Five practical use cases for dynamic predictive marketing
The use of predictive analytics has increased steadily over the past few years as marketers see the value in investing in this trend.
A study by Forrester Consulting reported that predictive marketers are 2.9x more likely to report revenue rate at a higher than industry average. These marketers are also 2.1x more likely to hold a commanding leadership position and 1.8x more likely to exceed marketing goals.
Predictive marketing is on the rise, and companies are starting to take note of the benefits of implementing predictive analytics in order to improve marketing campaigns.
While one of the largest benefits of predictive analytics is dynamic consumer segmentation, marketers are finding more ways to use data to their advantage when it comes to creating a stronger, more predictive campaign.
For those companies still on the fence about how predictive analytics can help improve overall growth and revenue, here are 5 practical use cases of predictive marketing.
1. Find ideal customer traits
Using “predictive personas”, marketers can determine behaviours and characteristics of their ideal customer. Using this method, marketers can figure out which people are likely to respond to certain campaigns.
Furthermore, this data can be used to find out what makes these buyers different from a buyer that bought a competing product. Predictive personas can also be used to prioritise these characteristics as well, which helps with segmentation.
2. Discover better quality leads
Predictive acquisition allows marketers to filter out leads. When paired with the sales team, the biggest problem marketers face is figuring out which leads are viable and which are not. Sales teams can get pretty frustrated if they have to dig through countless leads, especially if most of them go nowhere.
Targeting those at risk of leaving a brand, marketers can make sure to apply a campaign to keep them loyal
Predictive analytics help with look-alike targeting. Instead of manually going through past customers to find traits and similarities, predictive analytics can sort through data finding the highest quality leads.
Predictive can pull ideal customers and quality profiles to ensure leads that go to sales are more likely to convert; therefore, both marketing and sales can save time and resources.
3. Prioritise potential customers
Now that marketers can find out which leads are viable, it’s important to put them in order or priority since it’s near impossible to target them all at the same time. Predictive prioritisation helps organise in which order resources should be allocated for different campaigns.
The other issue is that new leads come in constantly, and marketers need to keep different groups segmented and updated in real time, which is where predictive analytics can help immensely.
Predictive prioritisation can look at an entire marketing automation system or CRM to organise and prioritise leads, so marketers can better organise various campaigns and know which to run first.
4. Determine what channels work
Product propensity analytics utilise data from behaviour metrics (social media campaigns, e-commerce statistics, etc.) and data on purchasing behavior to tell marketers what campaigns work and what don’t work as well.
Using data to see the effectiveness of a campaign allows marketers to then predict (not just guess) which customers are most likely to buy from which campaigns. Marketers can then put more resources towards those campaigns as they are more likely to convert.
5. Identify dissatisfied customers
Churn prevention is key for any marketing campaign. It’s important to find customers that are not happy and address their concerns. Predictive analytics can help prevent churn by using data to find these unhappy customers.
Targeting those at risk of leaving a brand, marketers can make sure to apply a campaign to keep them loyal.
This, in turn, keeps revenue streams high and churn rates low.
There are many use cases for marketers when it comes to predictive analytics. Now dubbed predictive marketing, companies are scrambling to find qualified candidates that can use data to drive a strong marketing campaign.
Using predictive analytics has proved financially beneficial for companies, and resources can be allocated appropriately, as marketers are no longer wasting time doing these processes manually. Predictive marketing is sure to be a buzzword in the next year, and the use case list will most likely continue on as data software improves
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