Analytics and Data Science
Each year, the search landscape grows increasingly more competitive as more brands compete for the number one position in the SERPs. As more and more brands are turning to their data to guide their digital marketing strategy, it’s safe to say we are in the ‘data-driven strategy’ era.
While there are more brands than ever using data to improve their performance, we’ve found that few are truly getting the most out of their data.
Making the jump from assumption-led strategies to data-driven marketing can greatly improve your performance, and it’s all too easy for brands to stop there. Advanced use of data requires a lot of company investment, in terms of time and often in terms of money, too. However, the return of reaching digital maturity is two-fold: companies who have reached full digital maturity see 30% cost efficiency savings and a 20% increase in revenue.
There are four key areas you can utilize data for improved online performance and reduced costs. Moving from beginner to expert through these areas will see your brand grow online, giving you an advantage against your competitors.
Attribution allows brands to see how much value different touchpoints in the customer’s journey have – i.e., how much influence they had on the decision to convert. Advanced attribution modeling allows companies to make better use of their budget, as they are able to identify where investment in channels will make the most difference.
Data-driven attribution modeling (DDAM) is arguably the most accurate from of attribution, as it analyzes your data to determine which channels and activity have the biggest impact on conversions.
There are several steps to move your brand from beginner to expert at attribution. Ensuring that data capture is correctly set up is the first step, as the predictive algorithms used in DDAM need access to all the data to build a true picture of attribution. This means setting up tags to track and measure against KPIs across websites, apps, technology and campaigns.
Joining up offline and online data is essential for full funnel attribution models. The customer journey spans multiple channels, so you need to include data from multiple sources including your website, app, CRM, social media, paid media, marketplaces.
With so much data involved, it’s crucial that you create a reporting solution that allows data to be available in a way which decision makers can see how changes in budget can impact performance. We use our proprietary reporting tool, ReportLab, as well as Data Studio to create custom dashboards that visualizes our clients’ data clearly, allowing for better decision-making.
You can use your data to create a digital strategy that allows you to reach the right customers, with the right messaging, at the right time, to maximize the chance of making a sale.
Where data-driven attribution allows you to identify which channels you want to reach your customers on; integrating CRM user data with analytics data can help you to identify which customers you want to reach.
CRM data can help brands to build a rich, in-depth view of who their online customers are. It can be used to build a user matrix of your customers and segment these into different audiences, or calculate the lifetime value of a customer based on their purchase behavior. These insights can be used to create robust remarketing campaigns with a high ROAS.
Having the right assets means you are able to show the right messaging to your customers, no matter where they are in the customer journey. Showing users personalized content provides a better user experience and increases the likelihood that the user will engage and convert.
For paid campaigns, this might mean showing users different ads and messaging depending on where they are in the customer buyer journey, or personalizing the landing page content depending on which audience segment the visitor sits within.
Automated bid strategies apply machine learning to your paid data to improve the performance of both your PPC and display campaigns, by adjusting bids to ensure your campaign hits a predefined target.
There are a few reasons automated bid strategies yield such good results. For one, they factor in a wide range of auction-time signals such as time of day or device. By using historical data, they can identify patterns and trends much more accurately than if it were to be looked at manually – particularly in the case of campaigns where there are huge amounts of data to be analyzed. They can also forecast performance and spend using campaign data, and adjust the bids accordingly to ensure targets are hit.
After reading through this blog, you might have found that you are utilizing data effectively in one area, but not in others. Likewise, you might identify that there is just one area you need to focus on, while you are experts in the rest. Or, you simply might not be sure where you sit within a digital maturity framework.
We are offering free Google Digital Marketing Maturity consultations for businesses to help identify where they currently sit within the digital maturity framework, and how they can progress to the next level.
This blog is part of our wider B2B Playbook that is designed to help B2B businesses with all aspects of their digital marketing from leveraging data, acquiring more traffic, creating assets that resonate and succeeding internationally.
In the data section we’ve compiled useful insights on everything you’ll need to know, from capturing data to building advanced machine learning solutions using your first-party data.