Knowing Your Shopper: Precision Marketing at Scale
There’s more to shopper marketing than coupons and samples, and if you wait until your shopper is actually in the store, you’ve already lost the battle. Technology has changed the way people learn about and acquire everything from socks to real estate. You have to meet them where they are, with information relevant to them, at a time when they are receptive to your message. The “best practices” ground is always shifting. You need to stay nimble, connected, and aware. On March 12, 2019 Supplier Community brought together a group of shopper marketing rock stars from a variety of suppliers, agencies and service providers to help participants do just that.
Our featured speakers included Tom Brydon, Regional Manager for Blis, and Melody Dickinson, Sales Digital Analytics for RSI, who shared tips, tricks, and important information on knowing your shopper and serving them with targeted, personalized messages through precision marketing and measurement at scale.
The Rise of the Smartphone
It all started with Steve Jobs, the pioneer who created a device that has changed the way society connects and interacts with the internet. To date, there is no other technological advancement that has changed and shaped the way we access information like the smartphone, and throughout the various iterations over the past 12 years, these devices have become a central part of people’s everyday lives.
When it comes to accessing the internet, the smartphone is now number one.
- Mobile or smartphone – 71%
- Laptop or notebook – 62%
- Tablet computer – 52%
- Desktop computer – 40%
- Smart TV – 21%
- Other mobile devices – 18%
Smart TVs and tablets are on the rise as well. Twelve years ago, this would have looked very different; desktop computers would have been number one. But society is changing. In another five years this will look very different again. That’s the nature of this industry.
There are about 270 million devices in the US at the moment, and according to a Deloitte study, each of us interact with our smart phones 52 times a day. So, if you multiply that by the number of devices in the US, that’s 14 billion interaction points every day across the US, and that’s what Brydon says location is tied to.
Gathering Consumer Data: Self-reported Metrics vs Actual Behavior
Location offers key insights into your shopper. Location is at the core of who you are. It’s a very honest representation of what you do every day. There’s no need to rely on metrics. The data is based on an individual’s behavior, and you can use that information to deliver important messages to your shopper.
When using location data, you no longer have to rely on such self-reported metrics as:
- Demographics – Age, gender, and ethnicity are important, but on their own don’t reveal a huge amount about your shopper.
- Attitudes – Religious, not religious, liberal, conservative – this information has no bearing on the type of toothpaste your shopper is going to buy.
- Lifestyles – The way your shopper wants to be perceived may not be who they actually are. For instance, they may spend time looking at online content that is health related, or browse workout clothing, when in fact, they have not set foot inside a gym for some time. Location gives you a good idea of who your shopper really is.
- Stereotypes – Because location provides so much information about your shopper, you don’t need to rely on stereotypes.
Location data shows you actual behavior to guide strategy rather than these self-reported metrics.
When building your location picture, it all starts with space, which in turn starts with a latitude/longitude data point that shows you an exact location on the earth’s surface.
Then you layer on place. Is there a building at that location point, and if so, what kind of building is it? Is your Walmart located there? Is it a gym, or a Starbucks? Your picture then becomes clearer through use of a place of interest database which can show you the nature of the services inside these buildings.
Finally, the most important part is the people, the people that you need to buy your product. These people decide the success or failure of your product, whether it will increase its distribution, or whether it’s going to suffer and eventually be pulled.
Understanding where they’re going through these different location points allows you to better understand who your shopper is.
Once you’ve built your location picture, you can talk about audience segmentation. Audience segmentation essentially boils down to three key data points:
- Places of interest (POI)
- Time and date
Your unique device ID sits and lives across these three data points, so you’ve got to treat the device, those set of numbers, as your shopper. As a side note, Brydon points out that while this might seem like a rather impersonal way of identifying the shopper, it’s important to make sure we protect their privacy and remain compliant with all data protection and privacy requirements.
Essentially that device is your shopper, and you need to find ways to interact with them that make sense to them in their everyday lives, because today’s consumers don’t see the difference between offline and online.
So, these three key points, linked to a unique device ID, build up a picture of who your shopper is. They tell you what location they’re in and what is serviced within that location. They also tell you when and how often your shopper does what they do.
To make that a bit more organic, Brydon shared an example of an everyday shopper he called Becky. At 8:15 AM, Becky starts her day off at the University of Arkansas. She then goes to Starbucks on campus, to the gym, then to a bar. Later that evening she stops at Chick-fil-A.
Becky represents your shopper. In this example, we know she’s a student. We know she likes to go to the gym and stay healthy, and then we know, based on her choice of restaurant, that she eats chicken. So, Brydon says they would serve her a Tyson chicken ad because it’s relevant to her behavior.
In another example, Brydon shares a day in the life of a mom. Monday through Friday she begins her day at school, a good indication that she is a mom. She goes to the gym, followed by a trip to a nail salon. She then stops at Walmart before going home, where she connects to her home Wi-Fi.
