In today’s marketing landscape, most meetings still revolve around those static marketing scorecards filled with rows and rows of data that tell the executive team where marketing has been, and where it currently is. However, on its own, this model does nothing to predict where marketing will (and could) be.
For this reason, many organizations are adopting predictive analytics into their marketing strategy. Predictive analytics combines historical data, statistics algorithms, and machine learning to identify the likelihood of future outcomes. At its best, it is designed to provide the best assessment for what will happen in the future.
Unfortunately, many businesses that currently use predictive analytics appear to have the wrong idea about its potential and what it’s supposed to do. A major myth regarding predictive analytics is that you need simply to plug in the data, and let the program run on its own and do the thinking for you. But this is not the case.
Lie: Predictive Analytics Tells You Exactly what Will Happen
One of the biggest misconceptions surrounding predictive analytics is that it produces perfect results, and immediately actionable insights. If such were the case, fewer businesses would fail. Predictive analytics is only as good as the data you provide it with. The more (accurate) data you can add into your model, the better the prediction will be—but don’t always take for gospel.
While it might be tempting, predictive analytics should never replace human judgment. Your analytics model may be responsible for producing insights into how you could be reaching your target audience better, but it is your responsibility as a marketer to dig into the data from time to time, use your intuition, and make a convincing pitch other than “that’s what the technology said” to get executive buy-in.
Truth: Predictive Analytics Models Customer Behavior
In its purest form, predictive analytics is designed to understand your customers according to the data, and make predictions based on that data. It models customer behavior so that you can understand trends, correlations, and other relationships that aren’t immediately obvious. You can apply a variety of models to your predictive analytics technology, but they’ll generally fall into three main categories:
- Cluster Model: a model that organizes customers into segments of shared behavior, demographics, and other variables.
- Propensity Model: used to predict a customer’s likelihood toward a certain action (e.g., unsubscribe, convert, engage, churn, etc.), as well as project the potential lifetime customer value.
- Collaborating Filtering: used in providing recommendations, and is based on a number of variables, including past purchasing behavior. Great for up-selling, cross-selling, etc.
Predictive analytics can provide valuable insight not only into who you’re dealing with, but also how best to deal with them.
Truth: Predictive Analytics Targets the Right People, at the Right Time, with the Right Content
This is perhaps one of the greatest challenges marketers face; however, it’s one of the simplest solutions predictive analytics can provide. Using machine learning to send personalized content at various stages of the customer journey is one of the most effective ways to demonstrate ROI for your marketing technology strategy. In fact, marketers who use predictive analytics in their marketing automation are twice as likely to identify high-value customers than marketers trying to make these decisions purely on their own.
So, how specifically can predictive analytics help you create a clear target, and answer those key who, when, and what questions? There are a number of models you can use to approach this challenge:
- Affinity Analysis: a data mining technique that discovers co-occurrence relationships among individuals or groups. Essentially, this technique explores the digital footprint of previous customers and groups, and fills in the blank (“You might also like _____”) for customers who have demonstrated similar behavior or interests.
- Churn Analysis: focuses on what specifically makes customers leave your site, or stop using your products or services, and at what point in the customer lifecycle churn is most likely to occur. Churn analysis helps you understand whether you’re delivering content too soon, too late, or to the wrong people.
- Response Modeling: uses data mining to detect similarities between responders from previous marketing campaigns to determine who is or is not likely to respond to a future campaign. This will help you avoid going after prospects with little to no potential and focus on the people who engage with your content.
While the above models are just a sampling of all the possible approaches to your predictive analytics technology, they all more or less help you make smarter decisions about your content, marketing, and personalization efforts.
*Bonus Lie: Predictive Analytics Is Only for Big Companies with Deep Pockets
While predictive analytics and machine learning are a driving force behind the success of major online retailers, insurance firms, and even global telecommunications carriers, this technology is becoming increasingly available to smaller businesses and organizations that want to be more forward-thinking.
In the past, you might have needed expensive storage space and software in order to run something even close to a successful big data program, but that is no longer the case. Solutions like cloud hosting, and integrated, user-friendly software has never made predictive analytics easily to implement into your existing marketing strategy.
Successful implementation of a predictive analytics model requires more than just a program; it requires true customer insight on your part, the vision to see data for what it really is, and the ability to make the right decision based on what you know. To learn more about how marketing automation software (including predictive analytics) can work for you, register for our webinar.