Mari Anne Vanella of The Vanella Group spoke on November 18th at Dreamforce 13 on Social Selling.
In her talk Mari Anne emphasises the need for Sellers to catch up and meet the Buyers at the right information stage.
As we know Buyers today rely less and less on reps for product information as they can find nearly everything on you and your competitors online.
It is therefore extremely important for Sellers to use Social Media resources to get to know their prospect better and to position themselves as experts in their field in order to engage the Buyer.
Mari Anne suggest three simple steps that every Seller enact immediately in order to kick start their Social Selling:
- Make sure your LinkedIn profile presents an expert in your field
- Research your prospects before meetings
- Learn how to interpret the information you discover into actionable discussion and discovery points
You can view here Mari Anne’s complete talk:
We are joining Qlikview for a virtual conference on November 19. All online, professionals, business experts and though leaders will discuss how business requirements shift and new developments in data analytics and visualization emerge.
Join us for this conference by registering over at Qlikview.
Ever longed for great quotes to go with your data driven presentation. Here’s a collection of great factoids pulled together by Gartner at a recent symposium from the awesome:
- Google’s ad revenue surpasses the entire U.S. print industry
- More people have mobile access than safe drinking water and electricity
… to the sobering
- 25% of all clicks are paid
- People who drive and text look away from the road for 4.6 seconds
Full list is available here.
Today we’re at Switzerland’s CEO Day, the annual watering hole of the Swiss startup scene. Roughly 500 people here - yeah that’s about the size…
Our co-founder Toni Birrer does a session presenting 30 great tools to develop great online products. Starting with marketing automation, CRM, touching on issue management, bug tracking, product development, hosting and more, he discloses in essence our operating stack here at Squirro.
For easy access find all the tools and a short description here.
Search engines often return multiple variations of the same story when you perform a search. You have to dedicate precious time parsing through them in order to get to what’s pertinent to you.
This is why we have just introduced the Similar Stories feature in Squirro.
Squirro filters out multiple instances of the same story and groups them together as one. Your stream of information is therefore less cluttered and it is easier for you to find what you are interested in.
Watch the video and see how the new Similar Stories feature works.
The new 1.16.0 version of Squirro is now live with great new features and functionalities.
Get your emails directly in Squirro. We created this feature in order to get newsletters directly into your Squirro information stream. You can also use this feature to forward emails from your mailbox and have them available in Squirro too.
Check out the screencast below and see how to include newsletters in Squirro:
We are also adding two new data sources with the latest release: StockTwits and Twitter searches.
With the StockTwits data source you can subscribe to the StockTwits short messages about any stock symbol directly into Squirro. This data source is configured automatically for stock symbols. In the “Add Topic” search you can use the new cashtag syntax to find stock news. For example: $ADBE.
We extended the Twitter data source with Twitter searches. With this you can get real time results for any Twitter search inside Squirro This is a premium data source, please talk to us if you want to know more.
The Squirro tools have been updated as well and you can get the latest version from our partner portal.
Text Analytics for Context Intelligence: How to get more relevant insights from your unstructured data.
by Cesare Allavena
Following last month’s article ‘Unstructured Data – Analytics’ next Frontier’ a lot of questions came on how textual analysis helps making sense of unstructured data.
The question has been around for some years.
Unstructured data represents roughly 70% to 80% of all data available to enterprises. Text analytics and context intelligence technologies such as Squirro allow users to extract meaning from unstructured data.
With today’s abundant computing power and emphasis on algorithms ever more precise statistical approximations are calculated. The resulting patterns are easily worked with in order to discover relationships and analyse unstructured content.
The ability to identify the most relevant information in unstructured data produces tremendous benefits: Cutting down on the amount of time knowledge workers dedicate to finding the intelligence that matters, enabling entirely new levels of decision making.
What is text analytics?
Text analytics is the process of analysing unstructured text, extracting relevant information, and transforming it into structured information that can then be leveraged in various ways. 
Because of the explosion of electronic data available the capacity to extract relevant information from large unstructured data sets becomes increasingly crucial.
Text analytics is based on the extraction of information implicitly contained in collections of documents or similarity-based structuring and visualisation of large sets of texts. 
There are many principles and techniques used in text analytics and here we will focus on two main ones, which are at the core of the Squirro technology.
According to Wikipedia feature selection is: “the process of selecting a subset of relevant features for use in model construction. The central assumption when using a feature selection technique is that the data contains many redundant or irrelevant features. Redundant features are those, which provide no more information than the currently selected features, and irrelevant features provide no useful information in any context. Feature selection techniques are a subset of the more general field of feature extraction.” 
It is a subtask of information extraction that seeks to locate atomic elements in text. It is often associated with entity recognition in which those atomic elements are then classified into predetermined categories. 
The aim of phrase detection is to extract from texts sequences of words, which occur together more times than we would expect co-occurrence due to chance. 
Let’s use the following text to illustrate the three principles and see how they are integrated in Squirro.
“Women’s entrepreneurship has hit a media tipping point. The question is: Is it just a passing media fad that will soon be a blip on the radar screen, or is it actually a real, fundamental economic force that’s reshaping the world? I think it’s safe to say that it’s the latter. Women-owned entities in the formal sector represent approximately 37% of enterprises globally — a market worthy of attention by businesses and policy makers alike.
Entrepreneurial activity creates growth and prosperity — and solutions for social problems. And today’s trends show that women will be a driving force of entrepreneurial growth in the future.” 
By using feature selection and phrase detection Squirro is able to create a “Smart Filter” for the above text in which it identifies the main features of the text and gives them specific weight.
