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Predictive analytics, as a science, is nothing new. According to Contemporary Analysis, it has been around since 1689, born out of a need to predict risk in insuring overseas shipments. More recently, predictive analytics has moved into the world of marketing, where it is quickly taking the industry by storm. Today, according to a Forbes Insights report, 86 percent of executives say they have seen a positive return on investment from predictive marketing analytics. Further, The Wall Street Journal reported that spending on marketing analytics is expected to increase from 7 percent of marketing budgets to more than 12 percent by 2017, while VentureBeat says that total marketing technology spend is expected to hit $32 billion by 2018.
The jump in spending on marketing analytics shows that busy CMOs would rather be given a solid recommendation than handed a set of data and forced to spend hours, days, or even weeks digging for their own conclusions. And therein lies the challenge for developers of predictive marketing applications: as decision-makers rely more on your predictions, you’d better be sure the underlying data is accurate. Reinforcing the point, the previously mentioned Forbes Insights report noted that only 13 percent of companies using predictive capabilities consider themselves to be highly advanced with the technology. Most, then, are not sophisticated and are relying on their predictive applications to get it right. How do they get it right?
Developers of predictive marketing apps use data science to identify the best leads, so reps and marketers can focus on the accounts mostly likely to buy and precisely target campaigns and messages to quickly and efficiently boost sales. The better the underlying data, the more precise the prediction, which leads to faster engagements, streamlined deal cycles, and more wins. To improve the precision of their predictions, app developers augment their customers’ data with supplemental data that introduces more signals and makes it possible to paint a more detailed picture of the prospect. And it’s incumbent upon them to choose the best data they can find—most accurate, rich, and relevant. Simply put, better data ensures more valid, higher-probability predictions.
Developers using the right market intelligence and data have found that it improves the quality of their predictions, which increases the value of their apps. As well it provides insights from news sources to create an even deeper picture of companies, contacts, and situations, enabling developers to build advanced predictions using data that isn’t available from other sources. But better data doesn’t come easy. Market Intelligence firms combs through over 40,000 editorial, news, financial, and social sources of information. Data points are then triangulated across multiple sources to determine which data is most reliable. For data that’s suspect, editorial staff of such marketing intelligence firms manually validates it to further ensure accuracy.
For customers, better predictions—from better data—magnifies and expands marketing potential, helping them target their efforts more precisely. Benefits include: Lear
- Increases marketing campaign effectiveness
- Increases lead-to-opportunity conversion rates
- Increases pipeline velocity
- Increases closed-won rates
- Increases accuracy of revenue forecasts
How are you building better data into your applications?
The post Better Predictive Marketing Comes From Better Data appeared first on Analytics India Magazine.
