Category: SearchListening

Why Search Listening?

The Neuroscience of Everybody’s Favorite Topic byĀ Scientific AmericanĀ researched that our favorite communication topic isĀ ourselves because it feels good. There is a better way to gather information – called Search Listening.

The traces of information that billions of people leave on Google, social media, dating, and even pornography sitesā€“finally reveal the truth. As Seth Stephens Davidowitz showed in Everybody Lies,Ā we can learn what people think and what they do by monitoring their behavior. These predictions based on Google Search volume have outperformed any research institute in 2016 and again in 2020.

Start understanding the stage of the funnel.

Search Listening Social Listening Funnel Reverse Engineer Innovation

Listening to Search Engines

Thanks to AI, Cloud Computing, and Superfast Networks, competitive monitoring is the new norm. Search listening can map keywords to need states, which tells us more than simple keyword lists and search volumes. We use the language that reflects how customers speak and generate multiple reports around long-tail, more editorial, or conversational searches.Ā 

When and where someone is looking for information, social support, inspiration, hard facts, or help tells us what they think and who they think is qualified as a trusted source. We use anonymous and aggregate data, not any individual data. Brand names, stock market prices, and shareholders’ information (Cap Tables) are public information. The same accounts for product names, price lists, and ratings in the shops. We find these insights in specialist forums and investment networksā€”no need for stalking or stealing private data. You have to know where to look for it and how to understand the data impact. And when you start analyzing this data, you get a different picture.

Search like itĀ“s 2021 - not 1999

For a long time, we have been using searching engines like this:
šŸ’šŸ½ You type in the first query, the Google Algorithm does it’s little magic, and you get a response. If you don’t like the outcome, you start the same process. You type in a second independent revised query. Again the Google algorithm does it’s little magic, and you get a new response. You can play this for a while. It’s time-consuming.

šŸ’šŸ½ Now, let the Google algorithm learn from the first query ā€“ so it’s becoming a compound query, meaning it combines more than one component query into a single SQL statement via a set operator. In our first example, let the algorithm know that we are on a smartphone with GPS enabled. That’s not a big deal in 2020. Around 2 billion people can do that and immediately understand that the dentist’s address has geo-information, which can be read by google maps.

šŸ“²Your digital assistant, formerly known as your smartphone, has a built-in superpower. It can take photos and has sensors; it knows if you are walking, running, or driving. It knows where it is, where you have been, who your friends are, and that you have an active UBER account. The system managed the complexity in the background. The user experience is seamless and takes just one minute ItĀ“s a Game Changer in B2B and B2C. šŸš€ Ā 

Start listening to implicit signals.

We can combine multiple data sources and identify implicit signals. Here comes an example of an implicit indication. If IĀ search for a specific category or brand, you learn a lot about my brand awareness,Ā my attitude, and my buying intent.Ā 

Technical Data can be an important signal and indicate a business impact. For example: maybe you heard of drones and small satellites monitoring physical car traffic patterns. They can tell you how many cars leave the factory or how many vehicles use the Tesla Supercharger Network, including the time of day and frequency. You can learn about Tesla’s šŸš• adoption rates, charging interval, and you can analyze these insights over time and compare different stations of the Tesla Supercharger Network.Ā 

Improve your target market sizing

By understanding what people really think, not what they say they think, you will be able to reshape your value proposition, distribution strategy, and partner strategy for the new region or vertical.

Target Market Sizing is key for business and marketing planning, and budgeting for all startups, especially when seeking third-party financing. Diligent preparation makes sure, that you go-to-market in the right niche. Target Market Sizing quantifies the financial potential of your business in money terms or consumption units. Target Market Sizing helps you refine business model canvas hypotheses. Especially in an international context , you can recalibrate your assumptions. Expanding internationally comes with its own unique set of obstacles. Expect language and cultural barriers. You will have to understand the different ways people communicate. Different tax codes, business regulations, and packaging standards in different countries.

Discover User Groups and Reasons for Buying 1
Discover User Groups and Reasons for Buying 2

Market Size Estimates

Total Addressable Market = Entire chain of buyer/seller relationships. Value of the relationships at the point of consumption.

