Examples of How Big Brands Are Using Advanced Analytics

Wojciech Prażuch

Aug 13, 2021 • 9 min read
Use cases of advanced analytics

As almost every aspect of business has become increasingly governed by technology, enterprises tend to generate a vast proliferation of data.

Coupled with this is the ever-present need to outrun the competitors, and so organizations have been looking to harness the power of advanced analytics to drive informed and strategic decision making.

Advanced analytics is closely related to the field of machine learning. Both concepts involve the use of computer programming fed by and analysing vast reams of data to identify patterns and deliver insights based on them. For example, most businesses today collect large amounts of unstructured data from their customer interactions, a practice that has come to be known as ‘Big Data’.

Big Data can include anything from web browsing history to social profile activity, and the sheer number of data points means it is too much for any individual to collect, collate and analyse themselves. Therefore, advanced analytics, powered by machine learning algorithms, has become an essential tool for businesses looking to derive value and insight from the unstructured data they collect, and consequently, determine future outcomes.

What is advanced analytics?

Let’s clear up the definition of advanced analytics. It’s an overarching term for the group of predictive modelling, statistical methods, automation techniques, and machine learning used to help organizations get more insight and value from the data collected.

By predictive analytics, these tools help many companies to forecast different market trends, predict customer behaviors or respond to different scenarios in a more agile and accurate way.

Business intelligence gives business leaders the ability to develop and act on advanced statistical models, planning for worst (or best) case scenarios and build an element of future-proofed resiliency into the operational structure of their enterprise.

Advanced analytics is covered by numerous areas, including:

  • Machine learning and artificial intelligence (AI)
  • Graph analysis
  • Data mining
  • Complex event and data processing
  • Searching and mapping for distinct patterns
  • Predictive analytics
  • Data visualization

Technology, of course, is always evolving, which means that this range of fields in which advanced analytics can be applied is growing all of the time. As new demands merge, so too do new solutions.

A variety of applications of advanced analytics in enterprises

There are numerous potential applications of advanced analytics to solve common business problems. Here are a few of the use cases:

Boosting customer acquisition and retention

With advanced analytics, data collected from customer interactions, market research and feedback can be used to identify trends in customer preference and behavior. With this insight at hand, businesses can more easily identify what they need to provide to their customers to meet their expectations, or how best to win over new customers by framing their products and services as solutions to prevailing trends and customer challenges.

Enhancing marketing efforts

In the past, marketing campaigns were much more of an art than a science. Before the era of big data analysis, it was much more difficult for businesses to precisely target their marketing efforts, and often this led to disappointing return on investment (if there was any return at all).

Thanks to machine learning and business intelligence, companies can now develop and execute more sophisticated marketing campaigns by monitoring transactional data, online activity, and real-time changes in customer behavior and preferences.

Advanced analytics allows marketers to adapt their messaging and targeting with agility and accuracy, making data driven decisions, and enhancing the chance of a successful marketing campaign.

Improving risk management

All businesses need to take risks in order to be successful, but the most successful are those that are able to effectively manage those risks and maintain resilience even as they evolve.

Predictive analytics with advanced analytics tools make this possible for any enterprise by providing the ability to forecast risks, model scenarios and provide a guiding light for the establishment of smarter, more strategic risk management plans.

Ensuring product development and innovation

Big data helps to open up new revenue streams for many companies by identifying areas for improvement of existing products, or niches for new products to fill.

Product design and development has always been best served by focusing on what customers need, and advanced analytics tools are capable of delivering a much more accurate picture of those needs than business ever had access to in the past.

Business intelligence can help to ensure all new product innovations are validated prior to development - saving on resource costs and increasing the chances of a higher return on investment once the product is launched.

3 examples of how brands use advanced analytics in practice

Amazon is perhaps the best example of an organization that has mastered data analytics. Amazon provides one of the most seamless and personalized customer experiences available.

They use business intelligence to make suggestions based on previous purchases, but also on what other customers have bought with certain items, browsing behaviour and other factors.

Another example of successful advanced analytics is Starbucks. Starbucks uses data analytics to help optimise its menu design and product offering. By aligning its product lines with customer preferences, the cafe chain ensures it can stay ahead of the competition.

Startbacks uses digital menu boards which allow the company to alter the products it features on its menu to strategically drive sales at different times, reflecting shifts in demands.

Over in the world of finance, American Express is using advanced analytics techniques to improve speed and performance in its service delivery. The company uses sophisticated predictive modeling to analyse historical data and over a hundred variables to predict potential customer churn, enabling it to speed up and optimize marketing efforts to retain these accounts.

This list of real world companies using advanced analytics goes on and on, but these three examples here are enough to show that it is applicable in a wide variety of business sectors and works for some of the most successful companies in the world.

Why is advanced analytics important for enterprises?

Advanced analytics is important for businesses because it helps to facilitate innovation - being able to predict, forecast and plan for changing market dynamics as well as improve business operations is crucial, and could hold the key to unlocking ongoing growth and success.

If organizations fail to utilize business intelligence, they risk falling far behind the competition. In a nutshell, advanced analytics makes it possible to make better, more informed business and strategy decisions with the help of predictive analytics - in many cases, these decisions and their outcomes can be modeled beforehand to give even greater assurance of results.

Tracking data on consumer behaviour leads to greater and more detailed information than ever before, producing a constant stream of data sets for businesses to utilize. In using advanced analytics techniques to gain insights from that data, they can form multilateral, incisive strategic plans for all areas and functions of the business, from customer service to digital marketing.

This is the crux of why advanced analytics is important to businesses: it helps to develop ‘self-serve’ capabilities and a foundation from which to launch better, more accurate business projects.

The future of advanced analytics for enterprises

The future is bright for analytics. As technology evolves, so too do the potential applications and capabilities of the data science tools that drive business intelligence. This is reflected in the 62% of organizations which reported themselves as planning to integrate advanced analytics into their decision making models in the near future.

Mark Cheng, Management Consultant, Sia Partners provides some insight into why companies invest in advanced analytics:

The future will be the combination of more complex algorithms, more data science, and a lot more process automation. If machine learning can take the error from existing processes and learn from them on its own, then the next processes will improve based on the prior changes.

The trend for advanced analytics for business is on an upward curve, and is going to play an increasingly important role in determining which companies succeed in creating a competitive and which ones fall by the wayside.

Companies like Amazon, with ambitions - and the resources to match - to dominate the global digital economy have already invested huge amounts of money and resources into understanding and leveraging advanced analytics. But even at the smaller end of business, companies can still leverage the power of predictive analytics for increasing business performance.

There is widespread confidence that advanced analytics, and data science in general, will become an even more competitive advantage for business in the coming years, as consumers continue to become more digitally native, turning to eCommerce in greater numbers and remaining glued to social media.

Consumers are aware that their information is collected in vast amounts, and as a result they expect to receive a targeted, personalized approach which understands their needs and preferences. As such, enterprises will need to become much more focused on personalization if they are to harness the power of advanced analytics to attract, win and retain new customers in the future.

If you wish to delve more into this topic of advanced analytics, read The State of Machine Learning in Fintech.

More posts by this author

Wojciech Prażuch

AI Consulting