2Know Magazine: Sharing KM Knowledge
2Know: Sharing KM Knowledge
June 2019 - Magazine No. 237
June 2019 - Magazine No. 237
Edition:

Have you ever heard of the term Digital Customer Experience? The term originally referred to the experience (i.e. process) the customer experiences when interacting with a brand's online digital platform. Nowadays, it refers to many types of audiences and digital interfaces.

Many perceive Digital Experience as a one-time event, yet it is actually an experience comprised from a number of 'paths' that the customer takes when interacting with the brand: online purchasing, social media, customer service, etc. These paths define the overall experience as either overall positive or overall negative. In this technological day and age, internet platform managers are investing much effort in deciphering the ultimate Digital Experience. They try to generate emotional connection via the platform, receive feedback and answer their various needs.


In order to attain the ideal Digital Experience, we must get into the users' heads and realize how they work, the reasoning behind their choices and how to make them enjoy their experience so that they wish to repeat it. Accordingly, many insights have been attained due to much research revolving this issue: improving customers' Digital Experience.

So here are 5 important insights I collected. I believe they can help us better understand the customers and what they're looking for:

  1. Get to know the user: get acquainted with the customer to tailor a unique Digital Experience for them. This might sound obvious, but many companies do not sufficiently invest on researching their customers despite the numerous available options.
  2. Key points: find the key points in which your users expressed contentment and/or got stuck. These enjoyable points are probably those that distinguish your business and promote it. Therefore, it is best to focus on them.
  3. Feedback: for you to successfully create a perfect Digital Experience, you must rely on the users and allow them to provide you with feedback. You can now improve the experience according to the customers' requests, which sends them a clear message: we care about you.
  4. Technology/infrastructure: users expect to receive a perfect Digital Experience. For example, even a mere ten second wait for a page to upload can send more than 50% of web-surfers away. Invest in your platform, it is the key to your success.
  5. Retaining the users' overall lifecycle: many platforms focus most resources on sales and marketing. While this does prove effective in expanding their customer circle, it also ignores equally critical stages like customer service and retaining current customers. In other words, Digital Experience isn't merely an initial process intended for newcomers. It is a constant and apparent investment in all customers and all users at all times.

In conclusion, success is measured in investment and care for detail: the more you invest, the more you will be rewarded for your efforts. You must understand that the process isn't complete once you've succeeded providing users with a successful Digital Experience. People constantly change their preferences, trends are being set daily, new technologies are being invented as you read this piece. Therefore, here's my tip: stay updated and keep on trying to improve your users' experience.

 

 

Written By Anat Katzenelnbogen

What is Natural Language Processing?

Understanding human language is a complex subject, since we express ourselves in various ways both orally and in writing. There are hundreds of languages, each with its own syntax, grammar, slang, etc. Furthermore, when writing, we tend to use abbreviating and drop punctuation. Other challenges we might face when analyzing speech include regional accents, mumbling, and terms acquired from other languages.

Natural Language Processing is a branch of Artificial Intelligence that assists computers in understanding, analyzing and interpreting human Language. This branch allows computers to communicate with humans in their own language and assess unstructured data related to language. For example, NLP enables computers to read text, hear speech, interpret it, assess emotion and define which parts of the text is more important.

 

What types of data can be analyzed and reviewed?

Structured data- data organized in orderly format. For example, data in SQL tables with columns and rows. They feature a relation index and can be easily charted.

Semi-structured data- data which isn't stored in a structured database yet features some organizational properties that simplify the analysis. A certain processing can arrange them in a certain context. For example, XML data.

Unstructured data- data which is not organized in a predefined manner or is not stored in a predefined database, from texts excerpted from medical records to texts published on social media channels.

Nowadays, computers analyze more language-based data than humans, considering the vast amount of unstructured data generated on a daily basis.

Automation is critical for text and speech data analysis.

 

What is NLP used for?

NLP technology plays a great role in Artificial intelligence applications, some of which implement machine learning combined with NLP for risk analysis, customer behavior review, Chatbots and fraud detection.

One of the most common uses of NLP is aimed at the various search apps, both audio and textual (for further details, click here). The search engine uses NLP to analyze the query and retrieve the most relevant answers. Users used to have to search by keywords. Nowadays, you can simply state complex sentences that then serve as the basis for your search. When searching by voice, the search questions are said, then recorded and processed by an interface which converts speech into text while filtering background noises, then analyzed for the different phonemes, adapted to words then finally converted to a sentence which becomes query. This query is sent to the system, which in turn sends the appropriate answer. In an age in which people can speak to Siri and Alexa as freely as they speak to one another, many people can benefit greatly from these apps.