We use two methods to determine the total addressable market. First, a top-down analysis relies on secondary market research such as market analysis reports to determine how many end-users meet your different characteristics. Then, bottom-up analysis to identify how many customers there are, as well as how many end-users each customer has.

Served Addressable Market = The part of the Total Addressable Market for which your business modelā€™s value proposition is strongest. The served addressable market is more clearly defined as that market opportunity that exists within a firmā€™s existing core competencies and/or past performance. A firm most likely can only service markets that are core or directly adjacent to its current customer base.

Target Market. The part of the Served Addressable Market segment with the most direct path to success. A segment of people considered likely to buy a product or service. A target market consists of customers that share the same buying motives and often as well follow similar characteristics, such as age, location, income, and lifestyle, to which a business directs its marketing.

Is there a correlation between racists and Trump voters?

You may not believe what the unbiased perspectives of millions of people can tell us. Search Listening is less subjective to unreliable responses, which can happen in surveys, or the desire to project a specific image rather than reality, which is a risk with social data.

Everybody Lies Image Search Volume and Trump Votes

Welcome to the world of Atlas Solutions and others

The mobile advertising ecosystem has experienced rapid growth in the past few years. The most important drivers are mobile devices and carrier networks’ technological evolution, enabling faster mobile bandwidth at flat rates. With the proliferation of smarter mobile touch screen devices and an open ecosystem of applications, mobile advertising connectsĀ billions of ad requests to ad spaces within milliseconds. More client cases about gathering insights in a world of ubiquitous computingĀ and Q&A.

Facebook has put the focus on Atlas' ability to serve ad campaigns, but also measure digital-ad performance in a way that goes beyond what traditional cookie-based ad measurement can deliver across devices.

Business Insider

Everybody Lies – Google Knows

Big Data, New Data, and What the Internet Can Tell Us About Who We Really Are from Seth Stephens-Davidowitz.

Everybody Lies offers fascinating, surprising, and sometimes laugh-out-loud insights into everything from economics to ethics to sports to race to sex, gender, and more, all drawn from the world of big data. Google knows more.Ā 

  • What percentage of white voters didn’t vote for Barack Obama because he’s black?
  • Do violent films affect the crime rate? Can you beat the stock market?
  • Who is more self-conscious about sex, men or women?
  • Do parents secretly favor boy children over girls?
Book Cover Everybody Lies

With conclusions ranging from strange-but-true to thought-provoking to disturbing, he explores the power of this digital truth serum and its deeper potential – revealing biases deeply embedded within us, the information we can use to change our culture, and the questions we’re afraid to ask that might be essential to our health – both emotional and physical.Ā 

All of us are touched by big data every day, and its influence is multiplying. Everybody Lies challenges us to think differently about how we see it and the world.

Ā©2017 Seth Stephens-Davidowitz (P)2017 HarperCollins Publishers

People have no incentive to tell surveys the truth.

Research Findings show: The more impersonal the conditions, the more honest people will be. For eliciting truthful answers, internet surveys are better than phone surveys, which are better than in-person surveys. People will admit more if they are alone than if others are in the room with them. However, on sensitive topics, every survey method will elicit substantial misreporting.

Search Listening monitors the traces of information that billions of people leave on Google and social media sites. Technology gives us a much better picture of what people think and what they do by monitoring their behavior. ItĀ“s finally David BowieĀ“s predictionĀ  ā€œAlien Life form, that just landedā€. And itĀ“s the wakeup call for Digital Healthcare and Predictive Medicining.

Predictive Healthcare and Digital Services

What does Predictive Healthcare mean?

The rapid aging demographic directly affect health outcomes for these growing economies. We need to adjust healthcare delivery pathways and financial resource allocation among competing groups of people or programs.

How resource allocation in Healthcare should be


Reactive Healthcare

  1. Seek health services when feeling ill
  2. Consumer sorts through different care options
  3. Data is then captured to confirm the diagnosis

Proactive Healthcare

  1. HealthData is captured via medical-grade wearables
  2. Care option reaches out if there is an anomaly
  3. Provider already has the historical dataset of relevant biomarkers and genetic predisposition

Google and Apple are into Healthcare.

