{"id":75057,"date":"2018-02-12T00:00:00","date_gmt":"2018-02-12T05:00:00","guid":{"rendered":"https:\/\/marketinginsidergroup.com\/uncategorized\/marketers-arent-using-prescriptive-analytics\/"},"modified":"2025-09-09T03:23:42","modified_gmt":"2025-09-09T07:23:42","slug":"marketers-arent-using-prescriptive-analytics","status":"publish","type":"post","link":"https:\/\/marketinginsidergroup.com\/content-marketing\/marketers-arent-using-prescriptive-analytics\/","title":{"rendered":"Why Marketers Aren\u2019t Using Prescriptive Analytics (But Should Be)"},"content":{"rendered":"

Predictive analytics fuel growth.<\/p>\n

Research shows, B2B marketers that use predictive analytics to develop their marketing strategies are\u00a02.9 times more likely<\/a>\u00a0to experience above industry average revenue growth rates. Their organizations are 2.1 times more likely to be positioned as an industry leader.<\/p>\n

Prescriptive analytics defines it.<\/p>\n

By 2015, the vast majority of B2B marketers, about 89 percent, were either already using or planning to implement predictive analytics within the next 12 months. With predictive marketing already proving itself as a power horse for driving up value-based KPIs like customer lifetime value and per customer profit margin, how much more efficient and effective would your organization be with the use of prescriptive analytics, the next level of analytical modeling? Why wait to be a late adopter?<\/p>\n

Prescriptive analytics essentially makes the data you use more valuable by telling you\u00a0what<\/em>\u00a0to use<\/em>. This transcends predictive insights, which reveal\u00a0what may happen<\/em>\u00a0if a specific decision is made. It\u2019s end-game (but not end goal) data science for marketers because it opens the door to an entirely new playing field of possibilities. So why aren\u2019t more marketers using it?<\/p>\n

The Marketing Priority Problem<\/h2>\n

Marketing technology is not a barrier to the use of prescription analytics but the\u00a0pace<\/em>\u00a0of martech evolution is. It was only recently, in 2013, that\u00a0Gartner called prescriptive analytics<\/a>\u00a0\u2018the final frontier for Big Data, where companies can finally turn the unprecedented levels of data into powerful action.\u2019<\/p>\n

We reached the next frontier pretty quickly and most organizations don\u2019t have the means \u2013 or the directly measureable incentive \u2013 to focus their priorities more on data science right now and play catch up.<\/p>\n

Being able to say, \u2018what marketing channel will yield the most customer engagement for this campaign?, ‘which price point will offer the optimal sales numbers and profitability combination?\u2019, \u2018which image should we use to increase the performance of our social media campaign,\u2019 sounds too good to be true. But it is possible. Early adopters are already benefiting from this more sophisticated level of data insight.<\/p>\n

As fantastic as prescriptive analytics would be to have in place, it has to take a backseat to other, more familiar, and more affordable, marketing priorities.<\/p>\n

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image source<\/a><\/p>\n

Even though it is exactly this level of analytics that will help marketers blow their lead generation, traffic and sales goals out of the water and take personalization and ROI tracking to a whole new level of amazing.<\/p>\n

The organizations that do invest more time and resources into implementing the right technology and then leveraging those platforms so they are able to get the real-life simulations, pragmatic AI guidance and other prescriptive analytics advantages will have to sacrifice other priorities to move ahead. But, the level of accuracy, risk reduction and depth of insight will be well worth it in the long run.<\/p>\n

The Data Science Mindset Problem<\/h2>\n

The other problem is mindset. Most marketers don\u2019t necessarily think like data scientists. A data science mindset is about testing and experimenting, but also asking the right questions. Says marketing consultant\u00a0Katrina Neal<\/a>, \u201cDon\u2019t rely on generic content best practices. You need original thinking and a test-and-learn culture to find your own unique blueprint that works for you.\u201d<\/p>\n

During a talk at the Intelligent Content Conference, she notes that data scientists\u00a0already<\/em>\u00a0are key in planning, creating and measuring content. For CMOs,\u00a0developing a data science capability<\/a>, either through an in-house team or consulting, has been on the wish list for years. For those who don\u2019t already have a data scientist, hiring one isn\u2019t that easy. Not just in marketing, but across the business world, data scientists are in high demand and short supply.<\/p>\n

With Big Data becoming increasingly digestible in the future, whether you hire a data scientist or not, there still needs to be a shift towards the way marketers approach data. This means, taking proactive steps to foster a more data scientific mindset right now is critical.<\/p>\n