Different Types of Data for Social CRM
When determining what Social CRM data you want to corral, it’s important to keep in mind the different types of data and what they can teach you. Many businesses use historical data to take an educated guess at future performance across similar metrics.
Forecasting, budget planning, and sales goals are often based on historical analytics data, but this approach can be limiting. To capture a well-rounded historical view for planning, it’s important to understand the difference between descriptive analytics and predictive analytics.
Descriptive analytics: This type of data identifies past events and the perceived or real factors that played into creating the events. The idea is to look for indicators of success and failure in past initiatives. It’s a reactive strategy to marketing planning — looking back, reacting to discoveries, and adjusting future plans to meet the past conditions.
Predictive analytics: This is a mathematical model that accurately predicts future results and outcomes based on hard facts. It’s easier (and typically more affordable) to keep existing customers than to generate new ones, which is why predictive modeling often leads marketing initiatives for promotions and offers. Taking what you know customers prefer and reaching them with what they want falls into predictive modeling.
Keep in mind that the past can’t always predict the future. Both descriptive and predictive modeling have their limits, in part because the following factors make tomorrow (or next year) a little different from yesterday (or yesteryear):
Today’s marketplace is ever-evolving and complex.
You’re gathering a lot of data at a rapid pace in social CRM.
Markets are moving targets that you can’t always accurately predict.
However, if you analyze historical data in the right context, you can develop a more tangible approach to planning. But it’s vital to distinguish the types of data you’re gathering and analyzing first. After you know what you have, you can draw conclusions and translate that data into actionable metrics.
|Descriptive Analytics Modeling||Predictive Analytics Modeling|
|Perceived outcome based on historical data||Accurate prediction of outcomes based on past outcomes with the
same factors in place