Data science as a service can help your organization serve data to the many people in your organization who need it, at the moment they need it, by organically surfacing data in employee workflows. But what does that really mean for your business?
Surfacing data organically in workflows allows workers with no statistical or quantitative skills to access the data they need to do their jobs in real time. The key is for the data to appear automatically while your staff goes about their day-to-day tasks. Information should not be something employees need to search for, run queries to get, or create equations to access. It is a matter of making the data a part of your employees’ everyday workflow.
Today, we see data marketing experts repeating a process over and over again, and it typically relies on predictive analytics to quickly provide data that previously may have taken a data scientist weeks or months to compile. The steps of the predictive marketing workflow include the following:
- A trigger for the analysis
- Data exploration (statistical correlations)
- Audience discovery and scoring (clustering and propensity scoring)
- Audience activation (make a highly valuable audience with a high propensity to complete an event available immediately for activation)
While that’s a mouthful of technical terms, when taken down to an actionable workflow, it is actually quite seamless for both the internal and external user. What the predictive marketing workflow comes down to is, “Understand your customers and then do something to make their experience better.” There’s plenty of data out there, and the tools are strong enough now that it should be as simple as that, and the whole point of these analytics tools should be to make it that simple.
In fact, this sort of thing has been done in the digital realm for ages, often to help customers find add-on services or products to their purchases. Take, for example, the common feature of recommending purchases. The trigger for an online retailer in this scenario is, of course, the customer’s searching for an item on the retailer’s website or a platform interface. Behind the scenes, without the customer’s knowledge or even a requirement that the customer do anything, an algorithm runs that can identify which other products the customer might potentially be interested in buying. The statistical correlations needed to run such algorithms are complex and would be terribly daunting for someone who does not have a doctorate in mathematics. Luckily for many online retail customers, the data science used to recommend products is triggered automatically, simply by searching for a product.
The initial trigger is anything the user normally does to initiate a process. For example, the workflow could be triggered when a customer calls into the call center. When the customer’s number comes up, the workflow could also populate statistical information about the customer, including recent purchases, survey results, and how likely the customer is to be calling with an issue based on that data. This can result in decreased wait times and better customer service.
Here’s a real-life example: One of our clients recently asked us to assess their checkout process. After examining hundreds of thousands of data points using our data science as a service tools, we discovered that cart abandonment was significantly higher among shoppers using Google Chrome as their Internet browser. We used this information to go through the checkout process in Chrome ourselves. When we were prompted by the page to enter credit card information, our team received a pop-up from Chrome that said, “Potential attacker.” This warning message was causing many users to abandon their carts rather than risk a potential security attack. After correcting this issue, the client saw an immediate increase in revenue from Chrome users. This was projected to equate to an additional $400 million revenue on an annual basis. Without data science as a service, it easily could have taken months to complete the math to make this discovery and the subsequent increase in revenue possible.
Implementing data science as a service solutions can improve your employees’ ability to serve your customers as well as identify potential opportunities. These solutions also can help increase your overall sales volume by allowing you to identify and suggest to your customers purchases that will complement their buying patterns. These seamless changes to workflows can get everyone the information they need quickly, leading to a more positive and rewarding customer experience.
As senior director of product management for Adobe’s Analytics Solution, Chris Wareham directs the teams responsible for mapping strategy and driving the innovation and growth of Adobe Analytics.
Wareham has nearly two decades of high-tech experience. In addition to more recent roles at Omniture, Micromuse, and IBM, he started his private sector career at Lucent Technologies, where he helped develop mobile telecommunications networks in Caribbean/Latin America and Asia/Pacific regions. Prior to that, he served as a squad leader in the US Army’s electronic warfare service. He holds dual Bachelor’s degrees from the University of Kansas, and was his class salutatorian at the Defense Language Institute, where he studied Arabic.
Subscribe to Data Informed for the latest information and news on big data and analytics for the enterprise.