Why data-driven insights can make a huge impact on your business (and your marketing strategy)

No matter what industry or type of business you are in, nowadays (and especially if you have an online presence for your business) you will most likely be collecting data in some way shape or form.  Using data driven analysis and insights in a meaningful way can become exceptionally powerful in making informed business decisions across the board as well as in your marketing and advertising strategy.

Business analytics – what is it exactly and how does it help with decision making?

There are four types of business analysis which are all useful in a marketing and advertising context – I will reference a good explanation of the four below taken from an article I came across at microstrategy.com

Descriptive Analytics

Descriptive analytics describes or summarizes a business’s historical or existing data to get a picture of what has happened in the past or is now happening currently. It is the simplest form of analytics and employs data aggregation and mining techniques.

Descriptive analytics can help identify strengths and weaknesses and provide insight into customer behaviour. Strategies can then be developed and deployed in the areas of targeted marketing and service improvement.

 Diagnostic Analytics

Diagnostic analytics shifts from the “what” of past and current events to “how” and “why,” focusing on past performance to determine which factors influence trends. This type of business analytics employs techniques such as drill-down, data discovery, data mining, and correlations to uncover the root causes of events.

Diagnostic analytics uses probabilities, likelihoods, and the distribution of outcomes to understand why events may occur.

 Predictive Analytics

Predictive analytics forecasts the possibility of future events using statistical models and machine learning techniques. This type of business analytics builds on descriptive analytics results to devise models that can extrapolate the likelihood of select outcomes. Machine learning experts and trained data scientists are typically employed to run predictive analysis using learning algorithms and statistical models, enabling a higher level of predictive accuracy than is achievable by business intelligence alone.

A common application of predictive analytics is sentiment analysis. Existing text data can be collected from social media to provide a comprehensive picture of opinions held by a user. This data can be analysed to predict their sentiment towards a new subject (positive, negative, neutral). The most common physical product of predictive analysis is a detailed report used to support complex forecasts in sales and marketing.

 Prescriptive Analytics

Prescriptive analytics goes a step beyond predictive analytics, providing recommendations for next best actions and allowing potential manipulation of events to drive better outcomes. This type of business analytics is capable of not only suggesting all favourable outcomes according to a specified course of action but recommending specific actions to deliver the most desired result. Prescriptive analytics relies on a strong feedback system and constant iterative analysis and testing to continually learn more about the relationships between different actions and outcomes.

One of the most common uses of prescriptive analytics is the creation of recommendation engines (think of the Google and Facebook algorithms that you’ve no doubt heard about!), which strive to match options to a consumer’s real-time needs. The key to effective prescriptive analysis is the emergence of deep learning and complex neural networks, which can micro-segment data across multiple parameters and timelines simultaneously. The most common physical product of prescriptive analysis is a focused recommendation for next best actions, which can be applied to clearly identified business goals.

These four different types of analytics may be implemented sequentially, but there is no mandate. In many scenarios, organizations may jump directly from descriptive to prescriptive analytics thanks to artificial intelligence, which streamlines the process.

The information from which you can ‘mine for insights’ can come in either qualitative or quantitative form, both serving a useful purpose in making smart data-driven decisions.

Qualitative analysis is more observation based such as 1:1 interviews or focus groups, it is non numerical and more investigative in its nature and open-ended, looking for characteristics.

Quantitative data analysis is more measurement based, using numbers and statistics to derive meaningful patterns and trends.

Therefore the importance of using data in driving your business decision making lies in analysing it consistently, turning it into actionable insights to create new business opportunities, generate more revenue, predict future trends, and streamline your processes and operations.  

In these times, the landscape is shifting constantly so if you are prepared to leverage all of the information and data available to you to make more informed and powerful data driven business decisions, you are going to have the edge to grow your business and stay ahead of the curve..

By implementing the right reporting tools and understanding how to analyse as well as to measure your data accurately you will be able to apply these learnings and insights to optimise and refine your marketing strategy. In essence it will help to take the guesswork out of your marketing efforts, provide the context for the content you produce, pinpoint the optimal channels and touchpoints you use and give you the ability to test and validate new ideas knowing you have some measures in place to evaluate and better understand what is working vs what is not!

If you don’t at the very least have your website analytics set up, now is the time to do so. We offer a service to get your Google accounts set up for tracking and measurment so get in touch if you would like some help with this!