For many marketers, the idea of integrating predictive analytics into their marketing strategy sounds out of reach. If you bring up the idea to a room of marketers, you are likely to hear phrases like “too expensive,” or “out of our budget,” or worse:
“We do not have the right people to manage something like that.”
When you look back at the history of predictive analytics, it's easy to understand why many marketing leaders might feel that way. During the first few years of big data’s meteoric rise in the marketing world, many struggled to keep up with the near-constant technological advances that seemed to pop out from around every corner. In the six years that Scott Brinker has tracked new marketing technology tools, martech solutions providers have grown from 150 to over 5,000. In the beginning, most companies didn't have an adequate budget to build a martech stack capable of predictive analytics. Nor did their marketing team have the right skills to meet the coding and analytical demands needed to extract quality insights.
Today, however, that's simply not the case. The flood of new technology has made building a martech stack set up for predictive analytics more affordable and has lowered the barrier to entry from a skills standpoint, too. According to Gartner, marketing analytics spend has moved from the #4 to the #1 marketing budget priority in 2018. That budget is not just going toward analyzing current customer activity; 2018 is set to be the year for more widespread adoption of predictive analytics, too.
You don’t need to be Google or Facebook to get on board and take advantage of predictive analytics either. With the right tools in place, your company can start forecasting customer behavior this year, and begin using that information to create more targeted marketing campaigns.
That's why the ReachForce team put together this post to show you the tools your business needs to make the leap into the world of predictive analytics in 2018. To help you better understand the role each of these tools plays in a successful predictive analytics strategy, we have broken down the process into three steps:
Step #1: Data Collection
As the name suggests, the first step in launching a successful predictive analytics marketing plan is to gather as much high-quality data as you possibly can about your clients. As a general rule, there is no such thing as too much data when it comes to predictive analytics. The more you know about your audience, the easier it becomes to forecast their future behavior.
That said, even a small amount of “bad” data can pollute your insights and cause you to take steps in your marketing plan that either have no effect on your customers or, worse, actually have a negative impact on your brand.
That is why building a foundation of solid, high-quality data plays such an important role in setting the stage for a successful predictive analytics strategy. There are two tools you will need to use at this stage to make it happen:
Web Analytics Platform
A web analytics platform — like Google Analytics or one of the 100+ alternatives — simply collects raw user data from your web traffic. That is it. Some tools will have reporting functionality, but at its core, the reason you are using a web analytics platform is not for help deciphering data (we will get to that in a moment), but rather as a simple means of collecting relevant information.
Now, defining what information you should consider “relevant” is where the various platforms will differentiate themselves. Google Analytics is free and a great starting point, but may not be able to get as granular with data as you might want. A paid alternative may be a better alternative if your budget allows. Why?
Because when it comes to predictive analytics, the more granular you can get with your data, the better your predictions are going to be. That is, of course, assuming that data is clean and of the highest quality, which is where this next tool comes into play.
Data Management Tool
If you regularly follow this blog, you may have seen our previous coverage of the relationship between clean data and predictive analytics. If not, here is a brief summary for you:
Good, clean data helps marketers create more targeted buyer personas, which increases the effectiveness of customer segmentation (the more you know about your customers, the easier it is to group them together based on commonalities). Better segmentation leads to higher conversions because the content you deliver becomes tailored to that specific buyer. Of course, as conversions increase, so does your revenue.
A data management platform like ReachForce collects the data from your web analytics platform along with other key inbound sources like lead capture forms (where data can be enriched in real-time using SmartForms) and social channels to build a single source of high-quality data to inform the rest of your predictive marketing strategy. It does that in three major ways:
- It unifies your data into a single source. Rather than data accumulating in siloed analytics platforms, ReachForce collects all of the data available to you through inbound streams and creates a single source view of your customers.
- It cleans your data. Duplicates and misinformation can skew insights and knock your marketing strategy off-kilter. A good data management platform scrubs your data clean of any “bad” information, ensuring you’re working with only the most high-quality information to formulate actionable insights.
