Predictive analytics in marketing: what it is and why you need it

Predictive analytics in marketing: what it is and why you need it

October 8, 2019

Maria Marinina

Digital Marketer

Know what your customers want most and what your company does best. Focus on where those two meet.

Kevin Stirtz

Kevin Stirtz

Analytics Consultant at Wells Fargo

This quote epitomizes the essence of successful sales. Effective marketing has always been about recognizing and anticipating customer needs. With the increasing application of big data, artificial intelligence, and machine learning in business, identifying customer expectations has become easier than ever.

This technological expansion has provided marketers with powerful tools to make data-backed assumptions about customer preferences and buying patterns. Today, predictive analytics experts helps businesses reduce risk and lift sales by answering mission-critical questions, such as:

“Who is my ideal target buyer?”

“What items do they typically look for?”

“How do they interact with my brand?”

“What might they need next?”

To understand how companies can gain that intelligence with technology, let’s take a step back to define predictive analytics software and understand how it works.

What is predictive analytics?

Predictive analytics is a branch of advanced data analytics that refers to the process of using data mining, statistics, and AI models for data analysis to make predictions about future outcomes. This analysis is done with highly-specialized software running on robust machines with high computing power that allows processing vast amounts of data accurately and fast.

In business, predictive models utilize historical and transactional data to identify patterns that make it possible to recognize risks, capture opportunities, and enhance decision-making.

4 types of data analytics

In 2012, Gartner published an iconic graph that illustrates various stages of data analytics, including predictive analytics. While some data analysts contest this model and propound a revised approach, it still neatly breaks down the evolution of analytics while enhancing its understanding on a high level.

Stages of data analytics maturity

All of the presented stages (or types) allow companies to traverse the path from raw data to meaningful business insights; each of them offers unique outcomes and serves a different purpose:

Descriptive analytics. As the name suggests, descriptive analytics is concerned with describing the current state of things. This type makes it possible to gain a comprehensive insight into existing data and break it down into smaller chunks for easier management.

Descriptive analytics can be applied by organizations to detect past events, such as engagement for marketing campaigns, customer attrition, retention, or sales KPIs. However, when used on its own, descriptive analytics provides neither root cause information nor visibility into why a given situation occurred. Think of Google Analytics, for example. It pulls data from a website and displays it to the user, without investigating the reasons for a high bounce rate or increased user engagement with a particular page. This cause-and-effect correlation has to be inferred by the user. Other data analytics types can help in that effort.

Diagnostic analytics. While descriptive analytics allows companies to discover what happened, diagnostic analytics examines data to determine why it happened. It drills down into dependencies between various past events and applies business intelligence to come up with the best possible explanation of why certain events occurred. Such information provides better decision support and allows companies to identify anomalies and draw conclusions on how to avoid blunders in the future.

Predictive analytics.The next data analytics type is the subject of this article. Its goal is to predict what is likely to happen. To do so, it builds on the findings of descriptive and diagnostic analytics. Personalized recommendations are probably the most obvious and widespread application of predictive analytics in business. Amazon, Netflix, or Spotify, they all boost their sales by forecasting possible future purchases for each customer based on what that person bought, viewed or listened to before. Such personalization also helps them drive more revenue from upselling and cross-selling thanks to a clever use of big data in ecommerce.

Prescriptive analytics. Prescriptive analytics literally prescribes what to do to prevent a future problem or capitalize on a potential opportunity. Because this method is so deeply embedded in the future, it is possibly the most abstract of all types, yet it presents tremendous value to businesses. Prescriptive analytics acts not only on historical data but also incorporates external information and a variety of complex tools and technologies as sources to provide recommendations. A simple example of its application could be price calculation engines that can assist companies with determining product prices that balance customer demand with optimized profit, or maintenance planning tools, which can anticipate downtime and recommend necessary actions to adjust maintenance planning and prevent failures.

How does predictive marketing work?

We have covered the basics of data analytics, including the predictive approach. Let’s now investigate the application of predictive analytics in marketing.

Predictive marketing comprises all tools, processes, and rules for applying AI-fueled predictive analytics to sales and marketing strategies. It works through collection and analysis of customer data from a growing list of data sources including CRM systems, surveys, social media channels, and other platforms of customer engagement. This breadth of knowledge is then applied to the entire marketing process spanning all the customer journey stages and every single channel of brand communication to identify future risks and opportunities.

The most exciting prospects in three years' time

The stages of predictive analytics

Every data analytics project is unique, not only due to the high level of sophistication but also because of distinctive data sets that each company provides as input. However, most implementations follow the workflow similar to the one presented below.

1. Project definition

This stage is about determining all the essential ingredients of the business intelligence project plan. It is vital to establish expected outcomes and deliverables, available data input and resources, and roles required to execute the implementation. As the outcome of this stage, a company should have a clear understanding of what it hopes to achieve when the project is complete.

2. Data collection

At this stage, companies gather all the data they consider useful as the input for their projects. Since data processing is the essence of predictive analytics choosing the right types, scopes, and sources of data is critical for the positive outcome.

For instance, if a project aims to refine segmentation for lead nurturing campaigns, it needs to operate on demographic and behavioral data collected from a variety of sources. These sources include traditional tools for collecting customer intelligence, such as CRM systems, face-to-face interactions, or over-the-phone engagements, as well as social media conversations, wearables, and other IoT devices. It is important to know that most of the times data is collected into a data lake and stored there in a raw, unprocessed state, which brings us to the next phase.

3. Data processing

Before any insights are derived, the supplied data needs to be scrubbed. It is quite a tedious and resource-heavy process, which however may bear the greatest impact on the project results.

