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5 Ways Predictive Analytics Make or Break a CRM System
Size doesn’t matter: why predictive analytics prove your customer relationship management methods are likely falling short
For a sales-driven organization, it isn’t the size of your data that matters, it’s what you do with it. No longer a discretionary luxury, predictive analytics are now the name of the game for those who seek to utilize customer metrics in a meaningful way to establish a tremendous competitive advantage, gain notable market share and significantly boost bottom lines. In fact, according to the 2015 State of Sales Report published by Salesforce Research, “smart selling fueled by predictive analysis is expected to jump 77% among high performers,” throughout 2016. Not only that but high performers are also four times more likely to use predictive analytics.
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About the Author
Lang Smith is the founder of Cloud Signalytics – a first-of-its-kind predictive intelligence software platform helping major franchise auto dealerships create highly precise, individualized customer profiles to maximize sales. He may be reached online at www.cloudsignalytics.com.
Source: https://secure2.sfdcstatic.com/assets/pdf/misc/state-of-sales-report-salesforce.pdf
All organizations and individuals have a history and within these experiences reside events which those persons involved would just as soon forget. These experiences typically involved significant loss or other discomfort that negatively impacted the psyche of the organization as a whole or the associated individual(s). Often, some key words and/or phrases become forever related to these painful experiences. Whenever these words are spoken or read, they elicit a strongly negative emotional response as individuals are jolted back into the memory of the disastrous event. Acting on this emotion, these individuals may strike out at or withdraw from the individual(s) using these words. Thus, these words or phrases have come to be collectively known as Purple Words.
Effective evaluation and control programs rely on a set of underlying behaviors promoting continuous performance improvement. While positionally dependent, these behaviors foster the continuous identification and resolution of performance improvement opportunities and shortfalls.
Too often, individuals align themselves with a particular statistic or data point as though it infallibly supported their position. In these instances, raw data is assigned meaning absent context from the surrounding environment and possibly in spite of flaws and biases in its collection. While the assignment of meaning to a particular data point may serve one’s immediate purpose, it often leads to erroneous conclusions and may result in undesirable outcomes.
“Information is data that has been processed in such a way as to be meaningful to the person who receives it.”
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