What do customers really want? That is the question marketers find themselves asking every time they set up a new campaign. With customers who are constantly changing their minds about how they like to engage with brands, and a never-ending flood of emerging trends to research and test, marketers today sure have a lot to keep up with!
This is why so many businesses have adopted "data-driven" initiatives to solve problems and aid in their decision making processes. Investing in analytics leads to better and more informed decisions for your marketing. Companies on the cutting edge are turning to machine learning and predictive analytics to help them understand and classify both historical and real-time data, which then helps them to better anticipate and address customer wants and needs. Furthermore, these capabilities are being used by marketers in the campaign setup stage to help them shape their marketing messages into something that will effectively reach customers through the clutter of content that is competing for their attention.
So how can predictive analytics help you shape your marketing message? In this post, we'll look at 3 ways that you can use predictive analytics in the decision making process to help you understand, classify, and forecast the wants and needs of your customer base.
1. Understand: gathering data from the recent past
Before you decide what your marketing message should be, you need a good understanding of who your targeted audience is and what they are looking for. This is the data gathering stage, where you are going to try and see what information you actually have to work with from the recent past, and what information you still need to get.
When you build predictive search capabilities into your analytics strategy, you find yourself with more data to work with, and more ways to analyze it. Predictive search capabilities allow you to gather specific action data about your customers across all platforms and channels, including web, mobile, email, or social media. Whether it is historical data or in real time, you can collect past purchases, personal data, contact preferences, search history, email engagement, social media interactions, and even click-through behaviors. Then, you can sort everything into one data set, giving you a true 360-degree view of your customers.
Once you have the data sorted, make sure to thoroughly clean and structure it to remove any outliers or records with missing information. This will ensure that your data only contains the relevant records and information needed to perform the analysis. Transforming your data into a visual display that uses statistics and graphs can also help you immensely when you get to the next stage.
2. Classify: validating trends in the present moment
The classify stage involves connecting the various data points you gathered to find patterns in your historical data, and then validating those patterns against present trends. Predictive analytics technology allows you to connect literally thousands of data variables, from which you can build elaborate segments and buyer personas that will make it easy to glean patterns from.
Depending on your research goal, marketers may look for patterns between different campaigns and channels, certain demographics groups and behaviors, or customers and their past purchases. Make sure you are asking detailed questions as you look for patterns so you know which ones are the most relevant to identify for the research goal you had in mind.
After finding the patterns in your historical data, you can then validate them against current purchase or engagement trends. Doing this will help you identify when trends started to shift and whether they applied to all or only certain segments. After comparing and interpreting the results, actionable goals can be defined that will allow you to choose the best model in the next stage.
3. Forecast: building a model to predict the near future
In this last stage, you will be using a model built from the validated trends to try and predict the future - not only what your customer wants and needs, but what they are most likely to do when they receive your marketing message. The goal isn't to have the model be 100% correct, but this is how companies are using the data that they have to gain an edge on their competition.
Once a suitable model has been built, your analytics team will need to integrate it into their daily routine as a way to aid in the decision making that surrounds and informs your marketing message. What you end up with is a clearer vision of the road ahead, with the possibility to make wiser decisions about what to do differently - or what to continue doing - with your current marketing message, as well as the future ones to come. What your customers receive is a more engaging message that reaches them at the right time and in the right way, and even more importantly, a company that better understands them... which is a company whose products or services they will consider worth their time and money!
Simply put, using predictive analytics during your decision making process results in more targeted and effective marketing messages that will engage your current customers and attract new ones. Is your company planning to add predictive analytics to their marketing agenda this year? Tell us your plan in the comments!
