Case Study: Using Unique Customer Data to Target Customers and Drive Conversions

folica 2Online paid search strategies are advancing beyond keyword and product optimizations, moving toward relevant and personalized advertising to targeted individuals, resulting in stronger performance and higher conversions.

Folica, an online hair care and beauty retailer, was hoping to re engage their existing customer base through an improved advertising experience. After exploring a number of agencies, Folica turned to Adlucent, a digital marketing and analytics company, for help. For more than a decade, Adlucent has been working to fulfill its mission of making ads relevant and helpful to all by understanding and serving the needs of each individual -- giving retailers like Folica the benefit of a more efficient ad program and consumers a better shopping experience -- a win/win for everyone involved.  

Folica wanted to reactivate dormant customers who had not purchased or engaged with the company in recent months, and to focus on retention efforts. While traditional agencies would increase spend or create new offers for this group of customers, Adlucent took a unique approach, instead leveraging Folica’s customer data base and Google’s newest solution, Customer Match. As the first agency to combine data segmentation with Customer Match, leveraging new and unique data insights was new but familiar territory for the team. 

“Like most retailers, we always want to grow our audience but also understand the importance of driving strong lifetime value among our existing customer base,” said Jackie Lampugnano, Director of Marketing & Communications at Folica. “We came to the table with great data about our audience; we just needed to find ways to better leverage those insights to drive performance.”

To start, Adlucent’s data science team performed machine learning techniques on Folica’s existing customer data -- which included transactional history, including previous purchases, devices used, items purchased, ad type clicked, customer loyalty, and lifetime value -- in order to create audience clusters to better target with search ads. Based on the analysis, four high value segments were created: Reactivate, Grow, Convert, and Retain.

“Using custom data points allowed us to get an in-depth, unmatched look at Folica’s customers,” said Laura Musa, Solutions Manager at Adlucent. “By importing Folica’s full CRM data into our own proprietary tool, Deep Search, we were able to see the full scope of their customers and accurately target their audiences, delivering the most relevant ads and helping to determine the optimal bidding strategy.”

After establishing the four audience clusters, the data on each segment was then uploaded to Customer Match, allowing Adlucent to bid higher or lower based on customer value. Each of the four audiences were then shown different paid search messages and offers based on what was most relevant to them, resulting in improved results for the dormant customer groups -- the Reactivate and Grow audiences. This strategy also drove improved efficiency in Folica’s loyal customer segments.

The Reactivate audience had an increase of 81% in Return on Ad Spend (ROAS) for non-brand and a 39% increase in ROAS for brand, proving that our investment in reactivating this dormant group of customers paid off and drove conversions. Non-brand conversion for this group increased by 109% and Revenue Per Click (RPC) increased by 150%.

folica 3The Grow audience was a huge success and drove an increase in Click Through Rate (CTR) of 860% and Conversion Rate (CVR) increase of 65% in non-brand, indicating that we were targeting and reaching the right customers. The grow audience for brand had a ROAS of 55.40. Improving ROAS shows us that our investment in this audience paid off and drove conversions.

Achieving high performance improvements for CTR across all audiences is a great indicator that we are reaching the right people through our targeted approach. The CTR for each of the audiences increased substantially for non-brand compared to non-customer match :

  • Reactivate Audience: 844% CTR
  • Grow Audience: 860% CTR
  • Convert Audience: 781% CTR
  • Retain Audience: 990% CTR

Our goals for retaining customers were excellent as well and we achieved an increase 340% Conversion Ratio (CVR), 225% RPC, 122% ROAS.

When looking at the overall success of our Customer Match campaigns across all segments for Folica the results are excellent. Here are some of the KPIs we measure to gauge performance overall:

  • ROAS: 214% increase
  • RPC: 167% increase
  • CVR: 151% increase
  • Average Order Value (AOV): 7% increase
  • CTR: 1346% increase
  • Cost per Click (CPCs): 15% decrease
  • Average Position improvement of 13% (CM 1.98 compared to 2.28 for non-customer match audiences)

A key focus for us with Customer Match is to increase performance for non-brand and the results were exceptional. Non-brand Customer Match Audiences compared to non-brand without using our targeted Customer Match Audiences include:

  • Non-Brand Only Segment CTR: 821% increase
  • Non-Brand Only Segment CVR: 204% increase
  • Non-Brand Only Segment RPC: 171% increase

Unique and relevant advertising proved to be very effective in reactivating Folica’s dormant audiences and driving conversions. Even among loyal segments, targeted messages demonstrated program efficiency while increasing purchases.

About Adlucent

Adlucent, a full-service digital marketing and intelligence company, helps brands acquire more of their best customers profitably through a mix of personalized marketing services, consumer data, predictive advertising technology, and advanced insights applied across digital touchpoints. Adlucent translates vast first and third party data into actionable consumer intelligence, answering questions like who are my best customers and how can I acquire more like them? These insights are used to connect brands with high value consumers through the right mix of touch points along their path to purchase. For more information on Adlucent, visit http://www.adlucent.com.

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