You’ve got data. You’re swimming in data. You’ve got Google Analytics. You’ve got email marketing, social media, voice-of-customer, SEO, paid search, and more analytics reports and business intelligence dashboards than you can check in a day. You’ve got a CRM database. What do you do with all of that?
“Big Data” or Just Overwhelming Data?
You might have heard a lot about “big data”: applying tools to manage huge sets of data that are intractable with standard tools.
For many types of organizations, you don’t truly have a big data problem. Chances are, your data can be handled with a few CSV files or relational databases, or some integration between existing systems. (Our programmers can help you out with those things. And if you do really have a big data problem, there are all sorts of fancy technological solutions like data warehouses and Hadoop clusters, too. But ultimately those are all just plumbing to get the data flowing where you need it.)
The real problem with data that seems “too big” is rarely with the tools and technology you need to get a handle on it; it’s almost always with having the personnel and expertise to be able to tease real insights out of whatever data you do have.
Data Science Services
With the right knowledge, you can use statistics ninja skills and algorithmic kung-fu to tame your data.
The technical term for “statistics ninja skills and algorithmic kung-fu” is “predictive analytics.” Predictive analytics encompasses a range of techniques for understanding your data. Data mining and predictive modeling take that set of data and figure out the patterns and anomalies: wins, failures, and opportunities. We can reduce the overwhelming data to some set of scores, rankings, or measures that make sense, and recommendations for acting on them.
Is this for my organization? Isn’t this just about e-commerce?
Predictive modeling tends to work best for organizations with detailed information about their customers and visitors: organizations that engage in relationship-driven marketing. This encompasses a variety of types of organizations:
- Retail e-commerce companies (including pure-play e-commerce and bricks-and-clicks organizations)
- Membership organizations, such as professional associations and advocacy groups
- Subscription services, such as cloud apps or subscription content websites
These organizations have different goals, but share a common characteristic: they (hopefully) develop long ongoing relationships with their customers, and have data about individual preferences and conversions over time. This gives us an excellent basis to form a model about future behavior.
Data mining can be useful in any situation in which there is a large amount of data, too much for manual analysis to be effective. This might be the case if you have a large email list, wide-ranging paid search campaigns, a ton of SEO keywords, or a bunch of social data that needs sorting out.
Especially if you have deeply detailed data, manual analysis may be exceedingly time-consuming to yield up the nuggets of insight in your data. If you’ve found yourself digging and digging in data from your web analytics, CRM, and other tools for hours and hours and getting no further than where you started, data mining and predictive modeling may be for you. Our team of engineers and analysts can mine your data using mathematical techniques and algorithms to uncover actionable business insights.