Brands have a wealth of data at their fingertips, from ad-server to website to CRM, consumer engagement with various touch points leaves a trail of insight not to be wasted. In the digitally connected world it’s important for businesses to harness this information to maximise their ad campaigns on each platform. Whilst it is estimated that consumers are exposed to around 7,000 ads in just one day, the question remains as to whether brands are really using the data available to them correctly and effectively. Yes, brands are using data to identify an audience, however, they aren’t considering what platform they prefer, what they recently posted on social media and where they made their last purchase, all at the same time.
In a crowded market, companies must take on a holistic view of consumers by connecting data to identify an audience and target them with the most relevant content, in the most relevant way possible. By bringing together a wide set of data, it is possible to not only run advanced programmatic strategies but also make transformative business decisions. Ultimately, brands must connect the dots between every inch of information available to make an effective change to their commercial outcomes. For me, there is a clear path for business transformation that uses data to solve commercial challenges.
Data would be pretty useless without someone there to analyse, decode and utilise it. The people behind it are the reason it works, but it’s not an easy job. In the current climate, those with the skills to use data effectively are hard to come by, so when someone does fit the bill it’s important they sit in the right place of the business to truly benefit from their skills. Solutions Engineers, Technical Engineers and Data Scientists are a few examples of people who are able to aid business transformation. Whether it’s translating commercial objectives into technical solutions, maintaining the architecture or running predictive modelling, each person has a part to play in the process of connecting data.
Now you have the right people in place, it’s time to consider the commercial framing. When looking at connected data sets it’s important to ask yourself what challenge is this looking to overcome. The more specific the business outcome, the better the design of the technical infrastructure that will be created. It will also allow for more precise KPI setting to ensure that the goals are being achieved.
For example, if a business wanted to increase the lifetime value of new users registering, the data pulled must work to achieve this. Be it delivering exclusive offers to new customers or tailoring the experience using what you already know about an individual. By investing in understanding consumer traits, there will be more opportunities to convert sales. It’s about taking a step back to figure out what your business wants to ultimately achieve and how data can achieve it.
Having decided what you want the outcome to be, the next step is to evaluate the data sets which can be used to evaluate behaviours. Typically, this would involve looking at consumer website interactions, CRM data and media data via ad-server logs. It is the combination of these first party data sets, in conjunction with any second or third party data, that provide the foundations for data discovery. Here, it’s necessary to think about any national privacy laws that could impact your data usage, in particular once the GDPR comes into law in 2018. Having set everything in place, it is then possible to build out a technical framework (think secure cloud computing infrastructure) to create a platform for data discovery.
The global impact of GDPR and what brands need to do
There are a number of techniques used to collect data that can be employed in order to meet the different business challenges.
Data is collected by on site pixel capturing variables from the page. The goal is to define the key audiences that will add value to the business, allowing for more specific creative builds as well as look-a-like modelling for scaling the first party data. Simply put, different consumer on site interactions are split into clusters, detail can then be added to provide further insight into each group’s behaviour.
Prediction and Segmentation
For more advanced results, data is collected by on site pixel capturing variables from the page, meaning you can get to know your customers next move via predictive segmentation. This is moving away from assumptive marketing towards more accurate outcomes. For example, knowing that a consumer group has a three times more likelihood to be a high value purchaser via their site interactions.
Life Time Value
Next would be to connect these insights. By connecting CRM data it is possible to look at the traits that define longer consumer interaction periods. Understanding the drivers to a client base that transacts over long periods enables businesses to react accordingly; be that via commercial messaging, optimising path to purchase, or controlling stock levels to name just a few.
Now that your connected data sets are in place with machine learning techniques for specific targeting, it must be activated into the programmatic landscape. Audiences can be pushed directly into DSP or DMP platforms for activation that will work to achieve the commercial outcomes originally set out. Ultimately, for this to truly have a successful business impact there must be close relationships between teams, be it trading, engineering, solutions, analysts, planning, sales, product; depending on the organisation.
In the digital age, there is a wealth of consumer data available to businesses, however not all of this is being actioned in the most effective way. While connected data may sound like a challenge, these steps can ensure the process runs smoothly to ensure insight is converted into intelligent output. Essentially, every part of the business has a part to play in delivering the client goals and, ultimately, transforming the business.