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A Customer-Centric Approach to Digital Marketing Analytics

Imagine you are a marketer for a retail client. The client has a loyal existing customer base, but wants to expand and attract new customers through digital media. Although all customers are important to the client, some are more valuable than others based on the frequency and revenue of their purchases. How can you optimize the portfolio of digital media to acquire new customers that will go on to have a high lifetime value?

Consider the initial purchase paths of two customers:

  • One customer, Thelma, begins her journey with a search for “colorful high heels” and clicks on a paid ad that takes her to the retailer’s website. She browses the site but doesn’t make a purchase. Over the next few days she is served remarketing ads for the retailer. She eventually conducts another search for the retailer’s brand name. She clicks on the retailer’s paid ad and converts.
  • Another customer, Louise, begins her journey with a general search for “sales on women’s dress shoes” and clicks on a paid ad that takes her to the retailer’s website. She also browses the site and does not make a purchase but signs up for the retailer’s email list. A few days later, she opens an email announcing deep discounts on boots on the retailer’s website. Later that day, she conducts a search for the retailer’s brand, clicks on a paid ad and makes a purchase.

After her first purchase, Thelma goes on to typically place an order once a month, usually in excess of $100. Conversely, Louise only purchases once every four months, usually for an amount less than $75. Thelma is the more valuable customer, but both her and Louise’s last-click was on a branded term, which is fully funded. How can we best use limited non-brand budget to attract more customers who are like Thelma than customers who are like Louise?

[whitepaper nid="3479" width="50%" align="right"][/whitepaper]While true insights cannot be made on a single conversion path, they can be made by analyzing the conversions paths of customers together. If we look for commonalities in the initial search path of a large set of customers who are like Thelma and see how those commonalities differ from the search paths of customers who are like Louise, we can identify channels and campaigns that attract higher valuable customers.

Analysis should not be restricted solely to last-click, but should incorporate events from all parts of the conversion funnel to get a holistic view of the entire customer journey. For example, we may find that high-value customers, like Thelma, typically have a detailed product description as a search query in the upper funnel where lower value customers, like Louise, do not. Similarly, the analysis may show that lower value customers are more likely to respond to messaging that involves words associated with cost-savings (sale, clearance, discount, etc.) than higher value customers. These insights can be used to shift budget from cost-savings keywords to detailed product-based keywords in order to optimize marketing spend.

Rather than starting with media and measuring how it affects conversions, putting the customer at the center of marketing analytics allows desirable customer traits and behaviors to be targeted, which can become a point of competitive differentiation. A customer-centric marketing dashboard enables integrated customer data to be cross-sectioned numerous ways to achieve agile comprehension of customer journeys, migrations, and behavior. To learn more about creating a customer insights platform, download our whitepaper, A New View on Media: A Customer-Centric Approach to Digital Marketing Analytics.