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Practical Applications

The opportunities to implement data mining and predictive solutions are endless across industries and business units. Here are some examples of how models are being used.

Customer retention

Predictive models on which customers have the highest likelihood of leaving your business and develop specific strategies around keeping them.

Rethink product development with data

Understand what customers are buying and create new products that will spearhead advanced purchases by customers of similar profiles.

Tie digital campaigns with customer behavior data

Learn more about your digital marketing efforts and how they correlate with actual customer behavior. Predict behavior based on marketing channel and assess marketing spend against margins. 

Take a data-driven approach to your budgeting and minimize the variance

Build your budget from the bottom up where any and all variances can be explained, allowing for better KPIs to track your performance throughout the year. Know your budget,  your variances, and your performance. Improvement starts with these measurements. 

Deeper dive into booking curves and behavior

A deeper dive into your booking curves will yield insights such as the various segments that have materially different booking windows or the elasticity between last minute bookers and planners. These deeper insights can help you build your base faster to yield higher rates. 

Determine least profitable pocket of customers

Data mine for the least profitable customers in your portfolio, understand their behavior, lifetime value, or their purchases. Then influence their behavior through new marketing strategies or change their products to yield higher margins. 

Create a Customer Lifetime Value (CLV) Score 

Having a CLV will help improve your marketing and financials. It allows you to know the various types of customers you have by segmenting the portfolio by similarly valued customer. The three elements of CLV are Recency, Frequency, and Monetary. 

Determine the levers of business demand 

Use internal and external data as well as environmental and competitor data to help determine what drives your business. How does a small change in your marketing or a change in your competitor’s tactics influence the demand for your products or services?

Predict who will likely default on loans

Determine the probability of a customer defaulting on his or her loan within a defined period of time. This will help manage bad debts by determining bad debt reserves at the customer level and establishing measures to change pricing or products.

Rank order your sales lead by profitability or conversion probability

By understanding what generally converts a sales lead to a customer, you can better manage your time and focus on what truly matters in building your business. 

Tier your real estate portfolio

Use historical sales metrics, home photos, market conditions, seller attributes and other external data to cluster your real estate portfolio.  Rank order properties and focus on those that have the highest propensity to sell while creating new strategies around those that have the lowest. Understand the empirical factors that influence home sales. 

Rank order your existing automotive customer base for new sales

Understand which of your former customer have the highest probability of coming back at a given time to purchase a new car and the anticipated value of the sale. Personalize your marketing campaign based on in-depth knowledge. 

Determine an estimated impact of your marketing dollars

Use Marketing Response models to determine the levers and sensitivity of your marketing investment through your different channels. Understand how pricing, marketing, competitor prices, sales initiatives, or market environment impacts your demand. If you drop digital marketing by X% and increase prices by Y%, how would that impact your business demand?

Take a data-driven approach to estimate unforeseen events

Use the lowest levels of internal and external data to assess revenue loss from unforeseen events and force majeure such as hurricanes, flooding, major renovations and other types of business interruption.

Predict which customers are least likely to stick to their wellness plan

Based on rehabilitation purpose, medical history, number of cancellations to date, distance from, work schedule, type of injury, and other parameters, determine the likelihood of a patient not following through their therapy. Create specific programs in advance to increase success and decrease the economic cost of failure to following through with therapy. 

Augment student counselors with models to predict student dropout

Use student records to determine the likelihood of a high school student dropping out without the need of a counselor going through all elements and seeing each student.

Understand and predict employee turnover

Use historical metrics, vacation usage, sick day usage, salary variances, and other measures to preempt employee turnover and proactively minimize your recruiting costs.

Use data to guide call center behavior 

Understand purchasing behavior of call center customers and create scripts that will yield higher average order per purchase. 

Incorporate weather in customer mix to determine demand levels

Use weather data to help understand demand and assess budget variances due to inclement weather. 

Simulate Your Portfolio To Determine Lower Bound and Upper Bound Risk

Use Monte Carlo simulations (technique used to understand the impact of risk or uncertainty) to incorporate the variability of all factors that influence your demand, sales, profit, or operations to estimate upper and lower bound forecasts and risks. 

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