Stop The Talking Start Listening

Why Search Listening?

Selfish men and women love talking about themselves and expose their social life disturbingly. The outcome is minimal. More truth lies in Listening to Search Engines. People's search for information is, in itself, information. When and where someone searches for facts, quotes, jokes, places, persons, things, or help can tell a lot more about what they really think, really desire, really fear, and do than anyone might have guessed.

Why do people spend the majority of their time talking about themselves?

The Neuroscience of Everybody’s Favorite Topic by Scientific American researched that our favorite communication topic is ourselves. Scientific American summarizes: 

Why do people spend the majority of their time talking about themselves? Because it feels good.

Samantha Boardman, a clinical instructor in psychiatry at Weill-Cornell Medical College, added. “We are social creatures, and by talking to one another, we feel more connected. “Stereotypes lead us to believe that women enjoy chatting more than men. According to science, it’s more nuanced than that. A test conducted to explore social interaction patterns found that women speak only slightly more than men in professional and social settings. Only when the number of people involved in the conversation is less than six. In large groups, men tend to dominate the conversation.

The downside: Who talks more and why is less clear. 

Start listening to Search Engines

When and where someone is looking for information, social support, inspiration, hard facts or help tells us what they really 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 networks. No need for stalking or stealing private data. You just 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.

How Search Engines started.

For a long time we have been using searching engines like this: 

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. 

Now, let the Google algorithm learn from the first query – 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 are able to do that and immediately understand that the dentist´s address has geo-information, that can be read by google maps.  

Your digital assistant, formerly known as your smartphone has built in superpower. It can take photos and has sensors, it knows if you are walking, running, or driving. It knows where it is and where you have been, who your friends are and that you have an active UBER account. This makes the whole operation a piece of cake. The complexity was managed by the system in the background. The user experience is seamless and the whole process can be done in a minute.  

The new model shows implicit signals.

The new model combines multiple data sources. We can identify implicit signals. An example of an implicit signal can be if I search for a specific category or brand. This tells a lot about my brand awareness, my attitude, and even indicates my buying intent. 

Technical Data can be an important signal and indicate a business impact. For example: maybe you heard of drones and even small satellites, which are monitoring physical car traffic patterns. They can tell you how many cars leave the factory, or how many cars uses the Tesla Supercharger Network, including the time of day and frequency. You can learn about the Tesla success (🚕 individual adoption rates, charging interval, and you can analyze these insights over time and compare it between different stations of the Tesla Supercharger Network. No Rocket Science.  🛸

 🖥 Here are some data sources for your inspiration. 🖥

Finally, you understand each stage of the funnel

Search Listening Social Listening Funnel.png

Social Listening connects to the social universe

Mapping Keywords to Need-States

Plain keyword lists and search volumes are not very strategic. We map keywords to need states. We use one or two seed words instead of full sentences to avoid bridge terms.  We use the language that reflects how customers speak and generate multiple reports around long-tail, more editorial, or conversational searches.

Welcome to the world of Atlas Solutions and others

The mobile advertising ecosystem has experienced a rapid growth in the past few years. The most important drivers are the technological evolution of mobile devices and carrier networks enabling faster mobile bandwidth at flat rates. With the proliferation of smarter mobile touch screen devices that are always connected to the internet and an open ecosystem of applications, mobile advertising connects billions of ad requests to ad spaces within milliseconds. 

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

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