After months of working on a marketing campaign, nothing is worse than realizing that you are not seeing the results you expected.
Unfortunately, many of us were there. We put all of our creative efforts, time, and numerous resources into a campaign that sounded like a great idea, but it was nowhere near the ROI or engagement we expected. In addition to the failure of our project, we had to deal with the uncomfortable scenario of sharing poor performance data with our teams.
No matter how hard you try, it’s impossible to know exactly how well a campaign is performing before running it. However, there is one strategy that comes pretty close.
It’s called predictive marketing.
What is predictive marketing?
Predictive marketing involves leveraging data on audience behavior, historical consumer research, purchase history, website analytics, and other areas to predict the outcomes of marketing tactics.
While predictive marketing sounds like a futuristic technology that you would only see on a show like Westworld, using data to estimate an outcome isn’t new.
Predictive marketing is powered by predictive analytics that date back to the 1930s. It enabled mathematicians and computers to calculate and analyze the possible successes, failures, and outcomes of various scenarios – such as health or weather conditions.
Later, in the 1990s, as brand analytics tools became more available, marketers from companies like eBay and Amazon began combining marketing data with similar formulas or algorithms to predict and strategize potential consumer behavior, purchases, and marketing campaign performance.
In the early 2000s, with the advent of big data, many more brands and online advertising platforms turned to predictive analytics and marketing technology.
Predictive marketing is all around us now. Below are just a few examples of these, along with explanations of how brands can use them.
Examples of predictive marketing
1. Forward-looking product suggestions
Have you ever considered buying a product, researched it, and then saw the same product – or a very similar one – in an advertisement posted on a social media feed, in your email inbox, on the streaming platform or in the banner of another website? ? You’re not alone.
Ecommerce site algorithms regularly collect data about your product interests based on what you’ve viewed or bought from them. These algorithms then use this data to predict which products you are most likely to buy next. This data is then used in the e-commerce ad or advertisement that a potential customer sees.
Do you need an example? Below is an EyeBuyDirect ad that ran on my Facebook news feed.
As an EyeBuyDirect customer, I have purchased many glasses with a style, shape, or pattern similar to the one in the ad above. For comparison, here are two of my recent purchases:
If I need new glasses, the EyeBuyDirrect ad would appeal to me as it shows product offers that I am very likely to see or buy.
Instead of showing the same ad or product to every audience, you can use predictive marketing tools to guide customers to products they might be most interested in.
If you are planning to get your business online and want to use predictive marketing to generate more sales, there are several affordable ecommerce tools that you can use to send predictive product suggestions to your target audience. You can learn more about it here.
2. Predictive lead scoring
Predictive marketing doesn’t stop after you’ve received a contact, customer, or lead.
Once you’ve created your contact list, you might want to continue marketing them or you may want to refer them to a sales rep. However, if you try to continuously market your brand to each and every one of your new contacts, you may be wasting serious time if they don’t seriously buy your product or sign up for more content.
To avoid giving too much time to unqualified leads, brands can use tools like HubSpot’s predictive lead scoring feature to analyze contact profiles and gauge which potential customers are most likely to close a deal in the future.
If you have a large database of contacts with varying degrees of interest in your product, brand, or service, the predictive lead scoring data above can help you understand which potential customers are prioritized first in your marketing or sales effort have to. This, in turn, could give you an edge over brands that waste crucial time and resources on deals that never take place.
3. Automated social media suggestions
A handful of social media tools, including HubSpot Marketing Hub, use predictive analytics and audience data to estimate and suggest the best times to post your content on a specific channel.
In addition to simple content timing suggestions, some tools go deeper into predicting content on social media. For example, when social media managers upload two or more images to the Cortex social media planning tool, the platform uses historical data to determine which colors of the photo are most noticeable to followers.
In addition to social media tools that can suggest strategies based on predicted results, social channels like Facebook, Twitter and Pinterest also offer some forecasting tools on their ad platforms.
For example, in 2018 news outlets received documents from Facebook stating that they had secretly introduced a “predictive loyalty” feature in their ads. The feature reportedly analyzes the behavior, interests, page likes, and other data points of Facebook users in order to distribute ads to people who are most likely to click, rather than ads just to a brand’s audience to judge.
