Digital Marketing Strategy
The number of changes to the digital landscape in recent years has highlighted significant scope for improvement in the conventional approach to digital marketing strategies.
Businesses eventually reach a plateau in performance with the commonly used ROAS targets and attribution models, overlooking multiple key touchpoints in the user journey and crediting conversions entirely to one touchpoint.
Once you recognize these gaps and flaws in your user journey, the next step is to pivot towards looking at individual product metrics and their quality to begin filling in the gaps.
The first key step is to build a data strategy to collect identifiable information. This makes it possible to stitch key parts of the user journey together and begin tying up touchpoints to move closer to your audience.
Changes to government regulations such as GDPR and movements in consumer privacy awareness mean that businesses must provide value in exchange for identifiable information. Most importantly, once you have that information, you must put it to use as effectively as possible. Making this your priority will naturally direct you down a path of digital maturity.
Google’s digital maturity benchmark report segments the journey of maturity into nascent, emerging, connected and multi-moment categories, essentially unlocking a deeper understanding of the customer journey as you move up the curve.
The report also revealed that more than 80% of the most digitally mature brands say that they have CEO sponsorship for data-driven marketing initiatives.
Going direct to your audience following at the point of interaction with your service is key to filling the gaps within lost third-party data. Whether it’s using push notifications or at the in-person check-in of a hotel, there are multiple opportunities to prompt users to provide rich data that can be used to build a greater understanding of your audience and evolve your strategy.
This process allows us to gather essential information at the start of the user journey, the middle and the end transaction. Each stage gated behind identifiable information allows us to join cookies together across multiple devices, people and months via soft login solutions and centralization of data.
Adopting this approach also opens the opportunity to join the user journey across multiple sales. If the same user returns to purchase twice more, we can use the data to tie up their behaviors. This is a key process that you will continue to develop over time and in stages.
Moving away from focusing on the number of leads, it’s about attributing their quality. When they happened, why they happened. This is where machine learning comes in. A call requesting a change to a booking isn’t driving lifetime value, a call to find out new info about a potential booking is and that will again vary by which product they want to talk about.
The power of machine learning makes it possible to crunch the data of millions of users, capturing the performance of extended periods in your business. You’re then able to estimate the likely future value of a user’s actions by statistically modeling the historical data of all the users that came before them.
This visibility makes it possible to make informed changes to your digital marketing investments based on individual performance. Each product will have a different performance profile and the heightened perspective gained from defining value by product naturally creates opportunities to optimize your marketing strategy toward the most valuable products.
Implementing a data-driven strategy and utilizing your audience data will naturally direct your business down a path of digital maturity.
Attribution modeling is one of the biggest challenges in building an effective investment strategy. With the growing changes across technology, government regulation and consumer privacy awareness, it’s becoming increasingly difficult to track a user.
From the first phone call or inquiry, you’re able to store the client ID in Google Analytics which you can then track back to uncover further data. It’s about asking the question, what earlier touchpoints can we tie up within this data?
The decline of cookies and rising privacy regulations evidence the importance of prioritizing first-party data and once it’s no longer possible to join data any further, we turn to behavioral modeling and machine learning to fill in the gaps.
Digital maturity is becoming increasingly important as we adapt to the new, cookie-less age of marketing. If you would like to know more about evolving your digital strategy, we go into more detail on the subject in our latest whitepaper.
Other topics covered in this whitepaper include: