Analytics and Digital Marketing
Analytics – the central nervous system of Digital Marketing
Decision making relies heavily on data in the digital age. And the digital age is one that creates data. Tons of it. About each person. It has been said that companies collect about 75,000 data points about an individual.
It is this data that powers the entire digital marketing process today. Because of this data, marketers are able to know their customers better and are able to send them more relevant and targeted messages. They can engage with the right people. They can send them the right message. The messages can reach them at the right time as well. They can send them the right offer. And these offers can be sent through the right channel.
Apart from this –
- Data helps a business gain a competitive advantage – you know how you rate against the competition and how you can overtake them!
- It helps a marketer focus on User Experience in order to boost digital marketing – data can tell you what the user clicks and hovers on so you know what works and what doesn’t.
- Data helps a business engage with its customers more meaningfully. Data helps you know your customers and therefore engage with them in a more meaningful way.
- Once you know your customers you can tailor your marketing efforts accordingly.
- It helps you find leads for marketing
These are some of the ways data helps a business.
The main benefit that data brings is that it helps a marketer really know his customer. Once he does that, marketing to him or engaging with him becomes incredibly easy. All this data is like gold in the hands of the right marketer. If he knew how to sift all the piles of data to find the nuggets that he needs, then he can derive all kinds of insights from it and base his strategy of engaging with him, on that.
So how does he do that?
With Analytics it is possible to discover patterns – meaningful patterns and make sense of all this data. We now have sophisticated tools that can sift through these mountains of raw data and find interesting insights. And this is done through some statistical modelling and some computer programming.
Some of the Analytics tools available include –
- SAS software with wide ranging capabilities from data management to advanced analytics.
- SPSS Modeler is a data mining software tool.
- Statistica: is a statistics and analytics tool that can do data analysis, data management, data mining, and data visualization.
- Salford systems has a range of predictive analytics and data mining tools specializing in classification and regression tree algorithms for businesses.
- Tools like KXEN drive automated analytics
- Angoss tools are easy to learn and use, and the results easy to understand and explain. It is very user friendly and is feature-heavy.
- MATLAB: is a statistical computing tool that does matrix manipulations, plotting of functions and data, implementation of algorithms and creation of user interfaces.
- Weka (Waikato Environment for Knowledge Analysis) is a popular suite of machine learning software.
These are some of the tools that are available and there are many, many more.
With these tools, you can process the data to derive meaningful and actionable insights. Without the insights, data is just data. But with analytics, you derive insights –
Broadly speaking, you get three kinds of insights.
Descriptive insights – These are all about giving you a description of things – they “describe”, or summarize all the raw data that is available and presents it in a form that us human beings can make sense of and understand. They pick up a moment and describe how things are at that time. So if you want to know or understand past behavior in order to understand future behavior, this is the tool you turn to. You can find out the total stock in an inventory, for instance. Or the sales figures over time.
Predictive is all about what will happen, as the name suggests. It is about understanding the future. Based on the data that it has been fed, Predictive tools have the ability to predict what is likely to happen in the future, based on the past. Based on probabilities, predictive tools will go through all the data and find patterns in it and forecast future trends – maybe in customers’ purchasing patterns or in their ability to repay loans etc. A good example could be the prediction of how a certain year’s holiday sales might turn out.
Prescriptive – this kind of analytics is all about giving you good advice. Based on the data, and the description of the past along with an understanding of possible behavior in the future, Prescriptive analytics offers solutions. It recommends what course of action to take – gives you options for you to take. It uses a blend of business rules, algorithms, machine learning and computational modelling procedures on historical and transactional data, real-time data feeds, and big data to offer multiple solutions.
And with these insights, it is possible to revolutionize the business process.
Some of the best effects include the following –
- Improve the decision making process both in terms of relevance and quality
- Speeds processes up
- Speeds up decision making
- Reduces cost
- Increases revenues
- Gives businesses a huge competitive advantage
- Aligns processes with business strategy better
- Dashboards with composite information across the enterprise in one spot
The combination of Analytics and data in the hands of the right people, asking the right questions and setting the right algorithms, can produce tremendous results. A business can be transformed entirely – no longer is anyone fumbling in the dark. Now every decision is powered by data and strategy based on this data. All it needs is the right people with the right thinking to set all of this in motion. No wonder executives with a keen business acumen and data scientists are highly sought after in the digital age! And Analytics is like the central nervous system that links it all together and powers the enterprise!