3 Surefire Ways to Leverage Customer Data
Big data will become so sophisticated that anyone ignoring it will be left behind.
One of the biggest business-related challenges brought by the COVID-19 pandemic is how fluctuating customer behavior has become. As the crisis lingers on, people generate new demands, shift their existing ones, and behave differently in their relationships with brands. And while there are broad trends that we could discuss (such as the boom of online shopping and the rise of curbside pickup) the reality is that it’s hard to predict how customers will behave.
At least without the help of big data, that is. I know it might sound outdated to hear this in 2021 but I think one of the lessons of 2020 was just how important big data is for companies of all sizes. With its help, businesses could navigate the harshest waters in recent memory and adapt by increasing their flexibility and dive deeper into what customers wanted from them.
Naturally, all of that is valid for this year which, up to this point, still feels like an extended 2020. What I’m saying is that big data is as crucial as it ever was (if not more), so you’d better start paying attention to it. Just consider that with big data I’m not referring to a process of amassing huge datasets but to the procedure of gathering and analyzing it to understand customers and discover opportunities and risks.
How can you do all that with big data tools? By doing the 3 things below, which will surely help you in leveraging the customer data you collect on a daily basis. (Trust me, we use these at BairesDev, a software development and QA outsourcing company, and they allow us to better serve our clientele).
#1 Segment for Value and Loyalty
Segmentation is one of the most basic tasks in marketing, yet it’s also one of the most essential. There’s an obvious reason for that — customers within any given audience aren’t homogenous. Creating marketing campaigns and brand experiences as if all your customers were the same is a gross mistake because there isn’t a one-size-fits-all approach for engagement.
Fortunately, big data can help you segment your audience into smaller groups according to different criteria. You surely already know segmentation based on typical criteria, such as age, gender, location, education level, and income. But those aren’t enough for today’s day and age. There are plenty of other variables you should consider to segment your audience but I’d like to focus on two: value and loyalty.
Segmenting by value will have you dividing your audience based on what they bring to your company. For instance, a client that subscribes to the lowest-paid tier of your online service doesn’t have the same value as the one that pays for a group subscription of the highest tier. It’s obvious that you should focus on the latter (without neglecting the former, of course).
In the same sense, you should start segmenting your audience by loyalty, something closely related to value. A loyal customer is someone who already made up his mind and has chosen your company over your competition. That means you don’t have to convince them to do business with you, which ends up being the best option (it’s far cheaper to engage with a loyal customer than to generate a new one). So, identifying those customers and keeping them happy is key — and that’s when big data comes in.
Through big data, you can collect data from your customer through plenty of channels (CRM, social media, website, emails, and so on) and group customers together based on value and loyalty. Once you’ve identified them, you should target them specifically. Valuable and loyal customers appreciate discounts, special offers, and invitations to exclusive events, things that can be part of your next marketing campaign.
#2 Invest in Preference Management Software
While value and loyalty are more company-sided factors, you shouldn’t forget about the customer side. And there’s nothing more customer-centric than your audience’s preferences. Learning the what, how, when, and why hiding behind your clients will give you a better shot at engaging with them at a more “personal” level.
Thanks to big data tools, you can collect that information with enough granularity to divide your audience according to their behavior, which can include their preferred channels of communication, their favorite services and products, their navigation patterns when visiting your site, and even the frequency of their dealings with you.
You can handle all of that data about your audience with preference management applications. These platforms allow you to gather your customers’ preferences across multiple touchpoints to better understand what they do when they engage with you. After that, you’ll have a data hub that will help you sort the information and create more targeted and relevant communications to engage with your customers when they are at their most receptive.
What’s more — those platforms can be enhanced through AI-based algorithms that will automate data collection and analysis and that can even help you automate other aspects of your website, such as automatic pricing and content adjustments based on the visitor’s preferences. This brings me to the next and final point.
#3 Maximize Big Data with AI
Artificial intelligence is rapidly becoming a standard in the business world mainly because its potential is finally starting to show. That isn’t more evident than with big data. Can you imagine the amount of time it could take you to collect, sort, and analyze the data manually? Fortunately, AI can do all that for you automatically without that much effort on your part.
That, however, is sort of a given when talking about big data. Where artificial intelligence truly shines when it comes to big data is with the security aspect of it all. First and foremost, AI is quickly becoming a preferred method to monitor systems and their multiple endpoints. A machine learning algorithm can constantly control a digital platform and act on the presence of certain triggers (suspicious behaviors, unauthorized accesses, and so on).
And then, there’s AI when applied at the edge. Given that you can use more and more devices to capture data (courtesy of the increasing presence of the Internet of Things and 5G), using AI becomes essential. The main objective of that is to make a first analysis right there, on the edge, decide whether the data is worth keeping or if it can be discarded right there. But it also has another goal — to stop any potential threat on its track right at the system’s entrance.
I know that security doesn’t seem like a direct use of customer data but think about it for a minute. People came to expect privacy and protection of their personal data. If you can’t offer a satisfactory level of security, then clients will leave you and your reputation will be forever tainted. Thus, by using AI, you can leverage AI with the peace of mind (yours and your customers’) that data is always protected.
Another Year, Another Evolutionary Step
Saying that big data will become a standard in 2021 is something of a naivete — big data is already a common practice across industries, so I can’t pretend that this year will be a breakout for it. However, and as it happens with technologies that are bound to be paradigmatic, big data will certainly take a step forward in the next 12 months.
With the pandemic consequences impacting every business under the sun, the need for increased agility will drive them to adopt big data which, in turn, will push for further innovation around it, both in terms of approach and uses. And there’s one more thing — big data will become so sophisticated that anyone ignoring it will be left behind. So, it doesn’t matter if you own an online hardware store or offer QA & testing services — you’ll need big data in your business life.
