Towards smarter data - accuracy and precision

sasha uritsky

Nov 01, 2014

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.

Interested in reading more? Check out our other blogs:

Social Strategy for B2B Companies

I am regularly approached by businesses that sell to other businesses to help them market and promote their brand on social networks.

And so I noticed that some of them have a vague idea of how social media works and the sustainability it offers. They often see social marketing as yet another way to advertise and sell their products, in the same manner they are accustomed to do on traditional marketing mediums. Not surprisingly it usually results in frustration.

While I saw companies successfully sell on social, they are typically limited to mass consumer oriented B2C verticals, such as fashion and apparel, travel and hospitality. There is a segment of online shoppers, sometimes called ‘impulse shoppers’, that makes purchases straight off the Twitter timeline, yet the majority of us go to social networks for different reasons. Certainly no one is buying an insurance policy, or a house, or a CRM solution there.

The success of social media and its importance for business is in its unique ability to build trust.

For B2B, as well as for the majority of consumer-oriented businesses, this is where the real value of social marketing lies. A more detailed discussion here

And so that means approaching social media strategically. First know precisely why you want to engage, understand clearly how it will help you grow the business. Then, if you are convinced of social media’s importance for the success of your business, start taking practical steps. Obviously very company is different, but here are some observations that are pretty generic:

- Plan long-term. Don’t expect results after one month. Not even after two months.

- Do not do social media just because ‘everybody’ is doing it. When people have strategy their choice is between social tools X or Y or Z. It typically comes early in the conversation. And when people say ‘I’ll try it for a month and see if it brings results’ or ‘I want to see how my friend/my competitor is making out before deciding’ it usually indicates a lack of strategy, because it implies a choice between tool X and doing nothing. In that case, better do nothing.

- Social media does not substitute sales. It is however one of the most efficient ways to grow sales Here is a good explaination

Social media’s importance for B2B business is increasing. More and more owners and executives are inquiring how they can succeed in the new environment. As usual, the earlier you start the better are the chances.

Top 5 Reasons to Use Artificial Intelligence Chat in Your Contact Center

1. Zero Wait Times

Do you suffer from long chat queues? How about any chat queues? Can you imagine a world where your customers are either immediately engaged in a proactive chat, or can easily decide to engage in a on-demand chat and get immediate service, no matter how busy your contact center is? Well, that day is today. With AI chat integrated into your contact center customers can be served immediately with no additional agent resources required. Now you can change your focus from calculating the time before a chat session starts to hcounting the number of chat sessions escalated to a live agent (We’ll give you a hint: It’s a lot less with Chat AI).

2. The High Efficiency Rate

The average chat session is completed in 42 seconds. With most questions being answered in just a few short seconds (Unless you’re Zappos, of course), Chat AI can quickly and effectively address most problems today, without having to engage a live agent. The savings in time and abandonment rate combined with the increased customer satisfaction, not only reduces costs, but delivers a measureable improvement in performance and customer perception.

3. It Really Understands Language

True Artificial Intelligence is not the robotic, sometimes irrelevant interaction of yesterday. Answers are more personalized, relevant, and complemented by the newly acquired ability to access multiple data sources to deliver the best possible responses to inquiries. The technology “learns” the longer it is deployed, as a result customer experiences improve with time without the need for additional investments in technology, people, or processes.

4. No such thing as a “Sick Day”

AI doesn’t sleep, it doesn’t get sick, you don’t need to train it, and it’ll never quit in the middle of a seasonal rush. You don’t even need to give it lunch, breaks, or let it go to the bathroom. Imagine how easy Workforce Management becomes when all you’re doing is flipping a switch. Scheduling becomes less and less of an issue over time, as the application continues to learn, making your investment more valuable over time.

5. 24x7x365

Imagine the increase in sales volume and support cases your business could handle if you’re able to offer 24x7x365 chat. And cart abandonment plummets when customers are offered chat. Today Chat AI can answer anywhere from 70-90% of customer inquiries meaning that only the most verbose requests require a human, and at an operational cost that is often 4 times less than the cost of staffing a contact center.