To identify typical customer paths and associate scenarios with them, the company needs to rely on the information. The company relies upstream on data science to identify behaviors and profiles. Strong and weak signals are then detected to predict each individual's behavior and the most effective personalized actions to respond to them. We can thus identify the products and services that may be of interest to them, the frequency of contacts they may accept and the time at which they may be spoken to, their favorite channel at the time t, or the risk that they may leave the store.
Traditionally, data science has also been used to identify segments that classify customers and prospects into these categories, such as high-value customers or fragile customers, whose probable behavior is known. This approach is gradually giving way to hyper-personalization. Formerly marketing animation tools, segments are then used as objectives.
Data science relies on multiple data sources: internet logs, purchases on all channels, reactions to marketing actions and campaigns (opening e-mails, clicks in messages or on advertising banners, the content of responses to phone calls, behavior on social networks, geolocation, and even data reported by connected objects. The multiplication of channels and interactions generates an explosion of data volumes. In a data science approach, we start by centralizing and reconciling all this information, previously confined in silos, by attaching it to a single client. The more accurate, available, and high-quality this information is, the more value-added information data science will derive from it. The technologies and resources available in the cloud today make it possible to collect and process all this data at reasonable costs.
Data science helps to highlight the issues at stake. Marketing people then take over to design customer itineraries and scenarios that will enable them to respond to them. These will then be implemented in the marketing platform which, by running these scenarios, will provide new data that will, in turn, feed data science.
- Leverage all internal enterprise and third-party data to develop a revenue management model
- Proposal of a pricing level that optimizes the company's revenue
- Steering and monitoring of results to adapt the repricing model in real-time
Customer segmentation and profiling
- Classification of each individual according to available attributes
- Optimization of customer knowledge and identification of a core target group
- Adjustment of the marketing strategy and personalization of messages for each segment
Attribution and contribution model for marketing levers
- Measurement of the weight of marketing levers in the conversion (purchase of a product, subscription of a contract ...)
- Analysis of the levers used by a consumer to identify the ideal path for optimal conversion
- Allocation of the marketing budget on the different levers to maximize conversions
- Prediction of future customer behavior to adapt marketing scenarios
- Detection and targeting of the most appetizing customers when purchasing a given product
- Identification of customers losing business in order to retain them (anti-churn score)
- Identification of products to be recommended according to customer profiles
- Adopting a one-to-one approach through hyper-targeting and highly relevant messages
- Reduce time to market and increase the ROI of marketing campaigns and programs.