Predictive analytics fuel growth.
Research shows, B2B marketers that use predictive analytics to develop their marketing strategies are 2.9 times more likely to experience above industry average revenue growth rates. Their organizations are 2.1 times more likely to be positioned as an industry leader.
Prescriptive analytics defines it.
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?
Prescriptive analytics essentially makes the data you use more valuable by telling you what to use. This transcends predictive insights, which reveal what may happen if a specific decision is made. It’s 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’t more marketers using it?
The Marketing Priority Problem
Marketing technology is not a barrier to the use of prescription analytics but the pace of martech evolution is. It was only recently, in 2013, that Gartner called prescriptive analytics ‘the final frontier for Big Data, where companies can finally turn the unprecedented levels of data into powerful action.’
We reached the next frontier pretty quickly and most organizations don’t have the means – or the directly measureable incentive – to focus their priorities more on data science right now and play catch up.
Being able to say, ‘what marketing channel will yield the most customer engagement for this campaign?, ‘which price point will offer the optimal sales numbers and profitability combination?’, ‘which image should we use to increase the performance of our social media campaign,’ sounds too good to be true. But it is possible. Early adopters are already benefiting from this more sophisticated level of data insight.
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.
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.
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.
The Data Science Mindset Problem
The other problem is mindset. Most marketers don’t necessarily think like data scientists. A data science mindset is about testing and experimenting, but also asking the right questions. Says marketing consultant Katrina Neal, “Don’t 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.”
During a talk at the Intelligent Content Conference, she notes that data scientists already are key in planning, creating and measuring content. For CMOs, developing a data science capability, either through an in-house team or consulting, has been on the wish list for years. For those who don’t already have a data scientist, hiring one isn’t that easy. Not just in marketing, but across the business world, data scientists are in high demand and short supply.
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.
- Using SaaS platforms with predictive modeling, like LeadSpace, Radius and Everstring, marketers can put their data to work and start experimenting with the possibilities of using more predictive and prescriptive analytics.
- Equipping your current team with the data science skills they need to put current and future platforms to use through training can be part of a long-term strategy as well.
This long-term, in-house approach isn’t just more affordable, it may be more practical for ensuring data insights and marketing goals are well aligned. The key with getting the most out of data for your marketing strategies is knowing how to use the numbers. Hiring an outside data scientist as a temporary consultant for example, may not translate into the most actionable insights. When your data science mindset comes from inside, it is more likely to be clear which metrics and actions are the most worthwhile from the perspective of your organization’s marketing goals.
Prescriptive Analytics Will Have to Happen at Some Point
CEO of Unmetric, Lux Narayan says, “Quite simply, enterprises should focus more on prescriptive analytics in 2018 because that is what produces a clear path from raw data to insights to the ideal course of action – potentially in real time.”
Whether you have fully-equipped and well-aligned in-house data science capability, or your organization is still deciding which SaaS platform to use to work with predictive modeling, there’s one undisputable takeaway. Prescriptive analytics will play a major role in marketing in the very near future.
- It makes a hyper personalized customer experience possible with localized data on consumption habits and customer patterns.
- Prescriptive analytics can lead to more impactful campaigns, allowing marketers to identify which strategic decisions to make to yield outcomes – this isn’t about following marketing trends and what works for other organizations. It’s about using data to identify exactly what will work for your brand, your audience, your situation.
- It can reduce business risk, revealing accurate assessments of what will work and why. As more data is processed, over time, prescriptive analytic models can become even more specific and accurate.
It’s too useful to ignore.
At this point in the digital transformation, I think we’ve all learned a golden rule with marketing technology: if it works, don’t wait to start.
The software market for prescriptive analytics is expected to reach $1.1B by 2019, jumping by 22 percent. Those marketing leaders that take advantage of this still relatively young stage of prescriptive analytics are going to come out ahead in a couple of years.
Are you already using prescriptive analytics in your marketing?