Demystifying Marketing Data Sources

Welcome to the exciting world of marketing data! If you’re new to marketing data science and analytics, it’s completely normal to feel overwhelmed by the vast amount of data available to work with. But remind yourself that you’re not alone in this journey! Like many professionals in this industry, I have also experienced the overwhelming feeling of dealing with vast amounts of marketing data.

In this blog, I will try to break down the most common data sources you’ll come across (or should ask for) on your marketing journey, using real-world examples to connect each one of them. My aim is to help you understand what they are and how they can be used. Understanding the key marketing data sources in our organization is not only essential, but also empowers us to unlock unique insights into our audience, enabling personalized marketing strategies, fostering trust with our customers, and providing full control over the data.

So, let’s dive in with a sense of optimism and curiosity!

First-party Data Sources:

Let’s start with first-party data sources in marketing. These are the data sources that are collected and owned by your organization directly from its interactions with customers or users; as simple as that. From my personal experience, I would say that mastering this section is crucial.

Make it a priority to dedicate ample time to managing and analyzing these data sources, as they are reliable and come directly from your own audience. It’s like a treasure trove that opens up as you invest more time in exploring and analyzing it.

1. Customer data from ERP’s and CRMs:

Consider this as your go-to resource, your ultimate source of truth on sales, revenue, and profit from new and returning customers. I cannot stress the importance of thoroughly exploring every nook and cranny of these data servers. ERP provides valuable insights into transactional customer touchpoints, while CRM offers socio-economic, geographic, and demographic data about your customers.

For instance, imagine you are working as a marketing data science and analytics specialist for e-commerce that sells sports equipment online. Your marketing communication team approaches you with a pain point - they’ve been struggling to understand customer preferences and behavior to tailor effective marketing campaigns. Where would you begin?

I would say, start by delving deep into the invoiced and non-invoiced transactional customer touchpoints from your ERP and CRM system. By analyzing this data, you can identify your most valuable and dormant customers; allowing you to create hyper-targeted campaigns that speak directly to their interests and preferences.

Imagine successfully performing customer segmentation using the recency, frequency, and monetary values from the ERP data sources and profiling each segment based on all the demographic variables in CRM. You’ve noticed that many of your most valuable customers are repeat buyers who frequently purchase soccer gear. However, their average order value is relatively low compared to other segments, and you’re curious to understand why. This is where the second data source comes into play-the web analytics data from your e-commerce website.

2. Web Analytics Data:

Web analytic data sources, such as Google Analytics (GA4, GA360), Adobe, and Snowplow, offer invaluable insights into customer behavior on your website. They provide information on web event-based customer conversion touchpoints, user journey mappings, and sales funnels based on how users interact with your website.

Let’s revisit the same example. By delving into your web analytics data from tools like Google Analytics (GA4, GA360) or Snowplow, you uncover that customers who visit your soccer section tend to browse and add multiple items to their cart but frequently abandon their purchase at the checkout stage. Armed with this valuable information, you collaborate with your web and app development team to quickly identify and fix the UX issues on the checkout pages that are causing this customer behavior.

Equipped with these insights, you take action and work with your marketing communications team to develop a targeted email marketing campaign that incentivizes your most valuable customers who are interested in soccer gear to complete their purchases. Additionally, the team creates personalized web, app, and SMS push notification campaigns that offer exclusive discounts on soccer gear, providing a seamless and personalized experience for your customers, and ultimately increasing the chances of conversion.

3. Customer Data Platforms (CDPs) or Marketing Automation Platforms:

To gauge the effectiveness of your marketing campaigns, you turn to the third data source in line - Customer Data Platforms (CDPs) or Marketing Automation Platforms. These platforms collect data from various sources such as web push, SMS push, browser pop-ups, and email marketing campaigns (For Ex: Blueshift, Marketo, or Tealium).

Let’s circle back to the same example here. You now delve into the event-based data feeds from sources like Tealium and are thrilled with the results. Your email campaign shows a significant boost in open rates and click-through rates, leading to a substantial increase in sales for soccer-related products from your most valuable customer segment. Moreover, you observe that web and SMS push notifications have resulted in increased website traffic and conversions, resulting in a significant rise in the average order value (AOV).

These insights from CDPs or Marketing Automation Platforms provide valuable data on the impact of your marketing efforts and allow you to make data-driven decisions to further optimize your campaigns for maximum results.

4. Unstructured Customer Service Data:

With the impressive results of your marketing campaigns, the senior leadership team presents you with a new challenge-understanding the behavior of inactive and dormant customer segments. They want to know why these customers haven’t returned to your e-commerce site and what pain points they may have. To tackle this challenge, you delve into the world of natural language processing (NLP) and turn to the next data source in line-unstructured customer service data.

