Social Engagement and Sales Accelerate When Using Intent-Driven Data

sasha uritsky

Nov 26, 2015

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.

Interested in reading more? Check out our other blogs:

AI unmasked: Have chatbots failed?

It is becoming increasingly popular to say that chatbots have failed and are overhyped.

While it is true that in many cases expectations from chatbots significantly exceed the results on the ground, the anticipation of chatbots’ demise are somewhat premature.

One of the main problems for chatbots is that the market is inundated with low quality solution providers who deliver low quality results. This happened because conversational AI seems to have low entry barriers. Unlike other recent technological darlings such as space technology or renewable energy, conversational AI is purely software and therefore does not require vast sums of initial investment.

What this approach is missing however, is that conversational AI, in addition to being a software, also requires an accurate understanding of how language works. And there is a limited number of people in the world that do have such understanding.

When conversational AI is delivered by AI experts who understand the way human language works, the results are good and convincing, just as how you would expect them to be.

Suffering from unsatisfactory product quality is a common problem for many new and emerging industries. The rules of the market dictate that most of the low quality players will eventually disappear. Poorly created chatbots will therefore not be around for too long.

See how a chatbot will help your businessnmodes.com

WHAT IS AI TRAINING

AI training is a critical part of conversational AI solutions, a part that makes AI software different from any kind of software previously created.
AI training is not coding.
Unlike all other existing software which is fully coded.

Let us consider a simple example:
We create chatbots for two companies, one company is selling shoes, another is selling cars. From the software standpoint it is one chatbot solution running as an online service accessed remotely or a program available locally. In both cases they are two identical instances of the same software (one instance for the shoes company, another for the cars company).
Yet, for the first company the chatbot is supposed to talk about flip-flops, summer shoes, high heels and so on. For the second company, however, the chatbot is not expected to know any of that. Instead, the chatbot should be able to support conversations about car brands, car models, should know how to tell Toyota Camry from Toyota Corolla, etc. This shoes and cars knowledge is not programmable. It is trainable. It is not coded, instead it is a part of language processing capability that AI solutions like chatbots have. And herein lies the major differentiation and advantage of the AI solutions compared to traditional software.

How to train AI?
There are several ways to do it. Sometimes AI system can train itself, improve its linguistic ability over time. It also can be trained by professional linguists. And in some cases, by the users. The latter is the desirable scenario because businesses know better than anybody else what they want their chatbot to talk about.
It is not easy, given the existing state of AI technology, and usually requires a high level of technical knowledge. You may have heard mentions of intents and entities in chatbot discussions. These are examples of linguistic elements AI training is currently based on.
Without proper understanding of what these linguistic elements are and how language acquisition process works in existing AI systems it is better to leave AI training to professional linguists.