The Customer 360 view provides companies with a holistic view of their customers, using different touch-points from customer interactions. This view can be extremely useful to visualize customer segmentation and profiling. Using predictive analytics tools, we are able to predict what customers would want to buy and how much they are willing to spend on certain products. A vital part of having a successful Customer 360 view, is having properly governed and cleansed data, as the ‘dirtier’ the data is, the less likely it is to be useful and product accurate results.
The customer journey is essential to understanding the customer, and how they interact with the product. It is a vital process that needs to be understood and documented in order to attract more customers and make their journey as smooth as possible. We have mapped and optimized the customer journey for a large financial institution, where they were able to reduce their overall cost by 20 percent.
Having the right data is the start of the journey, analysing the data and being able to make sound business decisions is the is the ultimate objective. When properly handling and analysing data, business users have the opportunity to intelligently cross-sell and up-sell their products. Revenue optimization is catered to companies with different products and helps market products to their specific customer segments. If the data is consistent then we can help build and route leads based on scoring. Only if the data is standard and normalized can the marketing analytics give the right business insights companies would need to optimize their revenue engine.
Recommendation Engine & Next Best Action
Recommendations engines are vital for understanding which customers to target for specific products. These engines seek to predict the ‘rating’ or ‘preference’ a customer might have for specific products, hence allowing companies to more efficiently market their products. There are two main approaches to these engines, collaborative filtering or content-based filtering. Collaborative filtering methods are based on collecting and analysing a large amount of information on users’ behaviours, activities or preferences and predicting what users will like based on their similarity to other users. Content-based filtering methods are based on a description of the item and a profile of the user’s preferences.