As we left the Industrial Age and entered the Internet Age, in other words, the Information Age, a massive information flow started taking place in our lives. The most important reason for this is that mobile devices, that is, devices that connect to the internet, have a bigger place in our lives with each passing day.
The continuous growth of mobile data, cloud computing, machine learning, and IoT contribute greatly to the increase in Big Data expenditures. By the year 2027, the revenue from “Big Data” is projected to be double what it is in 2019. According to data from Statista, as of April 2022, there were 4.95 billion internet users worldwide, which is 62.5% of the global population. Of this total, 4.62 billion were social media users. “In the third quarter of 2021, the mobile data traffic reached almost 78 exabytes worldwide, which is an increase of around 23 exabytes compared to the same quarter in the previous year.” In 2022, as the global Big Data industry approaches a net worth of 274,3 billion dollars, more than 8.5 billion searches are made on Google every day. Around 100 billion messages are sent every day on WhatsApp, and while 79 zettabytes of data were generated in 2021, this number is expected to reach 180 zettabytes in 2025. Meanwhile, 95% of businesses struggle with managing unconfigured data.
By looking at these numbers, it is possible to see that there is indeed an increase in information flow and data that correlates with the increased use of mobile devices. Back in the day, we used to regard companies as wealthy if they owned factories, buildings, and land, whereas today, these assets have now been replaced with companies’ data. Companies like Airbnb, Amazon, and Facebook (Meta) are among the most powerful companies in the world, and their biggest asset that makes them stand out is the enormous data they own. Companies can now produce meaningful data sets from big data and earn money through information flow. And with IoT, where we form connections with physical objects, entering our lives, data production will certainly continue to increase.
Information flow being free of charge, placeless, and timeless caused 3 fundamental approaches to emerge in marketing. The first one of these approaches is “real-time customer engagement”. This approach initially emerged with big data, but nowadays, it is a priority to process data in real-time. Although ‘big data’ provides valuable information, because of its immobility, it can quickly grow outdated in an age of speed where seconds and even milliseconds matter so much. Because pre-stored data does not reflect a customer’s current state of mind, it is not always possible to generate productive and compelling engagements with it. Whereas with real-time data analysis techniques, engagements with a much better response rate can be generated.
The second approach is providing personalized offers. In addition to speed, consumers now prefer personalized products and services that cater to their needs and goals. This is made possible through measuring and budgeting consumers’ behavior instantly when they occur. Thus, traditional marketing techniques, although not completely, are starting to lose their validity, and it’s becoming more and more important to offer personalized solutions to individuals. As a matter of fact, a new approach we refer to as “service-dominant logic” is starting to spread. This highlights the importance of generating insights from every consumer experience and offering information and value with any kind of product and service.
Once personalization met the Internet of Things, both our mobile devices and our devices at our workplaces and homes started generating data. This in turn started affecting segmentation. Segmentation is an important criterion that emerges during the Information Age to create homogenous markets that cater to consumers’ differing preferences in order to increase customer satisfaction. This also showed how transient the people in the personae companies create to use data in real-time are. These personae were created in line with traditional marketing methods; however, they no longer serve their need, and the transitions between segments are taking longer than expected. In fact, it’s no longer enough for segmentations to be rule-based or in real-time. It is at this point that we as Evam use “smart segmentation” to automatically predict customers, even if they have never interacted with a segment before, and direct them to the right segment through the help of artificial intelligence.
I want to demonstrate what the future of smart segmentation looks like with an example. Let’s say that we know a certain customer can look at their mobile notifications more frequently during the day, but that they check their emails after 6 pm, and almost never look at or respond to their SMS. Through smart segmentation, we can start learning more about this customer and getting to know them through their behavior, and place them in this segmentation with similar people without establishing a rule. This shows the importance of going forward with real-time data in order to reach consumers. In addition to smart segmentations, as Evam, we’re also able to automatically place individuals in different segmentations under customer journeys. For example, if we’re going to offer a $10 discount to people who make 3 credit card purchases worth $100, after we go ahead with the first two, we assess the people who remained at the second in a different segment and generate a campaign. And we can add a different segment by determining the people who are interested in this campaign but don’t quite meet the campaign criteria. When we can differentiate this, we’re able to make more interesting and relevant offers. So as Evam, we’re able to offer smart segmentation and classic segmentation that have been combined with real-time data. We have to think of segmentation and personalization as two interconnected applications. Segmentation groups customers according to their definable qualities. Personalization on the other hand, doesn’t target the groups that these customers belong in, but optimizes individuals’ unique experiences and messages.
Using real-time and smart segmentation, as Evam we’re able to make the transitions between these segments occur instantaneously. Hence, we are able to reach the target audience much more quickly and accurately. But even this is not enough, because a situation that is applicable for a group may not be applicable for an individual. That’s why we have to come up with strategies that are unique to an individual, which leads us straight to personalization. How do we do personalization? Evam uses the data from past behaviors, past purchasing experiences, and the behaviors or habits the customer has not displayed, and creates campaigns and provides solutions according to them, thus allowing offers to be made to customers. We can realize this as a marketing tool or just for communication. How Evam uses personalization is that when a customer accepts or refuses a personalized offer, it can present them the “next best offer” through the help of artificial intelligence, thus ensuring success.
Ahmet Kurtul , SEMEA Regional Manager