Based on her interest in beauty and health, which was determined by her location, Brydon says they would serve her an ad for Simple Hydrating Booster. But in this case, they would serve it on her home Wi-Fi when she’s in a more relaxed state of mind and more open to actually receiving the information displayed to her.
This particular ad includes a short quiz, and according to Brydon, if an action that will require more than just a click is needed, it’s best served on the home Wi-Fi where you shopper is in a much more relaxed state and able to complete that action on devices more suited to those types of actions.
After your audience segmentation, there are also a number of targeting layers.
You’ve got retail visitation and Wi-Fi browsing behavior. At this point demographics also become important, because when combined with this other information, they are key to understanding who your shopper is.
Another important aspect is app ownership targeting. Brydon says Blis has the ability to do this across both iOS and Android.
Do they have the Walmart app? Do they have the Target Cartwheel app? Do they have apps that are relevant to the theme of your program? Do they have apps which may be a female vs male type split such as ESPN, or a family type app such as Cozy Organizer?
Those types of apps give you a really good idea of who your shopper is.
You also have product and competitor purchases. It’s important to know who’s buying your product, who’s buying the product in your category, and also who’s buying your competitor’s product, because that needs to be added into the mix in order to try to regain market share.
Integrating the Demand Signal
Measurement is an important part of any campaign, and Brydon says they are constantly optimizing and measuring before, during, and after campaigns through Blis and RSI metrics.
And while RSI is known as a measurement partner, and measurement is certainly important, Dickinson says that measurement alone is not enough. By integrating the demand signal or actual sales data into targeting optimization and measurement, you can know you are doing everything before, during, and after your campaign to drive that incrementality in sales – something that is very important, especially when those are your KPIs.
Before the campaign, target smarter. Spend budgets efficiently around the stores that matter.
Use location BEFORE a campaign to drive spending efficiency.
- 25% top selling retail stores are targeted by the average campaign
- Those top 25% drive 45% of sales on average for the featured items in each campaign
- This is equal to 180% spending efficiency-45% of sales drive, 25% of stores targeted
There are a few things that need to be looked at prior to the campaign. First, it’s important to verify that the targeted stores are carrying the products being advertised. Then you need to determine what percentage of sales those stores represent for those items.
Once your budget is set, you’ll be looking at geotargeting. How many stores will your budget cover and which stores will you target? If you think about campaigns you supported, looking across retailer stores, some have sold a lot of product and some not so much.
To help determine which stores will be most lucrative for your program, look at the average sales from your retailer stores. Anything above that number will be stores selling more than average and anything under it will be those selling below average. So, if you’re charged with driving incrementality in in-store sales, the opportunity when geotargeting is going to be in that upper quadrant. That’s your sweet spot, especially if you’ve got a more limited budget.
According to Dickinson, during an average campaign measured by her company, 25% of retailer stores will be targeted, and those retailer stores will make up 45% of featured item sale – pretty efficient if you’re looking at targeting based on sales data.
During the campaign, optimize in real-time. Focus on what is working (mid-campaign) through real-time, store level data.
By Week 3, DURING a typical campaign
- 14% of stores trend significantly behind the average stores
- 16% of stores trend significantly ahead of the average stores
No matter how carefully you’ve chosen your stores, inevitably there are going to be some with a great response to media and some that lag behind. Dickenson says oftentimes they don’t know why, but what they do know is, by monitoring throughout the campaign, there’s an opportunity there to do something about it.
By understanding performance during a campaign, you can shift media to heavy up on the stores that are performing well. It will also alert you to stores that aren’t pulling their weight. Maybe the product is out of stock, maybe there’s a competitor roll back – something is going on. Dickenson says it doesn’t matter what the reason, just don’t invest there.
According to Dickinson, those media partners who are really looking to understand intra-campaign performance and optimizing ruthlessly are the ones that are driving the best returns so far this year.
After the campaign, measure automatically with consistent attribution for in-store sales measurement via POS data.
Use insights AFTER for future improvements.
- 70% of campaigns produce Feature Item lift between 3.8% and 9.2%
- 50% of campaigns produce Halo Item lift between 3.3% and 8.5%
- Large format retailers generate 61% higher incremental units than small format retailers
- The likelihood of Halo Item lift is the same whether Featured Items are new or existing
Dickinson shared a few of the KPIs her company has measured for recently, and these indicated that approximately 70% of campaigns have a statistically significant lift. She also stated that in 50% of campaigns, they observe some type of halo lift, other benefits to brand products, in terms of sales, whether it’s new items or existing items being supported.
Finally, they found that larger format retailers generate more volume movement than small format retailers. While this isn’t surprising, when they measured difference, they found it was as great as 61% higher sales.
