Building smart brands with analytics and data
As business analytics and data science are changing the world, people are intrigued by how they work in businesses. But before getting into detail, many are still confused about the differences between business analytics and data science. Keep in mind that business analytics is the umbrella term for business intelligence, meaning; business intelligence is within business analytics. With that, let us help you distinguish business analytics and business intelligence from data science.
Firstly, business analytics uses historical data to present retrospective reports for future operations’ current business needs. In contrast to data science, historical data is used to construct future prediction and generates product innovation with algorithms and analytical tools. To simplify, business analytics time focus is historical, and data science’s time focus is the future.
Business intelligence deals with structured data, usually delivered in reports to monitor interest areas. In contrast, data science deals with mainly unstructured, unorganized data in the form of statistical models. Business intelligence supports business decision-making by identifying trends and data science to plan future insights with hypothesis testing.
Now that you know the difference between the two, let us see how top brands use business analytics and business intelligence to their advantage.
Launched in 2008, the data-driven company Spotify; has to deal with large amounts of data due to millions of subscribers listening to music on their software. Spotify uses data analytics to examine and understand data trends to make decisions.
Bernard Marr mentioned that Spotify uses Application Programming Interface (APIs) to give its users’ song recommendations or search suggestions for finding new music according to their preferences or current music trends. This feature helps the business gain consumer insights, making operations function efficiently and effectively.
Data analytics helps Spotify observe and identify their users’ patterns and trends; from one point of view, this can trigger customer loyalty through observation, resulting in customer satisfaction and boosting its users’ acquisition.
Spotify also uses data analytics in ads; they introduced a ‘Skip’ button feature that allows users to skip through advertisements that they may find irrelevant to their interest. Data analytics informs advertisers of the user’s buying behavior, such as; variety-seeking behavior.
By identifying target groups based on their past interest in different ads, advertisers would reach a more receptive, engaged audience. Both advertisers and target groups on Spotify would benefit by having more relevant ad content.
A more targeted campaign, marketing the right products to high potential users, would be cost-effective and efficient for businesses. In short, data analytics helps Spotify focuses on the marketing of particular products based on the target user needs.
Predictive analysis is a type of data analytics that takes information and use it to predict outcomes. Uber – famous for offering transportation, food, package delivery, and more, has a pricing strategy called’ Dynamic Pricing,’ which uses predictive modeling to estimate real-time demands. Algorithms will monitor how long the journey will take and traffic conditions; prices will change according to the riders’ needs; this is also called surge pricing.
How does it work?
Uber explained; as the demand for car rides increases due to large numbers of demand, the fee will increase to prioritize those in need of a car ride; riders will then decide to pay or wait until prices decrease. The results are that these predictive analytics would improve operations, organizations’ efficiency, attract, maintain, and expand their number of profitable users.
The decision-making process is essential in every business, and how Uber does it is by applying insights through data analytics. To ensure a quality user experience, Uber had created an advanced tool that utilizes natural language processing and machine learning. The user experience management tool ‘Customer Obsession Ticket Assistant (COTA)’ helps support agents improve their trips after responding to ‘support tickets.’
Uber aims to improve on this and has developed ‘COTA v2’,.This updated version of COTA had produced more precise solution recommendations and faster services. Uber’s system has significantly improved its customer services and saves costs by streamlining its support ticket resolutions.
Data visualization in Uberpool
Data visualization comes in handy within business analytics for turning framework developments and mapping into data that drivers or users see on Uber. This visualization arranges complicated and complex outputs for organizations to present it in an informative manner.
THE Uber CEO, Travis Kalanick, claimed that the usage of Uberpool, a car-pooling service that allows users to find other users willing to share a ride. This will reduce traffic in areas where automobiles overcrowd the roads by reducing the number of drivers. Data visualization will provide public access to understand and recognize the convenience of this claim.
Founded in 1964, Nike is one of the world’s top-selling footwear and apparel company. They knew their supply chain and brand had to undergo a digital transformation to better connect with their customers. The focus was to have greater use of data analytics, improve e-commerce strategies, and better digital marketing campaigns to attract the right audience.
