Predictive intelligence might sound like something out of a science fiction film, but it’s a tool that is becoming increasingly commonplace. Marketers are using technology to anticipate what their customers want and deliver it to them before they are aware that they want it.
Retail furniture company Room & Board is utilizing predictive intelligence tools to improve company performance and more effectively connect with customers. The company integrated Salesforce’s Marketing Cloud technology, which uses customer traffic data and predictive analytics to suggest additional purchases to customers in real time. The result was an incredible 2900% ROI.
But that doesn’t mean that other businesses can’t learn from their example Although not all businesses will find the same level of success as Room & Board, there are still lessons that can be learned from their example. technology has progressed to a point where marketers can’t afford to ignore new developments, like a system that provides recommendations based on customer traffic patterns.
Customers who have a personalized experience with a company are more likely to continue doing business with that company, resulting in a positive return on investment. If you don’t know what predictive intelligence is, you should learn about it and the tools that are available.
What is predictive intelligence?
Predictive intelligence is a method of creating a customer experience that is catered to an individual’s specific preferences by monitoring their behavior. After collecting customer data, businesses use it to anticipate what the customer might want next.
If, for example, an online shopper has just bought a flat-screen TV, These new intelligence tools would detect the purchase of a coffee table or TV stand and send the shopper an email in real-time suggesting a matching product. This means that humans don’t have to think about each part of a customer’s profile separately, which makes marketing more efficient.
How Predictive Intelligence Works
Websites that recommend products and services to users based on their past behavior are an example of predictive intelligence. The websites ensure that every returning user is seeing product recommendations everywhere from the product search page to the checkout. The website’s recommendations are based on the study of customer behavior, such as items the customer has in a shopping cart or items the customer or other customers have interacted with or purchased in the past. The algorithm is usually more sophisticated than just relating data, and may take into account time, location, demographic distribution, and a whole host of other metrics, such as open rate, click rate, and opt-out rates.
Small businesses can also benefit from predictive marketing technology, as it is not only afforded to big companies. Our predictive marketing specialists use data from several sources to build marketing models for our business. The hackers had access to the company’s marketing and customer data, as well as information on the marketing efforts. The data analysts could then use their findings to predict the success of the company’s marketing efforts.
How it’s changing B2B marketing
In the past, marketers would assess leads manually to identify what stage in the customer journey they were at. This is when marketers manually assign a grade or numerical value to certain leads based on their analysis. If a marketer sees that leads who watch a product demo are more likely to buy the product, they might give the leads who watched the demo an “A” grade, and the leads who didn’t watch the demo a “B” grade.
This technique is the opposite of reactive lead scoring, which uses a customer’s current behavior to determine what they want and whether they are ready to buy. Lead scoring is a method of using big data to determine which leads are most likely to convert, so salespersons can focus on the leads that will provide the company with the most value.
The Benefits of Predictive Intelligence
Predictive intelligence is just as relevant to digital marketing as data science is.
First, predictive technology provides marketers with a better understanding of customer behaviors. Predictive marketing tools are slightly different than regular marketing data analysis because they make decisions without the data scientist having to interpret the data and make recommendations separately. Predictive marketing models make it easier for marketing decisions to be made by predicting which strategies are likely to work and which wouldn’t. Predictive marketing models analyze data to make predictions about customer behavior. For example, they can predict whether or not a customer will make a purchase, when and how the customer is likely to make the purchase, and other business-specific predictions.
Vendors who analyze customer data can help companies make marketing decisions like budget management, campaign planning, lead generation, and conversion strategies. Predictive marketing is more effective than gut feeling and guesswork because it analyzes large amounts of data from customers. The decisions from predictive marketing are more targeted and produce better outcomes.
The predictive analysis also brought us closer to automated marketing systems, which experts call prescriptive marketing. On this level, marketing systems automatically gather data and make decisions in real time. Activities that used to take a long time, like model generation, lead scoring, and updating customer insights, will now happen quickly. Since we could easily segment customers and deploy tailored marketing campaigns, we were able to substantially improve customer engagement at this level of proficiency. The obvious result of this is that we need to optimize our marketing budgets, improve lead scoring, and increase revenue from sales.
Predictive analytics can greatly improve a company’s marketing efforts in various ways, including marketing mix modeling, upselling and cross-selling, web optimization, customer acquisition, profiling, and retention, according to Jas Saran of Forbes.
Predictive intelligence is improving marketers’ ability to understand customer behavior. Here are a few of the tools marketers are using to become more efficient:
Machine learning
The main challenges, according to the study, were: – Difficulty understanding and using data – Lack of skilled personnel – siloed data What are the top three challenges, according to a 2014 Forrester Consulting study? – Difficulty understanding and using data – Lack of skilled personnel – Siloed data Analyzing data from customer interactions and applying insights to improve customer experiences was listed at 3 and 4, respectively.
As big data evolves, marketers are increasingly working with large streams of data that can be difficult for humans to manage. Machine learning is a solution to this problem. Machine learning is the process of training a computer to recognize patterns. This is done by feeding the computer a large amount of data, and then letting it adjust its program based on the new information.