Since the predictive ad news from Facebook, Twitter has also recognized that it uses predictive ad algorithms specifically for movies, television, and entertainment advertising.
In addition to predictive ad targeting, social platforms like Facebook and Pinterest also use algorithms to make predictions related to multivariate or A / B tests. With these types of tests, a brand will often send two or more variations of their ad. When the ad goes live, social media platforms immediately analyze which variation is clicked the most and predict which one will get the best conversion result. From there, the winning variants are shown in social media ads.
4. Tools to prevent customer churn
While many marketers primarily focus on attracting new customers, some may focus on creating content and offers that will continue to engage, retain, and even sell current customers.
However, sometimes it is difficult to tell when customers will need new, engaging content or when they are likely to churn. Because of this, some large companies have implemented predictive analytics, as well as marketing strategies, to identify and re-retain customers who are about to churn.
Take sprint for example. Back in 2014, when the cell phone giant was experiencing unprecedented customer churn rates, marketers and service reps began using predictive analytics tools to determine which customers were most likely to cancel their service. Once they did, they were able to target these customers with reintegration communications, messaging, and specials that would keep them posted.
According to a case study, Sprint’s forecasting strategy resulted in a 10% decrease in customer churn and an 800% increase in upgrades within 90 days of implementation.
While your brand may not be able to implement complex tools to predict customer churn, there are other ways you can use data to predict and prevent lost audiences.
For example, by tracking email engagement data and the likelihood of contacts opening or clicking on email, you can create a list segment of contacts at risk of opting out and email with Send re-engagement like the following:
5. Predictive SEO Tactics
As a marketer, a large part of your job might be creating blog posts, websites, or other online content to attract and convert audiences. Since search engines can provide great traffic and brand awareness for brands, you will likely want to create valuable content to display on page 1.
However, once you get to your high position on the search results page and have solid organic traffic, you can use predictive data to prevent future loss of your rankings and related traffic.
In this process, known as predictive SEO, content strategists use traffic and search ranking analytics to determine if a website is at risk of losing traffic momentum from search engines.
For HubSpot, our predictive SEO process includes using our at-risk content tool, which analyzes data from SEMRush, Ahrefs, and other software to determine when we are losing our ranking on search engine pages.
For example, if one of our posts appears first on a Google search results page and then steadily drops to third or fourth place, our content at risk tool may flag the post as at risk of losing search traffic.
This is what our table of content at risk looks like. If a blog post is experiencing declines that could possibly continue, the blog post is flagged as at risk in the Status table on the right in a formula in the table:
If you are a marketer who primarily focuses on web content, creating such a strategy will allow you to proactively monitor the performance of many websites at once, learn when old content really needs updating, or identify old content strategies or formats that are needed be revised – all before you lose large search traffic.
Do you want to replicate the predictive SEO strategy mentioned above? Here is a detailed post with the full step-by-step process that we used.
What to Know When Using Predictive Marketing
While predictive marketing can be a useful tool to justify a new tactic or strategy, marketers should keep important things in mind if they are planning to use them.
- It’s not perfect: Even if an algorithm or marketing formula appears to be providing accurate estimates 99% of the time, the fact that marketing strategies rely on human engagement to be successful can cause a prediction to be incorrect. While you can use predictive marketing data to justify investments or suggested strategies, you should have a plan of what to do if unexpected results occur.
- It can be expensive: While some predictive tools like HubSpot are affordable and easily accessible to smaller brands, other tools and predictive marketing projects that involve analyzing large amounts of data can become costly. Start with scalable, affordable prediction tools or tactics first.
- It requires data: While some tools like advertising or SEO software have access to historical consumer data, building your own marketing strategy from scratch may require you to have your own dataset. Gathering, cleaning up, and organizing this data so that a forecasting tool or algorithm can take advantage of it can take time that should be incorporated into your forecasting strategy.
Want to learn more about how predictive analytics and data can drive your marketing strategy? Click here for a handy blog post or download the free resource below.