Three Modern Marketing Analytics Use Cases
- John Morrell
- October 4, 2019
In the digital age, a modern suite of analytics and data is critical to creating digital campaigns that will be effective. New digital marketing analytics helps you measure the performance of your marketing channels and improve your marketing strategies. These three use cases will help you improve the return on investment (ROI) of your marketing efforts.
Product and Market Fit
Do your products or services satisfy the needs of that target market better than the products and services of your competitors? If your answer to this question was “no” or “I’m not sure,” your business may be suffering from the poor product or market fit. Let’s look at another definition from Josh Porter:
“Product/market fit is when people sell for you.”
Now imagine a company with hundreds or even thousands of products, and in rapidly changing digital and consumer product sectors. Such a company needs to constantly re-evaluate all their products and their position in the market.
A product or service that truly fits a market will be sold as much by customers and clients as by marketing and advertising campaigns. If your sales numbers aren’t consistently increasing or there isn’t positive word-of-mouth surrounding your offerings, your business is probably experiencing an inferior product or market fit.
You should assess the fit of your products or services by asking the following questions:
- Bounce or success rate. How quickly are people moving past your marketing campaigns and offerings?
- Qualitative data. How are customers responding to your product or service?
- Social proof. How does your product or service define trends in your market?
- Sales and repeat sales. Are people coming back for more?
- Sales-cycle length. How long does it take to sell someone on your product?
- What are the characteristics of the people genuinely reacting to your products or services?
As you can guess, answering these questions requires data from a variety of different sources, or perhaps re-using existing analytics assets, and combining them together to get a comprehensive answer to the questions. Data or existing analytics assets would come from:
- Web analytics
- Marketing campaign results
- Social media
- CRM, sales, and e-commerce systems
- Marketing automation
- 3rdparty data providers
It is critical to easily find these assets, understand how best to use them, and bring them together into this comprehensive view to truly understand your product-market fit, and determine potential product and service adjustments.
Customer acquisition can cost anywhere between $10 for the retail sector and $303 for the banking and insurance industry. Focusing on customer retention is cost-effective, and analytics can help you identify signs of customer churn and determine the best actions to take to re-engage a customer.
There are two critical aspects to customer retention: understanding the value of each customer, and tracking their behavior for the tell-tale signs of churn and wallet-share.
Prior to the digital age, one would look a customer’s value based on the “recency, frequency and monetary value” model. Modern organizations now look to examine and project the full customer lifetime value (CLV).
Knowing a customer’s CLV allows you to understand how much a specific client or customer is contributing in value to your business over time. Despite its importance as a statistic, only 34% of marketers fully understand what CLV is and how to use it.
Once you have the CLV model, you then need to apply behavioral analytics to understand how the customers are acting, and how best to get them to consume more products and services. As a result, you can implement effective marketing and customer retention strategies. Behavioral analytics need to be:
- Look at value over time
- Determining next best actions
- Continuously learning and improving
A comprehensive view of CLV and behavior needs a number of data and assets on:
- Full demographics
- Web behavior
- Social media behavior
- Call center response
- Marketing campaign responses
- Sales and orders
- Product fit
Using customer behavior analytics to its full effectiveness requires combining together the aforementioned assets. Some of these assets may come from traditional data and analytics sources, while others may come from data science. It is important to have a single source of your analytics assets to quickly and easily answer customer behavior questions. Missing a customer signal for even a day or even hours creates a risk of losing that customer.
Customers today are hammered continuously with marketing emails and advertising campaigns. If you want your business to stand out in all the white noise, your marketing campaigns need to be personalized.
Millennials currently hold the most purchasing power of any generation. The millennial generation as a whole wields $200 billion in buying power. Why is this important? Because brand loyalty increases by 28% among millennials who receive personalized marketing material. Furthermore, personalization can boost revenue by as much as 15% and reduce acquisition costs by 50% when utilized effectively.
Understanding and analyzing a comprehensive set of customer, demographic, and performance data can help you create personalized marketing campaigns specifically targeted toward your customers. By using personalization to your advantage, you can improve upon every aspect of your marketing strategy.
Creating this comprehensive view for personalization requires a number of key assets, including:
- Customer demographics
- Campaign performance and response
- Web behavior
- Product/service offer characteristics
With the digital age, it is critical for companies to re-examine their personalization, targeting, and marketing campaign performance at a rapid pace as consumer tendencies and behavior change on a dime. Finding, consuming, and building knowledge and trust in these assets is critical for the analytics team to answer these time-sensitive questions.
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Data transformation can be a difficult and complex part of the process when building a digital marketing dashboard. You may have solved the simple EL part of the process through data loader tools that can extract data from sources such as Marketo, Hubspot, Google Analytics, and Adobe Analytics and load it into your Snowflake data cloud. But that’s the simple part. Transforming this large, diverse, and complex set of data into something consumable in your dashboard is the difficult part. At least until now!!!
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