Google made another significant move into health research on Wednesday, unveiling a new smartphone app, called Google Health Studies, that lets Android users participate in medical studies remotely. The first study run through the app, called Google Health Studies, will look at respiratory illnesses like the flu and COVID-19. The Google Health Studies app isĀ now available in the šŸ‘‰Ā  Google Play Store.

The app will collect demographic data, like age, gender, and race as well. ā€œResearchers in this study can examine trends to understand the link between mobility (such as the number of daily trips a person makes outside the home) and the spread of COVID-19,ā€ Google wrote in aĀ press release.

The app will send data to researchers using a technique called federated learning, which will batch aggregated trends from multiple devices rather than pull information from each participant individually.

Health Studies is Googleā€™s answer to šŸ‘‰Ā Appleā€™sĀ Research app, which runs on iOS devices. Last year, it launchedĀ studiesĀ on menstrual cycles, mobility and heart health, and hearing. Apple also lets researchers build their own iPhone apps through itsĀ ResearchKit program.

Amazon is in Healthcare.

Voice Services are easy to use and reach users with limited technical understanding. Two major health systems are using Alexa in healthcare today and their predictions for voice assistants future. Machine Learning and Natural Language Processing are the next frontiers in proactive healthcare.

We can place sensors at any relevant touchpoint where the consumers interface with the health system. Their signals can teach an AI to spot signs of disease and trigger automated services. Wearables and scoring will be crucial for service authorization. Either as two-factor authentification for users and secure administration rights. This technology-assisted guidance will immensely help the elderly, who need more assistance and otherwise are left alone. According to research, 166 Mio use voice search on their smartphone, wherefrom 48,3 Mio use voice search daily. 129,7 Mio use voice search in the car, wherefrom 29,7 Mio use voice search daily. 87,7 Mio use voice search on a smart speaker, wherefrom 43,7 Mio use voice search daily.

older adult tech adoption 2017 versus 2019

Please check our blog for more thoughts onĀ Cloud Computing and AI – and get in contact if you want to know more about the potential of competitive monitoring and our onboarding process.

Scoring Methodology

Overview and Terminology

Smart-home devices haveĀ fourĀ mainĀ components:
  1. The deviceĀ – the hardware purchased.
  2. The mobile applicationĀ – the companion mobile application that interacts with the device (Android or iOS application.)
  3. Cloud endpointsĀ – Internet services that the device or the mobile application communicate with.
  4. Network communicationĀ – Network traffic between each component (local and Internet traffic).
Each of these components has properties that are used as features to compute a score. The score includes the own core software and partner services. Furthermore, the whole stack needs to reflect the applicable law in the European Union. We can support you with learnings from client cases. Please check our onboarding process and schedule a private meeting.Ā 
Device properties
The device properties the following:
  1. Internet Pairing – The configuration of network credentials to connect the device to the Internet.
  2. Configuration – The device configuration during setup phase, creating an account, setting up preferences, etc..
  3. Upgradability – The deviceā€™s upgrade options. Does the device update automatically or requires userā€™s interaction?
  4. Exposed services – Visible running services on the device, like UPnP, mDNS, HTTP server, etc..
  5. Vulnerabilities – Running services on the device that contain vulnerabilities, which are scored based onĀ CVSS.
Mobile Application properties
The mobile application properties are based on static analysis to identify three types of security issues.
  1. Sensitive Data – Sensitive data includes artifacts like API keys, passwords, and cryptographic keys that are hard-coded into the application.
  2. Programming Issues – Implementation errors and incorrect use of libraries include weak initialization vectors in cryptographic functions or guessable seeds to pseudorandom number generators.
  3. Over-privileged – Mobile applications request excess permissions that are not required or used in the application code.
Cloud endpoints
The cloud endpoint properties are based on the assessment of services that the device and/or the mobile application communicate with. There are three properties to inspect:
  1. Domain categories – Domain categories defines three main categories, namely first-party, third-party, and hybrid. First-party domains are endpoints that are owned and managed by the vendor of the product. Third-party domains are endpoints that use external services like Google Maps. Hybrid domains are endpoints that are run on cloud infrastructure like Amazon or Azure, but managed by the vendor of the device.
  2. TLS configuration – TLS configuration refers to the proper setup of TLS/SSL including the use of a trusted and valid certificates along with avoiding legacy versions of TLS/SSL with known vulnerabilities.
  3. Vulnerable services – The deployment of vulnerable services on the cloud endpoint includes the use of cleartext authentication, misconfigured services, exploitable services, or use of unsupported legacy operating systems as the host for the cloud endpoint.
Network Communication
The network communication properties are based on the observed network traffic between the three components, which are the smart-home device, the mobile application, and the cloud endpoint. There are three areas to inspect:
  1. Protocols – The use of third-party DNS, HTTP, UPnP, NTPv3, or custom protocols are considered under the protocol category. These protocols have security implications shown under the attack scenario section.
  2. Susceptibility to man-in-the-middle (MITM) attack – Identifies whether the communication between device-to-cloud, mobile application-to-cloud, or mobile application-to-device can be MITM attacked.
  3. Use of Encryption – Identifies whether the communication between device-to-cloud, mobile application-to-cloud, or mobile application-to-device uses or lacks encryption.


