Let’s cut to the chase: the buzzwords have gotten ridiculous. The broad use of vague terms like artificial intelligence and machine learning, without the context of what they actually are and how they work, can be misleading.
The buzzwords may make the data sound more exciting, but in order to truly reap the rewards of data science, you need to understand more than the industry jargon.
So let’s break data science down into the three basic ways it creates value for your organization—without the buzz.
1. Business Efficiency
Data science can also play a role in identifying your opportunities for improvement. Site friction monitoring, A/B testing, and other techniques can help you learn efficiently and effectively. Nearly every business has already started capturing an enormous amount of data—but very few of them are using that data to its fullest potential to create value.
Data science creates business efficiency in a variety of ways.
At its most basic, data science can provide real-time reporting that enables better information to be accessed faster and in more consumable formats. A simple example would be to consider the difference in value between two scenarios:
- Pulling data on all calls during the past month, extracting customer insights, and then refining your strategy; or
- Having relevant and actionable data about your customers streaming in real-time to the agent while speaking to the customer on the phone.
Given the scenarios, as you can imagine, you have many more opportunities to make a more efficient customer interaction if you’re better understanding your customers while you’re actually interacting with them.
At Clearlink, we are able to provide our sales agents with real-time data using our unique dynamic call routing system. By creating an information architecture that is fast enough to capture, transform, and send data to other applications in real-time, we are able to pull information from our incoming calls, understand what type of call it is, and put it on a screen for coaches and managers to see what types of calls their agents are currently on. This gives them the opportunity to offer help if it’s a topic their employee isn’t comfortable with, or to walk over and help ensure a top-tier service level for a high-value customer.
In addition to real-time reporting, data science uses historical data to help you predict what decisions will improve effectiveness, conversions, or profits.
Here are just a few ways you can use historical data to improve business efficiency:
- Maximize sales by determining what leads you should call first with a scoring model.
- Minimize profitable customer loss by using a retention model to learn which of your high-value customers might be on their way out—and determine what you should do to retain them.
- Save money on everything from employee salaries to energy consumption by observing and analyzing purchase behavior times to determine when your business should be open and when it should close.
- Better match customers to products or experiences—thereby increasing sales—using recommendation engines, customer similarities/segments, and pairing algorithms.
2. Product Creation
Sometimes, data can be your business. You have unique access to your consumers and their behavior, and the information you’re already gathering can be useful to other organizations as well. Data science can help you transform, analyze, and package that data in unique ways that create value for other businesses. When you gather any data for yourself, ask yourself if others would be interested in the data and how it could help them create value for their businesses.
This may seem counterintuitive: your data is proprietary information that you should keep to yourself, right? If you’re willing to do the work to find companies that aren’t direct competition but could still benefit from your data, you can use your data to generate additional revenue from assets that you already have.
Do you have relevant market research, data on consumer sentiment, or trends that you can analyze and share with other organizations in related industries? Can you consolidate signal data to identify the most valuable leads? If you’re a content creator that does not sell products, you likely have valuable customer data/interests that you could offer to non-competing businesses.
Keep in mind that some data will be proprietary and you will not want to or be able to share it. Keep your customers’ trust intact—if you tell them you won’t sell their data, don’t! Clearlink has strong privacy and non-disclosure contracts with our brand partners—and while we don’t sell data, we do benefit from others’ data points by packaging non-proprietary, non-confidential information in creative ways.
The data you own is of course an important input here, but how you present that information can be particularly valuable as well. This is probably the most diverse type of value creation provided by data science. Beyond selling the raw or consolidated data to other organizations, you can provide value through your interpretations of the data as well.
Try packaging the data in an interesting way to share with others. Even consumers like to see their own data when it is presented in a visually appealing way. You can draw more people in to your products and services if you can use your data to present it to them in a unique way.
This is the main way that Clearlink uses data as product creation. Our team of content creators is able to make unique, visually appealing graphics to go with our internal data—to share with our users onsite or with other publications.
3. Customer Experience
Using data science to improve customer experience is an application that is particularly near and dear to me. As a data science professional, it’s easy to think of data as data and forget about the people behind the data. It’s foundational, however, to being a good data scientist to remember that these aren’t just data points—they are people. The data points tell you their story. Their story gives you insight into how you can better serve them.
If you don’t think that using data to improve customer experience is effective or worth the effort, consider Amazon and Netflix and their product recommender engines. Not only does this use of data improve the customer experience by providing relevant, personalized information immediately, it also helps the companies re-convert their users at higher rates—a happy customer is a returning customer, after all.
Beyond that, a customer that enjoys interacting with your company is the best brand evangelist you could ask for. It’s a win-win. When customers see that you care enough to tailor your content or website experience to their needs, they are more likely to interact with it in a positive way and recommend it to others.
Customers will tell you in a number of different ways that they’re not getting what they used to from your company. Reduced engagement with your site, fewer logins, and interactions with other products can be huge signals that you’ve lost or are losing relevance to your customer. Using data science, you can build predictive models and machine learning algorithms to identify and distill these signals early on, all while prescribing retention strategies for those customers at highest risk of attrition.
Beyond Online Experiences
Customer experience extends far beyond online interaction, though. To create truly intelligent customer experiences, data science must play a role in optimizing those experiences online, in store, on a phone call, and in interactive chats.
One of the key services Clearlink offers is our “Online to Offline” capability. Tying your digital marketing dollars to experiences and customer interactions that happen in a brick-and-mortar store can be a difficult process. By stacking and integrating “beacons” and other google estimations with offline consumer demographic data, we’ve become pretty good at estimating this.
The power of understanding how your marketing and online spend directly equates to sales dollars in store is important to growing your business, and it provides huge inputs on how to optimize your marketing dollar investment strategies at the channel level.
Far too often we get stuck in the technical details of data science, artificial intelligence, or complex machine learning algorithms. It’s important to remember that your data must all be tied to customer and business value if any of these methods are worth anything.
Now that you know the basics, test your knowledge of the data science buzzwords with our handy buzzword glossary.