How to create a single customer view and personalize your marketing to an individual customer
Having a clear view of your customer is crucial for delivering personalized advertising to the individual and generating meaningful interactions.
Here are 2 steps businesses should follow to achieve a single customer view:
Web-tracking collects and measures data to understand how visitors interact with a brands website using cookies and tagging. Accurate data is important in understanding the customer journey. Bad data in means bad data out, if clients are to make informed decisions they need the information to be correct. The level of accuracy directly impacts on the effectiveness of the insights and the actions taken so it’s an essential component.
A cookie is a text file which you may download when you visit a site. This file is read by the website when the visitor returns, updating any relevant information about the visitor’s journey and subsequently informing the site which content to present the visitor going forward…
There are many limitations to cookies. As an example, privacy-conscious users may decide to delete their saved cookies, browse incognito or use tracking prevention features that are present in certain browsers, like Brave. These actions will prevent the website from tracking cookies,recognizing visitor behavior and the number of times visitors return correctly, which will then skew the tracking data. Each cookie is specific to that visitor on a particular device and browser. They cannot work or communicate between multiple devices and browsers, and so will be ineffective if visitors jump from device to device or switch browsers. The biggest gap is on mobile devices, as cookies don’t work on mobile. All of this will undermine the effectiveness of cookies in a web-tracking system.
A pixel tag is an image-based mechanism embedded within the website’s HTML code that will allow data to be transmitted to a server when it is loaded by the visitor’s browser; however, the purpose and intention of tags/cookies are similar.
Web-tracking can leave gaps in the user journey with gaps in the data leading to inaccurate reporting which can be misleading for a business. For example, cookies may tell us that there are three different people visiting a website. However, ‘people-based’ or ‘identity-based’ measurement will be able to identify that it is in fact one person visiting the website using three different devices. So how can marketing professionals link the number of visits with each individual to ensure they are delivering better brand experiences to their customers in the midst of data fragmentation?
2) Visitor Stitching
Stitching combines the multiple visits into one profile where we know it is the same person. These ‘visits’ are actually really one person on different devices rather three different people. It’s an incredibly important differentiation. Why should marketing professionals pay for three people when really, it’s only one? Stitching allows brands to gain a better, more accurate understanding of customer journeys so that customers get more relevant content and more personalized experiences.
One of the most common stitching methods is ‘probabilistic stitching’. This relies on pulling data from syndicated pools. These pools store cookies or ID’s that have been bought from companies. Algorithms then make calculations based on probabilities and match up different user ID’s to what they think is likely to be the same person.
While this method is often more accessible to smaller companies because it requires less time and infrastructure to set up, there are obstacles and limitations. Firstly, it is difficult to verify the accuracy of the data or audit it because you don’t have access to the raw data itself. Secondly, in Europe, it is now almost always in breach of the new GDPR regulations.
The other method is ‘deterministic stitching’. This uses information that customers have given permission to share with your brand. It may simply be a unique identifier (e.g. internal user ID, e-mail) that is matched against the raw data (cookies or another identifier). It combines the information into an individual profile based on data-driven insights to be sure they are the same person. There are no probabilities or assumptions involved. There are some limitations with deterministic models. It’s can be harder for smaller companies with a more basic infrastructure to keep people logged in, which is a challenge with a deterministic strategy.
At Fospha, we apply an advanced data-driven deterministic method. We work with companies using their first party data to stitch and overlay additional data sources such as order references, email addresses and offline user data. Our systems are fully compliant with GDPR legislation and we are able to measure and attribute customer journeys, creating highly accurate profiles and audience clusters which in turn allow for more personalized content customization. Interest level data means brands have more insight into customers so that they can share more relevant content and offers to improve brand experience and increase customer engagement.
Our measurement methodology allows us to differentiate between two different individuals who may log in on the same device. We are experts in helping brands stitch previously disconnected data together to create hyper accurate profiles and audience clusters. We do all this from a purely data-driven and consent led strategy, removing probabilistic assumptions. Fospha’s approach is unique compared to legacy MTA models, our ability to take insights, move fast and be agile helps clients be not just proactive but reactive to changing business environments and trading conditions. The Fospha solution is highly customisable making it more relevant to each business we work with.
If you would like to learn more about how to action on these insights and personalize your marketing strategy, get in touch at email@example.com