Data Science in Marketing and Business
In a second, Google processes over 99 thousand search queries, and thousands of photos are posted on Instagram. The amount of data on the Internet is skyrocketing. Being a resource for business and marketing, data is everywhere. Data science allows one to process and analyse data. It also helps to deal with business issues in retail, industry, banking and other fields.
When data science first emerged in the 70s of the last century, it studied the data life cycle. In the 2010s, computers could process a wealth of information. At that time, data science grew in importance and began to be included in the programmes of the world's universities.
Nowadays, data science helps business and drives innovation. In this piece, you will learn what data science is and how to apply it in business and marketing.
What is Data Science
Data science is a field of knowledge that studies and analyses data and looks for patterns to make decisions in practice.
Actually, data science is not exactly a science. Data is not a subject of data science, but a multidisciplinary resource (business analytics, mathematics, statistics, programming, strategic planning, system analysis) for gaining knowledge. Data science is closely associated with artificial intelligence, machine learning, big data and deep learning.
Data science and data technology
Data science receives data from the Internet, smartphones and other smart devices, special sensors. Other sources are corporate log files, archives, transactions history. Software is the primary tool for data scientists. It processes, structures, analyses data and visualises it in a format that is understandable to the end user.
Data science looks into the future — it gives forecasts based on data. It predicts demand for goods, user behaviour in the application, disease progression, climate disasters and so on.
How does Data Science Work
Data science processes data for analysis: organises, aggregates and arranges it for non-professional users. Data scientists extract information from arrays of processed data to find patterns, build hypotheses and model a picture of the future.
Stages of data science:
|Perspective||Setting a goal and a problem to be solved. What do we want from data?|
|Data collection and processing||Searching and extracting raw data from available sources.|
|Data preparation||Data storage and cleaning. Blank fields from databases and irrelevant information are removed. Raw data is transformed into a form that can be analysed further.|
|Data processing and modelling||Clustering, modelling and classification, searching for patterns. This stage reveals what happened in the past (descriptive analytics) and which scenario is possible in the future (predictive analytics). Methods of statistics, intelligent data analysis and machine learning "look" into the future. At this stage, modelling is applied — «what if» and «if… then what».|
|Data analysis||The following types of analysis are used: regression, exploratory, confirmatory, predictive, confirmatory qualitative, etc.|
|Data formatting||The data is presented to the end user as reports and visualisation: charts, graphs and so on.|
|Decision-making||Business decisions are made based on the data.|
Why does Business Need Data Science
Data science answers specific business questions by testing hypotheses and ideas. Data science:
- takes into account the amount of information that a person cannot process to make decisions;
- excludes subjective opinion in decision-making.
An unbiased answer to a business question protects from errors and misconceptions. The main thing is not the authority of the head, but facts.
Data science in business also:
- Studies the target audience. A company that knows the customer perfectly well offers the right products and outperforms its competitors.
- Forecasts trends (insights for new services and products).
How Data Science is Applied in Business
Data science forecasts demand and helps to optimise supply chains. It also reduces the risks of surplus goods when demand is low and shortages when orders are large. On the basis of data, production efficiently allocates resources, controls expenses and revenues and finds vulnerabilities — the causes of defects. Predictive analytics show when equipment will fail due to wear and tear and indicate when to carry out repairs to prevent this.
Logistics and transport
Optimisation of supply chains: reduction of delivery time, search for optimal routes and reduction of operating costs. As in industry, review of data forecasts demand for services of transport companies. Data science optimises warehouse space and reduces the risk of errors in storing and moving goods.
In transport, data science forecasts the volume of passenger traffic and vehicle occupancy and ensures safety — predicts breakdowns and accidents.
Example. Russian Railways has implemented data science technologies to predict dangerous failures of railway facilities. The algorithms simulate situations with more than 50 factors, assess the condition of railway tracks, the technical condition of trains and indicate areas for urgent repairs to avoid accidents and train wrecks.
In retail, data science provides:
- forecasts of demand for product groups;
- stock control;
- insight into trends and audience needs based on social media data and information about competitors;
- custom-tailored offers: which product to offer as an alternative or for additional sale;
- price forecasts.
Telecom sets a task for data science to personalise services and products. Data highlights what is important for the customer, how to optimise service packages. Mobile operators also use data to determine where it is better to place their sales points and cell towers.
Telecommunication companies have their own data science departments that create digital solutions not only for internal business needs.
Example. MTS offers customers a service for developing tourism that analyses 5,000+ metrics for audience segmentation.
In banks, data scientists examine data to determine the credit capacity of customers. Loans not repaid on time are a risk for banks. Data science forecasts how the customer will behave depending on the market situation, assesses the reliability of the borrower. Data helps banks to personalise services. Algorithms identify the user's interests by activity and offer services that customers themselves were not looking for.
An important task for data science is to search for the actions of scammers. In the case of large transactions, the system may suspend the transaction and ask for confirmation from the customer. Atypical purchases, several accounts with similar data will be under suspicion.
In this field, data research is not limited to offering suitable property options to the buyer. Data science analyses the documentation of objects: buildings, landing plots. Data forecasts the demand for real estate, the price in a given area, and investors decide which properties are worth investing in.
Data speeds up rental accommodation in services. Example. Airbnb analyses information from users: options they viewed and which ones they eventually chose. Data science forecasts the likelihood of booking in certain areas of the city.
A map with the probability of booking in San Francisco according to Airbnb data
How Data Science is Used in Marketing
Optimisation of platforms
Data science will show how users behave on different Internet platforms: websites, applications, social networks. Based on the data, the company understands how to simplify the purchase process and which elements to add to make the product more appealing to the customer. Which features to use to improve service. Data science indicates successful and ineffective content on the company's resources: which materials are read to the end and which are just scrolled through.
Audience research and personalisation
Data science studies customers in detail: interests, preferred communication channels, online and offline behaviour. Knowledge of the audience enhances the personalisation of offers: the company knows where the customer responds best to ads, which ads they respond to, how to organise cross-sales and additional sales.
Example. Facebook stores a huge amount of user data. It guarantees the accuracy of targeted advertising. Everyone on Facebook can set up what ads they want to see and remove topics that are not interesting.
Data science helps marketers to segment the audience in detail by dozens of parameters in a targeted manner. For example, to offer a product when it is relevant. If you know when your customer runs out of a product (pet food, household chemicals, car coolant and so on), then advertising the product at the right moment is a chance for success.
Data science helps marketing to get real-time analytics and quickly adapt to new situations (it changes strategy). It speeds up planning of future campaigns. Data-driven forecasts will answer how pricing will work and what price to choose to maintain and increase sales.
Accurate analytics and predictability of consumer behaviour save the company money. Ads are good for the company's budget when shown on time and on the right platform. Data science helps marketers to understand what content is interesting to the audience so that they do not waste resources on creating unnecessary one. There is always data to forecast future campaigns and optimise current ones.
Customer loyalty and retention
Attracting a new customer is more expensive than keeping an existing one. Data science shows when the customer starts being less active or stops communicating with the company at all. When the company knows the customer lifecycle, it sends personalised offers or useful content in time for retention.
Data science examines data and answers business and marketing questions to test hypotheses, ideas and launch new products and services.
In business, data science is a tool for objective decision-making instead of intuitive actions or subjective experience. Data science analyses information about the audience to create new services and products. In marketing, data science is about optimising platforms and budgets, personalising offers. It is a tool for analytics.
Data scientists work in industry, logistics, banking, telecommunications and other fields where it is possible to collect and analyse data and then apply it for business purposes.