These abilities can be used for other applications, too. For example, Facebook uses a tool titled DeepText to analyze posts published on the social network. DeepText is a text analysis engine that can detect positive and negative emotions expressed in written posts, automatically tag and retrieve themes from texts. The objective is to reach insights/conclusions from a text and in some cases even convert it to actual action. An example of this implementation is Chatbots response to a spoken question).

 

Google has its own NLP tools, including:

 

API Natural Language Cloud- this tool enables exposing the structure and meaning of the text. It allows retrieving information on people, locations and events to understand the information transferred through social media and client conversations. This tool allows classifying files by 700 predefined categories.

AutoML Natural Language- a machine learning NLP-oriented tool which enables creating personally adapted models for organizations to categorize, retrieve, and detect different elements in a text.

Here’s how it works:

  1. The user loads the various data into the computer (the more varied the examples, the more accurate the process)
  2. Runs the module adapted to their needs
  3. Assesses the received results. In case the received insights are insufficiently precise, update the module, then review the results again.

Source: https://cloud.google.com/natural-language/#how-automl-natural-language-works 

 

NLP and Knowledge Management

Both AI tools (such as Natural Language Processing) and Knowledge Management tools center around treating knowledge and information as a central component. Both are essentially intertwined. Without reliable knowledge and initial charting of the required templates, the machines would not be able to elaborate, create or implement knowledge optimally. Furthermore, the field of KM has generated the strategic processes that allow organizations to make the knowledge created with their automatic tools optimally accessible to all relevant parties.

 

References
https://machinelearning.co.il/172/deeptext/ 
https://www.sas.com/en_us/insights/analytics/what-is-natural-language-processing-nlp.html 
https://cloud.google.com/natural-language/#how-automl-natural-language-works 
https://curatti.com/artificial-intelligence-knowledge-management/ 

 

 

What is a group? What conditions are necessary for its success? What distinguishes a knowledge development group from other groups according to the above? This piece discusses these matters and attempts to shed some light on them.

Groups have many definitions, yet Ziv & Baharav (2001) attempt to present us with the main ones:

  • Sustained interactions with regular participants aimed at a common, unifying goal
  • Dynamic integrity based on mutual dependency
  • The group is more than the sum of its components
  • People that communicate during a certain time period. This leads to each member being able to contact all other members directly, face to face.

 

Kartrate and Xander (1968) claim that group success depends on meeting some of the following criteria:

  • Frequent interaction
  • Members' self-definition as a group
  • External parties defining the members as a group
  • Shared norms
  • Shared interests
  • Solidarity with others
  • A sense of value gained from the group
  • Aiming at a common goal
  • Sense of uniqueness
  • Typical action towards the environment

 

A knowledge development group must meet these criteria. This group must be as heterogenous as possible, contain organization members from various areas that meet every several weeks on predetermined dates. These meetings must center on determining what knowledge is vital for the organization in order to develop it and make it optimally accessible. Reading these theoretical definitions also brings up the dilemmas that occasionally rise when planning a group for knowledge development, policy setting or professional doctrine formulating purposes:

Yes! It is necessary to spread meetings over elongated periods, rather than hold three-day marathons. This allows interactions and processes to take place over time. This process enables in depth thinking, considering different possibilities and thought development.

Yes! It is vital to dedicate time to understanding the connection between the different members' work in order to understand and define how these different areas relate and affect each other.

No! occasionally inviting guest members isn't necessarily interesting or refreshing. The group pays a price for this lack of constancy in the relationship. Steady, consistent presence enforces the group's development towards attaining its objectives.

 

Yes! For a knowledge product to be professional and meaningful, a group must be valuable to its members. Group members should be proud to identify as such and view the product they wrote as unique in the organizational field. They should recognize the product for its unprecedented contribution.

 

In conclusion, although a knowledge development group is not explicitly focused on developing relationships and dynamics, these relationships nevertheless operate within it and tacitly affect its development and its success. A group director aware of the required settings will handle and improve them while promoting professional discourse, clear writing, the required structure and templates as well as appropriate accessibility. Dynamics and relationships are vital for knowledge development groups as meaningful roots that enable successfully completing tasks and meeting the standards the organization has set for itself.

 

References

 

Yalom, I. D., & Leszcz, M. (Collaborator). (2005). The theory and practice of group psychotherapy (5th ed.). New York, NY, US: Basic Books.

 

 

Written by Rom Knowledgeware
Fax 077-5020772 * Tel 077-5020771/3 * Bar Kochva 23 st., Bnei Brak Postal: 67135