Big Data Analysis is the greatest opportunity for marketing teams since the invention of the internet. It is the art of processing large amounts of structured and unstructured data – aka Big Data – to help organisations make informed decisions and gain a competitive edge.
Think about the data companies have been collecting for the past 20 years: number of transactions for each physical point of sales, customer data through fidelity cards, offline advertisement campaigns, and so on.
Now think about the data collected today: digital data. This includes information about online purchases, behaviors of website visitors, click rates, use of social media, geolocation and much more. The volume and variety of this data is beyond comparison and offers opportunities never seen before.
The growth of e-commerce platforms, social media and the Internet of things (IoT) for example is a wealth of information for those who know how to manage and process this data.
If you are not already incorporating Big Data Analysis in your marketing strategy, you are missing out on opportunities to make better and faster decisions for your business.
Making sense of Big Data with Analytics
Big Data refers to the humongous and ever growing volume of data generated by digital consumers and collected by businesses. The particularity of this data is that it can be structured, unstructured, heterogenous and so complex that manual analysis by humans is impossible.
For a time, this massive amount of data was incomprehensible and even though companies were collecting and storing data, they did not have the processing power to restore them into comprehensive information.
Recently, the emergence of more sophisticated technologies enabled us to extract valuable business information from Big Data collected over time. Big Data Analysis refers to the techniques, processes, tools and technologies to make sense of this massive amount of data.
It is an advanced analytical system that detects hidden patterns, correlations and other meaningful insights, enabling to pilot corporate activities in a data driven manner. This new form of business intelligence helps with decision making, improves precision and reaction time to market changes.
How to use Big Data analytics to drive your marketing growth
Consumers create data with every action they take online. For your marketing team, this is a great opportunity to better understand target audiences and improve the value of customer interactions.
Big Data analytics calls for a new approach to marketing research and customer segmentation. Two major customer data source are available:
- First party data owned by your company: it comes from customers who interacted with your company and lives in your CRM, website or social media analytics tools;
- Second party data owned by other corporations: companies now make a business of collecting and exchanging customer data (Google or Facebook for instance).
With more data available, along with the right tools to analyse and combine information, marketers can identify and target prospects more effectively. The increased granularity in customer profiles and data analysis in near real time lets us deliver a highly personalized experience to drive high ROI marketing campaigns.
However, a large volume of data does not automatically lead to a better marketing strategy or an increase of sales. Big data is the raw material essential to conscious decision making.
It is not the data itself that matters but the way is it manipulated to serve your marketing needs.
There are 4 different methods to approach Big Data analytics based on your business case and objectives. They are used at different stages of building your data driven marketing strategy.
1. Descriptive analysis: What happened?
Descriptive analysis is the most common form of data analysis. As its name implies, it is used to give an overview of past events in your company.
Think monitoring dashboards, sales reports or marketing campaigns analysis. Descriptive analysis uses historical data collected by your company and presents it in a comprehensive format so it can easily be interpreted by a business audience.
This type of data analysis allows you to learn from your past behaviors and better understand how your company performs in a given activity.
On its own, descriptive analysis is not used for business insight but lays the foundation for more actionable prescriptive or predictive analysis.
2. Diagnostic analysis: Why did it happen?
Diagnostic analysis is used to understand the root cause of an event or phenomenon. If your company has identified a particular problem, a diagnostic analysis will provide in depth insight regarding what caused the issue in the first place.
Let’s say the descriptive analysis of your website checkout process has highlighted a high rate of abandoned carts on your website. Is it because of the shipping costs, a website error, delivery times? There are so many possible causes it can be a daunting task to consider them all. A diagnostic analysis will study visitors behavior in depth and point you to the exact reason why they don’t proceed to purchase so you can quickly fix the issue and increase the conversions of your online shop.
3. Predictive analysis: What might happen next?
Predictive analysis leverages artificial intelligence (AI) and machine learning (ML) to produce business projections. Massive amounts of data are fed to statistical models trained to recognise trends and patterns. Based on recent and historical data, the model is able to forecast future business trends.
Predictive analysis is not an exact science, no algorithm can predict future events with 100% accuracy. The results are given with a level of probability that the prediction will eventually happen. The purpose is not to predict what WILL happen but rather what MIGHT happen in the future. It extrapolates data to predict a trend line.
Big Data marketing uses predictive analysis to understand customer behaviors and future trends, allowing them to plan their campaign accordingly and seize opportunities in real time.
4. Prescriptive analysis: What actions should you take?
Prescriptive analysis takes Big Data analytics to the next level. It is used to predict the possible outcomes of different choices of action.
After your predictive analysis, your team has probably produced various plans of action to take advantage of the upcoming trends identified. Prescriptive analysis allows you to choose the most efficient course of action for your business to reach its desired outcome.
It is a form of predictive analysis, but this time it forecasts the effect of multiple decisions you might want to take. Prescriptive analysis is very complex to implement and few companies are currently using it but we believe it is the future of Big Data Analytics.
Big Data and data science prediction: the impact on sales
Working hand in hand with data scientists that understand your business has become essential to stay ahead of competition. The ability to capture and analyse data efficiently allows companies to identify problems or opportunities to seize and take informed decisions.
Making an educated use of data has a direct impact on your sales performance. Here are a few examples of how companies leverage Big Data analytics to increase sales:
Adjust price in real time: Find the right price point for a product or service to take advantage of every sales opportunity and ensure maximal profitability. Dynamic pricing techniques are used to adjust the price of a product based on multiple factors such as customer interest level, seasonality, stock available or competitors’ price and promotions.
Reduce customer churn: Predict customer behavior to improve retention. Predictive analysis can study changes in customers interactions with your company or product and identify any signs of them stopping the commercial relationship. Reduce customer churn before it happens by giving customers at risk the attention they need.
Generate and prioritize leads: Target buyers that are actively looking for offers similar to yours by crossing data on existing customer profiles and prospects online activity. Predictive lead qualification creates a score based on ease of conversion and profit potential -aka lead scoring- to automatically assess and prioritize leads.
Repeat sales and cross sales: Identify customers more likely to buy multiple products. Find patterns in buyers behaviors and profiles to determine which customers are more likely to purchase your other products.
At Mowgli we can help you elaborate a better marketing and sales strategy based on data. We work with top notch data scientists to guide you through any stage of your Big Data journey. If you would like to know more about how we can assist, please get in touch!