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June 11, 2013 05:30 pm GMT
Marketo is coming out with its first launch since its successful IPO last month. Its a one-to-one marketing tool that applies machine learning for social campaigns that allows marketers to automatically adapt messaging based on the customer and the history of engagement. The engine for Customer Engagement Marketing (CEM) utilizes templates to create a content stream, a set of ideas for how to conduct a dialogue with a customer. For example, a customer buys his third video game. That might trigger the notice of the CEM that would then offer the customer a credit on a next purchase if he tweets about it. A larger incentive might come if the customer tweets three times about the game. The CEM has built-in rules and machine learning algorithms that gauge customer engagement, said CEO Phil Fernandez in an interview last week. The engine is designed to understand which sequence of messages are creating active, positive engagement. It is designed to deliver messages based on the customer and what content they have received in the past. It uses a drag-and-drop user interface that allows new or modified content to be placed in the stream. The system manages the timing and sending of the content. Companies with multiple products are often juggling different marketing efforts. The struggle comes with trying to get the right message out. Fernandez said the CEM allows the marketer to set up sequences for each campaign, tailored in a way so the system knows when to send a message and when it might be best not to send one at all. It comes down to how best to nurture the customer. Doing it manually is impractical as it means changing the rules on a constant basis to best suit the individual customer. There are also the pitfalls that arise. A customer might get a completely wrong message if a marketing automation system is set up wrong. The CEM has rules built in to adapt to the customers history. Metrics are provided that encompass the broad array of measurements marketers track when building campaigns. This allows the marketer to better tune a campaign with content that is most compelling to the customer. Machine learning has broad implications for business. It can be used in risk analysis, financial decision-making, and on an operations level to best determine where resources should be allocated. Its early in the game, and engines like the one
Original Link: http://feedproxy.google.com/~r/Techcrunch/~3/ocOPUgXGpG8/
Marketo's First Launch SInce IPO Is A Machine Learning Engine For Social Campaigns
Marketo is coming out with its first launch since its successful IPO last month. Its a one-to-one marketing tool that applies machine learning for social campaigns that allows marketers to automatically adapt messaging based on the customer and the history of engagement. The engine for Customer Engagement Marketing (CEM) utilizes templates to create a content stream, a set of ideas for how to conduct a dialogue with a customer. For example, a customer buys his third video game. That might trigger the notice of the CEM that would then offer the customer a credit on a next purchase if he tweets about it. A larger incentive might come if the customer tweets three times about the game. The CEM has built-in rules and machine learning algorithms that gauge customer engagement, said CEO Phil Fernandez in an interview last week. The engine is designed to understand which sequence of messages are creating active, positive engagement. It is designed to deliver messages based on the customer and what content they have received in the past. It uses a drag-and-drop user interface that allows new or modified content to be placed in the stream. The system manages the timing and sending of the content. Companies with multiple products are often juggling different marketing efforts. The struggle comes with trying to get the right message out. Fernandez said the CEM allows the marketer to set up sequences for each campaign, tailored in a way so the system knows when to send a message and when it might be best not to send one at all. It comes down to how best to nurture the customer. Doing it manually is impractical as it means changing the rules on a constant basis to best suit the individual customer. There are also the pitfalls that arise. A customer might get a completely wrong message if a marketing automation system is set up wrong. The CEM has rules built in to adapt to the customers history. Metrics are provided that encompass the broad array of measurements marketers track when building campaigns. This allows the marketer to better tune a campaign with content that is most compelling to the customer. Machine learning has broad implications for business. It can be used in risk analysis, financial decision-making, and on an operations level to best determine where resources should be allocated. Its early in the game, and engines like the oneOriginal Link: http://feedproxy.google.com/~r/Techcrunch/~3/ocOPUgXGpG8/
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