Threat Model

Attacker Types
The threat model assumes a network-based attacker ranked based on the following variations (high to low risk):
  1. Off-path attacker (Internet)
    1. The off-path attacker does not require direct access to the network where devices are deployed and can use n-day vulnerabilities or known flaws to mass exploit devices. These type of attackers are the most dangerous because of their capability of mass exploitation of vulnerable devices, mobile applications, cloud endpoints, and network protocols. (Internet)
  2. On-path attacker (Local Network)
    1. The on-path attacker is an attacker who has a presence on the network that the devices are deployed and can carry out direct attacks.
  3. Geographically proximity attacker (Next Door Neighbor)
    1. Geographically proximity attacker is an attacker whos physical presence is near to the deployed device and can carry out attacks against the initial device setup or using low-energy medium, such as Bluetooth, Zigbee, or ZWave.
Attack Examples
  1. Device
    1. Internet Pairing – A nearby attacker (type 3) hijacks the configuration setup over insecure Wifi or low-energy (Bluetooth, Zigbee, ZWave) protocols. A device requiring manual input of credentials or wired Internet connection is more secure.
    2. Configuration – An attacker (type 1 or 2) knows about weak device default configurations and uses this information to attack the device. A device that requires configuration before operating is more secure.
    3. Upgradability – An attacker (type 1, 2, or 3) can target vulnerable outdated devices that require manual or consent based upgrades. A device that automatically updates is more secure.
    4. Exposed Services – An attacker (type 1 or 2) has a bigger attack surface against a device with many running services. A device that uses a client model that runs no services is more secure.
    5. Vulnerabilities (CVSS) – An attacker can use one or many vulnerabilities in the device to expose sensitive information or gain control of the device. The vulnerabilities are in four categories (low, medium, high, and critical). The critical category means the device has an active vulnerability that has been exploited. The high category means the device has a serious vulnerability but hasĀ notĀ been exploited yet. The medium category means that the device has misconfiguration issues that could lead to information disclosure or device compromise. The low category means the device has minor issues like running legacy protocols or debug reporting is turned on.
  2. Mobile Application
    1. Sensitive Data – An attacker (type 1, 2, or 3) can extract private APIs or secret keys to gain privilege on a device or a cloud endpoint. A mobile application that encrypts and stores its sensitive data is more secure.
    2. Programming Issues – An attacker (type 1, 2, or 3) can exploit incorrect initialization of a cryptographic protocol to disclose sensitive information. A mobile application that adheres to correct practices is more secure.
    3. Excess Permissions – An attacker (type 2) can utilize excess permissions to disclose sensitive data about the end-users. A mobile application that requests only needed permissions is more secure.
  3. Cloud Endpoints
    1. Domain Categories – An attacker has a larger attack surface as the number of endpoints increase. Additionally, the risk of exposing private information or having privacy implications is higher when vendors use third-party resources. A large number of first-party endpoints increases the attack surface and expose the device to higher risk. Hybrid cloud endpoints run the risk of exposing user information to cloud providers. Additionally, they can suffer from outages that the vendor cannot control. Third-party cloud endpoints increase the risk of privacy implication. The more parties involved the higher risk of privacy implication.
    2. TLS/SSL Issues – An attacker can exploit weaknesses in public-key infrastructure-based communication like TLS/SSL to snoop or compromise the integrity of the communication. Self-signed certificates can risk impersonation by an attacker, especially if the endpoints do not implement certificate pinning. Name mismatch on a certificate indicates the incorrect configuration of TLS/SSL services, which can be exploited by an attacker. Vulnerable versions of TLS/SSL can leak information about the encrypted content, which can be used by an attacker to snoop or modify the communication between two parties.
    3. Vulnerable Services – An attacker can exploit vulnerable services on the cloud to gain control over smart-home devices remotely or infer sensitive information. Old unsupported operating systems (OS) can suffer from vulnerabilities that developers no longer support, which leaves the cloud endpoint exposed to attackers. Information disclosure from misconfigured cloud services can leak sensitive information about the services that will help attackers in crafting an effective attack. Cleartext authentication can be snooped by attackers (type 1 and 2) on the network and used to gain unauthorized access. Exploitable services can be targeted by attackers to gain unauthorized access to a cloud endpoint and control smart-home devices.
  4. Network Communication
    1. Third-party DNS – A third-party DNS provider can infer usage patterns and cause privacy implications to end-users. Devices that use local DNS services can be configured securely and end-users gain more control.
    2. HTTP – An attacker can snoop and actively modify HTTP connections since they do not offer integrity or confidentiality. Components that use HTTPS are more secure.
    3. UPnP – An attacker (type 2) can issue commands and control devices that use UPnP because UPnP does not provide authentication. Devices that opt-out of UPnP and use alternative controls (via HTTPS) are more secure.
    4. NTPv3 – An attacker (type 2) can modify NTP version 3 or lower protocol responses, which can break certificate-based security. Devices that use NTPv4 are more secure.
    5. Custom – Custom protocols are non-standard and can be weak based on an overlooked flaw. Relying on security by obscurity is a bad practice. Devices that use community-vetted and standardized protocols are more secure.
    6. Man-in-The-Middle Attack – An attacker can intercept communication between smart-home device components and modify the content, which could result in a compromise. Components that verify endpoints and pin certificates are more secure.
    7. Encryption – An attacker can snoop on communication between smart-home components and infer sensitive information. Components that use encryption across all of their external communication are more secure.


