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Marketing Predictive Analytics: Definition, Benefits, Applications, Tools

AI Predictive Analytics in Digital Marketing: Everything You Need to Know

AI Predictive Analytics in Digital Marketing: Everything You Need to Know

Over the past decade, predictive analytics has become a core marketing intelligence practice necessary to grow business and sustain competitive advantage. This artificial intelligence (AI) technology establishes a new phase of digital marketing evolution by applying data science and advanced analytics, which empowers businesses to grow by deploying data-driven predictive marketing decisions across multiple channels.

AI-enhanced marketing predictive analytics helps CMOs determine the best course of action, so they are able to faster achieve their strategic goals like brand awareness, social engagement, customer experience personalization, etc. While hypothesizing what to do next is a crucial aspect of any marketing leader, the value of predictive analysis and forecasting is often missed. We still tend to “go with the gut” when looking at an array of possible scenarios.

Read this guide to learn what predictive analytics in marketing is, how it works, and why it is critical to CMO’s success today.

Contents

As CMOs often deal with the vital question of “what best action should we take next?”, they have to consider many decision variables, constraints, and trade-offs. Predictive analytics and artificial intelligence together enable accurate decision-making for complex marketing problems by using mathematical models and representations of those problems. Sophisticated algorithms and machine learning then solve these models and deliver actionable recommendations to CMOs.
Marketing Predictive Analytics: Definition, Benefits, Applications, Tools

What is Marketing Predictive Analytics?

Predictive Analytics is a branch of AI-enhanced marketing analytics that aims to make predictions about the possible outcomes of a marketing program by combining historical data, like the past behavior of customers and their profiles, with statistical modeling and machine learning algorithms. Predictive analytics helps marketers understand which actions to take with a particular customer or a target group and how to optimize the campaign for high performance.

Modern marketers are working with tons of customer data coming from CRM databases, website/app log files, analytics tools, user content, social networks, or other first- and third-party data sources. To gain insights from this pool of big data, they use AI machine learning to find patterns and make predictions about possible customer intents and actions.

In particular, neural networks, deep learning, decision trees, and prescriptive analytics are the artificial intelligence technologies behind marketing predictive analytics that predict the best course of action for digital marketers to make complex decisions involving tradeoffs between business goals and customer intents.

Benefits of Marketing Predictive Analytics

As such, digital marketing powered with AI predictive analytics models can generate more customer engagements (opens, clicks, responses, purchases) and promote cross-sell opportunities. Machine-generated marketing predictions help attract, retain and nurture the most valued customers

Machine-generated marketing predictions make looking into the future more accurate and reliable than traditional customer analytics. As such AI can help digital marketers find ways to save money and acquire new clients.

In eCommerce, for example, predictive marketing models are used to generate relevant real time product recommendations for shoppers, forecast inventory needs, and configure store layouts to maximize sales.

In the travel industry, flight booking websites often use AI analytics predictions to forecast desired destinations and set ticket prices reflecting past travel trends. This information helps direct marketing efforts and campaigns.

Another example is hotels and recreation businesses using artificial intelligence predictive analytics models to estimate the number of guests in order to optimize occupancy and revenue.

In account based marketing (ABM), which has been adopted across many industries, customer predictive modeling is used for client intelligence, lead scoring, direct marketing, user journey mapping, and other activities. Prediction-driven ABM improves content personalization at every touchpoint as well as customer acquisition and customer retention.

Increase Sales and Retain Customers

Marketing predictive analytics creates the highest value for prospecting and sales-related activities. Every prospective customer is predictively scored for sales behavior such as past purchases, feedback, social engagement, page visits, clicks.

The scores then help marketers effectively plan and drive lead generation and nurturing activities across emailing, advertising, website, social networks, customer care, and other channels. In this way, predictive analytics delivers its unique competitive advantage to a range of marketing, sales and cross-sell activities.

Optimize Marketing Spend

Foreseeing a prospective customer’s direct engagement like email response or contact form submission is the most established use case of AI-enabled predictive analytics that delivers tremendous return on marketing investment. The value proposition here is straightforward. As marketers can suppress those prospects less likely to engage, marketing spending tends to be minimized, so profit goes up.

For example, Intersog, a US based IT company, lowered its cold outreach costs by 23% and increased response rate by 5.7% by integrating customer response modeling and advanced analytics in MailChimp email campaigns.

Reduce Customer Turnover

Customer turnover modeling can be another good use case of AI-driven marketing predictive analytics.

