VigourSoft helps Customer Analytics product companies build revenue-accelerating analytic platforms that enable enterprises in comprehending and interpreting their customers’ journeys, for activating winning marketing campaigns right from inquiry, acquisition, growth, retention to reacquisition.
The 360 Degree Customer View
Draws a more precise and vital image of your customer by way of combining behavioral analytics with conventional and unstructured information sources. 360-degree modeling yields a very insightful representation of your consumer.
Micro-Segmentation Powered By Behavioural Analytics
Machine Learning algorithms and statistical models can recognize customer behavior patterns as they occur, graduating from a static snapshot of limited customer segments. Dynamic micro-segmentation enables the discovery of newer customer identities, profiles, and categories which were hitherto unknown.
Customer Engagement Strategies
Securing the attention of your target profiles and prospective customers is a combination of technology, skill, venue and time. It is paramount, to meet them at the most appropriate venue, offering the product/service they need, with the easiest route to acquire it. Technology and analytics based customer engagement strategy can breach the cacophony to connect and stimulate action.
Customer Purchase Analytics
Understanding the “what”, “why”, “when” and “where” of customer purchases help sell better. It also fosters the creation of a strong and repeats consumer base.
Consumer Lifetime Value (CLV) Modeling
Behavioral and predictive analytics enables businesses to measure and estimate the value of a customer across products, brands, segments, channels and time. CLV models empower global brands in designing revenue-accelerating customer target programs and segments.
Customer Retention & Reacquisition
Customer acquisition, as well as customer loss, are both very expensive, so investing in forecasting and arresting customer loss is a great investment. Customer retention can be achieved by accurately identifying risk elements using smart predictive models and then applying remedial/retentive actions. In case customer loss does occur then predictive models can also be utilized to understand what will bring the customer back and when.