Retailers deal with a diverse audience. After all, no two shoppers are alike. However, predictive models built with audience segmentation and customer data enrichment can help marketers implement more focused and targeted advertising campaigns that resonate well with client expectations.
Speedeon’s analytics solutions are designed to help businesses reach the right audience at the right time. We have helped many retail brands succeed in their direct mail and other marketing campaigns by building powerful lookalike models.
One of our long-term clients, a leading furniture retailer, had already found their ideal customer with our modeling solutions. However, our team wanted to optimize it further with a more sharper model for their direct mail campaign. Here’s how we implemented our retargeting approach.
Reanalyzing the Targeted Customer Segment
We had already identified that new movers and pre-movers were the best customer segments to target because they are already prepared to spend and require less persuasion, especially when it comes to big purchases like furniture.
Also, these are customers transitioning into a new chapter in their lives. They will be looking forward to buying new furniture, among many other household products. The results were already applaudable, and the direct mail campaigns were proving to be effective. But, our team wanted to leverage the strategy further and increase the ROI with the power of data analytics.
As we dived deep, it was clear that not all movers will spend big or spend at all. So, we aimed to narrow it down further to find out the proportion of the pre-movers and new movers who will be receptive and responsive to our offers, as well as those likely to spend more at a single point of sale. For example, think of movers who wish to furnish their entire home with new furniture, as opposed to those who would be downsizing by moving into a small space.
So, it was time for a fresh model.
Additional Customer Attributes for a Sharper Model
Our primary model relied on customer attributes like household income and property value that directly impact movers’ purchasing decisions. While these were paramount, we wanted to add more layers of categories to enrich data and narrow it down into an optimal group.
So, we moved to additional customer attributes that may also have a say in consumer purchasing decisions. For example, dwelling type, auto purchases, and debt management were considered because these factors also influence buying habits.
The aim was to be more granular, more specific, and thereby, more accurate in targeting customers through our direct mail campaign.
Having a 360-degree view of customers is important to have a better understanding of how they will react to your campaigns. The more you work with customer data, the more you realize how different attributes, no matter how trivial they may seem on the surface level, affect your customer’s purchasing decisions.
Additional customer attributes are critical for segmenting your clientele into different groups based on their purchasing habits, preferences, and demographic characteristics and catering to their expectations accordingly.
The Importance of Customer Data Enrichment
Customer data enrichment is when you enhance the existing data with additional information for more context and to gain a well-rounded idea about your customer base. Essentially, you add more attributes to categorize your audiences further.
The customer data enrichment process often involves pairing first-party data with relevant third-party data and comes with many benefits. For one, it ensures your data is timely, relevant, and updated, making your targeting efforts more effective. It is also vital for creating predictive models to identify and target prospects who are more likely to be your customers.
More importantly, enriched data gives you a deeper understanding of your customers and helps narrow customer segmentation. This way, you can provide personalized experiences and relevant product offers to each segment. Ultimately, it will improve the customer experience and lead to high revenue.
The New Approach for Reaching New Movers
In the case of the furniture retailer, we appended more data for the algorithm to create a more fleshed-out yet specific profile of new movers. Based on the model, the company chose to target only the top 50% of movers and the top 60% of pre-movers in their footprint.
Adding more attributes not only helped us identify the more responsive group. We also determined which offers were more appealing to each segment to tailor our direct mail campaigns accordingly.
Better Data Leads to Better Results
Can you go wrong with a solid data-driven strategy? We think not!
The newer model helped us cut down marketing spending significantly without impacting the response rate. This was no leap in the dark because we knew exactly whom to target. With relevant marketing content and product offers, we increased the ROI of the direct mail campaign by an impressive amount.
The company benefited from an $83 increase in average mover spend and a $75 increase in average pre-mover spend. In addition to the rise in sales, the response rate of the pre-mover segment also skyrocketed by 58%. The results indicated that we had hit the right target at the right time.
Unlock the Power of Data Analytics
If your targeting efforts are bringing in good results, that’s great! But there’s always room for improvement. At Speedeon, we help our clients combine their cross-channel data and utilize our third party data to gain actionable insights and leverage their marketing campaigns. Once brands realize the power of data in acquiring customers, they don’t go back to the conventional strategies.
Want to discuss your direct mail campaign? Get in touch with us today!
