Customer & Marketing Analytics

Customer and marketing analytics is necessary for companies to retain their market share and leading edge. Advanced customer and marketing analytics enables your business to harness data to enhance your customers’ experiences – within a brand and across your organization’s portfolio – retain and grow your customer base, increase sales, and obtain insight into new product innovations. We implement analytic solutions that help you achieve your customer and marketing goals in essential areas such as estimating customer lifetime value, increasing customer retention, identifying unproductive campaigns, and expanding market share.

Our customer and marketing analytics consulting services can help you extract the most value from your data, whether you are just getting started with analytics or are growing in-house capabilities. Our proven track record has helped companies in many industries attract and retain customers and increase wallet share. We combine sound marketing principles with marketing analytics and customer relationship management (CRM) processes to give you a competitive advantage with solutions in the following areas:

Customer Lifetime Value

Lifetime value (LTV) is a forecast of the future dollar value that can be attributed to the ongoing relationship with a customer. Employing analytics, we identify characteristics of customers with a higher lifetime value and use these insights to inform digital spending decisions for paid search and targeted marketing campaigns.

Traditional LTV calculations rely on metrics based on subscription models and do not apply to the fluid purchasing behavior seen in consumer product goods. Rather than relying on churn-based methods, we designed a solution tailored specifically for products. One example used a collection of three models to predict the probability of a consumer to purchase, the dollar amount a consumer is likely to spend on the next purchase, and the likely number of days until the next purchase.

Cross-Selling and Up-Selling

Cross-selling increases sales by predicting which new products should be marketed to existing customers based on products they have purchased or are in the process of purchasing. Up-selling offers improvements, upgrades, complimentary, or natural progression products, but they are discovered from vast amounts of purchase and browsing data; the patterns don’t have to be curated by domain experts.  We can determine the origin of your most profitable customers and explore patterns in your transactional data to discover which products or services are frequently purchased together, then optimize return on investment with targeted marketing campaigns.

Customer Segmentation

Customer segmentation improves the effectiveness of marketing campaigns and decreases costs by grouping customers into segments based on meaningful attributes. Identifying customer clusters provides immediate insight into existing customers — how they are alike or different, and where to focus or how to pitch marketing efforts. Segments can also be informative inputs to models that predict who is most likely to purchase a particular product or service and identify the mix of customers that will maximize profitability.

Customer Retention

Retaining existing customers is crucial for every business. We use advanced data science techniques to determine root causes for customer churn and identify customers most at risk to prioritize marketing and customer service team workload, focus outreach to high-risk customers with appropriate incentives to reduce churn, and thereby increase market share and profitability.

Customer Acquisition

Our customer acquisition analytics uses demographic and behavioral characteristics to discover which customers will be most receptive to specific marketing offers.

Customer Experience Analytics

Our customer experience analytics zeros in on the most effective approaches to loyalty programs and customer retention. We develop insights into short-and long-term product reviews and ratings trends and find life-stage triggers that indicate your consumers are ready for the next assortment of products. Knowing which customers are likely to abandon your brand or organization (churn) provides an opportunity to reduce or avoid this loss.

Attribution Modeling

We use attribution modeling to connect marketing campaigns to your company’s ROI, to determine how sales can be attributed to the multiple touchpoints in the consumer journey. This provides valuable insight into which marketing efforts are paying off.

Channel Optimization

We use your data to understand which channels are delivering the most return on investment and optimize the allocation of your marketing dollars across the multiple channel options (on demand entertainment, digital, direct mail, etc.).

Marketing Campaign Analysis

We systematically monitor and analyze the effectiveness of your marketing campaigns and make adjustments to optimize the return on your investment. Justify your marketing budget for specific campaigns by demonstrating their success and determine when to discontinue or adjust unproductive campaigns.

Using an Agile development process and innovative analytic techniques, our consulting services help discover valuable insight from customer satisfaction surveys, analyze call center data, and build recommendation engines that enable more effective marketing campaign development, execution, and analysis.

Request a Consultation

Contact Us

Case Studies

Using Marketing Analytics to Enhance Customer Loyalty

A major computer technology provider wanted to assess the impact a printer supplies loyalty program had on customer lifetime value, retention, and market share in order to evaluate targeted marketing campaigns.

Results: The semi-supervised approach provided a more focused analysis of the survey comments that mattered for understanding customer sentiment and resulted in more focused action and improved customer loyalty.

Read the Case Study

Understanding Customer Sentiment

Understanding the “Voice of the Customer” is necessary for responding to customer needs and improving service, but it can be difficult and time consuming to identify the most actionable feedback. A major insurance company utilized a customer survey that covered 11 different topics. The goal was to gain actionable business insight that could be used to improve customer loyalty.

Results: The semi-supervised approach provided more focused analysis of the survey comments that mattered for understanding customer sentiment and resulted in more focused action and improved customer loyalty.

Read the Case Study

Improving Customer Retention and Profitability

The client’s risk model had been developed and deployed by dozens of expert statisticians over many years. The client was interested in whether any new insights would be produced by using modern machine learning techniques. To be adopted, the new model needed to have significantly better predictive performance and be suitable to run in the client’s production scoring environment.

Results: Measurements conducted using statistically significant control groups for multiple categories of customers showed that Elder Research’s solution decreased churn from 3.5 percent to 2.9 percent — the lowest level in years. The new models prioritized likely churners so that intervention was twice as effective as before and boosted annual profits by more than $1 million.

Read the Case Study