It was in 2014, when Airbnb faced a problem regarding their pricing model for rentals. Several variables such as seasonality, individual penchants and choice of location created a somewhat chaotic maze in front of them. The brand wondered how they can set an optimal fee which synched with the variables and also got them ample bookings. It used data science coupled with predictive tools to understand, apprehend and replicate the necessary changes.

This kind of an instance posits the fact that data sciences and predictive marketing analytics are interlinked.

Before delving into their core relationship, its better we see their individual-ness first!

Prologue – Data Sciences

What is it?

To answer this, we have to go back in history! When the world first entered the era of big data, there was an imminent need for its storage. This became the primary concern for industries till 2010. The processing of the data sciences created a solid framework where all data collected can be comprehensively stored, accessed and utilised.

Data science is nothing but an amalgamation of diverse tools, algorithms along with machine-learning principles that nurture the goal of discovering hidden patterns from raw data.

●       The job of a data scientist is to make decisions as well as predictions by utilising predictive causal analytics, machine-learning and lastly, prescriptive analytics.

●       Data science can be described as a forward moving approach or an explanatory methodology that focuses on analysing past/present data and predicts the future outcomes through informed decisions.

●       The “what” and “how” factors of events are answered by it.

What are the data sciences principles?

Being a multi-disciplinary arena, data science emerges as intersections of –

Business knowledge
Computer programming
Statistical/Machine-learning

The key principles of this field thus are –

Data as a strategic asset
This principle ideates on the fact that the data collected and stored should be considered as an integral asset of a company. For example, organisations operating with cloud-based solutions are extremely data-driven. They use data as a strategic asset only.

Following a systemic procedure for extracting knowledge
There has to be a methodological process which extracts insights from data. Thus, a procedure with distinctive stages having clear deliverables must be implemented.

Passionate about data
Companies must invest in individuals who are technologically adept at handling and streamlining data management. One cannot simply transform data into an insight without prior knowledge or industry expertise. Companies need data evangelists who comprehend the value of data and connect it with their technology and business.

BAB Principle
Data science literature puts a lot of stress on models and also on algorithms. The Business Analytics Business is a working principle which emphasises upon the business part of data science equation. Putting data science into a business context proves pivotal as it defines the problem and uses analytics for requisite resolution.

What are the Data Sciences applications?

There are several domains upon which data science builds its concept. Some of them are –

●       Healthcare

Tumor detection, organ delineation, artery stenosis are procedures employing different methods along with frameworks to seek optimal parameters for tasks like classification of lungs texture. Other zones of healthcare such as genetics, genomics, drug development require data science applications and machine-learning algorithms that simplify the process while adding a perspective to it other than reducing time-taken.

●       Target advertising

Across the vast digital spectrum where display banners, digital billboards and related ad objects abound, data science algorithms are applied extensively. Today, digital ads get a high CTR or Call-Through-Rate as they can be targeted based upon a consumer’s past purchasing patterns.

●       Recommendations of websites

Users are already familiar with similar product suggestions on e-commerce platforms such as Amazon. They help you to seek relevant products amidst the maze of billions of related items and thus enhance your user experience.

Other areas of data science applications include –

Advanced image recognition
Route planning for airlines
Speech recognition
Augmented reality
Gaming

The other side of the coin… Prologue to Predictive Marketing

What is it?

Essentially a marketing technique which uses data analytics to determine the strategies needed to enforce a high probability of success. It has a solid placing in marketing technology. Companies are using general business data, sales & marketing data and mathematical algorithms which seek out the right fit for their upcoming marketing actions.

●       Organisations utilising it has the advantage of making data-driven decisions to yield more accurate results.

●       Predictive marketing can empower a company towards more solid market information about their existing clientele. They can identify patterns that help them predict and forecast effectively.

●       It can work well as a measurement of advertisement and audience sourcing. This kind of marketing helps a marketer create campaigns and predict customer behaviour in terms of responses.

Guiding Principles of Predictive Marketing

Internalising industry context
Understanding the relevant market trends, the competitive advantage and business problems for a particular industrial segment can now be done with the help of big data initiative. Predictive analytics is used to determine proposed programs that a company can produce to incur a high profit margin.

Forecast seasonal customer behaviour
Predictive analytics are especially beneficial for online sales. Some of the most engaging e-commerce platforms use it to highlight the products that a consumer may want to buy with the help of these analytics.

Technology choice
It is essential for the marketer to select the various open and close-ended big data technologies so that they get the best-fit for them. Organisations adopting the best breed of technologies and tools can implement a strong analytical base.

Predictive marketing applications

●       Customer segmentation

This marketing format can assist a business to target accurately tailored marketing messages to target or prospective consumers, who shall most likely buy their products. Predictive analytics is vital to identify potential customers at a much faster rate than conventional techniques.

●       Risk assessment

Predictive analytics helps in building decision-support systems which predict the plausible business operations for a company.

●       Sales forecasts

Examining prior history, market-moving incidents and seasonality of customers, predictive analytics creates a realistic presentation of your organisation’s sales structure. Data mining anticipates your customer responses and their change in attitude by analysing multiple exclusive and inclusive factors. Sales forecasting is key to more than just the operational future of an organisation but its current working status as well.

Link between predictive marketing and data sciences – Where lies the connection?

Predictive intelligence is not new. Businesses have utilised highly advanced analytical tools to detect procedural loopholes, assessing risks and predict future trends of the market domain.

Today we have a new terminology – Predictive data analytics.

It is the process of using both historical as well as current data combined with machine-learning to forecast certain future outcomes beneficial for the business.

Predictive analysis uses reporting and monitoring to plan the strategies and campaign accurately. It is a marketing research approach which can change one’s business landscape by creating a positive impact upon the audiences.
Data Sciences collect and store data as identified by predictive intelligence and applies the same to further streamline business operations and revenue in-tune with market fluctuations.
Predictive marketing transforms all the scattered knowledge which Data Sciences collect and suggests future actions.
Integrating the diverse techniques such as data mining, machine-learning, modelling and artificial intelligence, the predictive analytics tools transform data at hand into concentrated marketing actions.
The connection between these two creates a system that helps to anticipate consumer behaviour, vital for any business. Moreover, it also optimises marketing campaigns created around certain factors which can impact the sales positively.
It enables a business to find valuable leads in their CRM and therefore, pitch them with offers to generate high conversions.
Predictive algorithms can forecast multiple likelihoods from a given base of data with proven scientific accuracy.
Big Data helps to merge marketing factors in an effective business model that across several outcomes.
[Factoid: Market research shows that about 58% of current generation marketers use predictive techniques to improve their email content. As a result, their ROI for email marketing takes a leap.]

Predictive marketing and Data Sciences add a rational dimension to determine “similar” groups of customer segments in new markets. They both help the marker to prioritise their customer. Marketers need to understand their customer behaviour so that they create a potentially loyal client base.

Learning and employing the best strategies where both Data Sciences and Predictive Marketing are put to use creates a winning business strategy for any business domain.

Budgets are rising higher every second and hence, marketing strategies must be designed to allocate business resources to and effectively lead conclusions of higher ROI. The emergence of predictive intelligence where data plays a crucial role is thus necessary for every marketing niche.