- It enriches your data. Remember what we said earlier. The more data you have available to you, the better your ability to forecast customer behavior. Unfortunately, some data may not be easily available to your company. You may have found through extensive testing that lead capture forms with more than five fields have a dramatically lower conversion rate and as a result, stopped asking for some information that would be valuable in predictive analytics. A good data management platform like ReachForce enriches the incoming data from your customers in real-time, enabling you to get the best possible picture of your customers.
Once you have high-quality data in-hand, the next step in the predictive analytics process is to actually analyze the data for trends your team can convert into actionable insights.
Step #2: Data Analysis
Collecting data is one thing, but actually understanding what the data tells you is something entirely different. For years, this has been the stage where many organizations have felt unprepared to manage the demands of predictive marketing. Real-time data analysis and trend identification generally extended beyond the limits of your average marketer’s job description and with the demand for (and salaries of) data scientists so high, most organizations did not have the personnel needed to make predictive analytics a reality.
Today, many predictive analytics tools are built directly into your martech stack. Plugins for everything from your CRM to your personalization tools make it so that predictive analytics can now be an accessory to the work you are already doing. Still, if you are going to take predictive analytics seriously, a standalone predictive modeling platform is a worthy investment. There are two routes you can go when it comes to investing in data analysis.
The Old School Predictive Coding Tool
Despite the widespread availability of self-service predictive modeling platforms, plenty of organizations elect to use their own predictive coding tools to delve into customer data and pull out insights. Proponents of predictive coding tools like the fact that you can manipulate your data however you see fit. The insights you draw can be tailor-made for your business, whereas with self-service tools you’re at the mercy of what insights the platform makes available (well, sort of — but more on that in a moment).
Of course, the downfall of a coding tool is fairly obvious; you need a data scientist (or a really savvy marketer) on staff to actually code the data and draw out those insights. If you are thinking, “well, could I not just hire a freelancer to set it up once and then be good?” the answer is, sadly, “no.” Predictive coding tools are open source platforms and as a result, are constantly being changed and updated. Your company will likely need a full-time data scientist to make a platform like this operable.
The New School Self-Service Platform
As marketing technology has evolved, the need for a coding tool has steadily diminished. Today, there are a number of different plug-and-play predictive analysis tools on the market that even an entry-level marketer could likely understand how to use. As these tools have risen in popularity, they have become more malleable. Rather than offering boilerplate insights, most tools allow you to drag-and-drop different data sets and scenarios to extract the same highly-customized data you would look for from a coding tool.
To be clear, this is not to say data science is no longer a valuable position in your organization. As we recently argued on the blog, 2018 is set to be the year of the data scientist, with more companies than ever looking to hire best-in-class analysts to their teams. However, like many marketing jobs today, data scientists are benefiting from the flexibility provided by machine learning technology, which has freed them from the traditional backroom job of coding for analytics to frontline research and customer engagement.
Regardless of which tool you use for data analysis, once you have the insights in place, it is time to put them into action.
Step #3: Taking Action
You have the data. You have the insights. Now you need to put a plan into action. At this stage, the full range of tools in your martech stack comes into play. From your CRM to your content management platform, your social media tools down to your digital advertising solutions, your entire martech stack can (and should) benefit from the insights you have collected and work together to create the most targeted marketing campaigns possible. Rather than go through each tool individually, here are a couple that will play a critical role in executing your predictive analytics marketing strategy.
A/B Testing Software
An A/B testing tool — like Optimizely — helps you determine whether the predictive insights you gather are valid (and subsequently, if you are working with high-quality data). Validating your insights with actual tests is an important step in the process because the data you gather from these tests actually feeds back into your insights and strengthens your strategy. If, for example, you’re testing to see the results of moving a call-to-action further up in a blog post and you find that doing so actually reduces your visitors’ on-site time by 30 seconds, that unexpected result can (and should) be used to inform your future approaches.
Of course, it would be impossible and completely unnecessary to manually trigger the actions you put in place based on your predictive insights. An automation platform that takes action based on specific rules you put in place based on your insights takes the responsibility off of your shoulders and ensures your actions are timely and well-executed.
Predictive analytics play an important part in the modern marketer's playbook, and it all starts with high-quality data. To learn more about how ReachForce SmartForms can help you optimize lead generation and improve your impact on revenue, sign up for a free trial and get a demo today.