Scoring Rubric

The scoring rubric outlines the weight distribution per property for each component. These can be reconfigured to emphasize important components across each deployment and their environment.
DeviceĀ (42 Points)
The device component score is out of 42 points. The scoring system is inverted so that higher scores signify worst grade and lower scores signify better grade.
  1. Internet PairingĀ (3 points)
    1. Internet pairing refers to the device setup where local-network credentials are passed to the device to connect to the Internet. The 3 points represent the following from high risk to low risk:
      1. Wifi – device broadcasts unsecured wifi to allow users to connect and configureĀ (3 points)
      2. Low-energy (LE) – device uses LE protocol to pair with mobile to device to configureĀ (2 points)
      3. Wired – device uses a wired medium to directly connect to the local networkĀ (1 point)
      4. Manual – device requires users to manually input network credentials to connect and configure the deviceĀ (0 points)
  2. ConfigurationĀ (7 points)
    1. Configuration refers to the device setup phase where the device requires to be configured before operating or default configurations are acceptable.
      1. Default configurationĀ (7 points)
      2. Forced configurationĀ (0 points)
  3. UpgradabilityĀ (4 points)
    1. Upgradability examins if the device requires manual, consent-based, or automatic updates. The 4 points represent the following from high to low risk:
      1. ManualĀ (4 points)
      2. ConsentĀ (1 point)
      3. AutomaticĀ (0 points)
  4. Exposed servicesĀ (4 points)
    1. Exposed services are services that run on the device and can be accessed directly from the local network. The 4 point bin distributions are the following from high to low risk:
      1. 5 or more servicesĀ (4 points)
      2. 3-4 servicesĀ (3 points)
      3. 1-2 servicesĀ (2 points)
      4. No servicesĀ (0 points)
  5. VulnerabilitiesĀ (24 points)
    1. Vulnerabilities are scored based on CVSS categories of low, medium, high, and critical. For each category, the scores are in the bins of 1-5, 6-10, and 11 or more. The point bin distribution is the following from high to low risk:
      1. Critical – 11 or moreĀ (10 points), 6-10Ā (9 points), 1-5Ā (8 points)
      2. High – 11 or moreĀ (7 points), 6-10Ā (6 points), 1-5Ā (5 points)
      3. Medium – 11 or moreĀ (4 points), 6-10Ā (3 points), 1-5Ā (2 points)
      4. Low – 11 or moreĀ (3 points), 6-10Ā (2 points), 1-5Ā (1 points)
MobileĀ (13 points)
The mobile component score is out of 13 points. The scoring system is inverted so that higher scores signify worst grade and lower scores signify better grade.
  1. Sensitive DataĀ (6 points)
    1. Sensitive data are counted per piece of information and their bin distributions are the following from high to low risk:
      1. 6 or moreĀ (6 points)
      2. 3-5Ā (5 points)
      3. 1-2Ā (4 points)
  2. Programming IssuesĀ (4 points)
