistent customer base for survival. Customer relationship management is an integral part of a marketing portfolio in any business. The major strategic decisions influenced by marketing are:
- Valuable and reliable customer acquisition creating value;
- Cross-/up-sell of products and services enhancing value; and
- High-value customer retention sustaining value.
Campaign management encompasses analysis of data for planning campaigns, campaign execution, monitoring campaign performance and incorporating lessons learned in enhancing business value from campaigns. The overall business objective of a campaign is explained in the Figure 1.
Marketing analytics is highly customer-centric and primarily utilizes statistical and advanced mathematical techniques in predicting future behavioral characteristics and product/service preferences of customers based on the historical information. Figure 2 provides an overview of components of marketing analytics. Since marketing is the key customer-facing entity in any business, marketing analytics analyzes the data in the following areas:
- Customer interaction data demographic, behavioral, attitudinal.
- Customer tenure in the system.
- Customer net present value.
- Customer product and service preferences.
Marketing analytics aids in structuring campaigns with higher success rates and also fine-tunes the campaign management strategy based on insights gathered by monitoring campaign execution and aftermath.
Components of Marketing Analytics
The three major components of marketing analytics are:
- Associate Finding the potentially strong association(s) amongst products and services preferred by customers based on the recency, frequency and monetary analysis of the historical associations.
- Profile Profiling customers based on their product and service preferences and based on the strength of associations identified as part of previous component.
- Predict Predicting customers propensity to respond/buy - Their future response behavioral trend prediction for targeted marketing.
These three components help in understanding prospects and customers across all stages of a customer lifecycle. Interestingly, this approach is cyclical, with the learning in each phase carried over to the next phase by a feedback loop, reflecting continuum learning, fine-tuning every successive phase.
One of the key derivatives of this analysis is gaining insights on customer-centric product and service affinities. One of the business objectives of campaigns is to understand the customers perception of products and services offered by the service provider. This approach helps in gaining deeper insights into customers preferences of products and services, product/service-based customer profiling, propensity to respond in future campaigns and also their perception of offered products and services. This additional information is beneficial for internal entities working on product development and service-providing strategies.
The Kernel of Marketing Analytics
The advanced statistical and mathematical techniques employed in accomplishing the three components associate, profile and predict play a crucial role in harvesting the business benefits. These techniques are tools in the hands of seasoned analytics stalwarts with sufficient expertise in marketing, and each one of them have their own merits. When the right set of techniques are utilized to tackle the relevant business problems, business benefits are realized faster.
This technique is traditionally used in recency, frequency and monetary analysis (RFM) to find out the frequent combinations of products and services as preferred by the customers. These frequent associations are identified by analyzing the transactional data of customers. Literature suggests a variety of association algorithms such as Apriori, Eclat, FP-Growth, and One-Attribute-Rule. The choice of an algorithm depends on a set of technical and functional parameters. In cases with increased complexity, a combination of algorithms would also be a better fit in identifying the frequent combinations of higher strength.
Profiling techniques. A variety of techniques are employed in profiling the customers based on their product and service preferences. These are primarily rule-based classification techniques and they are implemented in different scenarios as per the business needs. Decision trees, clustering, adaptive bayes network are some of the prevalent profiling techniques.
Predictive models. Predicting the propensity to respond/buy based on the existing customer profiles and their frequent preferred associations of products and services is highly challenging as this result plays a key role in campaign planning and execution. Most of the commonly used predictive modeling techniques are regression, support vector machines, bayesian network, neural networks, and decision trees.
Business Benefits of Marketing Analytics
- Create more effective marketing strategies. Targeting potential customers with higher propensity to respond makes the campaigns more effective.
- Develop bundled promotions and product offerings. Preferred associations of products and services as prioritized based on customer buying behaviors aids in designing profitable product-service mix.
- Reduce marketing efforts to unlikely buyers. Bring down the campaign cost by avoiding mass campaigning and targeting the customers with higher propensity to buy. Receive up-to-date information on product performance across the organization. Get the customer-centric product and service affinities as a derivative of campaign analytics approach.
- Gain insight into customers and their purchasing behavior. Customer profiling will identify common characteristics of customers with specific product and service preferences.
- Increase promotional profitability and campaign effectiveness. This increases the ability to measure, manage and improve overall service provider performance, enhancing the business value of service provider.
Marketing analytics is a lever enabling the service provider enterprises in creating business value, enhancing existing value and retaining value generated by their strongest assets valuable customer base. Marketing analytics approach reflecting Continuum Learning helps in accomplishing the business objectives of campaign management by gaining better insights into customers preferences and perceptions on products and services. These insights aid in devising successful campaign strategies, execution and monitoring for overall business value enhancement.
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