B2B Companies Need Deep Learning “Therapy” To Overcome Modern Data Challenges
The world has adjusted to the problems brought on by an abundance of data during the past 20 years. Our attention has primarily been on the practical difficulties of collecting and storing data, making it affordable and granting consumers the opportunity to experience secure, real-time access. The entire world is “on it.”
In this area, there has been a lot of innovation, from “hyper scale” data centers to data lakes and warehouses, to open data structures like data lake houses right now. The larger problems businesses face when attempting to find an always-on “single source of truth,”have not been touched by these amazing examples of data engineering.
It takes higher-order intelligence that encompasses identity, relationships, and truth to get to the heart of data unification. Although it could seem like a psychoanalysis, it’s not. We are discussing the upcoming difficulties that big data will face. All industries can attest to this. But how will these challenges at the next level show themselves in B2B?
There’s An Identity Crisis:
Businesses are currently extremely concerned about the possibility of a future without cookies. However, tracking anonymous consumers is only a small part of the identity dilemma that B2B businesses should be worried about.
B2B businesses shouldn’t limit their tracking to the anonymous ID of a Facebook ad clicker. They must keep an eye on all of the different ways they interact with people and businesses, including partners, vendors, and customers. In this instance, what we’re discussing relates to more basic B2B data concerning business activities like transactions, subscriptions, contacts, workers, and more.
Here’s what’s behind B2B’s identity issues:
- B2B enterprises continue to rely on “conventional,” offline identities (physical addresses, phone numbers, contact cards, and financial profiles), which must be synchronized with online identities on social media, the web, and mobile devices.
- B2B data is still mostly transmitted through CRMs and ERPs. However, because they rely on manual data entry by several departments, those systems frequently result in different representations of the same individuals or businesses.
- The number of rapidly expanding digital channels via which businesses engage with their surroundings only serves to stoke the fire (chat, email, mobile apps, product logins, Slack channels, Zoom chats, etc.). There are a tonne of “loose” identities left over. This not only tampers with personal data but also hinders any enterprise company’s attempts to constantly communicate with customers.
- Finally, compared to B2C identities, B2B identities are far more dynamic. People will change occupations and positions, businesses will open and close, acquire, merge, and change more quickly than before.
The difficulties don’t end there. As the Metaverse and other virtual platforms come into play, the question of “what is identity” will become even less clear. We already have statistical tools at our disposal to address these problems. Algorithms are used in B2B marketing to identify the specific John Smith who is the target client. B2B data will need a higher order of non-deterministic AI algorithms to solve for identity resolution at scale, though, as these identities become more complicated — less physical and more virtual, with an added layer of what’s real and what’s fake.
Relationship Problems: Using Data To Understand Connections
Never is data singular. It is brimming with links, connections, and history. Data science must be strong enough to not only find those connections—which people are built to make—but also to understand the significance for more context.
For instance, a B2B sales and marketing team would benefit greatly from knowing that a prospective customer’s business is owned by a larger corporation. It’s also quite useful to know relationships within and outside of the buying team (such as where people last worked or how they are related to other professionals from previous employment). The good news is that AI algorithms can be trained to recognise those links based on implicit clues even though explicit data about many of those ties may be difficult to come by. Again, once they are well-trained, machines are capable of performing that task on a huge scale. The models you create with the data are rarely constrained.
Discovering B2B network connections can be a significant help in implementing data-driven strategies. So much so that thought leaders spend hours debating, doing intricate analyses, and studying network analysis.
The Complex Truth: Sort Your “False News” Data Using AI
What is true and authentic is a topic that is frequently debated online, particularly across our social media platforms. An abundance of knowledge sometimes entails a flood of false information. Additionally, studies have indicated that as the amount of information increases, the quality is deteriorating due to bots, easy access to blogs, and content machines.
In the area of B2B data, we face comparable challenges, and we must train machine learning to uncover the truth. The CRM may recognise Amazon as a retailer when aiming its marketing at potential customers. However, the marketing strategy claims that it is a software company. A third system claims to be an IT firm because of its AWS operations. You can end up with three salespeople working independently on the same account due to multiple versions of one customer. Which is it then?
B2B should use AI to detect faulty data that could mislead its operation, much as social media platforms use their best data scientists to identify fake news and unreliable information in real time and on a large scale. While AI will be extremely helpful in identifying differences and indicating which is more likely to be real, the truth will frequently be arbitrary and require human monitoring to lead the machine. This can be shown in the case of determining whether or not Amazon belongs in the category of software companies. The best strategy will involve humans guiding the questions and AI making predictions, with the machine eventually taking over entirely.
Conclusion:
AI is continually evolving to meet the expectations, even while B2B data faces whole new difficulties that call for the most intense “treatment” and attention. Although the possibilities of AI are virtually endless, our data may be constrained. Only AI, in conjunction with human direction, will be able to address these escalating organisational problems and, ultimately, improve our data.
