Big Data

Sales & Marketing

Every organization’s primary goal is to grow Revenue using effective Sales & marketing. An organization’s Products and services are often highly complex, with customer needs similarly difficult to disentangle and understand. In this fragmented environment, Our data sciences solutions, using Machine learning, provide a sales force with the information to make better decisions about which customers to focus their efforts on, the ways in which to engage them and how to best differentiate product/service value from competitors. A business judgment-based segmentation can be made sharper with the help of data and information. And this cycle could be refined further with the suitable test and learn interventions. Listed below are some Data Sciences use cases that have benefited all types of organizations in growing their revenue

  • Prospect Segmentation: Prospects are segmented by their available attributes to form few meaningful segments for targeting purpose. In case of retail, these are their preferences, subscriptions, membership, attitudinal data. Once these “clusters” are formed, these clusters are further deeply analysed to understand their responses to certain stimuli. If the firm has past data on campaigns a look-alike model could provide this information. Based on that study, one could devise targeting strategy to optimize response and conversion. This kind of segmentation scheme can uncover details like type of channels and messaging to be used.
  • Campaign/ Marketing Effectiveness: In reality many firms may not have past data or the customer behaviour may change over time. One of the key tools to analyse campaign effectiveness is to analyse campaign performance with a test and control set up in mind. This coupled with a segmentation scheme help firms make continuous improvement to their targeting.
  • X-sell and U-sell Models: For a firm with diversified offerings, a key desire has been to cross sell additional services. Using predictive modelling techniques one can select a set of customers who are likely to respond to a x-sell offer and onboard customers for new services. A similar approach is available when the firm wants to offer an upgrade of an existing product/ services or persuade customers for additional sales. The up-sell models help archive this.
  • Preventing Customer Attrition: Continuously analysing customer behaviour to predict if there is any likelihood that the customer is going to defect could prevent losing valuable customers to competition. Most of the firms have rich information like their customers’ purchase pattern, amount, seasonality, location which could be leveraged for devising such a mechanism using predictive modelling.
  • Next Best Action/ Product: As part of ongoing CRM activities firms need to understand what the customer needs and act according to that as opposed to adopting a strategy that does not consider the customer need. With this kind of analytics, the ability to form a deeper relationship with the customer is a lot more in which the signalling to the customer from the firm is that “we understand your needs.”
  • Sales Analysis and Forecasting: In addition to having a micro level view of customer behaviour, it is important that sales analysis is carried out at an aggregated level possibly with a segmented view of products, customer and sales context to gain insights on which products are doing good, seasonal pattern, changing customer preference, and other macro trends. While one part of the analysis describes “what happened”, the same data could be used to forecast “what is going to happen”. This forecast could be used further to plan downstream activities like fulfilment/ servicing strategy.