Figure 1 Squirro’s visualisation of feature selection and phrase detection to create a “Smart Filter”
This is graphical representation of the selected features as part of a “Smart Filter”, where the most representative key elements form the basis of the filter and their relationship to each other and to the entire text give a specific weight to each (Figure 2).
Figure 2 The entities and phrases extracted are weighted
Entity extraction is exemplified by the phrase Global Entrepreneurship Monitor (GEM). Where GEM is associated with Global Entrepreneurship Monitor, the atomic element.
These principles contribute to the quality of the “Smart Filter” Squirro develops for any piece of unstructured data.
What are the benefits of text analytics and “Smart Filters”
Reduction of research time:
Using simple key word based search to look for information about a company, a brand or a market requires a lot of time. Searches need to be made in different languages and results need to be parsed for homonyms and duplicates in order to achieve some level of precision.
Instead text analytics provide the toolset to implement a more powerful enterprise search, where you do not have to manually phrase search queries (e.g. using Boolean operators), therefore the time needed to search through unstructured documents is drastically reduced.
These principles permit the creation of “Smart Filters” in Squirro that can be applied to any document to see which ones are the best matches.
By incorporating this technology in Business Intelligence dashboards or in Customer Relationship Management systems (CRM) for example users do not need to make any searches anymore, instead Squirro reads the elements selected in those systems to create “Smart Filters” to deliver within a dashboard or a CRM instance the most relevant information.
Information updates are made in real-time and users get all they need within one workspace, thus saving them up to 90% of time in the search for important insights.
Technologies like Squirro provide an easy to use environment to extract and curate knowledge from unstructured textual sources and deliver relevant insights for your business.
Simplification of processes:
The corollary of research time reduction is the simplification of processes whereby information is gathered and shared within a company and between co-workers.
Text analytics and context intelligence in particular improve the way work is done by providing better information.
Not only the information is delivered in real-time, but by using “Smart Filters” the information is more precise and more relevant.
Text analytics is at the core of Context Intelligence technologies like Squirro. It enables the access to the most relevant elements of unstructured textual data.
It is those elements that permit the understanding of that data and therefore empower knowledge workers to work with it to get better insights.
The consequence for companies is that they can now utilise all the unstructured data they have at hand. Use the insights from that data to have better understanding of the information they generate or consume, therefore having better and more effective decision-making processes.
 Text Analytics for Unstructured Big Data, Judith Hurwitz, Alan Nugent, Fern Halper, and Marcia Kaufman, Big Data for Dummies, http://www.dummies.com/how-to/content/text-analytics-for-unstructured-big-data.html
 Text Mining - Knowledge extraction from unstructured textual data, Martin Rajman and Romaric Besançon, http://liawww.epfl.ch/Publications/Archive/RajmanBesancon98a.pdf
 Phrase detection, Project proposal for Machine Learning course project, Suyash S Shringarpure, http://www.cs.cmu.edu/~epxing/Class/10701-06f/project-reports/shringarpure.pdf
 The Global Rise of Female Entrepreneurs, Jackie VanderBrug, Harvard Business Review, http://blogs.hbr.org/2013/09/global-rise-of-female-entrepreneurs/
Squirro is the leader in Context Intelligence, combining structured and unstructured data to provide the ‘Why’ behind the data. It’s context, which turns data points into a story.
Squirro delivers a personalized, real-time contextual stream directly to your workplace and enables you to curate a self-learning 360° context radar natural to use in any enterprise system. It’s based on Squirro’s digital fingerprint technology connecting personal interests and workflows while learning and refining as user interactions increase.
Achieving this reduces searching time by 90%, significantly reducing cost and allows for better, more effective decision-making.
Key Points about Squirro
- Delivering relevant information to you
Squirro is more specific than search: Simply click and combine existing and new data dimensions based on textual analysis to discovers new insights.
- Natural to use – Integrated in your environment
Squirro seamlessly integrates into your existing environment.
- Spotify your content – Deliver context and knowledge
Squirro creates a living collection that users can work with, analyse and visualize.
- An open platform integrates with your existing systems
Squirro is built as an open platform, easily deployable into your existing setup.
Customer Use Cases
The Market Intelligence App enables anyone to get in real-time an immediate overview of what is happening in a market or at a particular company. The app may be extended to include industry-leading sources such as Bloomberg, D&B, Reuters, etc. aggregated by topic of interest.
View the Market Watch video here.
Your hiring process is complex and you have a hard time identifying the first-in-class candidates?
The Squirro HR watch application enables users match and map CV’s and employee profiles with open positions with simply a few clicks. .
View the HR Watch video here.
The Service Help Desk App connects to internal data sources such as ticketing, help desk, service systems to detect patterns in unstructured textual data. These uncovered new data dimensions are made available to 3rd party systems such as Qlikview for further analysis.
View the Service Watch video here.
Connecting news sources with a financial dashboard allows for a click and point discovery of relevant news and background information on any stock. This app is particularly interesting if sources such as Bloomberg Chats, Reuters feeds, Stocktwits, etc. are connected.
View the Finance Watch video here.
Benefits of Squirro
Squirro’s benefits are:
- Up to 90% year-over-year reduction in time analysts and managers spend searching.
- An average 15% year-over-year improvement of the information cycle time, as compared to a 7% decrease among companies not using any context intelligence tool.
- In the case of applying context intelligence to sales processes improvements of key KPI’s like win rates by up to 14.6%, while shortening the sales cycle times by up to 8.7%
Current implementations consistently show positive returns in less than four months. An existing analysis and decision making capacity is significantly boosted by unlocking the exploration of unstructured data dimensions.
 Destination CRM 2012, Economist 2011, Wright et al., 2006, Cowley et al., 2005., Gartner 2012, Aberdeen Group 2012, CSO Insights 2013, own analysis