Magic Quadrant for Multichannel Campaign Management : 2015

The multichannel campaign management (MCCM) market comprises vendors that seek to orchestrate company communications and marketing offers to customer segments across channels, such as websites, mobile, social, direct mail, call centers and email. Capabilities include:

• Basic campaign management includes functions for segmentation, campaign creation, campaign workflow and campaign execution.
• Advanced analytic functions include predictive analytics and campaign optimization.
• Advanced execution functions include loyalty management, content management, event triggering, and real-time offer management in inbound and outbound environments.
• Digital marketing capabilities include ad management, content marketing, mobile and social marketing, Web, and email marketing. Digital marketing extends the marketing process through channels such as the Web, email, video, mobile and social applications, point-of-sale terminals, interactive TV, and digital signage and kiosks.
• MCCM offerings may also integrate marketing offers and leads with sales for execution in B2B and business-to-consumer (B2C) companies.

IBM, SAS, Teradata, Oracle, Adobe & Salesforce are in the leader's quadrant of "Magic Quadrant for Multichannel Campaign Management : 2015" report.

Note: Source Gartner

The Forrester Wave™: Big Data Predictive Analytics Solutions, Q2 2015

Predictive analytics uses algorithms to find patterns in data that might predict similar outcomes in the future. A common example of predictive analytics is to find a model that will predict which customers are likely to churn. For example, telecommunications firms can use customer data such as calls made, minutes used, number of texts sent, average bill amount, and hundreds of other variables to find models that will predict which customers are likely to change mobile carriers. If a carrier can predict the reasons why customers are likely to churn, it can try to take preemptive action to avoid this undesirable outcome.

This isn’t a one-time operation; firms must rerun their analysis on new data to make sure the models are still effective and to respond to changes in customer desires and competitors. Many firms analyze data weekly or even continuously. Game-changing insights start with asking creative, deep questions. Once the question has coalesced, use these six steps to answer them in a continuously improving predictive discipline

• Identify data from a variety of sources.
• Wrangle the data.
• Build a predictive model.
• Evaluate the model’s effectiveness and accuracy.
• Use the model to deliver actionable prescriptions to your business peers.
• Monitor and improve the effectiveness of the model.

SAS, IBM, SAP lead The Pack in ‘The Forrester Wave™: Big Data Predictive Analytics Solutions, Q2 2015’.