5 Ways That Everyday Marketers Can Act as Data Scientists
In marketing — as with everything else — there’s good news and bad news. The good news is that we have all the data we ever wanted. The bad news? There is so much data that we don’t know what to do with it. The usual solution is to hire more (and more and more) data scientists. But, due to the shortage of skilled data scientists and the costs associated with hiring them, this is neither realistic nor scalable. Instead, here are five methods that enable your existing marketing workforce to act on their data using intuitive technical tools.
1. Look for Intelligence in Your Tools
A common reason to contact a data scientist is to find insights in the data you should care about. Instead, look for a tool that surfaces not only trends that you suspect might be present, but also those that you never would have thought to look for. A useful tool will be designed so that your most liberal-arts-educated marketers — not data scientists — can use it, harnessing the power of machine learning to automate actions that help marketers better understand how customers interact with brands.
If you’re sailing on the ocean, there are layers and layers of animal and plant activity below that you’ll never see without advanced tools. Analytics tools, like anomaly detection, are those that surface insights gained from both shallow and deep layers of data. Not only can anomaly detection surface hidden insights, but also automatically prompt marketers to act, kicking off workflows that wouldn’t have been discoverable before. This serves as a powerful lever for a nontechnical marketer to add value to any organization.
2. Use Algorithms Instead of Rules
The problem with rules is that they are often generated by a dedicated analytics team working far from where customer interaction occurs. Rules are often broadcasted top-down and leave little or no room to adapt to local needs. More importantly, they can neither be generated nor changed in real time.
On the other hand, algorithmic tools should be used by the people closest to the action. These algorithms can process mounds of data very quickly, allowing marketers to optimize the customer experience based on ever-changing conditions. The very best marketers cannot accurately predict who exactly their customers are without going through teeming stacks of data — a project that could take weeks and even months. A machine, on the other hand, can indicate who has a high propensity to convert within a double-digit number of seconds. Save your time and money and empower your nontechnical marketers with smart tools that surface data on which they can automatically act.
3. Democratize Your Toolset for Optimized Experience
A rigorous toolset that can be operated across the organization by employees with varying skillsets can have a profoundly transforming effect. The Royal Bank of Scotland (RBS) is a great example. They have a dedicated team of digital-journey managers (DJs) who iteratively test and optimize customer experiences. They also invite executives from all over the company to act as guest DJs, and some of the guests outperform the regular team in optimizing critical key-performance indicators. Even those who have no background in data are able to help make experiences better for their customers. Because of this innovative approach to optimizing the customer journey, this 300-year-old institution competes extremely well in the modern digital age.
This is what a smart, well-designed tool can do. It is strong enough to be used by trained analysts but simple enough for a nontechnical employee as well. It also enables everyone to act quickly on insights as they surface.
4. Look Beyond Digital to Supercharge Your Data
To surface the best actionable insights, companies must look beyond digital data. Often, companies will run analytics on Web data and, maybe, throw in mobile and video. However, with an integrated tool, you can create a whole customer view by integrating data from channels such as call centers, onsite visits, and customer-relationship management systems. When you broaden your sources of information, you will broaden your understanding. It’s been said that more data always beats better algorithms. Why choose? Invest in a tool that helps harness the power of both.
5. Use Integration to Expand the Value of Analytics
Integration is about what you do with your insights. You learn from data and not only use it to optimize campaigns, but also to surface actionable insights that can be directed to others in different workflows — and in the tools they use. For example, a content creator can see that a particular piece of content works for males between the ages of 20 and 35. He didn’t search through analytics to learn that — it is overlaid in his content library, therefore, expanding the value of analytics to other partners within any given marketing department. Look for tools that let you put a play button on your data, empowering those in charge of the customer experience to have the ability to optimize it.
Our industry is experiencing a massive shift in the distribution of access to knowledge. This will spark a game-changing impact regarding how well a company can dynamically change and optimize their customers’ experiences. To enable this change, look for intelligence in your tools. The companies that get this right are going to dominate; the companies that don’t will be left on the side of the road.
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