Прогнозная маркетинговая аналитика как конкурентное преимущество16 февраля 2012
The report, “Divide & Conquer: Using Predictive Analytics to Segment, Target and Optimize Marketing,” assessed implementations and responses from hundreds of enterprises that have adopted predictive analytics for their marketing departments. The report was authored by Aberdeen analysts Trip Kucera and David White.
Comparing the top 35 percent of enterprises in the report with the remaining 65 percent – based on success of implementation and ROI – the top “leaders” scored a more than five-times advantage in year-over-year change in lifetime customer value than other enterprises in the report. Predictive analytics also enabled leaders to rank response times to customer queries at nearly double the rate of their counterparts, and an average percentage opt-out rate that was cut by 1.3 percent, according to the report.
There was also a difference in customization of marketing efforts from predictive analytics. Fifty-five percent of leader enterprises were able to offer customized offers to clients, compared with 33 percent of other enterprises using predictive analytics. And those leaders were able to apply behavior scoring to customer data at a higher rate (49 percent) than those less adept at predictive analytics (38 percent).
Aberdeen found that many of the tools and applications surrounding predictive analytics were the same among all enterprises surveyed. However, there was a stark contrast in organizational support and mandates to act on predictive data. With leaders, business managers were mandated to act on predictive information 45 percent of the time, but that was only the case with 30 percent of the remaining enterprises. In a different survey section, 45 percent of leaders were able to execute on predicted outcomes, though 28 percent of other organizations had that same directive and capability.
“When predictive analytics is used to support customer retention strategy, changes to operational processes will often be needed,” Kucera and White wrote in the report. “Changing a process changes the roles, responsibilities and reporting structure of the people involved in that process. That can be a challenge for some organizations.”
To close the gap with some of these disparities in predictive analytics for marketing, Kucera and White suggest paying increased attention to unstructured data, looking to more automation to enhance performance, and center an analytics plan around business expectations and processes.