By Jaime Veiga Mateos & Joshua Saxon
Studies show that the average consumer is exposed to up to 10,000 brand messages a day. And as marketers are presented with more and more channels to reach their customers, that number is growing rapidly.
By Jackie Lorch
At the ESOMAR Congress in New Orleans in September among other conferences and events in 2016, SSI’s booth in the exhibit hall was more crowded than usual. One reason was that SSI’s Senior Software Developer Chris Stevens was there, demonstrating a virtual reality experience that could well be part of the future of survey research.
By Alexander Shashkin
As we know, people do not always do what they say. This is especially true for online behavior. Together with the fact that people do not remember what they do online, this does not allow us to use traditional research methods to understand how people choose and buy products in the internet.
Passive behavioral data help to overcome this difficulty. More and more researchers have access to it and experiment with different possible applications of such data. Though, there is still a need to conceptualize the use of behavioral data as well as to bring more case of business value for it.
Our experience with tracking data at OMI started almost three years ago when we created large user-centric panel in Russia on the back of our access panel that consists of over 1 000 000 people. Desktop and mobile trackers were voluntary installed by over 30 000 participants. Now this panel is working on EnjoyTracking software, and we have three consecutive years of cross-device history of behavioral data. It includes URLs and search queries (clickstream data) for desktops as well as data from mobile browsers and apps. Clickstream data was enriched by social demographic variables known from the panelists profile.
Before analyzing the case I would like to bring your attention to the ‘building blocks’ that we use for behavioral data analysis (see Table below):
This means that in addition to social demographics, researchers can use behavioral variables (such as site visits, search terms, or apps usage to define the target audience. Along with the behavioral data we can ask specifying research questions to accomplish results in its usual format (ratings, indices etc.).
For example, you need to clarify what sites are popular among mothers with kids of 3-6 y.o. in order to choose web portal for a special project and make recommendations on its content. Then you follow three steps:
- Define target audience as “mothers with 3-6 years old kids”
- Build website top for this audience (by reach).
- Add Affinity Index for the websites
As a result you would have a full image of online behavior of particular audience such (as mothers with kids we had in our example) and where to find them to bring your message more effectively.
When it comes to the TA definition through visited websites and search queries, the most time-consuming task is manual or partly automated classification (building a code-frame) and coding these queries and the content visited during the relevant web sessions.
You can do more complex research studies, building them as a construction set using the ‘LEGO blocks’ described in Table 1. I would like to share two real examples of such studies:
- Digital segmentation and media optimization for a pharmaceutical brand.
- to describe the online audience of certain pharmaceutical product
- to perform digital segmentation
- to optimize online advertising strategy.
The audience of client’s product was defined as people performing searches for related key words (we called it thesaurus). The set of relevant searches was first brainstormed, then we found panelists who actually proceeded these search queries and looked at other relevant searches they performed in the same web-sessions. The audience was segmented according to their searches: for example, behavior of those who searched for the problem was significantly different from those, who searched for the brand. Each behavioral segment was described in terms of owned, paid and earned digital channel usage.
The study also allowed to rank different web resources inside each channel making it possible to optimize the brand’s digital presence, meaning that fully actionable results leading straight to the media planning were actually delivered.
- Path to purchase for a mobile device.
- to understand the strategies consumers use to search and buy mobile devices online. This would allow more targeted communication on particular stages of a sales funnel to the client.
First, we selected people from our user-centric panel who performed relevant search or visited relevant websites during the last six months. We realized that the purchase itself might happen offline. To define fact of offline purchase and offline factors we used qualitative research survey for respondents whose online history we followed.
On the second stage we segmented websites related to the topic into different categories (owned/paid/earned + shops, etc). We tried to understand the share of usage for each category of sites among segments that were relevant to the client: those who purchased online and offline, those who made expensive purchase as well as various social demographic and geographic segments.
We also analyzed path to purchase for the most interesting segments qualitatively (following the steps of the person URL by URL). Such analysis was followed by the series of IDIs to understand the reasons for certain steps in search/purchase process.
To summarize, online behavior tracking is an ultimate way to describe and understand the online audience of a brand or product. Researchers are able to 1) define the ‘internet behavioral profiles’ and consideration sets of the consumers to build digital segmentation, 2) better understand the potential brand or product audience in the Internet, 3) optimize online media strategy. Knowing the general media consumption of a certain audience is important for media planning, but knowing the media consumption around and during the search for brand-relevant information is crucial for understanding of the consumers’ decision-making. Combining behavioral data with survey research and qualitative analysis helps to understand the place of Internet in the purchase journey and help brands in developing successful digital strategies based on facts, not only words.
