The Downstream Benefits of Predictive Analytics
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Lately, when it comes to analytics, their predictive power has been all the rage, promising impressive forecasting power for businesses. As Tom Siebel, founder of Siebel Systems once put it, analytics gives decision makers the power to “see around corners,” and predictive analytics delivers on that potential. Marketing departments have been lining up to embrace the possibilities, from upselling customers for next-best offers to determining long-term profitability to sensing impending shifts in market conditions.
While the appeal to marketing seems straightforward, there are good reasons why other parts of the enterprise have a stake in predictive analytics efforts as well. That’s because insights go well beyond the bounds of marketing – and even the bounds of the enterprise itself. There are a lot of effects both upstream and downstream from marketing, and there is a pressing need for predictive analytics to be shared across the enterprise.
This is a key takeway from a recent survey of 306 executives I helped prepare as part of my work with Forbes Insights. The report, sponsored by Lattice, represents companies with $20 million in annual revenues or greater—30% report revenues exceeding $1 billion.
I spoke with Ravi Dhar, professor of management and marketing as well as director of the Center for Customer Insights at the Yale School of Management, about the reach of predictive analytics in marketing. Predictive analytics cover a broad range of applications, with the common denominator of being “able to predict what it would take to encourage a desired customer behavior,” he states. Such desired behavior includes a range of activities, from upselling, cross-selling or channel shifts, to customer migration from a bricks-and-mortar store to an online store. Additional predictive marketing approaches may include “optimizing prices, identifying customer needs more appropriately, machine learning, pricing analysis, unstructured data analysis, text analysis, social media and predicting what customers will end up buying.”
In the Forbes survey, executives were asked about the factors that helped deliver the success of their predictive analytics projects. In most cases, the survey finds, two factors come together to help deliver successful outcomes: effective technology choices (63%) and organizational support (57%).
There’s a good case to be made for making predictive analytics an enterprise pursuit. Sales managers need to understand who the most likely prospects are for new product or service offerings. Procurement managers need to know how much computing capacity, raw materials or supplies are needed to sustain the business in the months ahead. Human resource managers need to understand upcoming staffing requirements, and where skills demand will be. Operations managers need to be able to coordinate production schedules. Distribution managers require advance notice for shipping runs. Supply-chain partners need to prepare for potential surges in demand.
The enterprise scope of predictive analytics was spelled out by Paul Sallomi, vice chairman and the Global and US Technology Sector leader for Deloitte LLP, who I also spoke with for the report. “At the enterprise level, not all insights are directly affecting the marketing channel,” he says. For example, a jet engine may be equipped with sensors that deliver streaming data that helps predict repair needs in advance of having a problem while in flight. This is not a direct marketing concern, but it reshapes the level of engagement between the manufacturer and the purchasers of such products. While not under the purview of the marketing department, predictive “changes the way the provider thinks about the product, and the way the consumer thinks about the value from the product,” he points out. “It changes the sales relationship, and it changes how the product is marketed.”
Predictive marketing initiatives that have been under way for some time are delivering impressive results. A vast majority of executives who have been overseeing predictive marketing efforts for at least two years (86%) report increased return on investment (ROI) as a result of their predictive marketing. Only a handful, 5%, say there has been no or a decreased return.
Having access to plentiful data sources both inside organizations as well as externally is key to predictive marketing. As organizations gain more experience and advance in their predictive marketing efforts, the scope of enterprise data also widens. More than two-fifths of enterprises that have advanced analytics cultures say that most of the enterprise data across their infrastructure is available for analysis; this expands from about one-fourth to one-fifth of companies in the very early stages of their analytics journey.
The insights that are ultimately generated through predictive analytics engines tend to be widely distributed in enterprises leading the way with analytics. Close to three-fourths of executives with highly advanced organizations report they are sharing this information across their enterprise, with downstream operations such as design or production. This is where predictive analytics really begins to take off.
This article was written by Joe McKendrick from Forbes and was legally licensed through the NewsCred publisher network.