You’d create perfect marketing campaigns and would always know which of your leads would become customers.
It sounds too good to be true, doesn’t it?
Understanding customers with data science
Every day we create 2.5 quintillion bytes of information, according to IBM.
Instead of just storing all of that information away somewhere, organizations have realized it can be used to better understand consumer behavior and create predictive models.
In order to build these models, organizations rely on the power of data science.
Data science is a field of study that’s rooted in the discovery and extraction of knowledge from large volumes of structured or unstructured data.
In much simpler terms, data scientists are responsible for collecting, cleaning and categorizing data in the hopes of finding hidden insights.
This process is rapidly becoming a must-have for many industries.
However, finding answers to important questions isn’t as simple as typing a question into Google and getting a quick answer.
In many cases, data scientists don’t even know which questions to ask or what exactly to look for.
Picture data as a pile of Lego bricks. Without an image to know what the final result should look like, it’s difficult to know which pieces to use or how they fit together.
The same goes for data. We have lots of data to work with, but we don’t always know how to sort and categorize it.
In order to overcome this, data scientists must understand the business domain, the business levers, and which factors are at play when trying to sift through the mounds of data.
Mapping the future with predictive analytics
For decades, companies have collected information on their customers and target audience.
However, it’s only recently that we’ve developed the tools to mine the hidden insights locked away in this data.
These data-mining tools are helping data scientists and data analysts transform raw and unstructured data into actionable insights, helping us better predict consumer behavior and future trends.
While in the past statistics were commonplace in the decision-making process, we’re now seeing a shift toward machine learning and predictive analytics.
While data science focuses on the extraction of information from data, predictive analytics focuses on using data to predict and prescribe behavior.
The more information we gather on past behavior and occurrences, the better we understand the causes and effects of these occurrences.
This, in turn, allows us to recognize when a similar situation may arise, and how best to handle it.
The more information or details we collect and analyze, the more specific and accurate our models and predictions will be.
This has a wide range of applications.
It can track disease outbreaks, help marketing teams determine the success of a campaign, or inform sales teams how likely it is a particular lead will buy from you.
Neuralytics brings data automation to sales
XANT is working to automate the sales process.
Since 2006, XANT has collected anonymized data on every sales interaction occurring between our customers and their prospects.
In nine years, we’ve collected more than 85 billion sales interactions and counting.
We’ve also combined data from these interactions with external data, which includes information on things like geographical locations and stock prices.
All of this information is stored in our database and through Neuralytics, a self-learning engine, it’s converted into predictive and prescriptive insights.
Neuralytics is also what powers XANT’s Predictive Cloud for sales.
The Predictive Cloud represents the evolution of big data applications for businesses, allowing companies to realize the full potential of predictive technologies in their own applications, business processes and sales practices.
The Predictive Cloud allows data scientists to use our platform to help automate their data munging.
Neuralytics has the ability to offer you insights based not only on your own sales interactions, but those of everyone on the XANT platform.
Remember, the more data or details you have, the more accurate your predictions.
Want a deeper dive into the science behind sales success? Download the free ebook below.
Free eBook: The Science of Lead Scoring, Prioritization & Sales Success
79% of marketing leads never convert to sales. That means inbound reps waste a lot of time chasing the wrong leads.