Staples is collecting information about their corporate buyers’ preferences in a few distinct ways: with self-service shoppers via their website and their Easy Button as people speak into it. This helps Staples predict their customers’ shopping needs better. The more shoppers speak into the button, the more the bot understands what the customer needs and responds accordingly.
Artificial intelligence
An issue that marketers face is that they do not always have access to data that would give them a comprehensive view. AI can scour the internet for new data sources, like news articles, social media posts, and online databases. The more data that is available, the better marketers are able to find leads and to create content that is personalized for their potential customers.
Artificial intelligence is also a strong tool for personalization. AI can send emails specifically tailored to your customers’ brand preferences by using their shopping history and consumer profile. A survey conducted by Demandbase found that 80% of marketers believe that AI will revolutionize the marketing industry within the next 5 years.
Marketers believe in the power of artificial intelligence, but are unsure of how to implement it. 26% of marketers surveyed by Demandbase said they had a very confident understanding of AI, while the rest felt less confident. What would it be like to have a sports car in your driveway but not know how to drive it? This is the case for many marketers who are using artificial intelligence.
It is essential for marketers to know what trends are on the horizon if they want to use machine learning, artificial intelligence, and other tools effectively. Here are three trends that are driving change in B2B marketing.
3 Predictive intelligence trends
Predictive intelligence has been shown to result in higher employee engagement and customer lifetime value. According to a 2016 study from the Aberdeen Group, businesses that use predictive analytics tend to have average profit margins that are 5% higher than those who don’t use this type of data analysis, and 10% higher customer lifetime values.
As predictive intelligence becomes more popular among businesses, it is important to be aware of the trend’s implications in the coming years.
The rise of chatbots
As chatbots become more prevalent, they are increasingly utilizing machine learning in order to provide more accurate answers to customer queries. This results in a more natural conversation flow and happier customers. Users can quickly find answers to their questions about a product or service without having to search an FAQ page or make a time-consuming phone call.
Chatbots have become an important tool for businesses looking to automate more processes and provide the best user experience as possible. By automating processes, businesses can reduce the amount of time and resources spent on tasks, and chatbots can help to provide a more personalised user experience by engaging with customers and providing them with the information or answers they need. Chatbots have been growing in popularity, with $58 million dollars being invested in them in the first six months of 2016. In addition, Gartner predicts that by 2020, customers will hardly ever interact with an enterprise in person.
The technology will improve over time and the questions asked by customers will be used to improve their consumer profile so that companies have a better understanding of where the customer is in their journey and what potential services they might be interested in.
Intelligent apps
Machine learning will not only be a tool that tech giants like Facebook can afford. Intelligent apps will become mainstream sooner than you think. Most companies will soon be using apps that analyze large amounts of data rapidly and change their program based on new information. If you don’t believe that machine learning is going to improve customer experience for startups, just know that in 2016, TechCrunch found that 90% of startups they met with planned to use machine learning for this purpose.
There will also be an increase in demand for algorithms that power intelligent apps. Algorithmia is one such marketplace. Mashape is a marketplace for APIs that developers use to build intelligent apps. In the future, marketers will have to rely on algorithms to create apps that can provide personalized solutions to customers. This is because algorithms have the ability to learn and understand customer behavior better than any human could.
Marketplaces are important because they make it easier for people to buy and sell things. A more streamlined purchasing process will lead to quicker implementation of smart apps that rely on APIs.
Big data sees an increase in investment
A survey from DNV GL – Business Assurance found that the majority of organizations are looking to increase or maintain their investment in big data between now and 2019. Most companies worldwide see big data as an opportunity rather than a threat, according to the survey. Companies that invest more in big data are seeing an increase in efficiency and better decision making. Businesses are also turning to big data to capture and store important information about their customers.
How Predictive Marketing Improved E-Mail Marketing and Website Engagement
Email marketing campaigns that make use of predictive intelligence tend to result in the highest revenue influenced. Predictive marketing allows marketers to create more accurate emails by using data to predict which customers are more likely to engage. This results in more emails being opened and fewer being sent to customers who are not interested. but has changed the game by empowering the marketer with specific information to create personalized emails which have better open and engagement rates.
Did you know that “back-in-stock emails” and emails about abandoned products have higher click-through rates than other types of emails?
Websites that use predictive marketing models can improve engagement and sales by studying the behavior of site visitors, especially around web assets like ad banners, product pages, and action buttons. The information they get from visitors and customers can help them improve the web experience.
In a nutshell, predictive marketing is all about using data to predict when a customer is likely to make a purchase, and then using that information to help optimize things like pricing and other factors that influence customer behavior. Amazon reportedly achieved a 30% increase in sales when it introduced recommendations based on its predictive analysis model.
Which technology will you take advantage of?
Predictive intelligence technology, such as machine learning and AI, are no longer foreign concepts you can ignore. They are not going anywhere and they are changing digital marketing. Both options enable marketers to develop custom-made solutions for clients and remaining aware of emerging trends can enable your company to become more productive in the future.
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