The post-Covid world has witnessed a surge in almost all sectors of e-commerce activity, emerging technologies and improved advertising techniques such as Omni Channel advertising shoulder the blame. The deployment of AI and Machine Learning concepts to B2B and B2C models have resulted in improved conversion rates and enhanced sales figures.
Marketing and advertising have come quite a long way, from the humble Out of Home (OOH) format to the next-gen Web3 iteration, the world has witnessed it all. Looking for new customers has its own set of challenges. Taking into account the emergence of new trends and technologies, picking out the right set of people who shall be interested in your product/service can be a bit tricky. The universal set of customers has become more diverse than ever before, diversity seemingly has been increasing exponentially in this digital age.
While AI and Machine Learning have driven efficient results, there’s more to their deployment than automation. The surge in AI reliance for acquiring new customers and engaging them has become more noticeable. Close to 56% of leading marketers believe the fact that AI-based data-driven marketing is more accurate and efficient than experience-based practices.
Lead generation in the age of big data, with streams of zero, first, and third-party data calls for efficient and out-of-the-box methods. While techniques like Lookalike Modeling look and work great on paper, their deployment often calls for improvement.
Lookalike Modeling: The Basics
Speaking of Lookalike modeling’s proposed enhancement, one should first understand what it is in the first place.
Your zero-party data strategy might be working well for the existing set of customers, but what about the ones you’ve yet to discover? This is where Lookalike Modeling comes into play. Lookalike Modeling assists the advertiser in identifying and targeting those segments that act and behave in the same way as their current customer base. The best part is that the predicted segment hasn’t been reached out to yet.
This technique not only allows the advertiser to tap into new segments but also helps them streamline their strategies on the current customer base. Omni Channel advertising techniques can benefit from Lookalike Modeling a lot since the deployment of customer-centric content across multiple channels can also garner a similar response from the segments that are projected to be potential targets.
The Omni Channel advertising technique has become a mainstay of a number of industries today. A good marketer with the right know-how can retain 90% of the customers using the Omni Channel Advertising technique alone. This reliance on Omni Channel also necessitates the usage of Lookalike Modeling.
The aforementioned points on Lookalike Modeling paint a very good picture for advertisers. However, one shouldn’t rely solely on the current practices alone. Being aware and on par with the upcoming trends can not only benefit your existing marketing strategy but can also help you with the updates you’ll be making to the said plan in the future as well.
Here’s what the future of Lookalike Modeling might look like
The complexity of algorithms driving complex systems only tends to increase with the passage of time. As of now, a gazillion systems might be running on the same algorithm as yours. So, how would you establish the differentiating factor and gain the edge?
Well, in such situations, the data becomes the differentiating factor. This is where your zero- party data strategy can be taken advantage of. Data has always been the buzzword, but the addition of terms like “privacy,” “encryption,” etc. to the mix has turned out to be a strong driving force when it comes to attracting new customers.
Lookalike Modeling’s fuel, when enriched with state-of-the-art encryption techniques and improved zero-party data inclusion shall give rise to accurate targeting and acquisition of customers sharing the same interests as the ones from your current campaign.
The future of Lookalike Modeling is nigh, and this is what you need to know:
The paradigm shift is inevitable
For the most part, supervised learning when combined with Lookalike Modeling gives decent results. The problem, however, arises the moment an unlabeled data set is fed to the system. This reduces the chances of exploration of attributes that might not be visible at first glance. The inclusion of unsupervised learning after going through a series of supervised cycles has been shown to yield accurate results. Since unsupervised learning doesn’t work with labeled data sets, the discovery of hidden patterns is carried out by the system itself.
This technique gives rise to the discovery and probable targeting of traits that are subtle but important for the most part. Some subtle traits might even turn out to be the new identifiers for the data set that you’re willing to target in the upcoming days.
The human clearance won’t lose its viability
While the shift from supervised to unsupervised learning is inevitable, the idea of eliminating human clearance cannot be achieved. The reason being that human clearance at the end of the day might put the entire process back on track if it has deviated from the way before. The reliance on unsupervised learning has its benefits, but one cannot be entirely sure of the results shared after the modeling process.
Certain aspects if added to the set of potential customer behaviors might end up creating a different persona altogether. And if the modeling process continues to work upon the wrongly evolving set, the results will start to vary, and in some cases, they might even vary drastically. Hence, the need for human intervention has been prophesized.
Creation of sub-models with evolving privacy measures
Data privacy has not only become mainstream but has also become one of the major factors that has led to the adoption of new and safe data extraction methods. Industries are focusing more towards the self-declared aspect of data than the extraction process. Your zero-party data strategy will do wonders if done the right way, establishing faith is a key component of the extraction process.
However, the extraction of said zero-party data has to evolve with the evolution of privacy regulations as well. This brings into account the deployment of sub-models. Think of them as smaller repositories of self-declared data for every venue where the user shares it. With the passage of time, more repositories shall appear. Such repositories shall stitch together all the pieces of data to create a unique profile that is as accurate as it can be.
To sum it all up
Lookalike Modeling as of now is going through a critical phase of evolution that’ll propel the techniques used even further. Being an advertiser, you must heed the trends and may even have to keep an eye on the recurring themes that might give rise to new trends. The realm of big data is changing, with Cubera’s approach toward Big Data Monetization, and the creation of an ecosystem that benefits the users and advertisers, it is safe to say that your advertising campaign shall sail smoothly.