Creating marketing campaigns without data analytics is akin to playing darts while blindfolded. You might hit the bullseye, but you might also miss by a mile.
Marketing campaign analytics allow businesses to understand their audience’s behavior, create more engaging customer interactions, and maximize return on investment (ROI). Instead of throwing darts at random, every campaign is backed by an efficient, data-powered strategy.
But ecommerce marketers face many challenges when analyzing marketing campaigns, from fragmented and inaccurate data to difficulties with attribution modeling—not to mention complex data analytics tools.
Structured Query Language (SQL) helps marketing teams overcome these challenges and extract eye-opening insights from vast volumes of data. This will support:
In this guide, we will explain 10 ways to use SQL for campaign analytics and introduce a way to get the benefits of SQL without having to learn it.
SQL is a programming language that allows you to access, retrieve, and manipulate data in a relational database. Here’s how to use it to run advanced analytics and boost your marketing campaigns.
One of the greatest advantages of SQL is its ability to dig deep into audience data. This provides you with granular insights into your customer base, allowing you to make data-driven decisions when crafting your next campaign.
SQL queries can filter customer data to create audience segments that share common characteristics, such as geographic location, purchase history, or lifetime value (LTV).
Then, you may analyze each segment by querying the data for specific events: website visits, email engagement, usage of promotional codes, and more.
Using these insights, your team could develop a campaign targeting customers with a high LTV or another segment entirely.
Marketers use various platforms, like Google Ads, Meta Ads, social media, and email marketing tools, to run ad campaigns. Each platform generates unique data about ad clicks, website visits, and other types of conversions.
SQL retrieves these insights from your database, allowing you to see conversions from all of your campaigns in one place.
For example, a SQL query can uncover how many people clicked on a specific ad, how many of those clicks led to a purchase, and the total revenue generated by the campaign.
Using more advanced queries, your team can also create marketing attribution models to understand which customer interactions drive conversions and how many times a prospect engages with their ads before becoming a customer.
By scheduling periodic SQL queries, it’s easier for marketers to track conversions over time.
With such granular insights into conversion events, you will quickly identify underperforming and high-performing campaigns. You will be able to optimize campaigns in real time and adjust ad spend to improve ROI.
Marketers are under constant pressure to develop fresh approaches to attracting and converting customers. Experimentation is key to alleviating this pressure, helping businesses boost their ROI by 20% or more, on average.
Consider a furniture retailer that wants to increase the conversion rate on their website’s product pages. The marketing team generates a hypothesis that the “add to cart” button and financing options aren’t visible enough. This hurts the user experience, leading to lower sales.
Instead of changing the website elements and hoping for the best, the marketing team develops an A/B test to see which version performs better. With SQL, they analyze the results and determine the variation that leads to more purchases.
Similarly, you can experiment with different email subject lines, text message copy, and welcome offers. Each new experiment will gather more data on the customer experience and lead to more successful campaigns.
SQL allows marketers to identify abandoned carts on their website by querying the database for carts where the customer added items but failed to complete the purchase.
Research shows that customers abandon roughly 6 out of 10 carts when shopping online. Recapturing just a fraction of these customers will lift revenue and lower customer acquisition costs.
Marketers achieve this by setting up SQL trigger conditions to automatically email customers who have abandoned their carts for over an hour. This ensures the email reaches the customer while they’re still interested in the product.
An abandoned cart email by Grove Collaborative.
SQL can also use customer data to personalize abandoned cart emails. This includes items left in the cart, special offers, or tailored recommendations to entice customers to complete their purchases.
Dynamic retargeting campaigns display ads that include items most recently viewed by the customer. SQL powers these campaigns by:
Instead of targeting customers with generic ads, SQL allows marketers to capture their audience’s attention with items they’re already interested in. This moves customers down the funnel more quickly, and it lowers your customer acquisition cost (CAC) because you will target audiences with greater purchase intent.
Upselling and cross-selling help ecommerce businesses keep a healthy LTV-to-CAC ratio. They increase the average order value and nurture customer loyalty by exposing them to more of your products.
