With the advent of big data technologies, previously unavailable data sources are now fodder for analytics. Data like viewership, multidevice, IoT, telemetry, usage, and clickstream data need to be considered.
As with structured data in the past, making sense of this data requires enrichment of it to make meaning. With viewership data, for example, how is watching a show defined? Is it when the channel is active? What happens with a customer is viewing content from the DVR versus live?
Regardless of the challenge to use the data, the promise of its value is great. Companies should be looking to enhance knowledge of their customers with understanding of what they’re viewing and how they’re browsing. Segmentation, retention strategies, and upsell strategies can all be improved with more perspective on customer behavior. The development of new products and services can be made more efficient by pinpointing individual consumer preferences.
One of the main goals of the next generation analytics organizations is to turn the goldmine of big data sources into gold.
Customer segmentation is a key tactic for understanding current and future customers and tailoring your business model to their needs. There are several different types of customer segmentation, but one of the most powerful models is Customer Type Segmentation.
In his book, What Customers Crave: How to Create Relevant and Memorable Experiences at Every Touchpoint, CX expert Nicholas J. Webb champions the concept of customer type segmentation, which groups people based not on demographic or profitability data, but on what they love and what they hate.
Imagine you’re the store manager for a technology company and you try using traditional methods to segment a group of ten middle-aged white males. It’s likely all ten would end up in the same bucket, and you’d be tempted to create a one-size-fits-all customer experience for the group. But that’s an old-world approach in this new world of customer-centric thinking.
If you follow Webb’s advice and get to know those ten people based on what they love and what they hate (customer type segmentation), you’d see something far different.
- Three of them may be eager to spend hours in your store trying out the latest gadgets; we’ll call them our “Techy” segment.
- Four may be busy working professionals who want speedy service without all the frills; let’s call them “Corporate.”
- And the last three may be focused just on cost; we’ll call them “Thrifty.”
With these three segments clearly defined, you can create relevant and memorable interactions for each one. Whether Techy, Corporate, or Thrifty walks in the door, you’ll have an experience waiting for them.
Southwest Airlines is a great example of a company using customer type segmentation to build an incredible customer experience. On every flight, they’re delivering specific, relevant experiences for frequent flyers (priority boarding, Wi-Fi), families (low fares, family boarding), and casual travelers (free TV, snacks, and drinks).
But how do you actually do that? How do you figure out what your customers love and what they hate? Most organizations invest huge sums of time and money into primary research activities – customer surveys, interviews, and focus groups. And while these are powerful tools for understanding the motivations and mindsets of your customers, it’s difficult to do them on an ongoing basis.
What if there was some other way to figure out what your customers think about your products and services?
Enter Big Data. Your viewership, multidevice, IoT, telemetry, usage, and clickstream data can be mined to paint a picture of who your customers are, what products and services they’re using (or ignoring), and how they’re consuming the products and services you’re providing.
For example, you could use all your viewership data to understand at a granular level which channels your subscribers are actually watching. Hidden away in this data are eye-opening customer segments that you can use not only to sharpen your marketing efforts, but also to adjust and tailor your over-arching value proposition to customers.
Additionally, you could use multidevice and IoT data to better understand how your customers are consuming content. Synthesizing these massive data sets into meaningful, consumable segments will enable you to meet customers where they are and create memorable touchpoints and experiences for them.
Product and Service Development
If your organization is like most MSO’s, you probably have a handful of key video bundles: a basic package, a mid-tier bundle, and a premium collection. While bundling “must-have” channels with niche offerings is core to this model, there are a number of different ways you can determine your bundles.
One way to stay ahead of the competition and develop market-leading offerings is to leverage your customers’ viewership data to put together a package they actually want. In other words, instead of basing your product development on what you think they want, base it on the content they’re actually consuming.
That will tell you what your customers want, but there’s more to the story. To truly differentiate yourself from your peers, you’ll want to leverage your big data assets to understand how your customers are consuming your products and services.
Are they watching live content on the TV or streaming it a day later on their iPad? Are they using multiple products or services at once? Is their Internet package sufficient to support the volume and types of streaming they’re using?
Understanding these usage habits and consumption patterns will enable you to identify gaps in your service offering and provide targeted product improvements that actually move the needle for your customers. It could even provide a “lightbulb moment” that helps your product development team come up with a completely new product.
Needless to say, in this era of market consolidation, disruptive innovation, and price pressure, big data analytics can give you a leg up on the competition when it comes to improving your existing offerings and developing new ones.
Churn Prediction & Prevention
Lastly, your customer viewership, multidevice, and usage data can be used to predict and prevent customer churn. In this era of cord-cutting, all MSO’s are intently focused on retaining their highest value customers. But that is easier said than done. In many cases, organizations don’t know