You analyze unstructured customer reviews, NPS surveys, chat logs, call center recordings, social media feeds, and email transcripts using NLP techniques such as text clustering, keyword extraction, and sentiment analysis. Through this audience text mining process, you uncover significant negative feedback from dormant and inactive customers regarding issues with delivery and stock availability of sports equipment in categories such as Outdoor Sports and Bikes. This valuable insight was buried deep within the unstructured customer service data, but you promptly share it with the leadership team.

The leadership team takes action and works diligently to address the identified bottlenecks in the customer service and post-purchase departments. As a result, the net promoter score of the organization improves from 75 points to 80 points within three business quarters, demonstrating the positive impact of leveraging unstructured customer service data and NLP analysis.

5. Customer-centric data from Loyalty Programs, Coupon Codes & Social Media Ads:

As you reflect on the work you have done and the improvements in customer satisfaction and engagement, you share your concerns about the remaining inactive customer count with the head of the marketing communications team. In response, the marketing communications team comes up with a plan to create engaging social media and Google shopping ads that resonate with the target audience, along with refer-a-friend gift vouchers, coupon codes, and loyalty programs. These initiatives aim to further enhance customer loyalty and drive sales.

To effectively analyze and leverage the data generated from these campaigns, you start thinking about how to stitch the actions and inactions of customers to these initiatives in your customer data lake. This brings you to the next data source in line-customer-centric data from loyalty programs, coupon code redemptions, and social media ads, such as Facebook Ads, Google Ads, and Twitter Ads. These data sources provide valuable insights into customer behavior and preferences, allowing you to make data-driven and customer-centric marketing decisions.

As a result of incorporating these additional data sources into your marketing strategies, your overall customer experience further improves, customer loyalty increases, and your sales and revenue soar to new heights. Your company’s marketing efforts become even more data-driven and customer-centric, thanks to the power of leveraging first-party data sources. These actionable insights from various data sources enable you to optimize your marketing strategies and achieve remarkable success in the highly competitive sports equipment market.

6. Product-level or SKU-level inventory, sales, and profit data:

Lastly, this data provides you with insights into the performance and profitability of your products at a granular level, allowing you to make data-driven decisions to optimize your marketing strategies further. By analyzing this data, you gain a deeper understanding of how each product or SKU is performing in terms of sales, inventory levels, and profitability.

This information enables you to identify top-performing products, assess their profitability, and make informed decisions about inventory management, pricing, and promotions. You can also identify underperforming products and take proactive measures to address any issues, such as adjusting pricing, improving marketing strategies, or making changes to the product assortment. I think that’s pretty much everything about first-party data sources. Let’s quickly look into the other two types of data sources in marketing.

Second-party Data Sources:

As you navigate the complex landscape of marketing data, you may come across second-party data sources along the way. These data sources involve direct data sharing between two trusted parties, allowing them to complement and enhance each other’s datasets.

For example, your e-commerce website could establish a partnership with a popular payment gateway provider, granting access to payment data from their customers to enhance your targeting and personalization efforts. Or say, the same e-commerce website that sells sports equipment could collaborate in a joint campaign with a vendor, such as Adidas, where they share their customer data to target prospective buyers with personalized offers and promotions. This is another example of how second-party data can be utilized to enhance marketing efforts and create mutually beneficial partnerships.

Third-party Data Sources:

Finally, I will briefly mention some popular third-party data sources I have encountered in marketing. These data sources are collected by other organizations and licensed to us for the purpose of market research and analytics.

1. Market research data

Market research data can be obtained from sources like Roy Morgan, which provides customer preferences at the suburb or mesh block level. This data can be overlaid on our in-house customer segments for profiling purposes.

2. Public government data

This includes data from organizations like the Australian Bureau of Statistics (ABS) or the United States Census Bureau (CB), providing demographic and psychographic insights such as age, gender, income, interests, and purchasing habits of prospective market segments.

3. Data aggregators

This includes data on price competitiveness from sources like Google Merchant Centre, or data from web scrapers and SEO keyword aggregators, which can help us understand market trends and competition.

4. Location data providers:

Sources, like Near, provide geographical data points that can help us identify customer movement patterns for personalization and targeted advertising.

Congrats if you have made it this far! I think that’s pretty much everything I would like to convey for now. I hope this real-world example gave you some insights into the most popular first-party, second-party, and third-party data sources that we use in the marketing data science & analytics industry. By leveraging these data points in your marketing efforts, you can unlock their true potential and drive improved results for your business. Write to you soon.

Thank you