Predictive Analytics in Marketing: Hype or Reality?

Latest surveys report that the use of predictive analytics in marketing has increased after realizing its importance at the business level.

Organizations looking to stay in a dynamic world use data analysis to predict upcoming shifts.

Despite being predominantly used, there is a threat that it can become burdensome for users. 

The worldwide market for predictive analytics will have a value of nearly $23.9 billion by 2027, with a CAGR of approximately 21%.

According to the report by Gartner, predictive analytics was considered the CMO’s first technology priority.

Predictive analytics in marketing involves predictive targeting through predictive modeling, data mining, and machine learning concepts to make predictions related to market trends, user behavior, and campaign outputs.

Companies adopt predictive analytics to spot risks, streamline workflows, and uncover hidden opportunities that can lead to growth and success.

Predictive analytics uses location data, contextual data, first-party data, weather data, real-time data, and historical data to predict shifts in the future.  

For example, assume a popular but low-margin new product.

A look at margin contribution may indicate that this new product does not contribute much, but the affinity analysis and RFM analysis might indicate that selling more of such low-margin products generates more overall sales and profit as it makes customers feel excited about the organization’s whole product line and drives up the frequency.

Predictive analytics in marketing requires the right data to be gathered from sources like your website, email campaigns, social media platforms, POS system, and demographics, and that’s the starting point.

What’s equally important for predictive analytics is the choice of the right analytical approaches to answer the right questions, as the example of the low-margin product earlier in the note indicated.

Is Predictive Analytics Accurate?

This query arises in your mind as organizations possess enormous amounts of data for predictive modeling.

In addition, it is illogical to question the accuracy of predictive models without having complete knowledge of their working processes.  

According to Ms. Masood, CEO & co-owner of Tara AI, data is the beginning and end of decisions made using predictive analytics.

Examine the methods used to collect the data, and the data quality, and inquire whether or not you are using cleaned data.

She also adds that CEOs shouldn’t forget to keep all these elements in mind when making predictive decisions.

Iba Masood also says that the responsibility of CDOs and CIOs is to ensure that the data is completely cleaned before management and CEOs start trusting recommendations suggested by systems associated with predictive analytics.

According to the Co-founder of Hexe Data, Mr. Krzysztof Surowiecki “Well-planned forecasting and neatly executed predictive analytics programs are trustworthy to deploy predictive analytics in companies”.

“Moreover, he can’t trust poorly incorporated predictive analytics”. Hence, perhaps that is the major reason why users are hesitant to make predictions. In a few cases, they fail to check if they were done correctly.

What is the Biggest Assumption in Predictive Analytics?

It is necessary to inspect the assumptions made while creating predictive models. The business manager should continuously ask about the major assumptions and the situations in which we reject them.

The responsibility of organizations and analysts is to frequently check to ensure whether the major elements added to assumptions have been modified over time.

The major belief in predictive analytics is that upcoming results are replicas of the past. Moreover, they might lose importance over time.

Time is the most important reason why assumptions are false. The model doesn’t forecast perfectly if it was built years ago.

The assumption may not be considered legitimate if an important variable is excluded from the model and that variable has substantially altered over time.

The accuracy of assumptions made is a major element of a predictive model, along with the correct data and statistical model, and neither the company nor the analysts can afford to neglect them.

Benefits of Predictive Analytics

Predictive analytics is used to research, inspect, and understand different aspects of marketing. It can be used by companies to:

Predict customer churn

Predictive analytics tools are used to instruct the machine data sets to predict customer loyalty.

By looking at the previous and present data, companies can easily predict churn. 

Increase sales

By looking at the behavior patterns of humans, predictive analytics boosts business profits. 

Understand the needs of clients

Predictive analytics minimizes the number of corporate risks by collecting insights related to the success of fresh products, getting knowledge of the businesses they work with, and evaluating the future demand for something to find new opportunities.

Analyze the performance of the campaign

Through this process, companies can discover visitor behavior and their purchasing habits.

Find out the possibility of product purchase

By keeping product purchase possibilities in mind, businesses can develop consumer segments so that they can reach out to them in an efficient way to get better outcomes.

Challenges in Predictive Analytics

Right data collection

Businesses use predictive models to avoid mission-major issues, including machine malfunctions and manufacturing delays.

These models work better if valuable data is added to them. If any employee of a company captures and transmits partial or inaccurate information, then there is less chance of getting the right output.

To avoid this issue, it is recommended to set robust processes around quality assurance and data collection.

Creating a powerful strategy

Majority of the businesses don’t know how to use predictive analytics and implement it without a clear business objective and goals.

They need to have a clear idea of which pain points they wish to relieve using this technology.

The solution to this challenge is to run pilot research and talk with predictive analytics vendors to make sure they are selecting the software that supports their initiatives.

Identifying a friendly solution

Most data analytics programs need firms to follow a step-by-step process before they shift from Level A to Level B, from early preparation to data cleaning to complete model deployment.