According to Dickinson, the above data was qualified through more than 1,300 campaigns her company has measured, across 115 CPGs with 20 retailers. To date, they have measured over 5 trillion impressions.
So, how does all of this look in the real world? To illustrate how these principles actually work for a company, Brydon shared three cases studies of campaigns conducted through Blis.
- Skincare Products inside retail stores
Objective: The campaign objective was to drive sales of skincare products among 18 to 24-year olds at selected retail stores, with a focus on regaining market shares from lapsed and competitor purchases.
Targeting Solutions: Blis first identified devices seen at retail stores within the past 30 days. They then accessed previous purchase data to refine those shoppers into groups of lapsed purchasers and competitor purchases.
- Store visitation was x2 among 25 to 35-year olds vs. 18 to24-year olds.
- $.94 Incremental ROAs
- 21% Foot traffic uplift
- 13.5% Featured item % sales lift
- $1.64 Cost per visitor
In this example Brydon said their goal was to drive sales of skin care products and regain market share among 18 to 24-year olds by targeting people who were lapsed and competitor purchasers.
They collected devices observed at stores during the prior 30 days, then filtered those into defined behavioral groups to target against.
The key thing they learned was that store visitation was twice as high among the 25 to 34-year olds as it was for the 18 to 24-year olds. This was a valuable, actionable insight for the brand and product to help refine and target against in future campaigns – that they needed to put more dollars toward the 25 to 34-year-old audience group.
The final results for this campaign included a 21% uplift in foot traffic and a 13.5% featured item sales lift.
- Soap Brand – Veteran Charity Foundation inside retail stores
Objective: Promote the affiliation between a soap brand and a veteran charity foundation that donated $1 to military families for every purchase of soap product limited edition packs.
Targeting Solutions: Blis began by collecting devices seen visiting participating retailers in the previous 30 days and cross-referencing these with multiple behavior data points to create retailer audience segments of previous soap products/low price point competitor purchases, household income of $30,000 – $100 k, interested in charitable giving, and a veteran in the household.
- Exceptional Sales Results largely attributed to combining multiple behavior traits with a popular product and ethical cause.
- $1.41 Incremental ROAs
- 17% Foot traffic uplift
- $132K Total item sales lift
- $2.08 Cost per visit
This example was a bit different because it had an ethical element, a type of campaign Brydon says he always enjoys working on. The goal was to promote the affiliation between this soap and a veteran charity, to which the company donated $1 every time a customer bought one of these special edition packs.
By building up different behavioral traits and data points, including people that had an affiliation with veterans or had donated to charities in the past, plus layering on household income data and insights, then comparing to products at the lowest price points, they were able to create an effective campaign and make it work in harmony to reach the right shopper.
The final results for this campaign included a $132K of total items sales lift, along with a foot traffic lift of 17%.
- Ice Cream Pints inside retail stores
Objective: Drive sales of pint ice cream products within retail stores
Targeting Solution: Devices of females aged 25 to 45 were collected during visits at select retailers over 30 days. Additional data sets were layered on to further refine the collected audience for previous purchases of pint ice cream and competitor ice cream brands. Zonal targeting was also carried out.
- Most sales lift driven by milk chocolate hazelnut (18%) flavor. Most incremental unit sales driven by milk chocolate vanilla (38%) flavor. Both important to optimizing toward sales lift vs IROAS.
- 14% Foot traffic uplift
- 13.2% featured item sales lift
- $1.47 Cost per visit
According to Brydon, they took a different approach in this example – a zonal targeting approach that employs three different areas.
The hot zone is in-store, right where you want it to be. Ideally, you’ve kept your brand top of mind with targeting throughout the shopper’s journey.
At this point, they served shoppers messaging that generated a reaction, let them know this was the product they should buy, made the purchase as easy as possible, and finally, got them to purchase the brand.
The comfort zone is at home, connected to Wi-Fi, and would include cross device targeting. For this campaign, they utilized that Wi-Fi connection, not necessarily to drive action, but to keep the brand top of mind.
The idle zone is on the go or in transit. This would be primarily smart phones. Shoppers were targeted with messaging designed to convert them to that hot zone, to get them in-store purchasing the product, using a digital means to create a physical reaction.
The final results for this campaign included a 14% foot traffic uplift and a 13.25% featured item sales lift. Additionally, they found that Chocolate Hazelnut had the best sales uplift, but Milk Chocolate Vanilla actually drove the most incremental sales units.
According to Brydon, the days of using only digital means to define whether a campaign was successful are gone. Impressions, click through rates, helicopters, digital helicopters – all these things, while useful, need to be tied to physical, real world actions.
And Brydon says that’s really the power that today’s shopper marketers have. There is so much data to action against now, and even better, we can measure it. It’s amazing, and for all the shopper marketers out there, it’s a great time to be in the shopper marketing industry.
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