So how does data analytics help?
Nike allows consumers to sign up for a membership, getting access to ‘Member-exclusive’ products. These marketing programs are the main component of the business strategy to attract and maintain customers while collecting data to produce predictive analysis models. Because of this strategy, product recommendations pushed in ads or emails will be personalized. This feature had attracted the target audience and subsequently grew the platform’s popularity.
Even when customers purchase, machine learning algorithms can assess their purchasing behavior or patterns. Social media data in a specific geographic area helps Nike to decide where to build new stores and what products deserve more marketing efforts, etc.
Besides that, Nike also captures and pinpoints demands through health data on their exercise routines. Nike manages to transform its wealth of business data into information to understand its customer’s needs, customize products, and offer health packages. Ultimately, product development is significantly improved.
Now that retail businesses incorporate business analytics and Artificial Intelligence(AI) into their workforce, technology has become essential for a company to succeed. Stores like Nike would take this opportunity to be more efficient and effective with business analytics to better their innovations and enhance their customers’ online experience.
Starbucks – an American multinational chain of coffeehouses, with about 90 million transactions happening per week, must be a lot of data to manage. This is where business analytics comes to help.
According to Socialbakers, the company has developed an AI technology initiative called Deep Brew, to improve the business operations and customer experiences. Deep Brew aims to give analysts a pool of data to operate processes, creates personalized experiences for baristas and customers based on their preferences, location, weather, etc.
How does Starbucks personalize experiences and improve customer services?
For Starbucks to personalize each experience, they control a rewards program and a mobile app that gathers information regarding its purchasing behavior. If their location were elsewhere, the point-of-sale system would know where the customer is. The recommender system will then give suggestions about preferred orders by the customer to the barista before the customer places an order.
One engine that supports Starbucks is a cloud-based AI engine called the Digital flywheel program; it is the intel that recommends products to customers and gives personalized offers.
According to Forbes, another engine that aids the company is Starbucks’ virtual barista named ‘My Starbucks Barista’ on its mobile app. Its primary focus is to serve as a communicative platform for customers to place an order using AI algorithms. Virtual baristas will receive the orders as either voice commands or messages.
With a long queue for orders, this addition of AI-driven business analytics will assist Starbucks with the efficiency that will boost customer experiences and services.
The American multinational beverage corporation Coca-Cola, was founded in 1892. They are known for manufacturing and selling its well-known beverage ‘Coke.’ Data analytics, a business analytics branch, is widely used to drive customer retention. How is this possible?
Data and business analytics play a significant role in product development and marketing; with social media, emails, phone calls, Coca-Cola retrieves information on customer’s preferences or interests and uses that to re-adjust their approach. Such as creating relevant content for targeted audiences, helping them attract and stay connected to their consumers.
HBS Digital Initiative gave an example of how Coca-Cola found a way to collect data from consumers back in 2008 with a self-service fountain drink machine, allowing consumers to order their customizable drinks from their smartphones.
And with this innovative product accompanied by data analytics, Coca-Cola decided to launch a new beverage flavor ‘Cherry Sprite.’ In other words, these fountain machines had gathered information about consumers’ preferences and generated insights into the company’s future product development.
Coca-Cola relies on AI, business insights, and data analytics to strategize their business, develop their product and give assistance to the decision-making processes while staying relevant to the future’s expanding digital transformation.
Embark the journey towards AI-driven business
Always start with clean data. Without an integrated data infrastructure, you will never be able to become a data-driven organization truly.
Organizations of tomorrow will need to hire data-literate talents who know how to structure and organize data for insights development and capture value.
Without a data science team working closely with key business leaders in an organization, the effort will not see true ROI. Business leaders need to firstly identify a roadmap towards their digitalization effort and be supported by the data science team for execution.
If you are keen to develop a data-driven framework for your organization with strategic alignment between different stakeholders, write to us, and we shall provide you with more in-depth case studies and programs to help your organization accelerate your data-driven adoption internally.
For more details on CADS’ proprietary Data Driven Organization model, visit www.thecads.com/data-driven-organization