This process consumes time and results in more errors because if any step is executed incorrectly, it could impact the whole result.

To resolve this issue, it’s better to have a system that automates some parts of these actions to reduce the number of steps that are carried out by the firm.

How Do Companies Conduct Predictive Analysis?

Predictive analytics can be conducted by companies using a systematic approach, as mentioned below:

Defining goals & objectives

It is meaningless to develop a predictive model without having a clear focus on what needs to be achieved with it.

To define business objectives and goals, companies have to discover a problem to solve.

Data prep & profiling

Data prep describes the structure of data, and data profiling includes getting an idea of what’s there in the data.

As the number of data sources increases, it is more crucial to focus on data quality to make sure that the data used for the predictive analytics model is of good quality and matches the set of objectives set by the company.

To initiate this process, businesses need to

a) Collect present data

b) Organize data for data modeling

c) Clean data

d) Review the quality of the data

e) Determine the goal

Model the company’s data

Modeling the company’s data allows it to develop, train, and test an ML-based data model to forecast future events or specific results.

Validate results

The firms need to ensure that they are comfortable with the outcomes before deploying the data model into operations because a bad model leads to questionable results.

Deploy a predictive analytics model

Now, deploy a model in a real environment and allow it to start working. Operationalize the outcomes by inserting them into applications where they are used later.

Model monitoring

Soon after deployment, it is crucial to monitor its performance.

Constantly review the business’s predictive model and set it up with the capability to adapt as the data changes.    

What is Predictive Analytics Modeling’s Impact on a Business?

Let’s highlight the business advantages of predictive analytics in marketing:

Accurate prediction of trends

By analyzing huge quantities of user information, market data, and social media sentiments, marketers can forecast upcoming trends in the industry faster than their competitors.

Intelligent segmentation of clients

ML-driven models can discover invisible links between clients’ data points and result in better decisions related to clustering.

Lead scoring

It is considered one of the best use cases for predictive marketing analytics.

The process includes the use of previous clients’ data to rank discovered prospects based on their chances of converting.

Ad and content recommendations

The majority of eCommerce businesses (including Amazon and Zalando) and streaming services (such as Spotify and Netflix) are making use of collaborative filtering to display related products, songs, and series recommendations.

In general, collaborative filtering uses history to make perfect recommendations for cross-selling or upselling.

Is Predictive Analytics a Better Method than Others for Predicting the Future?

Expert opinion says that, when compared to different methods, predictive analytics is more data-oriented.

Technology helps organizations focus more on new opportunities to improve products according to customer demand and maintain long-term relationships.

Predictive analytics uses measurements like customer lifetime value and others for the growth and success of a business. Hence, it’s a big thing to launch in the market to solve clients’ problems.

How can Predictive Analytics Become Useful in People’s Buying Patterns?

Predictive analytics solutions will enable companies to do real-time analysis of the previous click-through behavior of consumers, product preferences, and shopping history.

Predictive analytics, when combined with ML, can offer the most related outcomes and recommendations to eCommerce & retail users.

However, the online behavior of all consumers isn’t the same; it will differ according to their habits and preferences.

This technology will evaluate numerous variable factors in a consumer’s behavior and produce the preferred responses and engagement from consumers, making their shopping experiences more customized.

Moreover, firms use this technology to conduct customer sentiment analysis about pricing to come up with a decision on the optimal pricing of products.  

Predictive Analytics Industry Use Cases

Predictive analytics is widely used by different industries for their growth. Here are some use cases:

Manufacturing industries

Maintaining sensor records and consistent logs can assist in discovering probable breakdown places in the industry and the necessary steps to minimize such probabilities.

Financial sectors

Insurance companies, banks, and housing loan providers use both statistical tools and machine learning to forecast cases related to fraud detection and credit risk before the loan approval process.

Predictive analytics in finance is used to predict expenditures, cash flows, and taxes.

Healthcare

Detecting chronic diseases, treating patients with such diseases, and predicting the chances of the spread of a few diseases in the upcoming days according to the change in the environment.

Sales & Marketing

Introducing a fresh product in a market or discovering the requirement for possible modifications in the present product according to the details gathered from numerous sources.

HR

Choose the most relevant workforce and forecast each employee’s performance. 

Why is Predictive Analytics a Must-have Tool for Modern Marketers?

The predictive model empowers modern marketers to discover the most promising or high-value leads, group audiences according to their behavior and interests, and improve marketing campaigns.

This ends up with higher conversion rates and ROI.

With the availability of no-code predictive analytics SaaS solutions, even teams with no technology background can benefit from predictive analytics services without depending on any specialized knowledge.

Advanced AI, coupled with predictive analytics, will play a crucial role in customizing marketing efforts.

Deep analysis of 1st- and 3rd-party datasets would be significant to acquire predictive insights on CLV, consumer behavior, etc.

Why Do Marketers Struggle with Data-Driven Decisions in Predictive Analytics?

According to some reports, it’s not possible to completely trust predictive analytics in marketing.