Alexander Shashkin, PhD in Sociology, is CEO of Online Market Intelligence (OMI).
By Terry Lawlor
There’s a lot of talk about the Internet of Things (IoT) at the moment. As tech gets smaller and smarter, so grows the network of physical objects that communicate and interact with the external environment, logging data about almost anything and everything.
Ericsson predicts that by 2018 the number of IoT connections will outstrip the number of mobile phones in use globally. By Ericsson’s recent estimate, by 2021 there will be: 9 billion mobile subscriptions, 7.7 billion mobile broadband subscriptions used for IoT and 6.3 billion smartphone subscriptions. That’s a tremendous amount of data bouncing around.
What does that mean for insight?
Increasingly these “things” are going to provide ways for companies and Market Researchers to gain insights about consumers, employees, and customer experience. That may be through wearable devices such as health trackers, intelligent products such as smartphones or cars, or fixed location items such as appliances. Certainly there’s been a huge increase in the last year or so of people making use of such devices and there’s been a lot of talk in the market about exactly how businesses can harness the resulting data.
The data received and transmitted by these devices will help businesses to shape their products and provide tailored offerings and better deals. Some of this is already in use, such as car insurers enabling customers to use a device that encourages safer driving practices to reduce premiums.
There’s also Amazon Dash, recently announced to be coming to the UK, which enables consumers to place branded buttons at key locations in their homes so they can quickly order replacement products such as washing detergent. The general expectation is that it will morph into a process that no longer requires the customer’s input, with the order automatically being placed at the right time by the Dash service.
So how does this impact your Market Research, Voice of the Employee or Voice of the Customer program? IoT provides an opportunity to enhance existing research by adding contextual data to direct respondent data. In effect it offers a new window into the needs and satisfaction of customers or respondents with the product or service provided by the brand.
What’s really interesting is that IoT takes the concept of “in-the-moment” responses to a new level. From an insight perspective this provides some interesting opportunities. Firstly, businesses can trigger surveys to respondents based on specific actions taken by a connected device. Secondly, businesses can integrate data collected by the IoT device with survey responses to build up a more holistic view. Longer term, who knows where this may lead. Why not automatically capture my emotional and physical state at the point of sale, using my contactless payment device, without ever asking me a question? Or just make it easy for me to request alerts and/or submit feedback as and when certain conditions are detected?
Making this a reality will bring a multitude of challenges. There will be more and more data sources at the edge of the Internet, generating huge volumes of data, and even bigger data integration challenges. These smart devices need a smart hub, to bring data together and enable the analytics that generate the insights.
Right now, there are a couple of main scenarios for researchers and analysts to get to grips with.
• Objects that collect information: These are smart objects that collect information through their sensors. They share the information to the IoT network that then provides recommendations to the respondents. In this case the IoT user or IoT respondent can accept the recommendations or reject them. An example is car “app-cessories” that can provide collision notifications, fuel consumption information and vehicle health data such as oil pressure, gearbox wear, etc..
• Objects that collect information and take actions in real-time: These objects collect information through their sensors, they analyze the data and are able to take actions in real time. The objects take actions on behalf of the IoT respondent using rules set by that respondent. As well as the expected evolution of Amazon Dash, the connected home is a good example as it contains IoT thermostats or alarms that take real time actions such as turning the heating on or off, or sending an alert to a security service if the alarm detects movements in the property.
The possibilities presented by the IoT seem almost limitless, and no doubt there will be a lot of false starts and wrong turns along the way. What’s clear is that asking questions and then analyzing responses to plan action will be increasingly augmented by a world of devices that can provide a level of insight that was unimaginable a few years ago.
It’s an exciting time. There are so many ifs and buts involved, particularly around things like privacy, but while elements of the Internet of Things may seem a little on the creepy side, I think there’s a lot to look forward to!
Terry Lawlor has the responsibility of all aspects of product management, including strategy development, product definition, and product representation in client and marketing activities. Terry is a seasoned and highly professional enterprise software executive who possesses a wealth of expertise in the Market Research and customer experience markets.