Our company (www.nmodes.com) is a bit like Croatia (#WorldCup): Everybody pay attention to other teams with shiny and glitzy products. But we work our hearts out, deliver quality results, humbly and respectfully advance forward. And at the end of the day we are winning. 20+ customers, full trust and glowing testimonials from customers, 200% growth in the last year. All is achieved completely organically.
One of the most central causes of missed growth opportunities and overspending is a failure on the part of businesses to create strategies that are tailored to the intent of the consumer. Recognizing and harnessing visitor intent brings increased engagement with relevant messages and calls to action.
Once a business identifies purchase intenders it can create content that aligns with their needs and desires in order to increase the likelihood of conversion. Consequently it can pick up on pre-sale signals from visitors in the research phase and drive lead-nurturing initiatives accordingly. The ability to identify this spectrum of visitor intent is key to creating relevant engagement campaigns that drive sales.
nmodes has been at the forefront of delivering consumer intent to businesses.
We sort the intents based on conversation topics, called ‘streams’.
Here is a stream of people looking for a hotel:
A stream of people who are getting married:
A stream of people thinking of going on a cruise:
There is a huge amount of information out there. And it is growing. To make it efficient and increase our competitive advantage we need to evolve and start using information in a smart way, by concentrating on data that drives business value because it is accurate, actionable, and agile. Accuracy is an important measure that determines the quality of data processing solutions.
How accuracy is calculated?
It is easy to do with structured data, because the requirements are formalizable. It is less obvious with unstructured data, e.g. a stream of social feeds, or any data set that involves natural language. Indeed, the sentences of natural language are subject to multiple interpretations, and therefore allow a degree of subjectivity. For example, should a sentence ‘I haven’t been on a sea cruise for a long time’ be qualified for a data set of people interested in going on a cruise? Both answers, yes and no, seem valid.
In these cases an argument was put forward endorsing a consensus approach which polls data providers is the best way to judge data accuracy. This approach essentially claims that attributes with the highest consensus across data providers is the most accurate.
At nmodes we deal with unstructured data all the time because we process natural language messages, primarily from social networks. We do not favor this simplistic approach, as it is considered biased, inviting people to make assumptions based on what they already believe to be true, and making no distinction between precision and accuracy. Obviously the difference is that precision measures what you got right, and accuracy measures both what you got right and what you got wrong. Accuracy is a more inclusive and therefore more valuable characteristic.
Our approach is
a) to validate data against third party independent sources (typically of academic origin) that contain trusted sets and reliable demography. Validating nmodes data against third party sources allows us to verify that our data achieves the greatest possible balance of scale and accuracy.
b) to enrich upon the existing test sets by purposefully including examples ambiguous in meaning and intent, and providing additional levels of categorization to cover these examples.
Accuracy is becoming important when businesses move from rudimentary data use, typical of the first Big Data years, to a more measured and careful approach of today. Understanding how it is calculated and the value it brings helps in achieving long-term sustainability and success.