This is because conditions alter, and the predictions quickly become outdated due to alterations in the economic climate and other disturbances.

Many people think there is no option to link predictions with behavior.

Predictive analytics is imperfect after the introduction of the time factor, so prescriptive analytics is essential.

To use predictive analytics, numerous organizations rely on data science teams rather than marketing people.

This needs the data science and marketing teams to be aligned to execute as per plans. This creates problems for marketers.

Reports state the causes for the failure of data projects to match the expected goals of marketing.

Here are some causes:

Inaccurate selection of data and sluggish model building: Studies state that the technical team was overloaded and failed to fulfill the needs of 42% of respondents.

The model’s development was too slow for 35% of people.

Outdated models, which are dangerous and even unusable: For models to be useful to marketers, they must be continually reviewed and updated.

If models aren’t updated, predictions may be dangerous or even erroneous.

Models and data updates can be automated. The majority of companies rely on manually operated data science workflows since they may not have realized the importance of automation.

The Predictive Analytics Revolution in Social Media Marketing

Following are the major examples of predictive analytics in social media marketing:

Identify potential clients

The major social media platform Facebook makes use of this technology to create lookalike customers.

Details related to your followers are gathered from pixel tags, business pages, and mobile apps.

Based on the profiles of your best customers, the algorithm finds viable customers to present your content.

Optimize marketing efforts

Predictive analytics for social media are used to design more powerful marketing approaches.

The goal of this technology is to assist companies in understanding their customers’ dislikes, interests, likes, and preferences.

Enhanced user engagement

Predictive analytics in social media can analyze data from business pages of social media accounts to highlight the type of content that matches their niche audience and the types of interactions that lead to improved engagement.

Better data-driven business decisions

This kind of analytics can help firms understand the outcomes of their marketing processes and assist them in making data-driven decisions on maximizing their marketing spend and spending their resources.

Additionally, this analytics is used by firms to get an idea of the impact of social media influence on their overall operations and give insights on improving their strategies.

The process of incorporating predictive analytics into social media involves three steps, such as:

a) Setting KPIs and Objectives

b) Choosing relevant technologies and tools

c)   Developing a data-controlled culture

Is Predictive Analytics the Future of Marketing or Just Hype for 2023?

Nowadays, platforms for predictive analytics are getting more automated, requiring less human involvement.

Does this indicate that ultimately humans will not be able to do anything but relax while the machines monitor everything for them? No, because humans are still needed to support machines in setting up aims and layout goals in a mathematical way.

No matter how superior machines become, they will never be able to react to illogical commands.

Furthermore, machines won’t be able to fully understand why people want certain tasks performed, which is a major element of any marketing approach.

So, is predictive analytics the future of marketing or just hype for 2023? In the future, machines will keep increasing the reach, speed, and accuracy of marketing automation and predictive analytics, but they will still need logical, responsive people to give them instructions.

Boost Your Market Research Strategy with Predictive Analytics

Let’s discuss how these predictions apply to market research using business intelligence and, more especially, online research:

Market researchers can use this technology to obtain more useful forecasts from existing and past online information and move further to improve strategies related to traditional research, including target groups, information from research communities, and collecting the relevant respondents.

“Cross-validation and algorithms are used by the predictive analytics model to sort all data”, and “ignore useless data and highlight critical variables”.

This technology can be used to analyze any of the data that market researchers collect, such as market research online communities, texts, detailed information about participants, and both online qualitative and quantitative research.

Also provides useful information regarding upcoming clients, market trends, and qualitative markets.  

Predictive Analytics: To Burden or Not to Burden Users?

Different techniques such as association, clustering, regression, forecasting, classification, correlation, and statistical approaches have been used by predictive analytics to forecast business results without burdening its users. 

It is possible to determine whether predictive analytics burden a user or not after looking at various factors.

The technology requires special attention to automate complicated data analytics operations to minimize the burden on users.

However, if anything goes wrong while implementing predictive analytics, it can become a burden to users by providing erroneous results.

Hence, managing the balance between transparency and user-friendliness is critical to ensuring that it’s a boon to users.

How Express Analytics’ Predictive Analytics Models can Shape Your Business?

Express Analytics is a US-based predictive analytics consulting firm that takes a customer-centric approach to all projects, ensuring that each solution is customized to the expectations of the business.

The company customizes its services according to the operational requirements of every organization. 

Conclusion:

Predictive analytics is trend, and continues to expand; it’s a key asset for marketing professionals to learn, understand, and implement all the action plans and filter the hype from trends.

If someone tells you that ML is used by predictive analytics, you have to identify what the machine will be doing, what you will be doing, and how it relates to your operations and technical team.

Don’t feel shy about asking questions and clearly describing your goals. The more you understand what to achieve with predictive analytics tools, the more you’ll enhance your business productivity.

References:

Predictive Analytics in Marketing: Benefits and 7 Use Cases

Predictive Analytics in Marketing

How Predictive Analytics can Save Your Business