The bells are still a ringin’! Welcome to Part 2 of our location data for holiday shopping series. In Part 1, we looked at how location data can be used to understand and predict visitation patterns during the peak holiday shopping weeks.
In Part 2, we’ll show you how to connect with the shoppers that are coming in the door during this critical period. Specifically, we’ll cover:
- Learning your shopper segmentation
- Creating value-add messaging and marketing to these key customer segments
- Maximizing your holiday marketing budget with efficient targeting
Location data helps brands get shoppers in the door, deliver the right messaging to serve them, and find where they are to reach more like-minded customers. Read on to learn more!
Customer Profiles: Learning Your Actual Shopper Segmentation
Winning market share during the holiday season starts with understanding the shopper that sets foot in a store. Who is that shopper and why should they pick you during the holiday season?
Many companies develop target shopper personas based on who they want or think will walk in the door. Location data unlocks the ability to create these personas based on shoppers actually visiting your location.
Below is an example of customer profiles from a Target store in North Carolina. These customer profiles are specific, targeted, and actionable. For the purposes of this article we’ll mainly focus on the two largest: “Upper Suburban Diverse Families” and “Blue Collar Suburbs.”
As the store approaches the holiday season, access to these shopper personas becomes highly strategic:
- What is the location’s unique holiday selling proposition to Upper Suburban Diverse Families, which represent >20% of shoppers?
- How is that different from the value proposition to Blue Collar Suburbs, the second largest shopping segment?
Shoppers in the Upper Suburban Diverse Families (USDF), which have a household income of $80,000, may have more disposable income than shoppers in the Blue Collar Suburbs, which have a household income of $55,000. The USDF segment also has more multi-person households and higher home ownership.
Thus, the selling proposition to the USDF segment needs to reflect homeowners and families with above average income while the selling proposition to the Blue Collar Suburbs needs to account for a higher share of individuals with lower household income.
For Target, an example of the selling proposition to each group could be:
- Upper Suburban Diverse Families: Premium gifts for the whole family
- Blue Collar Suburbs: Gifts at value prices for every shopper
In a competitive retail environment, every advantage counts. The more precise a team can be in its planning and preparation, the higher the likelihood that the customer will connect with their value proposition during the holidays and spend more.
Shopper segmentation data allows the team more time to spend planning for the holidays and less time guessing whether they’re planning for the right shopper.
Tailoring your Marketing Messaging with Customer Segmentation
When a brand knows the customer coming in the door, it can develop the marketing strategy to help increase sales per visit and increase repeat visits during the holidays by asking:
- What products will these key segments be shopping for during the holidays?
- Why does your brand uniquely connect with that segment?
Brands can tailor their holiday messaging, promotions, and marketing to these customer segments.
As an example, let’s return to the shopper segmentation graphic for the Target location in North Carolina with the descriptions of the top two segments.
Clicking through each customer profile reveals the demographics and characteristics of those segments.
For example, Upper Suburban Diverse Families have a household income of $80,000, have high home ownership rates, and are well educated. Blue Collar Suburbs have household income of $55,000, are 40% single-person households, and have a home ownership rate of 53%.
As we start to understand each segment, we can look for opportunities to create personas that capture larger portions of the customer base.
For example, four of the shopper segmentations presented in the graphic can be further grouped into two personas that exhibit similar characteristics.
- Upper Suburban Diverse Families + Wealthy Suburban families have similar demographic characteristics and will likely have similar holiday shopping characteristics.
- These two groups represent 36% of customers
- Blue Collar Suburbs + Rural Average Income also have similar demographic profiles by age & income and can be a second group.
- These groups represent 25% of customers
In this example, we’ve now accounted for over 60% of customers with just two groups. We can develop holiday strategies, messaging, and promotions that speak directly to the needs of those groups and have a higher chance of gaining holiday share.
The strategy & messaging for the first group, with higher income and higher rates of families, may be more oriented around driving purchases of toys, games, or presents for their children.
For the second group, where the rate of single person households is higher, promotions on personal items like electronics or self-care might have a better chance of drawing the shoppers in the door.
These differing strategies can be reflected in places like the weekly ad flyers that Target produces, the tone and product promotion of different TV ad campaigns, and the personalized offers that the company develops through its Target Circle program.
Customer profiles provide the starting point and teams can use them to find the largest opportunities to connect with like-minded segments, and this is all powered by location data that unlocks insights into these key customer insights.
Targeted Marketing with Trade Area Analysis
The final step after learning the customer profiles and developing the messaging to reach them is to know where more of these customers can be found.
Trade area data solves this question. It pinpoints the areas where shoppers live and work. This data allows brands to amplify their marketing efforts in the right places.
Below is a representative map from the Target store referenced earlier shaded by the areas where most customers live.
The blue pin in the graphic is the Target location. Areas shaded in blue represent block groups where customers live, with darker shading representing a higher share of customers.
What we find interesting looking at this graphic is that customers tend to travel from their homes in the southeast, but the location acquires few customers from the northwest, as evidenced by the blue shading that extends to the bottom right of the marker, but very few shaded areas to the top left.
Instead of simply marketing to anyone within a 5 mile radius, Target can get a better return by doubling down on the areas where the majority of shoppers travel from in order to access the store.
Leading up to, and during, the holidays, this knowledge becomes an opportunity to get in front of the right households at the right time through out of home advertising, mailers, and other forms of targeted marketing.
Access the Data with Unacast Insights
Collectively, this information identifies who is shopping at a location and where to find them. This makes marketing during the holiday season highly targeted and strategic, and it’s all powered by the simple-to-use Unacast Insights platform.
Customer Profiles and Demographics are accessible in the Venues section of the platform and do not require coding skills or a data science background to interpret.
Likewise, trade area data is also found in the platform, which can be adjusted to Home or Work locations.
Shopper profiles — while relevant year-round — take on added importance during the holiday season given its outsized impact on annual revenue. Developing clear customer profiles based on reliable data can support teams in the holiday planning season to ensure the store messaging, marketing, and targeting reflects the shoppers at that location to give them the highest chance of holiday success