Scalable Yet Personalized

sasha uritsky

Aug 02, 2016

How to offer businesses and organizations a solution that personalizes and scales consumer interaction process at the same time?

Personalizing the user relationship process. Today end users and consumers demand to be targeted individually and to be approached based on their actual interests. nmodes AI (Artificial Intelligence) powered solution helps organizations accurately identify user needs in real time. Our solution delivers information on each user individually thus providing the necessary level of personalization required of the successful customer service.

Scaling the user relationship process: Once the organization identifies a user and a problem that needs to be addressed, next step is reaching out to that user individually. Currently this is a manual non-scalable procedure. nmodes AI (Artificial Intelligence) solution provides automated assistance to human personnel, including substitution when deemed appropriate, thus making the entire process scalable.

Today more than 90% of all organizations and businesses rely on solutions based on keywords, even though these solutions provide low quality results not sufficient for the new generation of personalized scalable services.

nmodes solution enables sustainable delivery of high quality results, with x5 costs reduction and up to 45% increase in conversation (engagement) capacity.

Interested in reading more? Check out our other blogs:

Social Engagement and Sales Accelerate When Using Intent-Driven Data

nmodes delivers consumer intent from social web to businesses. In real-time.

That is, if your company is selling cars we will connect you with potential customers out there that express intent to buy a car.

nmodes has partnered with a medium-sized travel company to help grow their social web sales channel. Our approach is to deliver consumer intent relevant to the company (people planning vacations, going on trips, flying to various world destinations, etc) and develop engagement strategies maximizing the impact of this consumer information.

Here are the results based on 4 months of data:

• The most efficient way to achieve short-term sales turned out to be individually targeted promo campaigns. For example, our travel partner created an attractive vacation destination package, and nmodes helped to spread the word on social media to those intended going on vacation.

A typical conversation start leading to promo offering. nmodes intent-based solution made it especially easy to target only relevant end users:

• The response rate varies geographically.

Canada - 20%

USA - 64%.

• The conversion rate is consistent across all locations and is slightly above 4%. When concentrating on vacation packages we were targeting 20-50 prospects daily, resulting in 2-4 sales per week, averaged $15,000 /mo or $200,000 /year.

• The potential for this particular market segment (all-inclusive vacations) in the US is at least x10 higher.

• The engagement was based on the combination of intent-based data and location data.

An intent-based sample for European destination package, travelers from USA:

While working with companies from various verticals we proved that intent-based data paired with location data offers a powerful opportunity to drive sales aggressively and accelerate business growth.

nmodes is best equipped to ensure that your business can benefit from this newly available power.

Beware the lure of crowdsourced data

Crowdsourced data can often be inconsistent, messy or downright wrong

We all like something for nothing, that’s why open source software is so popular. (It’s also why the Pirate Bay exists). But sometimes things that seem too good to be true are just that.

Repustate is in the text analytics game which means we needs lots and lots of data to model certain characteristics of written text. We need common words, grammar constructs, human-annotated corpora of text etc. to make our various language models work as quickly and as well as they do.

We recently embarked on the next phase of our text analytics adventure: semantic analysis. Semantic analysis the process of taking arbitrary text and assigning meaning to the individual, relevant components. For example, being able to identify “apple” as a fruit in the sentence “I went apple picking yesterday” but to identify “Apple’ the company when saying “I can’t wait for the new Apple product announcement” (note: even though I used title case for the latter example, casing should not matter)