Every day we generate data at work (emails), at home (website and app usage) and on the road (location). Companies have found creative ways to collect, structure and draw valuable insights from this data... valuable insights that can be monetized directly or power more complex business models.
Let’s take Kreditech as an example, a company that has no assets (property, inventory, machines) besides the data that its customers voluntarily provide (including their browsing history). Kreditech will tell your bank how risky it is to loan you money based on your browsing behavior (including how you navigate the Kreditech website)1. Over the past 4 years, Kreditech’s predictions were good enough for it to fundraise close to $400 million! By now you must wonder, how can I transform my data into dollars? You can either sell your data or generate more revenue from your current marketing activities.
The Marketer’s Data
While traditionally the finance department has been known as the single source of truth for key decision-making in organizations, data generated and collected by the marketing department have a big role in assessing and influencing an organization’s brand health and sales growth. Nowadays, marketing data has three attributes that confer power to it: it’s granular, it’s real-time and it’s actionable. Marketers can now reach each customer with the right message at the right time.
Data Hygiene
Let’s dive into the 6 key steps in the data lifecycle that must be respected to obtain the best results. We address the main concepts below.
1. Business Objectives and Key Performance Indicators
Before thinking about what data to collect, the marketing and data teams should clearly define their business objectives and the questions they’d like answered through data. This will help avoid one of the biggest pitfalls of the data-era which is data hoarding. Trying to collect everything will dilute efforts and create significant distraction from business priorities. KPI definition is at the core of this step. Setting those metrics is an art and a science at the same time spanning both marketing (brand recall, customer acquisition, conversion rate) and business objectives (sales, margin, average order value).
2. Collection and data quality
While collecting the data from the various systems it is important to make sure that data-quality principles are respected:
3. Integration
During the integration phase we make sure to create integrations (think of them like pipes) between the different systems to aggregate the relevant data we need as they are generated. These integrations would also power real-time decisions across systems where rules have been defined.
4. Analysis & Segmentation
Various analysis techniques can be used to draw insights, identify patterns and segment customers based on similarities in behavior.
5. Deployment
Data is consumed in three main ways within the marketing department: i) analysis & reporting, ii) customization (website/app, creative, product recommendation) and iii) media-buying (budget allocation, real-time buying). This is where actions are taken based on the insights generated in the previous steps.
6. Impact Measurement
Closing the loop, after deploying the data (changing check-out process, using different creatives per customer persona and redistributing budget across channels) measurement comes in to confirm whether the actions taken are actually yielding the expected results against the KPIs defined in step 1.
Data Hygiene
Let’s explore a few common strategies to maximize the marketing ROI.
1. Data-driven creative strategies for customized customer messaging. As we’ve seen in our February article, advertisers were able to enhance their sales by delivering the right message to each subsegment of their audiences.
2. Leveraging ad servers for deduplication as we’ve seen in our March article. Some advertisers realized they were wasting up to 32% of their budgets over-targeting the same audience.
3. Extended-CRM for higher visibility on the customer journey across various channels. Merging-adtech with owned channel analytics allows marketers to build a database of golden records which provides them with insights across channels and allows them to answer questions such as: “how many of my loyal customers visit my stores after seeing my latest marketing campaign?”
4. Incorporating internal commercial information to drive bottom-line results. The real-timeness of the programmatic buy can deeply impact the operating model of some businesses by optimizing towards key business metrics. As we can see in this Hertz case study, GA360 was used to optimize car-utilization rates and profitability which are far beyond traditional digital marketing metrics.
5. Using attribution modeling to obtain the best media allocation. For a very long time, digital advertisers relied on the last-click to determine top performing channels. However, that approach can be misleading especially when complex decision-making is happening in the consumer’s mind. It would be risky to believe that a car buyer who probably spends a month researching different car models is persuaded by a single ad to go visit a certain carmakers’ showroom. In fact, in MENA, up to 72% of car buyers who start with one brand in mind end-up buying another brand! Attribution-modeling allows advertisers to test different models by giving varying levels of credit to each step in the customer journey. This informs them on how to rebalance the marketing budget across channels for a better overall performance.
Conclusion
In summary, data is a very powerful tool in today’s marketer’s arsenal. There are multiple ways to deploy it but it needs careful planning and maintenance. “The Value of Data” report conducted by professors from Harvard Business School and Columbia University found that data-driven marketing services employ around 676,000 people and power an industry of $156 billion in the US alone. How are you or your organization currently leveraging data to drive your business and capture your fair share of that value?
