What is Advanced Analytics?

Gartner, the leading IT research and advisory company, places advanced analytics as a top business priority. Does advanced analytics deliver?

Marketers view analytics as a tool that speeds up decision making and gets them ahead of competitors. Marketers can certainly do a lot of things with the help of analytics. But how would you go about increasing analytics use in your marketing strategies? The answer is advanced analytics.

The analytics we have come to known somehow revealed the true value of how big data works. Huge companies like Google, eBay and LinkedIn were said to be among the first to experiment with the use of big data. Analytics has become an approach that delivers real results.

Analytics is said to be of value due to the following reasons:

  • It reduces costs
  • Speeds up and improves decision making
  • Innovates new products and services

What is Advanced Analytics?


Advanced analytics has existed for 20 years but it has only been used to its full potential after the upsurge of big data in the marketing industry. Now the interest for this has accelerated and become one of the fastest growing segments of business intelligence and the analytics software market.

Advanced analytics employs analytical techniques applied to research data. Data is used to explore the hidden relationships and patterns to be used in decision making later. The data received from the analytics are then used for inference of future occurrences in sales, lead converts and products.

Advanced analytics is based on math principals and started out as descriptive statistics. Most businesses used advanced analytics as a corrective and reactive way to proceed with business ventures. Advanced analytics, applicable for so many problems around the world, is now a major player in digital marketing used by small, medium and big enterprises.

Purpose of Advanced Analytics


The use of the old business intelligence model is to inform businesses of their past performance. It simply provides data for analysis. However, today, BI has grown into something much better. BI can now give businesses a 360 degree view of their customers’ behaviour and their possible business path.

So now it serves as a trouble-shooting strategy instead of just being an information provider like in the past. The approach it now follows is something in line with business problem solving. Gone are the days of predefined BI analysis formats and presentations. Now, the problem is raised first then analytics is used to delve into the data and provide business insights.

Most advanced analytics software now allows user-friendly interfaces and utilises data to find answers to user questions. Multiple sources are usually pulled out by the software to help gather and select the best process and answer to the question.

Advanced Analytics and the Data it Uses


If you have already used the traditional business intelligence model, you may have noted that the software gathers from a data warehouse. Data is then categorised based on geography, time frame and search query. The data and BI report is seen as a whole.

Advanced analytics differs in that it may view small and large data. The characteristics of customers can be analysed, starting from the segment level. This way, businesses can start making priorities to improve personalised marketing and improve their investment returns. And instead of setting aside unstructured data such as that found on social media, they can now be used for further analysis and even segmenting. In fact, social media is one useful part of the campaign and becomes a wealthy source of timely and complete data.

Do Advanced Analytics Techniques Matter?


Advanced analytics utilises five types of techniques. Most of these techniques are future-oriented and aimed to support data-driven decisions, minor or major, of entrepreneurial ventures. The five techniques are:

Descriptive analysis. Descriptive analysis boils down to the question of what happened. It aims to understand the underlying phenomenon. It also aims to figure out the typical characteristics of customers and products that customers pair together when they buy something.

Diagnostic Analysis. Diagnostic analysis often delves a little deeper and tries to access the reasons behind phenomena. It helps answer the “why”. Like why customers abandon a cart or why many customers pulled out of a subscription from emails. This information is lifted from the patterns of behaviour found in big data. Software draws the inference based on the granular data analysis.

Predictive analysis. Predictive analysis essentially predicts what will lead to human input, decision and finally action. It aims to analyse the relationships between factors. It also analyses possible outcomes based on the data lifted from the data bank. The software, after using this strategy, then forecasts and estimates a value. This is useful among big companies but can be valuable as well among small to medium entrepreneurs. It may be able to predict customers who are likely to buy the products or predict which product may sell less at a sudden turn of events.

Simulation and Optimisation. Simulation helps businesses get real-time operation imitations. Characteristics and processes are also put under the software’s scrutiny and simulated. After the process, the results will be summarised. The most notable problems will be given the most priority and the best possible ways to execute a new venture will also be considered. This can be valuable when launching new products and services. Simulating customer behaviour and other external factors will help you come out with the best possible solution or route.

Prescriptive. Prescriptive is simply the answer to every problem. This technique gets straight into the suggesting what the business should do. It can offer decision support and decision automation. This prescribed decision can be used right away and be put into action.

Making Analytics Work For You


According to a study by Andrew McAfee and Erik Brynjolfsson from MIT, companies who use big data and analytics to their advantage show a productivity and profitability rate increase of 5-6% higher compared to their peers. But given the statistics and even the outlined benefits of advanced analytics, many are still unsure of how to proceed.

Experts have come up with this fool-proof guide to making analytics work for you:

  1. Choose the right data
  2. Build models that predict and optimise
  3. Transform your company’s capability

Now, given this valuable information, are you willing to try advanced analytics for your business?


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