How Next-Gen Analytics Will Impact Customer Interactions

In 2000, Peter Lyman and Hal R. Varian carried out the first study of its kind, aiming to find out, in computer storage terms, how much original data was created globally in one year. They found that in 1999, the world produced about 1.5 exabytes (equating to 1.5 billion gigabytes) of unique information.

Fast forward 18 years and we have lived through data explosion. Now, we easily create more data than this in a single day. According to IBM, we now produce 2.5 billion gigabytes of data each day, and this growth isn’t showing signs of slowing down.

Organizations are responding to this onslaught of data in ever more innovative ways. NASA has recently announced that it relies on its vast ‘Lessons Learned’ database, a collection of knowledge and experience from previous missions, when planning future projects and expeditions into space.

This reliance on this “big” data is reflected in many other sectors. IBM has analyzed data from the World Health Organization to understand how local climate and temperature affects how malaria spreads, while Mt. Hood Meadows ski resort in Oregon has inserted tags into lift tickets to help them understand which lifts and runs are most popular at which times in order to reduce queues.

And all this is without even touching upon the thousands of algorithms that consumers and businesses alike unknowingly use and every day, from social media feeds on Facebook to Google’s infamous and mysterious PageRank algorithm.

These innovative uses of data lead to the question: where can big data go from here? As time goes on and more data is created and used, we will see a shift from big data being used solely within the realms of engineering and software development to something which can help to streamline processes, provide better customer service and calculate risk.

Here are a few ways in which big data is likely to shape interactions between businesses and customers in the future.

Personalization

In the past, it was not uncommon for local store owners and grocers to remember the names of their customers and ask after their families when they visited. Now, with many of these interactions happening online, retail customers may feel that this personal touch has been lost.

With so much competition, this absence of meaningful relationships can make it very difficult for businesses to attract and retain customers. Targeted, personalized marketing allows a digital relationship to be built where it isn’t possible to create this face-to-face. One successful example of this is Netflix, which is successfully channeling the potential of big data by analyzing its customers’ viewing habits to make informed suggestions on what they should watch next.

 

We’re likely to see an increase in the use of big data for personalization. We’ve already seen a rise in the use of social listening tools in recent years, which look at relevant conversations on social media. These allow organizations to gauge consumer behaviour in a shallow way, but it doesn’t enable them to truly understand their customers through knowing their likes, dislikes and motivations.

However, big data analytics tools go beyond this and can analyze a customer’s entire digital footprint, providing businesses with complete insight into their interests, activity, and future behaviour. Advanced big data and text analysis enables businesses to now derive meaning from unstructured data to understand what consumers like, what they are passionate about, how they want to be communicated with, what occasions they have coming up and who they spend time with.

Not only can they identify that an individual is interested in sports, they can discover that they love football, support the Texas Longhorns, and have their son’s graduation coming up. All this and more helps companies to deliver personalized marketing communications which will lay the foundations of a long-term, sustainable relationships, much more effectively than blanket campaigns or demographic targeting would.

ID Verification

According to the United Nations Conference on Trade and Development, the number of global online shoppers is expected to grow by 50 between 2013 and 2018. With an increasing number of purchases taking place every day, the need for strict ID verification will also grow. Industries including gaming, retail and food and drink all sell age-restricted products online, but many businesses involved don’t have thorough processes in place.

In fact, a recent LexisNexis Risk Solutions survey of 200 senior ecommerce professionals revealed that over 61 of respondents use self-certification, in the form of a ticked box or data of birth entry to verify the age of their customers.

This lack of strict ID verification is a real issue for a number of industries. The gaming industry in particular is affected by state-specific age restrictions. However, achieving a balance between ease of usability and effective ID verification processes can be difficult. Organizations are understandably keen to keep the process of purchasing a product or registering for a service online as simple and efficient as possible.

This is where big data comes in. While it’s easy enough for users to create fake email addresses or accounts, it’s almost impossible to imitate a thorough, active and interconnected digital existence. With so many customers now living their lives online, we’re likely to see companies work with consumers to utilize this. Big data analytics tools allow companies to assess the quality and quantity of data about a customer to ensure that it’s consistent, meaningful and real.  This use of big data can help to verify that customers are who they say they are, without compromising on usability.

Fraud Prevention

As well the retail and gaming sectors, the financial sector is another area in which big data analytics tools can be used to avoid identity fraud while making the consumer journey easier. The process of identity verification to avoid fraud is often lengthy, with consumers frequently having to provide utility bills or disclose personal details to apply for a loan or bank account.

Again, big data can help here. Rather than requiring customers to show evidence that they are who they say they are, businesses can use big data analytics tools to make life easier for consumers while avoiding fraud. These tools allow businesses such as those in the banking sector to look at the data readily available about an individual online and check these details against, for example, a list of known fraudulent emails. This is all done in real time and behind the scenes, meaning that the customer experience isn’t disrupted.

So, as the big data industry grows, we’re likely to see more businesses harnessing the power of the digital footprint. As a result, data analytics will play a role in many more of the interactions between businesses and consumers.

With so many big data innovations in engineering and healthcare, it seems like a natural step for businesses to implement these tools to strengthen relationships with their customers. And if this leads to more meaningful interactions, efficient consumer journeys and increased brand loyalty, it can only be a good thing.