Marketing Analytics

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Marketing is hugely important for a business to succeed. Being able to clearly define marketing objectives and accordingly prioritize on marketing spend is one of the major challenges marketers face. And in order to tune their approach, marketers need important metrics from various business functions to determine marketing effectiveness. Below is an attempt to categorize some of the generally applied analytic techniques that can be used to measure the marketing performance.

Our first step in this analysis would be to identify relevant data sources and develop automation capabilities to streamline data into well-defined repositories. Next, we could use a combination of descriptive and predictive analytic techniques to gain insights. And further we could integrate different models and automate their execution to perform prescriptive analytics for continuous monitoring and feedback.

Marketing drives sales and sales in turn should help improve marketing strategy. Let’s look at some techniques to identify sales patterns and then work on improving our mix of marketing activities.

Sales
Applications Applicable Tools/Techniques Required Measures/Expected Results
Sales Performance (Descriptive) Visualizing data using Time Series Analysis and other metrics using standard/ad hoc reporting and operational dashboards that cater to different audiences Use accumulated data over time to learn about correlations and identify patterns
ARIMA models for time series data
Sales Performance (Predictive) Simple and multiple linear regression techniques for forecasting and simulation Determine future possibilities and predicting events to make more informed decisions
Customer Service
Applications Applicable Tools/Techniques Required Measures/Expected Results
Customer Acquisition and Retention Logistic Regression (Churn Analysis) Using historical data to identify ingress and egress of customers
Customer Segmentation Cluster Analysis Identify potential markets and improve on promotion, product, pricing and distribution decisions
Decision Trees
Hypothesis Testing
Product and Brand Feedback Text Analytics using Natural Language Processing Toolkit from Python Analyze unstructured data from social media platforms such as Facebook, Twitter, Yelp etc.
Sentiment Analysis using Stanford NLP
Customer Loyalty Logistic Regression Understand customer behavior and improve decisions around targeted promotions
Multivariate Analysis using Factor Analysis, Principal Component Analysis or Canonical Correlation Analysis
E-Marketing Clickstream Analysis (Traffic and E-commerce-based) Improve conversion and sales
Drive email marketing campaigns
Google Analytics for website statistics Search engine optimization (SEO)
Channel adaptation

Note: The above mentioned techniques can always be used across a set of problems depending on their applicability.

After analyzing the results from our analytical models, we have to take measures on improving crucial marketing activities such as generating leads, demand creation and product promotion. Further, above analysis could be used to design and implement marketing strategies including product and brand promotion, pricing strategy, distribution and customer service. And the findings can be employed in improving questionnaires and other mechanisms of collecting marketing data and customer feedback to learn about product performance and brand value.

With these new analytics capabilities, we can make predictions much more accurately and provide our marketing teams with new ideas to drive promotions and boost sales.

In general, adoption and effective application of these analytic techniques is challenging. Building the right analytics should be informed by industry knowledge and subject to the business function in context. However, this is a process which requires constructive iteration over a long term and in most cases should lead in optimizing marketing performance and delivering tremendous value to the organization.

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