It’s getting near dinner on a Tuesday night. The pantry is empty, which means a run to the store. You could head out anticipating that inspiration will strike, grabbing ingredients and hoping that it all comes together. It’s an expensive way to shop, a less organized way to cook, and the outcome is uncertain. Or, you could pick a recipe, select the right ingredients from the shelf, follow the instructions, and get dinner on the table. It’s efficient, less wasteful, and the results are, more or less, certain.
Maybe analytics seems a far cry from a Tuesday night dinner, but hear me out. In essence, many businesses face similar problems, such as keeping customers happy. Why should everyone have to create analytics to address those problems from scratch? That’s the idea behind productized analytics. For example, if you need analytics on customer abandonment, how do you know what data is needed? Some companies sit down and try to figure it out. Others take a more direct path by going to those who’ve done it before and finding out the relevant ingredients and approach. For example, you’ll need payment history, download history, prospect profile details, subscriber information, and marketing contact history. Those data elements combined with field-proven algorithms yield results.
In other words, there are important analytics that are available as fully productized, ready-for-production solutions that accelerate time to value, reduce risk, and minimize resource demands. When you know the ingredients and have a recipe, you’re more confident of your ability to get results in a timely fashion.
Customer satisfaction is a good example of an analytic that is dear to many businesses and where productized analytics has much to offer. By combining a prescribed set of multi-genre analytics, you can explore their positive and negative impact on customer sentiment. You do not need to invent this system to arrive at an actionable satisfaction index for each customer.
A tried and true approach begins by looking at the events a customer may experience across all the channels a company has to offer. Did the customer make purchases on their mobile device? Did they visit a retail center and make a purchase? Was there an event that drove them to complain to the call center? Was a call from the customer dropped? Has the customer interacted with the website and in what way—accessing support or product information, or reading a FAQ about terminating their subscription?
This is a similar pattern seen across many industries. Customers interact across different channels creating millions, billions, even trillions of events from which the organization can dynamically take the measure of customer satisfaction and spot negative or positive trends. Those satisfaction indexes, in turn, can complement traditional, quarterly metrics such as net promoter score.
A real-time, productized analytic enables organizations to quickly expand their understanding by bringing together the right data and data model with algorithms for text, path, pattern, graph and statistical analytics. These analytics can be run at scale every day to uncover unique patterns that can be put directly into operational action to reduce churn and enable you to up sell, cross-sell, and grow your business.
Most importantly, these analytics have been done before and are there for the asking.
Big is so often the operable word in analytics—big data, big project, big insight, big undertaking. While it is safe to assume businesses would like to see big results, too, we don’t always need big effort and big experimentation. Often fast results are even more desirable. If you can identify well-defined criteria for success and specific business benefits to achieve, a productized analytics approach might just be the recipe for success. Best of all, it gives organizations an opportunity to learn about these complex analytics while deriving results. Productized analytics are not just a commodity that you install—they offer a recipe for solving specific problems with analytics. As you get more familiar and dexterous in the analytics kitchen, it is possible to then start pushing that foundation in new directions.
Chris Twogood is Senior Vice President of Product and Solutions Marketing for Teradata Corporation. He is responsible for marketing Teradata products (database, utilities, and platform), Aster Products and Hadoop software – as well as Teradata services (professional and customer services). His responsibilities include overseeing technical field sales support teams.
Chris has twenty-five years of experience in the computer industry specializing in Data Warehousing, Decision Support, Customer Management and Appliance platforms.
He started his career with NCR in retail Point of Sale solutions (POS), and then moved to AT&T to manage channel development and strategic partners. Then, Chris joined Teradata where he managed strategy, application definition, marketing, product requirements/management, platform solutions, and product marketing.
Chris holds a Bachelor of Science degree from California State University at Long Beach with an emphasis in marketing. He resides in San Diego.