With billions of websites now online, consumers have a plethora of choices when it comes to how they purchase products and services. As smartphones and other Internet of Things (IoT) devices become the norm rather than the exception, businesses need to reach out to their customers in more novel ways that create an omni-channel journey. Consumers demand relevancy from businesses and ensuring a personalized or targeted experience is the best way to ensure you stay ahead of the competition.
Google Analytics advocate Adam Singer has said that an average consumer does not make a purchase before consulting 10.4 sources, which typically cover in-store visits, search engines, and business websites.
We have more data in the world now than ever before. In fact, the majority of that has been generated in the last few years with a booming social media, messaging, app, video, and image marketplace. For businesses to really get close to the customers and achieve a competitive advantage, they must invest in “Big Data.”
What is Big Data?
Big Data is the exponentially growing volume, velocity, veracity, variability (the Four V’s of Big Data) and complexity of information that we now have in the world. It is essentially derived from the digital world we live in and refers to all the ways that we need to collect, process, store, and analyze data to drive business decision making. Given the potential for utilizing data in real-time, there is an obvious link to customer experience, with the consumer demand for relevancy.
If you go back 20 years, marketing teams may have had some data about who buys their products or how their email campaigns were. Today, they can store all of this as well as social media data, images, videos, documents, speech, online behavior, mobile data, geolocation data, and much more in one place. When used correctly, each customer can be presented with an experience specifically tailored to them.
One of the biggest cases for using Big Data in recent years has been in marketing. As brands collect so much information about their customers, they have a possibility to create contextual journeys based on their behavior. For example, as marketing teams learn how customers behave online through platforms like Google Analytics, they can craft highly targeted communications. Long gone have the days that everybody in the list gets sent the same email content. It would make no sense for a shoe company to send a 20-year-old male cross-country runner the same products as a 75-year-old woman who loves ballroom dancing (let’s assume they have a very wide target market).
Marketing communications now do a much better job of delivering something relevant to their customers, driven by Big Data. This does an amazing job of driving retention and customer loyalty through maintained levels of engagement.
“Recommender" systems take marketing preferences to the next level. One of the most popular use cases of the technology is Netflix. The framework for the online streaming service is almost solely based on Big Data. Subscribers to the service are all recommended shows using masses of data to work out what they will like. Unknown to many, the Netflix show “House of Cards” was actually generated using data to predict and know the director, actors, and type of shows users like. What is fascinating is that the idea came BEFORE the show because data told Netflix it needed to be produced based on analytics. Without seeing a single episode, Netflix committed to multiple seasons. Why would they do that? Well, as it turns out, by analyzing their expansive amount of data, they were able to make several accurate predictions. The biggest one being that the viewers of the original British "House of Cards" also watched a lot of movies starring Kevin Spacey.
Think about how powerful that is. Today, we see many of these types of shows and they dominate Netflix. Amazon recommends products, Spotify recommends music, and grocery stores now recommend food items to their customers. All of this has only come about because of Big Data technology and is an application of a field known as “predictive analytics”.
Turbo-Charging the Contact Center
Big Data can spot patterns that might otherwise go missed. One of the costliest aspects of businesses is call center operations and staff. Having the right people, handling the right calls is fundamental to efficient operations. Big Data can take each conversation and understand why customers are calling. This then will be combined with key performance indicators such as average handle times and first call resolution. For example, if lots of customers are calling in about shipping issues and taking 10 minutes per call, it will point towards bigger systemic issues that the business needs to resolve. Without Big Data, they wouldn’t have had the ability to get this insight and any business not leveraging Big Data is missing huge opportunities for operational efficiency.
Understanding Customer Sentiments
In an omni-channel world, it is important to know how your customers feel and attempt to connect with them at an emotional level. Big Data platforms will take multiple data sources including customer feedback, surveys, telephone conversations, emails, social media comments, and live chats. This information can give an accurate picture of customer sentiments towards your brand. There are many uses for this insight like changing call scripts, providing customers with communications that speak to them on a relatable level or fixing issues they are speaking negatively about.
Enhanced Pricing Strategy
Small changes in pricing can have a big impact on your business bottom line. With so much competition, it is an aspect of the buying decision that customers are very sensitive about. Traditionally, teams have relied on in-house data to make decisions about their products. Now, they can leverage competitor information, social data, and market trends that previously were very tough to access.
Big Data analytics can now be used to analyze customer data, identifying behavior, and elasticity with respect to price changes. Digital companies can offer targeted pricing based on online consumer behavior. Techniques like market basket analysis can also be used to predict future demands of a product and offer them at a competitive pricing.
This post was meant as an overview on some of the ways that Big Data can accelerate customer experiences. Ultimately, there is no definitive answer as to how your business can use Big Data as it depends on the industry, channels you are using, and where your customers are. However, in collecting, processing, and analyzing large volumes of data, there are a multitude of opportunities for businesses to gain a competitive advantage.