In the time before “big data” became a component of every smart marketer’s campaign strategy, the idea of predictive analytics marketing likely seemed like something out of a dystopian, 1984-esque novel.
The thought that enough data on consumer activity exists for marketers to accurately forecast trends in buying behaviors would have Orwell turning over in his grave. Knowing that marketers use that data to improve conversion rates (and thus increase revenue) might just be enough to bring him back from the dead.
The truth, however, is that predictive analytics marketing is a win-win-win scenario.
The Win-Win-Win Scenario
Marketers win because predictive analytics marketing pushes campaign success through the roof. A 2016 report shows marketers who employed predictive analytics met objectives 55 percent of the time, whereas marketers who did not showed a success rate of just 30 percent.
Salespeople win because lead quality improves. Predictive analytics marketing means better targeted campaigns leading to higher conversion rates and more sales.
Finally, clients win as well. Customers escape the annoyance of advertisements for products or services that mean nothing or do nothing for them. Remember the early days of the internet where you could not visit a website without being bombarded with generic ads? A new car when you take the train? A shaving kit when you are a woman? A vacation in Arizona when you live in (wait for it...) Arizona?
Big data and predictive analytics marketing changed all of that for consumers. Now, marketing efforts are specific to you, your needs, and your buying behaviors. Can it feel a little “big brother” at times? Maybe. But the end result for marketers and consumers alike is a far superior, increasingly efficient marketing strategy tailor-made to meet the needs of both sides.
This article aims to dive deeper into the world of predictive marketing analytics. From what exactly “predictive analytics” means, to the impact analytics have had on campaign design, to the steps your business can take to begin leveraging predictive analytics as part of your marketing strategy, you should walk away from this article with a comprehensive understanding of predictive analytics’ role and importance in the future of marketing.
What are Predictive Analytics?
A short definition goes something like this: predictive analytics are a composite view of historical customer data, segmented demographically to forecast future buying behavior.
In non-marketing jargon, that means companies track your activity online through any number of channels - keyword searches performed, social media, clicks on a website, previous purchase history, etc. - and bring all of that information together to paint a picture and gauge where you are in the customer journey. Are you a tire-kicker, just poking around for information and not yet ready to shop for a solution? Or are you currently comparing competitive services in hopes of making a buying decision in the very near future? Either way, marketers are compiling that “big data” and combining it with demographic information (age, gender, company, role), to market solutions to you more effectively. That is predictive analytics marketing in a nutshell.
It is important to note that predictive analytics rely heavily on consistency. As a result, best-in-class predictive marketing strategies do not put too much weight in any single data point. Marketers leveraging predictive analytics care about the amalgam of your activity and demographic data to create a profile of you as a buyer. They care about tectonic shifts in buying behavior, not small changes in any one particular area.
The next big marketing revolution is here, and it is riding in on a wave of customer data more powerful than ever before. Savvy marketers armed with predictive analytics are changing their approach to campaign design with impressive results.
How Predictive Analytics Have Revolutionized Campaign Design
The Smartforms blog recently covered some tactics on how to best prepare your team for the “coming B2B marketing revolution” known as predictive analytics, but this section goes a bit deeper by analyzing the impact predictive analytics marketing has had on campaign design.
First, consider budget. Before big data, marketing budget was spent cautiously and experimentally. Proving return on investment was difficult, if not impossible; therefore marketing teams needed to test the waters with incremental budget spend across many different marketing channels to figure out what resonated with their target customer base.
Predictive analytics marketing completely changed the way marketers spend their budgets. Because several key questions for marketers can be answered through big data, budget can now be spent with confidence. Attributing ROI to a specific campaign no longer requires guess work; predictive analytics tell marketers - with a variable degree of certainty - exactly which campaigns are likely to produce the highest yield. Allocating budget to those campaigns equals better conversion rates, more successful salespeople, and ultimately, happier customers.
Next, consider the role of predictive analytics in campaigns focused on customer acquisition. Aside from the previously mentioned advantages of a better understanding of your buyer journey based on historical data composites, predictive analytics marketing gives marketers unique leverage in two key areas:
- Knowing Where and How to Reach You Best. Demographic data enriches a marketer’s understanding of the proper channels to deliver offers where the chances of you seeing them and taking action are at their greatest potential. Whether that is through social channels (like sponsored posts in Instagram or Facebook), email campaigns, banner ads on popular websites for your demographic, or any number of other avenues, marketers can virtually ensure visibility in the places you frequent the most.
- Knowing What Offer You Are Most Likely to Respond to in the Moment. Time kills all deals: when marketers know they have a lead on the hook likely to convert, there is no time to be wasted testing pricing options. Predictive analytics provide clear data on the proper price point for your target customer and allow you to market your offer to them when and where they will see it.
Finally, big data is often primarily seen as a tool for customer acquisition, when in fact the value derived from predictive analytics can be equally as transformative to your client retention strategy. Churn prevention often hinges on the same predictable, measurable data:
- Usage of your product/service measured against KPIs
- Demographic data (company size, industry)
- Onboarding/access to training materials
- Engagement with thought leadership
- Perceived ROI - access to reporting and communication with a relationship manager
If predictive analytics can help marketers target potential leads and convert them into customers, the same concept can be applied to creating a profile of an “at-risk” customer based on historical data.
This represents the tip of the iceberg of how predictive marketing analytics have impacted and will continue to revolutionize the way marketers design campaigns. Whether it be with a focus on customer acquisition or retention, big data undoubtedly opens doors for better, more focused conversations with the clients likely to benefit from your product or service.
Of course, big data means nothing if it is not accurate. Consider the critical role quality data plays in predictive analytics (and how you can ensure yours is of the highest quality).
The Importance of Clean, Enriched Data in Predictive Analytics
You might know all the lingo and feel comfortable splicing and dicing data to gain insights into your customers, but at the end of the day, predictive analytics mean nothing without quality data. With predictive marketers 1.8 times more likely to hit targets than their counterparts, you cannot afford to have anything but the highest quality of data.
The ReachForce blog recently covered data’s role in predictive marketing, but to summarize the key points regarding data quality, you need to focus on three C’s: completeness, consistency, and cleanliness.
Completeness means no gaps in your data. With customer information coming in from any number of different channels, managing that information in one central location and enriching that data with a tool like Smartforms means you are working with whole data when analyzing predictive analytics.
Consistency means a uniform way data is represented across your data management system. No duplicates, no irregular formats, no incorrect contact information. Ensure your data is consistent across all contacts, not a hodgepodge of information gathered across numerous channels.
Cleanliness means verified information for all of your contacts. Data hygiene can make or break the success of your marketing campaigns, so focusing on using a data management platform that validates your data is a must.
By maximizing your data’s completeness, consistency and cleanliness, you are doing the legwork necessary to make predictive analytics marketing campaigns work for you. Data quality needs to be a non-negotiable before even considering launching predictive analytics campaigns. Otherwise, you risk doing more harm than good by wasting time and money implementing campaigns for the wrong targets or with incorrect information.
Luckily, ReachForce SmartForms works to provide real-time lead form data enrichment. In combination with the ReachForce Continuous DataManager, ReachForce works to unify, clean, enrich, and activate your data to ensure you are ready to launch the most effective and efficient predictive analytics marketing campaigns possible.