Yesterday I wrote a post about Google Natural Language vs Watson Natural Language Understanding, Given that it was 2178 words you might think it contained a lot of info on why understanding natural language from unstructured text…….but no, so thought it was appropriate to write one.

I wanted to elaborate on some business examples in this post and below you will find two examples: Product Information Cognitive Enrichment and how to Utilize Natural Langauge in Customer Relations.

What is Natural Langauge in this context?

About 80% of all data in the world is unstructured (dark data), that is information that is in text (like documents, blogs, intranets, social media etc), video and sound. This information has been difficult to draw value from, up until now.

The concrete result of utilizing a natural language service like Watson NLU or Google NL is that you can surface information that previously was hidden and not accessible to draw valuable and actionable insight from.

Things you can enrich your information with is the following amongst other things:

  1. Is it a positive or negative text?
  2. What emotions are present in the text, is the writer angry and what is he / she angry about. Is it a product or a person etc.
  3. What products, persons, companies etc are mentioned in the post.
  4. What category does the text belong to. Is it a text about sports, business, tech etc.
  5. How do things relate to each other, is a person angry about a product from a company in a specific city?

But what about classic search, like intranet search or Google search? CanĀ“t Google Search already do this?

Neither of these are good at managing these types of questions, they are good at relevancy, but not in understanding the meaning of the text.

Try imagining the result of this question in a Google search or intranet search:

“Show me the 10 most positive reviews of hotels in Stockholm that are close to the Royal Castle”.

If a service like Watson Natural Language Understanding have enriched all those reviews, an answer is returned in a wiff with ranking and everything.

Other examples can be:

  • “Which of our products are mentioned in negative terms the last week that is related to our new shop in Amsterdam”
  • “Which of our products are most mentioned in business-related articles “
  • “Which brands are most related to our product X”
  • “How does our product compare against our competitor X in terms of sentiment in the financial sector”

What about measuring mentions in Social Media etc?

It is also important to note that a simple question like “Which brands are most related to our product X” is not always a simple search for mentions of the two entities (brands and product name), but two things are different from the “dumb” keyword search.

  1. Is the product and the brand actually related to each other in the text?
  2. Is the product or brand really a product name or brand name? Let us take two Swedish companies as examples, Ericsson and IKEA and use the following two sentences as examples:

    “The politician Peter Ericsson and Ivar Andersson are traveling to Kivik on Tuesday for meetings”.

    “Ericsson has signed a new agreement with IKEA to implement their technology in the Kivik product range starting 2019”.

    You can replace Ericsson with Apple or many other companies, same frustration. The IKEA product can be replaced with most companies products since many have names that already exist.

What a natural language service does is that it understand that the first example with Peter Ericsson is not the company Ericsson, but a person. It also understands that Kivik is a city and not a product. Therefore it does not show up in a cognitive query, but would most certainly show up in a classic social media mention search.

In the other sentance, it understands that Ericsson is a company and Kivik is a product, that is Natural Language understanding and it will have a great impact on the value we get from information.

Integrate cognitive enriched information in your business applications

Now lets put this in a wider perspective where this information is combined with other sources of information like already existing solutions like structured data (BI / Analytics etc). Most companies can predict their product sales, but uncertain things always appear and most of the time it is hard to know why, at least prove the gut feeling you have, with combining enriched unstructured data with existing structured data new insights surface.

Example: Suddenly and unexpectedly sales drop for a product, this without any logical explanation. Predictive analytics can only use existing structured data to present predictions from, the unexpected is hard to predict and to find an answer to. This no more. By using customer service conversations and external sources like weather, news, social media etc we can suddenly see that the product is mentioned in negative terms and that it is due to a new feature that was launched a few months ago, but due to the change in weather seasons, the reaction did not surface until now. This information was found in customer service conversations as well as in social media information and weather data and when combined a the root-cause was found and the product could be fixed and sales returned.

It might sound like a far-fetched scenario, but did not want to make it too simple since most companies are complex and want to draw insight from the reality not only a simple “What products are mentioned in negative terms on Twitter”, but rather put it in context and connected with sales and the “why” question as well. The example above surfaces actionable insight that was not possible before.

Product Information Cognitive Enrichment

Product information tends to be very static and does not always match how the customers refer to the product. Often product data has internal terms and a lot of unstructured data is not possible to find.

Let us say you are looking for a yellow chair from a furniture store. We are heading to Google and enter a search for a yellow chair for bedrooms. I have tried this search with a few furniture companies and it is very similar, the result is that high in the ranking you first get the chair category and then the bedroom category (or the other way around). When you click a link and move on to the site it is often not the yellow chair you find, but a category with 30 pages of chairs or similar, somewhere we have lost the connection to the bedroom as well as the colour yellow.

I know all companies product data is different as well as how they work with Google, but if you work at a large company that sells products, I think you can relate to the challenge.

What I discovered when I tried several alternatives is that Pinterest surface at the top for these types of searches on Google, why?

Simple, it is because Pinterest has user-created product information. To put this in context for this post, the information on Pinterest is by default already cognitive enriched information, the product description and data is from humans, not a product database with only structured data.

So, if our product information would have been enriched with cognitive capabilities, how would that look.

Product data that is enriched with cognitive capabilities would connect the customer with the right product in one click instead of endless scrolling and headache.

Since this enriched product info know that the chair in the image is yelly and also know which chairs that is well suited for bedrooms, it is simple query to answer.

The result is higher conversation, more sales and happier customers.

Nice and dandy, absolutely, but is it really that simple? Overall I would say yes! If we break it down there are a few actions that is needed.

  1. Enrich existing product information with cognitive information from unstructured data such as editorial content, product images and existing product information. Could also be combined with behavioral data from Google Analytics or similar.
  2. To gain full value subject matter experts (in this case probably interior designers and product salespeople or similar) need to train a Machine Learning algorithm that understands what furniture that fits with others and also what colors that match other colors. We need to teach the algorithm to act as a designer. This to replicate the inspirational feeling we get when moving into a furniture store and not to put a pink toilet brush by the bedside.
  3. Access to data. Sounds easy, but can be a challenge.

Natural Language in Customer Relations

To enrich all customer interactions we have is also a great example where natural language creates value. Which customer service ticket should I start with, is the customer angry or happy (or angry when he / she started and happy when the ticket is closed), is it a critical business problem, is it regarding a high priority product etc etc. Is the answer already existing in our knowledge base so we can speed up the amount of time utilized on the ticket?

And equally important, all the information that was previously hidden is now available for us to use in dashboards for insights, customer satisfaction (without surveys!), product problems, time saved etc.

To enrich unstructured information with cognitive capabilities is of value for most companies and I dare to state that every single company can benefit from investigating how it can benefit your organisation.

So, lets start to empower your organisation with the real value in your unstrucutred information.


Top image from my favorite coffeeshop, Koppi in Helsingborg