When it comes to marketing, you might wonder whether your current marketing actions will still work in the upcoming year. Customer preferences are constantly changing, and you need to be able to adapt to those changes quickly.
A great way to do that is by utilizing predictive marketing. It forecasts future customer behaviors to help you make data-driven decisions and optimize your marketing process.
In this article, we’ll explain what predictive marketing is, how to use it, its benefits and challenges, and how to implement it.
Predictive Analytics vs. Predictive Marketing: What’s the Difference?
While the two terms are often used interchangeably, predictive marketing takes predictive analysis to the next level and has a much broader implication.
Predictive analysis uses predictive models and techniques, such as machine learning, to get insights into future outcomes.
Predictive marketing uses predictive technologies to analyze historical data and identify potential customer behavior in the future. It helps a business make better, data-driven marketing decisions in the long run.
How Does Big Data Enable Predictive Marketing?
Essentially, a predictive marketing expert – usually a data analyst, data scientist, or analytical firm – will gather business data from various sources, which is then analyzed alongside the company’s customer and marketing data.
Using the data gathered, the predictive marketing expert will create a model that best suits the business. With it, it will be possible to predict customer trends and optimize customer journeys.
Predictive marketing work is widely used on eCommerce websites to recommend specific products and services to their users based on their past purchasing behavior.
The actual content of the recommendations was developed thanks to a combination of sophisticated algorithms, machine learning, and other statistical techniques. It relates data with location, time, demographic distribution, open rate, click rate, opt-out rate, and many more metrics.
Examples of Predictive Marketing
Let’s take a look at some use case examples of how effective predictive marketing techniques helped companies achieve better results.
Airbnb
Airbnb has grown 43000% since its launch. The most significant contribution to its hypergrowth is the use of data science to create predictive models. Airbnb’s data science team came up with a four-step process to leverage predictive analysis, ultimately leading to their rapid growth.
- Use historical and real-time data to create models and find out which marketing strategies will work effectively.
- Create a plan to utilize the data gathered as effectively as possible.
- Utilize A/B testing to confirm whether the findings were correct and determine how they can be maximized.
- Measure the results and repeat the process.
Amazon
Amazon obtained a patent for its new predictive ordering technology. Their AI is advanced enough to know what consumers will buy before they do, so it essentially orders products before you purchase them.
The technology, known as anticipatory shipping, utilizes Amazon’s massive customer data to determine what customers want. It analyzes past purchasing behavior and items that you’ve looked up but not purchased.
The items will then be sent to their delivery hub, resulting in consumers getting what they want faster, thus significantly improving their shopping experiences and automating Amazon’s work.
What Are the Benefits of Predictive Marketing?
Develop Smaller eCommerce Businesses
Large eCommerce companies like Amazon and eBay use predictive technologies to offer personalized suggestions of products or services their customers might want to purchase.
With predictive modeling, small eCommerce companies can also optimize their business and provide a better customer experience.
Data scientists and marketing teams can generate algorithms and models to provide real-time and personalized recommendations to different consumers. Luckily, there are several affordable SaaS solutions available to boost their marketing efforts.
Take Better Decisions
For predictive marketing analytics to work, companies need to look at their past performance to develop accurate and new marketing strategies.
It involves acknowledging their past mistakes and ensuring they don’t happen again. To do that, it is typically necessary to go through customer feedback, reviews, and complaints to get an accurate picture of your previous marketing plans.
The data helps businesses implement best practices, leveraging their marketing outcomes and consumer engagement.
Minimize Unnecessary Costs
Predictive marketing lets companies know which strategies and channels work best for each customer. Thus, they can focus their efforts and budget on those areas instead of investing in less effective marketing strategies and channels.
Create Advanced Data Segmentation
Thanks to social media, and site analytics, businesses have more information sources to get leads and predict customer preferences.
By combining the data gathered from different channels, data scientists can create a more in-depth customer segmentation, enhancing the intended marketing message.
As a result, businesses can focus their resources on people who are already interested in their brand and willing to purchase, generating more conversions.
Improve Lead Scoring
For business-to-business companies, getting better lead scoring is one of the most important benefits of predictive technology. Lead scoring ranks the company’s prospects using a scale that represents their value.
To determine lead scores, a company needs to gather information about their prospects and determine their purchase intent, customer lifetime value, promotion response, and profitability.
The sales department will then use this information to prioritize which prospects they should contact.
Challenges of Predictive Marketing
Predictive marketing has become more prevalent over the past few years, but it’s a complex skill to master. Therefore, when implementing predictive marketing, there are a few challenges to consider.
Predictive technology requires skilled data scientists who have a great understanding of Phyton, R, and statistical modeling.
In addition, there has to be a strong collaboration with marketers and other teams to develop and improve the models and, consequently, the business strategies.
Using predictive analytics requires plenty of steps, which are:
- Data preparation
- Data cleansing
- Identification of important columns
- Finding correlations
- Understanding different algorithms
- Choosing the right algorithms
- Deciding the right properties
- Ensuring that the data is correct
- Understanding the output
- Re-training the algorithm with new data
- Solving imbalanced data
- Deploying the model
- Predicting in real-time
- Integrating with primary applications.
Luckily, there are plenty of predictive marketing tools that are simple to use and affordable.
How to Utilize Predictive Marketing in Your Marketing Strategy
There are various use cases for predictive marketing, regardless of the business type. Let’s take a look at some of the most used methods:
Purchase Predictions
As mentioned in the Amazon example, companies can utilize predictive marketing to determine when a customer will purchase. The information received can be used as a deciding factor on whether the customer needs further marketing and sales efforts or not.
It would be a waste of resources to send promotional emails to customers that are already willing to purchase your product anyway. Thus you can focus on sending those emails to other customers that still need convincing.
Open Email Prediction
Emails are still one of the most effective communication channels. Open email prediction means that you can predict whether a customer will open your following email or not. Knowing this information allows you to adjust email communication frequency to reduce the customer’s chance of unsubscribing from your email list or newsletter.
Optimal Email Time Prediction
Predictive analytics helps you identify the exact time you should send an email to your customers.
By knowing the precise time to send your emails, you can leverage untapped data, have marketing automation for email distribution, and increase click-through rates.
In-Session Prediction
Customers who are on the brink of conversion usually follow similar behavioral patterns. Predictive technology allows you to identify customers who just need to press the purchase button and help them continue their customer journey by providing incentives.
The incentives can come in various forms, but usually, they will come in a limited-time offer to further convince the customer to make the purchase.
Conclusion
Predictive marketing technology utilizes machine learning and artificial intelligence algorithms to gather customer data, identify profitable leads, and analyze marketing campaigns.
Predictive modeling also allows you to appeal to your desired customers and create a personalized and actionable response to any predicted situation.
In this article, you’ve learned what predictive marketing is, its benefits and challenges, and how to implement it in your marketing strategy.
All that’s left to do is try out the predictive technology. If necessary, hire a data scientist and see how it can significantly boost your business’s success.
