What's the probability for Customer X to buy product Y next week?

As customers leave behavorial traces when interacting with you through your platform, your support services, your marketing and your sales teams, you can collect valuable data to infer what other products a customer might be interested in. You can create clusters of customers based on data, to help you approach them in a manner that maximizes your chance for closing deals. Given enough coherent data, you can predict the likelihood of a customer to buy a specific product or a variant thereof during some timeframe. Using this information, your sales team can approach a client directly and close the deal. Our data-driven services can also help you increase sales by suggesting additional products this client might be interested in.

What customers are most likely to switch to a competitor by the end of the month?

Equally important as winning new customers, is making sure that existing customers remain with you. Sometimes, small marketing efforts including small gifts and promotions can help to keep customers from leaving. Given enough relevant data, a prediction model can help estimate the likelihood for a specific customer to leave in the near future. A marketing specialist can then pull a list of customers whose probability for leaving by the end of a payment cycle exceeds 80%, and target these with promotional campaigns. For example, a cellphone provider might be interested in knowing the people likely to switch to a competitor by the ended of their current contracts. A prediction model for this use case might incorporate internal data such as call history, cellphone locations (estimable through cellphone towers) and behavior of the social circle (e.g. if key friends whose contracts end earlier switch to a competitor, the likelihood is increased). Additionally, we would include external data such as cellphone coverage of the provider and its competitors for frequently visited places (home, work, friends' homes) or the buying power of the region a customer lives.

What's the consumer sentiment for Product X? And how will it change?

Whenever Apple presents their newest iPhone, they stream the keynote live to the whole world. During these times, the social networks, such as Twitter and Facebook, buzz with the most recent revelations by the technology company. Sentiment Analysis allows to track the polarity of the sentiment over time. For example, during such keynotes, new features typically get discussed in sequence, which lead to consumer sentiment around the Apple brand to rapidly rise and fall. Besides during large-scale events, consumer sentiment can give an interesting insight into what the market thinks about something, and how it is developing over time. Upwards trends in market sentiment are typically associated with an increase in sales, while the negative is true too.

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What's the probability for Customer X to buy product Y next week?

As customers leave behavorial traces when interacting with you through your platform, your support services, your marketing and your sales teams, you can collect valuable data to infer what other products a customer might be interested in. You can create clusters of customers based on data, to help you approach them in a manner that maximizes your chance for closing deals. Given enough coherent data, you can predict the likelihood of a customer to buy a specific product or a variant thereof during some timeframe. Using this information, your sales team can approach a client directly and close the deal. Our data-driven services can also help you increase sales by suggesting additional products this client might be interested in.

trending_up

What customers are most likely to switch to a competitor by the end of the month?

Equally important as winning new customers, is making sure that existing customers remain with you. Sometimes, small marketing efforts including small gifts and promotions can help to keep customers from leaving. Given enough relevant data, a prediction model can help estimate the likelihood for a specific customer to leave in the near future. A marketing specialist can then pull a list of customers whose probability for leaving by the end of a payment cycle exceeds 80%, and target these with promotional campaigns. For example, a cellphone provider might be interested in knowing the people likely to switch to a competitor by the ended of their current contracts. A prediction model for this use case might incorporate internal data such as call history, cellphone locations (estimable through cellphone towers) and behavior of the social circle (e.g. if key friends whose contracts end earlier switch to a competitor, the likelihood is increased). Additionally, we would include external data such as cellphone coverage of the provider and its competitors for frequently visited places (home, work, friends' homes) or the buying power of the region a customer lives.

trending_up

What's the consumer sentiment for Product X? And how will it change?

Whenever Apple presents their newest iPhone, they stream the keynote live to the whole world. During these times, the social networks, such as Twitter and Facebook, buzz with the most recent revelations by the technology company. Sentiment Analysis allows to track the polarity of the sentiment over time. For example, during such keynotes, new features typically get discussed in sequence, which lead to consumer sentiment around the Apple brand to rapidly rise and fall. Besides during large-scale events, consumer sentiment can give an interesting insight into what the market thinks about something, and how it is developing over time. Upwards trends in market sentiment are typically associated with an increase in sales, while the negative is true too.