After months of working on a marketing campaign, nothing’s worse than realizing you seeing the results you anticipated.
Unfortunately, many of us have been generally there. We’ve put all of our innovative effort, time, and numerous assets into a campaign that sounded like a great idea, but acquired nowhere near the expected ROI or engagement. Then, along with watching our project fail, we’ve had to deal with the awkward scenario of revealing bad performance data with the teams.
No matter how hard a person try, it’s impossible to know exactly how well a marketing campaign will do before you run it. However , there’s a strategy that gets pretty close.
It might be called predictive marketing.
While predictive marketing sounds like some futuristic technology you’d just see on a show like Westworld , using data to calculate an outcome isn’t new.
Predictive marketing is motivated by predictive analytics, which usually dates back to the 1930s. This enabled mathematicians and computers to calculate and evaluate the possible successes, failures, and results of various scenarios — such as health or weather conditions.
Later, in the 1990s, when analytics tools became more available to brands, marketing experts at companies like eBay and Amazon began to mix marketing data with comparable formulas or algorithms to predict and strategize close to potential consumer behaviors, buys, and marketing campaign performance.
Within the early 2000s, with the existence of “Big Data” many more brands and online advertising systems embraced predictive analytics plus marketing technology.
Now, predictive marketing is all around all of us. Below are just a few common cases of it, along with explanations showing how brands can leverage it.
Examples of Predictive Marketing
1 ) Predictive Product Suggestions
Perhaps you have considered buying a product, researched it, and then saw exactly the same product — or a very similar one — in a advertising that showed up on a social media feed, in your email inbox, streaming platform, or another home page’s banner? You’re not alone.
Ecommerce site algorithms regularly collect data about your item interests based on what an individual has viewed or purchased from. Then, these algorithms make use of that data to predict which products you’re more than likely to buy next. This data is then used in the particular ecommerce ad or advertising a prospect sees.
Require an example? Below is an EyeBuyDirect ad that appeared upon my Facebook News Feed.
As an EyeBuyDirect client, I’ve bought many pairs of glasses with comparable styles, shapes, or styles to the pairs seen in the ad above. To compare, here are two of my latest purchases:
Basically needed new glasses, EyeBuyDirrect’s ad would be very attractive to me because it shows item offerings I’m very likely to see or buy.
Rather than showcasing the same ad or item to every audience member, predictive marketing tools can help you to immediate customers to products they may be most interested in.
If you’re about to bring your business online and want to use predictive marketing in making more sales, several inexpensive ecommerce tools enable you to send out predictive product suggestions to your audiences. You can learn more about them here.
2 . Predictive Lead Scoring
Predictive marketing is not going to just stop after you get a contact, customer, or business lead.
Once you build up your list of contacts, you’ll want to continue advertising to them or potentially direct them to a sales rep. But , if you try to market your own brand continuously to every individual one of your new contacts, you might waste serious time when they aren’t serious about buying your own product or signing up for more content.
To avoid giving a lot of time to unqualified leads, brand names can use tools like HubSpot’s Predictive Lead Scoring feature to analyze contact data users and estimate which potential clients are most likely to make a deal later on.
When you have a huge data source of contacts with different levels of interest in your item, brand, or service, predictive lead scoring data like this above can give you insight where prospects to prioritize inside your marketing or sales attempts first. In turn, this could give you a leg up on brands that will waste crucial time plus resources on deals that will never happen.
3. Automatic Social Media Suggestions
A handful of social media marketing tools, including HubSpot Marketing and advertising Hub, use predictive analytics and audience data in order to estimate and suggest the best times to post your content on the given channel.
On top of basic content timing suggestions, several tools go even much deeper with social media content predictions. For example , when social media managers upload two or more images towards the social media scheduling tool, Cortex, the platform will use historical data to determine which photo’s colors will be most eye-catching in order to followers.
On top of the social media tools that can suggest strategies based on predicted results, social channels like Facebook, Twitter, and Pinterest also offer some predictive tools inside their ads platforms.
For example , in 2018, news outlets obtained documents from Facebook uncovering that it secretly launched a “Predictive Loyalty” feature within its ads. The feature reportedly analyzes Facebook consumer behavior, interests, page likes, and other data points to circulate ads to people that will had the highest likelihood of clicking them, rather than just leading ads to a brand’s viewers targets.
