During the last years, customer journeys have become increasingly complex. An example: Brand awareness may start with advertisement in traditional media (e.g. print, radio, TV) or an online campaign, but most likely these channels will have to play together. Consumers may then look for more information by browsing online product reviews, asking friends or consulting a local retailer. While comparing product features and prices, they may discover new brands or alternative products and start their journey all over again. And at some point they will make a purchase decision, deliberately or with the help of a little nudge. And all this can happen in a local store or an online shop.
This complexity is not just about the vast amount of different touchpoints, it is primarily about the many ways in which they can be combined and connected, to lead consumers down the customer journey. And to make it even more complex, different customer segments may prefer different journeys. Among all the possibilities, there could well be a relatively well-paved path to success for each of them. Therefore, it is no wonder that particularly researchers are expected to give orientation.
This challenge has many facets and data analysis is definitely one of them. Calculating the impact of single experiences on the overall brand perception or purchase likeability in a Key Driver Analysis can become quite complex. Attributing the success of a conversion to a single effort with Multi-Touch Attribution models can start to feel like an art rather than a scientific method. Even though these questions also deserve some attention, in this article we’ll focus on the challenges for data collectors, like Norstat.
The first challenge consists in getting an overview over all relevant touchpoints. These may be completely under the control of the brand (e.g. website, outlet stores), managed by the brand even though the medium is not owned (e.g. advertisement), but also completely out of a brand’s control (e.g. word of mouth). Even for medium-sized brands, the amount of different touchpoints can be somewhere in the hundreds and this makes it really hard to get a comprehensive overview.
The second challenge consists in asking ourselves how we could collect data about each of these touchpoints. Possible options are either asking people (active data) or measuring / observing (passive data). And it’s not at all clear which option is the better. You definitely have to ask, if you can’t measure the interaction or engagement with a certain touchpoint. However, asking has some downsides especially when it comes to perceptions below a certain threshold of attention that still affect the subconscious of consumers. But also measuring has its downsides. Very often, passive data is too superficial and doesn’t reveal anything about the emotional engagement with a touchpoint. Knowing from geo-data that someone has been next to a billboard doesn’t mean the billboard has been perceived.
And that brings us to the next challenge: How can we get access to as many data points as possible for a single customer journey? You may use Cookie tracking to monitor the activities of a visitor on your website or measure the amount of ads a consumer has been exposed to. You may track the comments of consumers on Social Media and perform a sentiment analysis on whether they are talking positively about your brand or complaining. You may trigger a short online survey after each purchase has been made on your website and ask about the customer’s experience. Whatever you do, there is probably no “one-size-fits-all”-approach and you have to become a bit creative to get all data collected.
But even if you manage to get everything together, you’re running into the next challenge: How can you connect all these dots to get to see the bigger picture? This is where our Integrated Data Services come into play. In the simplest case, you are able to collect data about every touchpoint for each individual in your study. This would allow you to perform a plain analysis. Unfortunately, it is rarely the case to have such a comprehensive data set. Most often, you’ll have some data for each touchpoint and overlapping samples of different touchpoints, but, apart from that, mainly missing values and data gaps. Modern statistical procedures will now allow you to estimate these blind spots from the known connections (e.g. Lookalike Audiences), but will also set requirements for data collections: You’ll have to have enough cases in these sample overlaps to draw the inference on the general customer.
Last but not least, we’re always collecting data for those who analyse and work it. This means all data has to meet some additional requirements in data analysis, especially finding a common KPI for all touchpoints to make them comparable. Given all the challenges above, this can become really tough. Other projects expected us to deal with different streams of live data and make all information available in an online dashboard. So depending on what the data should be used for, the last challenge consists in preparing all data for its users.
Taken all the points above into consideration, it should have become clear that research into customer journeys may turn out to be very different for different brands, different customer segments and different media environments. It’s probably not possible to map a path that works for everyone. This is why our project managers will be happy to discuss an individual solutions with you that exactly meets your requirements. Get in touch with us and learn, what we can do for you.