The trouble with data is that it is big, dense, and difficult to understand.
If you want anything contextual or relevant from data to use for your marketing, it requires the services of a data scientist or analyst. They can pan for nuggets of data gold and melt it down and turn it into something valuable for other organizational departments to use.
Doing all that is time-consuming, complicated, and prohibitive to almost anyone without the correct skills and patience.
Though data experts are undoubtedly able to uncover additional (and treasured) insights, the process often lacks the promptness and responsiveness that marketers need when they want to query and explore their customer data. If an organization were to lean on the IT department to report on data, the results would probably be static and lack the sort of interactivity needed to break their data down to a granular level.
Thankfully, it doesn’t have to be this way.
Modern train-of-thought analytics facilitate data for use by non-technical people, making datasets easier to access and manipulate and to see results far quicker.
The key to the user-friendliness of these new analytics tools is visualization, which gives context to understanding the data. Those platforms enable marketers to drill deep down into customer data with easy-to-comprehend diagrams, statistics, and other reporting graphics to understand behaviors, segments, trends, and demographics.
This type of data analysis plays well with marketers, who enjoy an iterative approach of asking questions to extract insight. An initial query may not tell you much, but with train-of-thought analytics, the results could spark a second or third question (and more) to really dig down and unearth useful knowledge.
Want to know who your best customers are? What they buy? How often do they buy and what is the likelihood that they will buy again? There are countless ways to approach your path of inquiry.
Let’s say a golf-equipment company is looking to get a better understanding of its customers and how to target them with appropriate messaging. An initial query might reveal that an attention-grabbing percentage of its audience are women age 30-60. That may not be who the company envisaged as a typical customer. More investigation would then be required.
Following a series of further questions, the marketer could then discover underneath this top-layer information that those women are mostly the wives and daughters of golfing husbands, partners, and fathers, and these women are buying golf-related gifts for birthdays and holidays. That conclusion is backed up by spikes in sales before Father’s Day in June and on the run up to Christmas.
Most likely, those particular customers would not be responsive to frequent messages throughout the year. (After all, they may not share the same passion for the sport as the person they are buying gifts for.) Even so, this knowledge could lead to the creation of timed, targeted campaigns and ads tailored to those buyers around strategic dates. These campaigns can be further honed by analysis to identify customers by location, targeting those geographically within the region of some of the country’s most popular golf courses.
Using train-of-thought analytics to achieve a deeper understanding of your customers is crucial when an organization wants to ensure its messaging is relevant and personalized.
For example, those aforementioned golfing customers may not find messages about golf club sets useful as their partners are likely already armed with the most important pieces of equipment. However, emailing coupons or offers to save money on a golfing break at a luxury hotel may be far more appealing.
With customer data simplified and visualized, marketers can make more informed and smarter decisions.
Another example is a children’s clothing company that can find and exclude customers without school-aged kids within the short window for “Back to School” campaigns. And an auto dealer can target customers coming to the end of their existing payment plans or those with children at an age where they can get a driver’s license and need a first car, and so on.
If your company is collecting accurate up-to-date data, marketers can ask any other number of questions, depending on the journey their queries take them.
The success of any organization can be seen to be proportional to the speed in which its data can be collected, correlated, analyzed, and acted upon. Train-of-thought analytics helps break down organization barriers and empowers marketers to take the many streams of data available out of the hands of IT, enabling marketers of all levels of capability to start making use of it.
Now, the right people can find their own gold, drilling down into very specific information and creating very personalized communications with customers.