Since Facebook’s predictive advertising news, Twitter’s also acknowledged that it uses predictive ad algorithms specifically for films, TV, and entertainment-related promotions.
Aside from predictive ad targeting, social platforms like Fb and Pinterest also use algorithms to make predictions associated with multivariate or A/B testing. With these types of tests, a brandname will often submit two or more variants of their ad. When the advertisement goes live, the social media marketing platforms will immediately evaluate which variation is clicked on the most and anticipate which will have the best conversion outcome. From there, social media advertisements will begin to display the winning variation.
4. Customer Churn Prevention Tools
While many entrepreneurs focus primarily on getting new customers, some might focus on creating content and products that continue to engage, keep, and even upsell current clients.
But , sometimes, it can be difficult to tell when customers require new, engaging content or when they’re likely to churn. That’s why some major companies have implemented predictive analytics as well as marketing strategies to recognize and re-engage customers which are about to churn.
Take Sprint for example. Back in 2014, when the cell-phone giant saw an all-time high customer churn rate, marketers and service reps began to use predictive analytics tools to determine which usually customers were most likely to cancel their service. After they did this, they were able to target those customers with re-engagement communications, messaging, and special offers that would keep them signed up.
According to a case study, Sprint’s predictive strategy led to the 10% decrease in customer churn, and an 800% embrace upgrades within 90 days of implementation.
While your brand name might not be able to implement complex, customer churn prediction tools, there are other ways you can use data to predict and prevent lost audiences.
For example , by monitoring email engagement data plus which contacts are more unlikely to open or click on emails, you can create a list portion of contacts that are at-risk of unsubscribing and send a re-engagement email like the one below:
five. Predictive SEO Tactics
As a marketer, a major part of your job might involve creating blog posts, web pages, or other online content aimed to catch the attention of and convert audiences. Due to the fact search engines can provide major traffic wins and brand understanding to brands, you’ll likely want to produce valuable content that shows up on page one.
However once you’ve landed your high search result page position and gained solid natural traffic, you can use predictive data to prevent the future loss of your own ranking and all the visitors that comes with it.
This process, known as predictive SEO, is when content strategists use traffic and search ranking analytics to determine if a web page is at risk of losing its visitors momentum from search engines.
Pertaining to HubSpot, our predictive SEO process involves using our At-Risk Content Tool — which analyzes data through SEMRush, Ahrefs, and other software program — to determine when we’re losing our ranking upon search engine pages.
For example , if some of our posts shows up within the first spot on a Google search result page, then continuously goes down to spot three or four, our own At-Risk Content Tool may flag the post as in danger of losing search traffic.
Here’s what our At-Risk Content spreadsheet looks like. If a blog post begins to see declines that could potentially continue, the formula in the spreadsheet information the blog post as “At-Risk” in the Status column around the right:
If you’re a marketer who focuses primarily on site content, creating a strategy like this may proactively help you monitor the performance of many web pages at once, learn when old articles really needs an revise, or identify old articles strategies or formats that require to be re-worked — just about all before you lose major search traffic.
Want to replicate the predictive SEO strategy over? Here’s a detailed post with all the full step-by-step process all of us used.
What to Know When you use Predictive Marketing
While predictive marketing can be a handy tool for justifying a new technique or strategy, there are important things marketers should keep in mind when they want to leverage it.
- Difficult perfect: Even though an algorithm or marketing formulation seems to give accurate estimates 99% of the time, the fact that advertising strategies rely on human engagement to succeed can cause a prediction to be wrong. While you may use predictive marketing data to justify investments or proposed strategies, you should have a plan designed for what to do if unexpected outcomes occur.
- It can be costly: While some predictive tools, such as HubSpot, could be affordable and easily accessible in order to smaller brands, other tools and predictive marketing tasks that require analyzing large amounts of data can get costly. Make sure to start with scaleable affordable predictive tools or tactics first.
- It requires data: While some tools, such as ad or SEO software have access to historical consumer information, creating your own predictive online marketing strategy from scratch might require you to have got your own data set. Gathering, cleaning, and organizing this data so a predictive tool or algorithm may leverage it can take quite a lot of time which should be built into your predictive strategy.
Want to learn more about exactly how predictive analytics and information can fuel your marketing strategy? Click here for a convenient blog post, or download the particular free resource below.