Companies have long leveraged customer data to increase engagement, drive sales and add to the bottom line. Typically, historical data was used to determine what actions had already taken place and the factors that contributed to those actions. A company would then build its next campaign or marketing strategy based on these historical factors.
Today, business intelligence and data analytics are much more forward-looking and predictive. Rather than focus on the past, we can use data to forecast what will likely happen in the future and how customers may act. The variety and volume of data being generated in rapid fashion require a shift in how the data is stored, handled and analyzed in order to leverage it effectively. In this article, some of the newest features of data storage and analysis will be discussed, as well as why these tools are important for helping you create targeted marketing efforts that drive sales.
1. In-Memory Analytics: This is an important concept that is critical to understand in an era of big data and fast analysis. Where and how you store your data is important. Analyzing big data that is stored on a hard disk is time-consuming, slow and does not support the rapid-fire decisions needed for today's marketers. In-memory analytics means the data is stored on a computer's RAM (random access memory) rather than a hard disk. This allows the system to run algorithms more quickly and get results faster. Due to the availability of cheap memory, it is now possible to perform analysis in near real-time. For example, when a customer is purchasing an item and using a loyalty card, in-memory analytics can help the marketer run an analysis to quickly determine which product offers to attach to the receipt for the customer's next visit. Faster analysis means fewer missed opportunities to capitalize on customer behavior as it happens.
2. Leveraging Mobile: Some of the world's most cutting-edge marketers are leveraging the Wi-Fi signal from a customer's cell phone in order to better understand how the customer moves about the store, as well as where and when the customer purchases items. Even more specific data can be gathered if that customer is a member of a loyalty program. For example, if the customer used his or her phone to register or log into a loyalty program app, the marketer may recognize that device when it enters a restaurant and begins transmitting a Wi-Fi signal. The data on a customer's purchasing behaviors and movement throughout the restaurant can then be integrated with the historical and demographic data already known about the customer to create a more customized line of offers targeted at him or her, even while that person is still seated. This data gleaned from a mobile device can also help companies determine where to place products and the best store layout for maximizing purchases.
3. Column-Stored Databases: Traditional databases are referred to as row-stored databases, because each record is stored in a row and fields are stored in columns—much like a typical Excel spreadsheet. But a column-stored database reverses this and stores all the values of a field in a row, which allows for much faster calculations because the software only needs to read one row. Companies will find column-stored databases useful any time analytics are run, although real-time analytics will see the biggest benefit from this format.
Collaboration: The ability for multiple platforms to work together is critical for maximizing the use of analytics, and allows a company to dive deeper into its data to more accurately target customers. Your company may have a traditional data warehouse platform that is good at transactional and operational reporting and dashboards. But today, big data platforms are required to work with a high volume and variety of data elements, as well as an analytic platform that can perform rapid, complex analytics to support the iterations required by data scientists and business managers. Companies are gathering more data and demographic information than ever before, but the data is not effective or helpful if the right platforms are not used for analysis. Collaboration among these three types of platforms will bring about the much-needed performance improvements with a lower cost, quicker turnaround and lower error rate to generate better results for your marketing campaigns.
Making Sense of the Trends
These emerging methods for storing and analyzing data will help you perform more accurate analysis of your data more rapidly, and just as important is communicating the results effectively. Pages of analysis aren't helpful if you have to spend hours staring at a spreadsheet to understand it. Whether your company works with an outside or in-house analytics team, the analysts should have a strong understanding of data visualization, which helps push the right information to the right stakeholders so they clearly understand the most important results and how to take action.
Data changes quickly and drastically for many companies. By having the right infrastructure to harness and make sense of the business intelligence, you will have the best data available for targeting customers more effectively with the right offers that drive results.
Nagendra Sastry is head of analytics at Santa Rosa, Calif.-based IQR Consulting, a data analysis services, analytics solutions and business strategy consulting services provider. Reach him at Nagendra.Sastry@IQRConsulting.com.