Introducing Fiind Data Integrity Rewards

Introducing Fiind Data Integrity Rewards

Artificial Intelligence, Blog, Data Science, Machine Learning, Predictive Intelligence
Data tells your customer’s story  Data tells a story. With Fiind AI and our >1 billion data signals, you can quickly learn your customer’s story and how it aligns with your solutions. In order to tell that story well so you can rely on it to engage your customers, we put a lot of care into the integrity of our data even as it changes over time, and constantly seek new ways to improve.  Our focus on data integrity can be the difference between knowing if your customer is located in Philadelphia or Berlin, or a company of 15 or 50,000 employees. It’s critical for you to connect with the right business opportunities in the right way.  Data integrity: Always be improving  The Fiind AI platform itself is always learning. One of the ways Fiind AI uses artificial intelligence and machine learning is to improve not only the data it discovers…
Read More
3 Ways We Solve for the Influence of Human Cognitive Biases in AI & ML Models

3 Ways We Solve for the Influence of Human Cognitive Biases in AI & ML Models

Artificial Intelligence, Blog, Data Science, Machine Learning
In recent years, we have seen artificial intelligence (AI) being used to solve complex analytical problems and introduced in a growing number of fields in ways never before thought possible. We find ourselves amidst machine learning (ML) and deep learning algorithms deployed for specific cases to replace the need for humans and mimicking human functions. These algorithms are designed to process innumerable input parameters, assess the various possibilities by running multiple iterations before identifying the best solution. All the above steps occur within fractions of a second. Consequently, these scenarios call for high accuracy and high precision models which leave almost null to zero chances for error. How confident and comfortable are we, when machines take over these responsibilities that we thrust upon them? As organizations, we often tend to focus…
Read More
The trouble with MQLs…

The trouble with MQLs…

Blog, Data Science
How many times have you had trouble in agreeing upon what is actually a marketing qualified lead (MQL)? Marketing produces content, generates leads, and more so leads that qualify the agreed upon job titles, company size, revenue, etc. Yet, you hear.. Sounds familiar? The issue is not just because the attributes of an MQL is continuously evolving, but because MQL has its own set of shortcomings – which you might come across especially when you apply MQLs in an ABM scenario. We prefer to look at accounts as MQAs (Marketing Qualified Account), as that would equip marketers with effective information on whether a particular account qualifies to get into the ABM cycle or not. The problem with MQL is it is focused on the contact, whereas MQA gives you a…
Read More
Customer data – are you turning into intelligence?

Customer data – are you turning into intelligence?

Blog, Data Science
There is a lot of customer data collected these days. But, how much do we really know about our customers? The very fundamental aspect of marketing is to give customers a better experience and align with their interests in real time. However, a part of our customer understanding comes from what we think is valuable to our customers, average deal size, repeat purchases, etc. But usually, these insights are narrow and not in real time. Therefore, the key question is – how much of what we know is fact, and how much is fiction? Data and its reliability It is absolutely important for marketers of today to be data-driven, and organizations are beginning to make large investments in customer data platforms. While it is one thing to capture data, ingest…
Read More
Data quality first, smart applications next

Data quality first, smart applications next

Blog, Data Science
From the times we have been offering data enrichment solutions, we have come across a lot of companies that do not have the data they precisely need in their CRM and marketing automation systems. It can be due to missing lead information, or data has become old and irrelevant over-time and many more reasons. As one would expect – wrong data leads to reaching out to a wrong audience, thereby low sales conversion ratio, etc. This leads to two huge problems: There is a significant cost incurred due to bad data ($8.8 mil per year, as per Gartner) It becomes very difficult to predict where your revenue is likely to come from Fix your data quality first While martech is focused on smarter applications, we need to build a process…
Read More
Dark data – a missing link to improving customer experience

Dark data – a missing link to improving customer experience

Blog, Data Science
The primary objective of the marketing department in any organization is to facilitate great customer experience and ultimately make it the biggest differentiation from the competition. If you look closely, there has been an increase in the number of customer experience software and platforms but the real improvement hasn’t really improved a great deal over the last two years. Are we looking at the right data? Marketers have increasingly become data-driven but for a long-time, the approach to data has been passive, i.e., we always looked at past data or siloed data within a specific system. But today’s marketers and salespeople are harnessing technology advancements such as distributed data architecture, in-memory processing, machine learning, artificial intelligence and so on to unearth insights in real-time, which was unimaginable few years ago.…
Read More