Author: Loretta Jones
Everyone dreams of predicting the future, and this is especially true of marketers. Knowing how likely a person is to become a customer, and why, is the Holy Grail for marketers. So it’s no surprise that predictive analytics is an increasingly popular topic.
Predictive analytics uses big data and machine intelligence to calculate how likely a specific outcome is based on customer data and historical actions. While this isn’t exactly the same as predicting the future, marketers can draw conclusions from predictive analytics to improve their key campaign metrics.
As the amount of available data grows, predictive analytics has the potential to become even more valuable. However, it can be easy to get lost in the sheer amount of information. Here are four tips to get the most out of your predictive analytics efforts:
1. Smaller Data = Better Data
Organizations collect data with various methods and sources, and analyze it to bring to light trends, insights, strengths, and weaknesses–but that goal isn’t realistic when there’s simply too much data and not enough resources to parse through it.
To create measurable campaigns, marketers need data that’s actionable. This is where big data can struggle–there’s so much of it that individual insights can get lost in the weeds. By analyzing big data with specific categories and goals in mind, however, you can break data sets down into small slices, or “small data,” which helps you focus on insights that are practical and actionable.
As marketers, we use data to maximize the alignment between our buyers’ expectations and needs to the value we’re able to provide them with as little friction as possible. We can break down our data on a couple of different levels to achieve this:
- Data to define your customer persona, or who your target audience is (e.g. segment, vertical, target market, company size, geography)
- Data to define what they need for success (e.g. tools, resources, content) and how you can help them
For example, a B2B organization might use big data to evaluate its most common customers–say, small and medium-sized business owners or sales managers. Digging into those customer profiles manually, or with an analytics solution, can uncover even more details: demographics that can be used to create customer profiles, behavioral clues (e.g. pages clicked on when they’re most likely to open emails), and the problems that they’re immediately focused on solving, such as acquisition, employee retention, or increasing profits.
2. Maximize Small Data with Predictive Analytics
Once you’re looking at the right data in the right amounts, predictive analytics can help you identify and analyze usage patterns. For example, an online retailer might use predictive analytics to discover the different ways that their customers interact with them such as how often they log on to the website, use features like search, or contact customer service. Of course, these different events present new opportunities. Potential customers might search for a specific term when they’re ready to buy or search two related items and be primed for an upsell–cues that marketers can use to inform their campaigns.
Using predictive analysis, you can gain a better idea of what to look for and optimize your campaigns for the best possible outcomes. Let’s say a B2B technology provider that provides software with a freemium subscription model wants to quantify how likely trial users are to become paid subscribers. The company can match the behavior of trial users to their customers who have paid for a subscription. By analyzing this data, the company can identify trial users that are most likely to convert and then target them with personalized emails and other offers, like tips for using the software or a time-sensitive discount for a paid subscription.
3. Improve the Customer Experience
You can discover a lot of information about your potential and existing customers through your conversations and interactions with them. This information can be added to the full data picture and linked to particular outcomes, like renewals and increases or decreases in business. This allows marketers to further refine their message and identify customer characteristics that tend to lead to positive outcomes.
For example, certain customers may respond better to certain content, but it’s also true that some customers respond better to certain message delivery platforms–text, email, phone. This is the type of information, contained in small data, that can have great value when paired with predictive analytics. Someone who doesn’t want to be bothered over the phone can receive a text; someone who prefers that personal touch can get a call.
Predictive analytics can also identify things like engagement markers, which can be critical for turning website browsers into customers. For example, let’s say that the B2B company that provides a freemium subscription finds that its best repeat customers tend to log in to the platform multiple times per day during their trial period. If a trial user isn’t logging in at all, someone from your team can contact that person and offer to answer questions or provide assistance. Predictive analysis can flag markers like these at specific time periods–the first week of the trial, day 15, or five days before the trial ends. By comparing this data with what your best customers are doing, you can truly understand your prospects’ expectations and work to exceed them.
4. Pair It with Marketing Automation
Basic marketing platforms can populate an email with a customer’s name, company or birthday. However, to truly provide value to your buyers, it’s critical to go above and beyond the status quo by using a sophisticated marketing automation platform to segment and deliver personalized content, offers, and messages that address each buyer’s unique challenges and goals.
By combining small data, marketing automation, and predictive analytics, you can gain even deeper insights about your prospects and customers, and use these insights to deliver messages that change the game. Marketers can use predictive analytics to uncover the users who respond in high volumes at different stages of a campaign, and then use a marketing automation platform to personalize and refine outreach to those customers. Those messages can also hone in on the content that customers are most likely to engage with and automatically display the most relevant content.
Data is only as valuable as the customers it helps you gain–and keep. It’s still not possible to predict the future, but when predictive analytics is used with small data on a marketing automation platform, you can target specific segments that share similar interests and needs. This puts marketers in a better position to acquire leads or contacts, generate ROI, and ultimately retain satisfied customers.
Have you started applying predictive analytics to your marketing campaigns? I’d love to hear about your experience below.