“There’s no reason why the simple shapes of stories can’t be fed into computers.”
I don’t believe in market research. I don’t believe in marketing the way it’s done in America. The American way of marketing is to answer to the wants of the customer instead of answering to the needs of the customer. The purpose of marketing should be to find needs — not to find wants.
People do not know what they want. They barely know what they need, but they definitely do not know what they want. They’re conditioned by the limited imagination of what is possible. … Most of the time, focus groups are built on the pressure of ignorance.
As people go about their day to day lives they are creating data, always. Changes in body temperature, credit card usage, opening the refrigerator all create data. This data operates in what some have called the ‘data shadow’ – we know the data is there but it’s difficult to grasp on to, like a shadow.
However, in recent years the reduced cost of and increased availability of technology like arudnio & makerbot, as well as the increased digitalisation of data and the pervasiveness of internet connectivity has started to make the data that previously resided in the shadow available to use. Which means that we can now passively collect data on changes in body temperature, credit card usage the opening refrigerators in realtime.
To see if this idea of tapping into the data shadow using readily available technology was feasible and most importantly, could produce something of use a experiment was conducted. The experiment aimed to passively measure the frequency of tea drinking amongst a convince sample of ‘1’ – me.
A Twine from Supermechanical was use to collect the data. A Twine is a electronic box which sensors are connected to it. The Twine then sends the data from the sensor via an wi-fi connection to a website.
To collect data on tea drinking a mag(netic)-switch was connected to the twine. And a magnet was attached to the bottom of a ‘Tannersville General Store’ mug. A receptacle highlighted as the primary tea drinking device used by the participant.
Every time the mug was taken on or off the magswitch, the Twine sent a message to a website, which passed the signal on to another website, which formatted the data and placed in in a Google Spreadsheet for storage and analysis.
Tea drinking data was collected over a 3 day period in November 2012
The summary results are divided into two sections. Mug level – results associated with drinking of the mug of tea as a whole; and Sip level – results related to individual sips of tea. There being multiple sips per mug.
Average number of cups of tea drank per day: 3.3
Average time to drink a cup of tea: 0:36:26
Average number of sips per cup: 11.2
Average time to sip a cup: 0:00:06
The experiment succeed in accessing data that was inaccessible before – e.g. number of sips per mug of tea. Which in turn gave us a accurate description of tea drinking behaviour which would be difficult to obtain by other means.
This approach has the advantage of capturing behavioural data at a level of accuracy and detail which surpasses that of self report data.
However, whilst the approach seems to be very good at capturing what is happening, it tells you very little about why it is happening, and it’s recommended that this be partnered with other approaches. E.g. Using the data from the Twine to help design a qualitative discussion guide – “You drink more tea on a Monday, why is that?” or trigger a mobile survey when the Twine is triggered.
Unlike several of Eugene’s rivals, which put together sentences by imitating people they have spoken to before or by searching through Twitter transcripts for conversational ideas, Veselov has given his bot a consistent and specific personality. “He has created very much a person where Cleverbot is everybody,” says Carpenter.
1. Object (or conclusion): Take the knowledge that we already know about the anomaly we are trying to explain and use this as a starting point. For example, if we are looking to understand why a particular group of people is different from other groups and our previous research says they like cats and tend to live in the US, the machine is programmed with this information (location US and lover of cats) and then pulls all the information we have about cat lovers in the US, from the internet and any other sources we have access to.
2. Relation (or rule): The next step is to link this existing knowledge object to another object, to perform some sort of “logical leap” to connect one idea to another. There are different types of relationships or rules that can be used to link ideas together. We currently focus on a few different sorts of relationships, one of the most basic being word proximity. For example, if we look at all the information related to cat lovers in the US and we find that the noun “knitting” frequently occurs in close proximity to “cats” we can infer that there is some link between ”cats” and “knitting”. There are many other types of logical leaps you can make using different sorts of textual analysis include spotting linking verbs, using dates or other meta-data like tags, introducing elements of randomness, or indeed using a combination of different relation types.
3. Object (or precondition): Using this newly connected knowledge to create a hypothesis. The final part of the puzzle is to analyse the information in away which balances looking at objects/knowledge which have lots of connections and those which are obscure enough to suggest that some form of competitor advantage could be gleaned if the connection and preceding hypotheses were proved to be valid and reliable. You couldn’t rely simply on the strength or frequency of connections between objects to discover potential insights because the objects with the strongest connections tend to point to obvious or common-sense hypotheses, which would prove to be perfectly valid, but wouldn’t provide much a advantage for a business because they are so well known.
If you take a walk around some of London’s more well heeled neighbourhoods you’re likely to run into one of these. It turns out these things are pineapples and they’re everywhere in the capital. Throughout the 18th and 19th century pineapples were rare & expensive. As such, they were added to buildings as symbols of hospitality and wealth.