While customer retention is a top business priority in many organizations, especially in the B2B sector, promoting effective retention incentives, such as a discount offer or loyalty program, can be quite costly.

You can spend a lot on re-marketing, social and email campaigns trying to persuade dozens of your clients and deliver your special offer. But this initiative would be more effective if the offer was delivered only to those customers most likely to leave. How do you know which accounts are about to leave? Why are they leaving?

Predictive analytics enables you to rank existing customers by loyalty level and identify those who are most likely to stop buying from you. AI analyzes customer behaviors or actions and makes predictions regarding possible turnover within your customer base. With AI-enabled customer retention predictions, you are able to grow your client base and focus only on key accounts.

Six Applications of Marketing Predictive Analytics

Organizations of all sizes across industries leverage artificial intelligence predictive analytics to make smarter marketing decisions. Your organization can benefit from many applications of AI predictive analytics depending on which type of marketing decision you choose to improve — that is, how predictive marketing modeling will best apply to drive data-driven decisions within your organization.

There are dozens of predictive analytics applications in digital marketing, and this number is growing as data science and artificial intelligence evolves. In this article, we list 6 most pervasive cases that deliver tangible benefits to marketing teams — that is, advanced customer analytics, predictions and behavioural insights.

1. Customer retention

How many existing customers are likely to stay with the company and continue using its product or services. It helps build customer loyalty, improve customer lifetime value, increase sales, and minimize turnover rate.

2. Customer marketing

What direct channels such as email, social media and strategic ABM is best to deliver the personalized messages to individual consumers. This helps not waste time and money on disinterested or unqualified accounts.

3. Customer journey mapping

What a specific customer, prospect or a group of will like or dislike while engaging with the product/service/company. This helps marketers see their business from the customer’s perspective and optimize conversion flows and journey maps.

4. Behavior-based advertising

Which advertisement a given customer or target group will engage with, based on their past behaviors and future intents. Such predictions optimize campaign performance and increase return on ad spend.

5. Email outreach

Which emails a customer will open and take further actions through the conversion funnel. With these AI predictions, email marketers can send finely-tuned relevant offerings to the right audiences at the right time.

6. Lead scoring

Rank potential clients (leads) and create scorecards that represent the perceived value each lead represents to the business. Predictive models use lead behavior data such as website visits, social engagement, white paper downloads, email clicks.

Top-12 Predictive Analytics Tools for B2B Marketers

Here are 12 most popular AI-powered predictive analytics tools for B2B marketers:

  1. Google’s Prediction API is a logical first choice of B2B marketers that allows to integrate predictive analytics with any Google Analytics account. It is inclusive of all the Google products and services, including Gmail, Calendar, and Google Ads.
  2. Alteryx a suite of data management and analytics tools that help businesses simplify, automate and accelerate analytics processes.
  3. Predictive.io provides a suite of products for marketing automation, optimization, data science and machine learning
  4. Radian6 is a predictive marketing analytics platform that lets you build sophisticated algorithms in minutes without any coding, programming or scripting skills. Radian6 is designed to monitor social media and study customer opinions on products and services in real-time.
  5. Tableau is one of the leaders in the BI analytics space, offering cloud-based tools that enable users to build interactive data visualizations, advanced analytics and advanced data discovery capabilities.
  6. Bime Analytics is a B2B data management platform that combines the power of machine learning with the ability to identify high-impact indicators and patterns discovered from big datasets.
  7. Google Trends gives you access to aggregated search volume data for specific keywords or keyword phrases. This tool is especially useful if you are running a campaign for a new product that has not yet been launched.
  8. Marketo is a cloud-based application that helps create intelligent email programs.
  9. IBM Watson Analytics provides cloud-based predictive analytics solutions that help businesses analyze, visualize and understand customer data to predict behaviors, patterns and trends.
  10. Adobe Analytics is a cloud-based predictive analytics solutions. It combines predictive modeling with actionable insights from your data.
  11. Salesforce’s Einstein Analytics Platform allows you to integrate the power of Einstein into your CRM software. You can then access predictive analytics deeply integrated into Salesforce’s platform.
  12. Oracle Data Mining leads the way for businesses to find the insights hidden within their CRM data.

In the past, predictive analytics was limited to advanced statistical techniques and complex mathematics. However, recent innovations have streamlined this entire process and brought it within the grasp of even non-technical people.

With the explosive growth in social media and the ability to use real-time data for analytical purposes, there is no wonder that advanced marketing analytics is one of the hottest areas of business development. In addition, the explosion in new tools and technologies has made predictive analytics more accessible to CMOs and marketers executives.