    1. Programming issues refer to any incorrect implementation or use of libraries.
  3. Over-privilegedĀ (3 points)
CloudĀ (92 points)
The cloud component score is out of 92 points. The scoring system is inverted so that higher scores signify worst grade and lower scores signify better grade.
  1. Domain CategorizationĀ (12 points)
    1. Domain categorization is divided into three categories and their bin distributions are the following ranked from high to low risk:
      1. Third-party domains – 76 or moreĀ (5 points), 26-75Ā (4 points), 1-25Ā (3 points)
      2. Hybrid domains – 46 or moreĀ (4 points), 16-45Ā (3 points), 1-15Ā (2 points)
      3. First-party – – 46 or moreĀ (3 points), 16-45Ā (2 points), 1-15Ā (1 points)
  2. TLS ConfigurationĀ (30 points)
    1. TLS configuration scores the certificate, ciphers, and key-exchange algorithms used by the TLS service. The scores are in three categories and they are the following ranked from high to low risk:
      1. Self-signed certificateĀ (10 points)
      2. Name-mismatch certificateĀ (10 points)
      3. Vulnerable ciphers and KEAĀ (10 points)
  3. ServicesĀ (50 points)
    1. Services on the cloud endpoints are scored based on the weaknesses in the following 4 categories ranked from high to low risk:
      1. Exploitable serviceĀ (14 points)
      2. Cleartext authenticationĀ (13 points)
      3. Information disclosureĀ (12 points)
      4. Unsupported OSĀ (11 points)
Network (28 points)
The network component score is out of 28 points. The scoring system is inverted so that higher scores signify worst grade and lower scores signify better grade.
  1. ProtocolsĀ (8 points)
    1. Protocols are graded based on use in the following five categories ranked from high to low risk:
      1. Use of 3rd-party DNSĀ (2 points)
      2. Use of non-standard custom protocolĀ (2 points)
      3. Use of UPnPĀ (2 points)
      4. Use of HTTPĀ (1 point)Ā ** HTTPS!=HTTP
      5. Use of NTPv3Ā (1 point)Ā ** using version 3 or below.
  2. MITM AttackĀ (10 points)
    1. MITM attack scores are based on the communication between three network directions, device-to-cloud, mobile app-to-cloud, and mobile app-to-device. The scores are given if the communication was successfully MITMā€™d during the evaluation. They are ranked from high to low risk:
      1. Device-to-CloudĀ (4 points)
      2. Mobile Application-to-CloudĀ (4 points)
      3. Mobile Application-to-DeviceĀ (2 points)
  3. Network Encryption
    1. Encryption is scored on three categories and three score distribution. The three score distribution is no encryption, partial encryption, or full encryption and the three communication categories are the following ranked from high to low risk:
      1. Device-to-Cloud noneĀ (4 points), partialĀ (2 points), fullĀ (0 points)
      2. Mobile Application-to-Cloud noneĀ (4 points), partialĀ (2 points), fullĀ (0 points)
      3. Mobile Application-to-Device noneĀ (2 points), partialĀ (1 points), fullĀ (0 points)