Data gathered at the previous stage is typically rather chaotic. It can be structured (e.g., aligned in a neat table, with all sections and labels in place) but most of the time it’s unstructured, with some attributes missing and data appearing in various formats (audio, video, social media). Such unstructured data is difficult for machines to process, and therefore it requires some managing before algorithms can digest it. Compliance is another aspect to be considered. Especially in the context of a marketing project, it is of crucial importance to ensure strict observance of data protection laws when processing personal or sensitive information.

4. Modeling

Modeling is a core part of a data analytics project. It’s where ‘the magic happens’. This process refers to the creation and extension of data models that define how an organization should collect, update, and store data.

Models are used to understand the logic within data and formulate predictions based on the drawn conclusions. You may picture them as abstract graphs that organize data sets and determine the relationships between them. They range from linear models that work with two correlated variables to complex machine learning neural networks that are capable of independently processing large data volumes to identify subtle correlations and infer from them.

5. Interpretation

Once various statistical algorithms and models have processed data, the output can be interpreted and presented to answer the business questions asked at the beginning of the project. Visualization is applied to ensure a better understanding of predictions. Finally, the obtained intelligence should be translated into actionable steps and incorporated into strategic marketing planning.

Average lift in conversion rate for sessions influenced

7 examples demonstrating the value of predictive analytics in marketing

Predictive marketing is hardly a new phenomenon. Individual methods and applications have been around for years, used by savvy marketers to enhance products and improve targeting of their marketing campaigns. However, in recent years, we have seen a remarkable evolution of data science and analysis, accompanied by the development of supporting technologies such as cloud computing, which made predictive analytics accessible and affordable to almost every business.

As data intelligence is permeating every industry, forward-thinking companies shouldn’t overlook the incredible potential that predictive analytics can bring to their marketing. It’s impossible to collect all the use cases that it can help resolve. The technology can drive sales and growth across every vertical and can be applied to a myriad of use cases that marketers may implement to bolster up their campaigns and content and create compelling end-to-end customer experiences across every touchpoint.

High Performers in marketing are 7.3x more likely to be satisfied with their ability to use data to create more relevant customer experiences.



Predictive marketing analytics can aid modern marketing teams in the following aspects:

1. Improving lead generation and scoring

Thanks to such predictive analytics techniques as predictive scoring and identification models, companies can prioritize leads and prospects based on their similarity to existing customers or the likelihood of engaging and following up with a purchase.

2. Optimizing marketing campaigns

Through analyzing demographic and behavioral data, predictive analytics enhances lead segmentation so companies can target leads with more individual, specific campaigns that offer higher conversion rates.

3. Enhancing content distribution

Predictive analytics can also be leveraged to identify content types that resonate most with each audience, and determine which channels provide optimal results in terms of engagement and response rates.

High-performing marketers are 9.7x more likely than underperformers to be completely satisfied with their ability to personalize omnichannel experiences.



4. Predicting and decreasing churn

Churn, or attrition, rate indicates the percentage of customers who abandon a brand or discontinue a service. To keep it low, companies can implement predictive analytics and identify signals that may indicate that a customer is likely to drop out. With this intelligence, businesses can act in time, turning up the value of their services to retain hesitant customers.

5. Augmenting products

Data-driven, in-depth understanding of customer expectations for each target group allows companies to tweak and adjust products and services to correspond closely to what customers need.

6. Boosting upsell/cross-sell revenue

Obtaining insights into customer demographics and behavior creates a fantastic opportunity for maximizing revenue through upselling and cross-selling. By observing and analyzing the relationship between particular purchases, companies may refine advertising and recommendations to compel customers to add an extra item on top of what they’ve just bought.

7. Identifying new market opportunities

By implementing more advanced analytical models, companies can also formulate predictions on future trends and growth opportunities. What products and services are likely to catch on in the next season? Which markets offer the strongest growth potential? Those are the kind of questions that predictive analytics can answer.

Amazon Thanks to the continuous investment in data analytics solutions, Amazon knows its customers so well it can predict with great accuracy that a person is going to buy a given product long before it happens. The company holds a pending patent for its “anticipatory shipping” service, which will trigger package delivery before customers have even hit the “buy” button.
Netflix Netflix has honed data-empowered product personalization to perfection. The streaming service is using predictive analytics to prompt customers to watch “what they might also like,” based on viewing history and demographic data gathered from a variety of sources. According to the company, over 75% of the content watched by its viewers is based on personal recommendations.
adidas The clothing giant has turned to a comprehensive AI-based analytical platform to grow its digital sales from zero to 90 million active subscriptions today. The predictive component was one of the main features of the project designed to foretell buyer preferences and expectations and act on that knowledge with tailored offerings.
Macy’s The department store chain invested in a predictive analytics solution a few years ago to better tailor results for the registered users of their website. Within three months from the implementation, the retailer saw an increase of 12% in online sales, just based on the data-powered capability to anticipate customer needs

Unlocking marketing value with data-based predictions

A few years ago, advanced data analytics powered by AI was only accessible to large enterprises. The implementation cost was inhibitive, and there was a lack of scalable, affordable solutions that could be used by smaller companies as a framework for the development of predictive analytics tools catering to their needs.

Fast forward to 2019, and even small companies can afford an investment in predictive analytics to transform every aspect of their marketing with data-based predictions, from lead generation to ROI forecasting. Thanks to smart data analysis, they can stamp out useless data and build valuable insights to inform their sales and marketing strategies. By tapping into these benefits, they strengthen the bond with customers and secure sustained growth.

Are you ready to start with predictive marketing?

How about your business? Is it ready for success with predictive marketing? Reach out to us if you are interested in learning more about custom predictive marketing solutions.