Personalization is the key to successful upselling and cross-selling. SQL enables you to analyze historical data and identify purchasing patterns. For example, it can determine which products customers frequently purchase together or which items customers tend to upgrade.
This allows you to suggest complementary products based on the items in the customer’s cart or recommend upgrades based on previous purchases.
Apply these personalized offers at checkout or add them to your email campaigns or another customer touchpoint.
For example, Apple cross-sells USB adapters and insurance to customers who are about to purchase a MacBook.
Setting a campaign benchmark allows you to better evaluate the performance of future marketing campaigns. To set benchmarks, use SQL queries to analyze historical campaign data.
First, identify the key performance indicators (KPIs) you need to benchmark your marketing performance, such as click-through rates (CTR), conversion rates, email open rates, or ROI. Then, apply SQL queries to retrieve and calculate these metrics.
You can glean more complex insights by selecting specific audience segments and marketing channels to pull data from.
For example, a SQL query allows you to benchmark the CTR of previous Black Friday text messaging campaigns.
This type of benchmark is most helpful when developing new campaigns with goals similar to those used to calculate the benchmark. It allows you to experiment and measure the impact of different marketing strategies.
SQL commands can analyze historical data to generate predictive insights that forecast different types of future events and trends.
Before making any predictions, however, we need clean data. Use SQL to remove inconsistencies, duplicates, or errors that could affect the accuracy of the predictions.
Experienced SQL users can then leverage the programming language to build machine learning models that will generate predictive analytics.
Then, use the model to run different queries. For example, it can forecast sales for the upcoming quarter or predict the lifetime value of customers in the company’s loyalty program.
If this sounds too complex, there’s an easier way to generate forecasts. With Triple Whale, marketers and data analysts don’t need to build predictive models. Our AI data platform gathers the metrics from all of your ecommerce and marketing tools into a single dashboard.
Then, you can ask our chatbot Moby to make a prediction, such as “Forecast my total sales for the next 12 months. Use the last 24 months of data.”
Calculating the churn rate helps ecommerce businesses develop better customer retention strategies and build a profitable business.
Your data repository includes information about when customers joined, their purchase frequency, and when they stopped engaging with the business. With SQL queries, we can analyze this data to understand the average churn rate.
Your business’s definition of churn depends on unique criteria, such as the amount of time since a customer’s last purchase or interaction. It also depends on your business model. For subscription-based businesses, a customer is considered churned when they cancel their subscription.
We can go one step further and segment customers based on demographics, purchase history, or engagement level. Once we calculate the churn rates for different segments, it’s possible to identify patterns that indicate why and when certain customers are more likely to churn.
Then, we are able to target at-risk customers with special offers to retain them.
Campaign analytics dashboards allow marketers to access important metrics in just a few clicks.
SQL-powered dashboards update in real time, preventing you from using old marketing data to analyze campaigns. And you can tailor the dashboard to display only the most relevant insights and metrics.
One way to create a real-time SQL dashboard is to implement a business intelligence tool. These tools integrate with your database and retrieve data from your customer relationship management tool, website analytics, and other sources.
Triple Whale’s data platform also allows you to build a customizable dashboard containing real-time data from different ecommerce and advertising platforms.
As an ecommerce marketer, you might have had little experience with data analytics so far. Learning SQL will help you bridge this gap and open doors to the insights you need to boost campaign performance.
Online courses are an easy way to pick up SQL basics quickly. But to take your knowledge to the next level, regular practice is a must. HackerRank provides a variety of SQL challenges at all levels. It’s also helpful to join online communities to get tips from other learners.
If you don’t have time to learn SQL, you can still access detailed marketing analytics with the help of Moby.
Note: Triple Whale will provide this section
Whether you’re digging deep into audience segments or creating personalized customer journeys, SQL is a game-changing tool for ecommerce marketers.
But if SQL queries are too intimidating or time-consuming, you can turn to Moby. The AI chatbot takes natural language prompts and returns insights into advertising campaigns, industry benchmarks, revenue, and much more.
Get early access to Moby today.
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