Customer Acquisition

Customer Data Platform Strategy and Implementations, Customer Propensity Models, Predictive Segmentation

Customer Lifetime Value Growth

Price Optimization Models (Dynamic Pricing), Upsell/Crossell Propensity Models, Highly Granular Product Demand Forecasting, Business Intelligence Dashboards and Integration, Customer Lifetime Value Forecasting, Customer Journey Analytics

Customer Retention

Churn/Retention Model Analysis, Customer Attention Models based on CLV 

Change Management and Education

Culture Change, Data Science Training, Technology Review, Data Security Review

Data Audit and Strategy

Data Quality Review, Third Party Data Amendment, Data Architecture Review

A Few Case Examples

Customer Data Platform for a Products Company

A global powersports company offers a wide range of motorcycle, ATV and jet ski products. Each of these products is at a price point that requires significant offline and online research and review by the consumer. Further, once purchased, the product has a lifespan that can include accesorization, maintenance, upgrade, and repurchase. By implementing a customer data platform that will enable multitouch attribution, Magnetic Sky provides the client with the ability to track each prospect and customer as a single unique entity, anticipate their interests and promote to them directly. This results in higher rates of customer satisfaction, loyalty and ultimately lifetime value.

Customer Acquisition for a B2B Media Company

A “customer acquisition” company offers key assets in the way of Search Engine Optimization (SEO) algorithms and a call center to convert customers captured on client websites into paying customers of a range of client businesses from cable TV firms to financial services companies. The company struggles with being able to predict who among the prospect base is most likely to become a customer. By building a propensity model to project customers that are most likely to purchase a given product or service, Magnetic Sky can assist the company to strategically target prospective clients; thus saving the company and its clients (and the end consumer) wasted time, money and resources.

Customer Lifetime Value Growth for a B2B Services Company

A national print services roll-up is operating in a consolidating market, but with the necessary capital to aggregate it into one nationwide efficient provider. Presently, it is operating at breakeven profitability and struggles with the optimization of marketing and sales resource allocations with respect to the value of its diverse client portfolio. By conducting a customer lifetime value (CLV) model that looks at past value and projects the future value of each client, the company is able to segment clients for appropriate marketing, sales, pricing and promotion allocation. This allows them to invest more in its high “CLVs” and re-evaluate the business of the negative value clients. The resulting CLV prediction model will be applied as part of the due diligence period of each acquired company.

Customer Retention for a Media & Telecom Company

A global direct broadcast satellite service provider operates a Latin American division where services are offered predominantly on a prepaid basis.  Customer churn was substantial, resulting in losses on the provision of equipment to activate the satellite service, and the impact was felt on the company's valuation as a result. We analyzed subscription figures in the two largest markets and concluded that by adjusting subscription periods from a monthly basis to a 28 day period, enabling renewals to occur on the same day of the week as the original subscription was created, retention could be improved by more than 20%.

Marketing Personalization to Healthcare Providers

A digital medium for healthcare providers (HCP) seeking perspectives on procedures and medications to prescribe patients engaged us to "close the marketing loop" enabling them to provide content specific to the demonstrated interests of each HCP subscriber. We built the recommendation engine that powers content to the site (and other properties) based on browsing behavior and content similarity. We also developed a measurement framework to quantify lift from their pharmaceutical sponsors' campaigns.

Growing Customer Lifetime Value at a Regional Bank

A large regional U.S. bank sought to grow the average value of its customers to the bank without simply raising its fees.  We first conducted a historical Customer Lifetime Value analysis and then extended that to a projection of each customer's value.  The model was made to be continuously updated.  A second section of the project looked for signals among account balance and activity patterns to project share of wallet as well as cross-sell propensity.  Customers scoring highest on the index were campaigned for cross-sell of the banks other products with great success.

National Hotel Chain Intercepts Competitive Customers

A national hotel chain suffered from the growth of online travel agencies.  The growing trend towards booking hotel travel last minute and via OTAs that aggregate points for loyalty to their sites negatively impacted the client's margins and retention.  Our client needed to increase direct booking rates without sacrificing price with significant discounts. We constructed a method to profile the chain's most profitable and loyal clients, identifying characteristics such as demographics, frequency of travel, location of travel, proximity to hotel properties, time of day booked, method of booking, as well as clues toward lifestyle circumstances (e.g. travel sports families regularly requiring accommodations nearby competitions).  We then acquired a list of 15 million guests among competitive properties over the last 12 months and overlaid our "best customer" profiles on that list.  Lookalike profiles were developed for targeted campaigns directed at this competitive guest group, resulting in significant increases in organic growth of direct bookings.

National Retailer Identifies Individual Customers

A national retail chain with both online e-commerce presence and a nationwide footprint of brick and mortar stores was unable to identify individual customers that were either shopping in its stores or on its website.  The client needed a basic "golden record" capability to unify email, credit card, name, loyalty card, and address identifiers. Further, in cases where this information wasn't completely present, or was incorrectly entered, fuzzy logic algorithms were necessary to probabilisitically determine that in fact the store was dealing with the same individual.  These identifiers were unified in a Hadoop database which processed quickly enough that a customer could be recognized among the master "golden record" database before their transaction was fully processed, enabling the client to deliver real-time individual customer propensity-specific campaigns that optimized client profit and the customer's experience.