Score Calculation

The score calculation is an aggregate of the total points in each category divided by the total possible points. We use an inverse scoring system, where higher scores mean worst security posture. To generate the scores give to each device, we subtract the score fraction from one, which gives us the assigned scores. We use the general cutoffs to assign a grade letter (A – 0.9+, B – 0.8+, etc.). To calculate the score follow these steps:
  1. For each component (device, mobile, cloud, network), sum up the points.
  2. Divide the sum by the total number of possible points for the component category.
  3. Subtract the results from one.
  4. Apply grade assignment based on the letter cutoffs.
Device A:
  1. In the device category it got 7 points
  2. In the mobile category it got 1 point
  3. In the cloud category it got 28 points
  4. In the network category it got 10 points
The scores are the following:
  1. Device score: 1-(7/42) = 0.83333
  2. Mobile score: 1-(1/13) = 0.92308
  3. Cloud score: 1-(28/92) = 0.69565
  4. Network score: 1-(10/28) = 0.64286
The grade assignments are the following:
  1. 0.83333 => 83.33% gets a B
  2. 0.92308 => 92.31% gets an A
  3. 0.69565 => 69.57% gets a D
  4. 0.64286 => 64.29% gets a D

How do vertical search engines get their results?

What search engines need to build their lists

Search Listening is the new superpower, which raises the question: How do these engines work? Search engines follow linksā€‹. Links connect pages and documents, much like roads connect villages and cities. By following these links, search engines collect data to show to their users. But how do they do it?

Search engines consist of three parts: theĀ crawler, theĀ index, and theĀ algorithm. The process begins with a crawler that travels the web through links and pages. The crawler mainly looks forĀ content-headingsĀ andĀ site-structure.Ā 

The crawler goes around the internet 24/7, and when it passes through a website, it saves the HTML version of a page in an enormous database – called theĀ index.Ā 

TheĀ indexĀ is updated every time the crawler comes around again and finds new information. In Google’s case, the crawler revisits your site more or less often depending on how often you change things and how important Google thinks your site is.

How do search engines prioritize and rank?

Search engines use anĀ algorithmĀ for this. TheĀ algorithmĀ takes the data from the index and calculates many factors that predict whether a result will be useful for the searcher.

Like we maintain our websites to say relevant, Google never stops re-evaluating their indexing database and algorithm. While we’re talking about links, both internal links (links from one part of your website to another part of your website) and external links (links from a different website to yours) have a tremendous effect on search engine rankings.

Site-speed,Ā site-security, andĀ quality of contentĀ are essential. But Google makes changes to the importance of all these different factors almost every day. Therefore, crawlers follow all the links on the web that they have access to continuously. This way, they can find new sites to index and change outdated information.

What is special about vertical search?

Vertical search engines use the same process but obviously follow a fundamentally different objective. Like any generic search engine, vertical search engines consist of crawlers, an index, and an algorithm. But vertical search engines are trained to optimize in an extraordinary direction. You cannot find the same level of information in generic search engines as in vertical search engines. But you will find more about vertical search and Searchlistening in the BlogĀ and our Support Section

/ – or here in GermanĀ 


The use of these Internet pages is possible without any indication of personal data; however, if a data subject wants to use special enterprise services via our website, personal data processing could become necessary …Ā