Data Science & Analytics

Leveraging the Value of Your Data

Your data is a valuable strategic asset that can provide insights to maximize business performance. We help you drive positive outcomes by extracting actionable insights and new understanding of your business. We use analytic tools and statistical techniques including machine learning, predictive modeling and other advanced data science methods.

We help you answer key questions like “What happened?”, “Why did it happen?”, “What is likely to happen next?” and “What should we do differently?” to help drive positive outcomes. Our experienced consultants will provide advice, guidance and recommendations to get the most value from your data.

Improving Performance Through Data-Driven Decisions

Customer Insights

Unlock the power of data to answer critical questions about your customers and solve your most important challenges. Practical and actionable insights and recommendations to help you implement solutions to improve business processes. Identify insights, relationships, trends and patterns in your data through descriptive analytics, data appends, segmentation, data enhancements and data visualization.

Customer Lifecycle Management

Optimize the customer lifecycle of acquisition, growth and retention using machine learning and advanced analytics to maximize customer value. Efficiently acquire long-term, profitable customers. Grow revenue and profitability through targeted up sell and cross sell campaigns. Retain your best customers with targeted retention programs, and identify your most loyal customers for differentiated service. Manage the customer experience across touchpoints and channels to improve customer satisfaction and experience.

Profitability

Maximize overall profitability through strategic pricing with the knowledge of how customer demand changes in response to variation in pricing (price elasticity). Develop Lifetime Value (LTV) and profitability models to estimate revenue, cost and profitability considering price, product mix, cost of goods, service costs and acquisition costs. Simulation and forecast tools will help to accurately forecast and plan revenue, costs and margins.

Optimization

Make the best use of sales and marketing budget to maximize value of investment and understand where incremental spend will yield the highest return. Optimize your business with statistical models based on marketing mix, sales funnel, channel, campaign, promotional offer, business processes, staffing, site location and other pertinent factors.

Market Research

Qualitative and quantitative research services to help you better understand your customers’ and prospects’ attitudes and motivations. Measure, track and improve key performance metrics around customer acquisition, retention and satisfaction. Competitive insights to identify product preferences essential for launching new products or enhancing services.

Analytic Operations

Forecasting, measuring and model tuning to maximize ongoing value of data science solutions. Manage automated processes for continuous data collection, monitoring and evaluation.

Case Studies

A telecom client was experiencing significant attrition across its consumer base. They wanted to understand drivers of churn and implement a program to proactively intervene with likely churners.

Approach

We performed exploratory data analysis to understand and profile customer and behavioral data and formulate the best analytic approach. We developed a machine learning model to dynamically predict the probability of voluntary (customer initiated) and involuntary (client initiated for non-payment) churn at an individual customer level. The outputs from the model also provided valuable insights into profile characteristics of churners vs. retainers.

Results

The model enabled our client to make significant changes in acquisition tactics to shift marketing and spend from high churn channels and profiles resulting in higher early tenure retention and lower costs. The predictive model was used to focus retention efforts on at-risk customers and focus up sell programs on those found to be secure. These efficiencies allowed our client to target at-risk customers at a nearly 45% cost savings.

A consumer services client was launching a customer relationship management initiative to understand and guide the customer journey. Their goal was to establish a framework to manage customer communications to improve the customer experience and engagement.

Approach

Tru-Clarity developed a segmentation model using customer profile, engagement and purchase data as well as 3rd party demographics. Five customer groups with distinct behavioral and profile characteristics were identified from the data. Key determinants of group membership were level of purchases, tenure, and engagement history including recency, frequency, channel and content.

Results

From these insights, a lifecycle strategy was developed for onboarding of new customers, nurturing to encourage initial engagements across multiple channels, retention programs targeting at-risk points in the customer journey and personalized up sell of tenured customers to increase value.

A subscription based client was experiencing widely varying customer acquisition costs and retention rates and was concerned about declining profitability. A framework was needed to provide transparency into profitability in the consumer base and evaluate and manage the trade-off of acquisition costs and customer quality.

Approach

We developed a Lifetime Value (LTV) model to forecast revenue, costs and profitability for an acquisition cohort over a ten year period. Retention curves at a sales channel, affiliate and demographic level were modeled from historic data. Fixed and variable costs were considered in the model so that a profit and loss statement (P&L) could be generated.

Results

The client used this model to quickly assess acquisition costs versus quality. They terminated a number of unprofitable affiliate relationships and renegotiated terms on low to marginal affiliates. Marketing spend was optimally redeployed to the most profitable channels. The model has served a number of additional purposes including providing insights into cash flow and breakeven point by acquisition cohort, supporting revenue forecasting and analyzing variances of actual versus budget.

A client wanted to define the best customer profiles for advertising and direct marketing. In particular, they wanted to use these profiles to identify customers that cancel service early in their tenure. They engaged Tru-Clarity to identify prospects that will most likely become “good” customers.

Approach

During the exploratory analysis phase of this project, we worked with the client to refine the definition of “best profile” as a combination of tenure and successful payment criteria. We developed a binary logit model using machine learning techniques to apply a score to a 3rd party data set indicating likelihood of matching the best customer profile. The model used demographic, behavioral and geographic attributes.

Results

The client used insights from the model to support media buys to better reach their desired prospect profiles. The model scores were used for direct mail campaigns that efficiently reach their target audience. Using the model reduced direct marketing data spend by nearly 50%.

An eCommerce client was considering raising prices but was concerned of the impact on new customer acquisition and potential losses of existing customers. They asked us to quantify the impact to sales volume and revenue under various pricing scenarios.

Approach

Using historic pricing and sales history, we developed a price elasticity model to estimate base and promotional price elasticity by product considering seasonality, day of week, sales channel, acquisition offer and other factors. Numerous internal metrics of existing customers were also analyzed including tenure, cyclical spending behaviors and total revenue. The model quantified expected change in revenue and volume for a given change in price.

Results

The model strongly indicated that customer demand was inelastic on most products meaning the client could raise prices with the higher revenue from the price increase more than offsetting the decrease in sales volume. We also delivered a simulation tool to support sensitivity analysis around various scenarios. The client conducted extensive price testing and then rolled out across their product lines. They were able to increase pricing by nearly 15% in less than 9 months.

A financial services client with a recently launched B2B business unit needed help in determining what lead generation efforts were performing well and what types of businesses their direct sales force should target. Although Salesforce.com was being used to track opportunities, data management practices around lead management and marketing hindered their ability to readily answer these questions.

Approach

Tru-Clarity developed an analytic data set from all marketing activities including email campaigns, conferences, webinars and white-paper downloads. We performed a match back from the data set to sales opportunities and sales conversions and also appended industry code and company size to each record. From this process we identified specific campaigns that consistently yielded the best opportunity and conversion rates and highlighted many sources to scale back or eliminate. In addition to the industry and size data, we developed profiles for the best prospect targets with higher engagement and conversion rates and higher revenue levels. We also worked with the Salesforce administrator to develop better data management practices and training for the sales team.

Results

As a result of this analysis, the client reallocated over $1 million in marketing spend to more productive use, implemented a more disciplined targeting strategy and revamped data management practices in Salesforce.