What It Is
Portfolio Advantage is an in-depth portfolio analytics solution that incorporates data mining, machine learning, and financial modeling to provide comprehensive levels of actionable insights for your business.
This service is ideal for companies or teams that need in-depth insights of their portfolio and/or business but do not have the necessary data science or business analytics personnel. This could even work for those organizations that do have an in-house team, but not the bandwidth or domain knowledge to fully execute extensive analyses. Executives turn to this service to yield insights to their portfolio without interrupting their data science teams or as a substitute for a business analytics task force.
Below are some examples on how you can leverage the Portfolio Advantage to gain insights, make impactful decisions, and grow your business.
Know Your Customer
Knowing who your customers are and understanding their purchasing patterns will help you build brand loyalty and drive more sales. Generate deeper insights beyond the CRM standard by creating customer segmentations and tracking behavior by incorporating propensity models, scorecards, and financial measures.
Customer Segmentation & Profiling. Understand the Who, What, When, Where, Why, and their financial footprint.
Probability models to determine likelihood of return or attrition.
Customer lifetime value scorecard for tracking behavioral shifts and micro-target. Scorecard helps assess the recency, frequency, and monetary elements of your customers.
Create clusters of personas to micro-target and personalize marketing campaigns.
Portfolio Risk and Opportunities
Businesses that manage loans/leases or subscription/membership based portfolios need to fully understand their risks and identify segments that are diluting their bottom line. Generally, such insights lead to managing bad debt, attrition, better forecasting measures, or stronger loyalty programs.
Probability models of attrition, return, or lapse.
Segmentation of products/segments by aggregating by the customer level data and probability models.
Tier customers by risk and opportunities and create strategies around each tier.
Forecast portfolio impacts based on new strategies and shift in portfolio. Create an Analytical Commercial Playbook based on sales, marketing, and portfolio risk/opportunities.
Data-Driven Product Development
Utilize look-alike modeling to find and attract groups of people with the same purchasing patterns as your most profitable customers. Optimize bundles to increase advanced purchases and yield higher average order value. Get a data-driven sense of price, value proposition.
Apply market basket analysis to understand what products are most often purchased together.
Reverse engineer the commonly purchased products to determine the associated customer profiles.
Understand the value behind the aggregated purchases and the distinct customer profile associated with those purchases. Is it price, is it value, or is it convenience? Understand the value proposition.
Assess the financial sensitivity and long term impact of new products and how it impacts the portfolio.
Tracking A Digital Footprint to Profitability
Understanding which marketing channels have the highest impact to your bottom-line leads to sound pricing structure, product development, and marketing strategies. Determine the value of your marketing dollars and take a more insightful approach to your bottom line growth.
Analyze digital marketing data for quality and effectiveness.
Determine the most valuable lifetime marketing channels.
Develop portfolio analytics and distributional assessments of margins by product.
Evaluate which are the effective marketing campaigns.
Pricing is based on the business objective(s) and the extent of data mining, modeling, and financial assessment involved. Generally, prices are project-based and dependent on scope. A thorough process in determining full scope will be conducted prior to initiating Portfolio Advantage.
A few elements affecting pricing:
Depth, quality, and acquisition of data
Number of business objectives and their complexity
Number of models (data mining, machine learning, and financial)
Associated BI and IT support