Big Data is a term used to describe the growing volume, speed, variety, variability, and complexity of huge blocks of information that also grow exponentially over time. In short, such data is so large and complex that none of the traditional data management tools can store or process it efficiently. For marketing and sales departments, big data is a new paradigm that must be adopted to fit customers' digital needs. Big Data also refers to the analysis of descriptive, predictive and prescriptive information of large volumes of data, which facilitate evidence-based decision-making. By combining Big Data and advanced analytics with marketing and sales management strategies, organizations can have a substantial impact in some of these areas:
- Customer Knowledge: Big Data can provide information not only about who your customers are, but also where they are, what they want, how they want to be contacted, and when.
- Customization: With bulk data exploration and behavioral pattern analysis, you can identify customization preferences for your customers. This is possibly one of the most commercially desired use cases in a Big Data project.
- Campaigns: Having access to geolocation data, social networks, and shopping traces it is possible to make advanced models of campaigns that in real time execute actions for their customers.
- Customer retention and loyalty: Big Data can help you discover what influences customer loyalty and what brings them back again and again.
- Optimization and better marketing and sales performance: Through processes in Big Data, optimal marketing expenditure can be determined across multiple channels as well as continuously optimizing marketing programs through testing, measurement, and analysis.
- Production and Operations: Demand models can also automate orders in production, inventories, or operations, allowing you to better plan your resources.
The challenges related to the effective use of Big Data can be especially daunting for marketing departments, and most analytics systems are not aligned with the data, processes, and business rules of the commercial department. Consider the following recommendations in adopting a Big Data project for short-term results with objective management outcomes:
- Big Data Architecture: At the beginning, you'll need to prepare your company's system architecture to enable the extraction and visualization of large volumes of data. This is your first major achievement and in this objective you will order many of the bad practices that exist in many companies such as non-centralized data, data with little or no quality, information silos that had never been integrated, among others. This first objective will give the first results, order, and control to the commercial, technology and general management department.
- Information Dashboard: Today, many companies continue to perform Business Intelligence analytics only with their department’s information, not through dashboards with unified information from multiple departments. These processes also remain manual. Why not deliver a dashboard with centralized information that displays in a single panel the main indicators of a department? This would start to give some visible results to management.
- First Analytical Models: Take an inventory of the main indicators and define what type of analysis I need/can extract for each of these indicators. For instance, start with descriptive models, are the short implementation models and will help your managers get the first results to facilitate decision making and then develop the predictive models. Don't start the other way around since you risk wearing down your team and not being able to show short-term results.