As customers have more alternatives to source from, they become more demanding in their shopping behaviour. In response to the change, companies are forced to differentiate themselves from competitors in order to retain their customer base. Innovations are one of many different means that companies exploited to distinguish themselves from others in the market.
In business, typical questions most companies often try to answer include:
what
items customers are likely to buy together
what products should be cross-sold to customers when they bought specific items
what products should be placed together on the shelf to stimulate buying
To cope with the circumstance, emerging analysis methodology like Data Mining started to be widely employed by many big players in retail sector. The methodology helps them gain insights of their companies from their own data and in turns gives them all the competencies sufficient to answer the above questions.
INFOLYST's Data Mining consulting team consists of highly-respected renowned Computer Science experts specialised in data analysis. The team has also experienced developing a number of sophisticated data analysis software that help them in knowing the logic of Data Mining algorithms rather than just consider Data Mining software tool as a black box.
Combining the Data Mining consulting team with our business professionals whose experience embrace in business, marketing, consulting, and financial, INFOLYST is well qualified for the business-oriented data analysis in this advanced technology era. The output of the analysis will give businesses lots of insightful information, allowing them to plan your business strategies more effectively.
Other sample Data Mining applications are;
to classify customers who might respond to a particular campaign to detect fraud in credit card using to predict customer churning to identify what action will earn their loyalty back if likely to churn to select which customer should be credit card approved to segment customers into groups of similar behaviors to identify propensity to buy
what
items customers are likely to buy together
