Overcoming Business Challenges by Leveraging Location Data: A Case Study
There’s more to shopper marketing than coupons and samples, and if you wait until your shopper is already 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 Amplify Retail Executive Consultant Allisha Watkins and GroundTruth VP Mark Fleisch, who shared a case study that leveraged location data to overcome specific challenges during the launch of a new brand with multiple items in a new-to-company category.
As one of Watkins’ first projects as an independent consultant, this project was a clear example of how, in today’s highly competitive market, launching a new product is not for the faint of heart. But fortunately for Watkins, her vast experience with consumer marketing, a great partnership with GroundTruth, and a bit of creative thinking proved that the biggest challenges can also produce the biggest results.
According to Watkins, this particular client was looking to launch a new brand with multiple items in a new-to-company category.
This was a large, established company, but despite the fact that it had its own shopper marketing department, brand department, and an independent agency, it was primarily data driven and not very marketing savvy. So, the CMO approached Watkins to assist them in taking advantage of an opportunity to do something different, knowing that if they continued to think about things the way they had in the past, they were simply going to get what they had gotten in the past.
The company’s biggest competitor had a 60 share in this category and had been around since 1999. There were also two other brands that had tried and failed to launch in this same category the previous year – a fact Watkins’ client was unaware of. This new line of products was also priced above category.
In addition to the new item launch, the ultimate goal was to grow overall category sales. Success for the retailer would not involve stealing share from the competition, but rather seeing growth in incremental sales for the category.
Watkins also stated they were given only 500 stores and 10 weeks to complete this challenge.
After being presented with the challenge, Watkins determined that the solution lay in identifying the shopper behavior, including the mindsets and barriers relative to the category, and developing effective targeting solutions.
She needed to understand the shoppers’ emotions and identify the real-world behaviors that existed in these shoppers’ lives. She had to reach them throughout the purchase journey and make sure the campaign was connecting with them in meaningful ways in order to drive the conversion they were looking for.
Because this was a very analytical, finance driven organization, every single dollar that was put into the plan had to show the ROI. Watkins said there was a formula she and her team had to plug into that scrutinized everything to the tenth degree.
It was also important to understand store level performance. They were going against a competitor that had a 60 share in the category and very, very large budgets. Because the 500 test stores were critical to the success of the program, Watkins had to know where they were moving the needle, which stores were performing well, and why they were achieving those results.
Location and the Shopper
In order to better understand the shopper, Watkins partnered with GroundTruth to employ advanced targeting solutions which would help ensure they were only speaking to consumers who actually shopped at the 500 test stores. This allowed them to really connect and develop a message based on the mindset and behavior of these stores’ actual shoppers
They also leveraged offline data sets, which included not just the sales data, but also where shoppers were going and their visits to the stores – information that was then optimized throughout the campaign in real time.
One of the important aspects of this campaign was adjusting the media based on store inventory. Since this was a new launch, they leveraged first scan technology in order to understand when the product was available at each of those 500 stores. Because they were using media, such as retailtainment, they needed to make sure they weren’t driving to markets where the stores were out of stock.
By utilizing these advanced targeting solutions, Watkins says they were able to determine even before the client did when some stores were out of stock, and then share that information so the media could be adjusted accordingly. They could also leverage that location data for retargeting any markets that did not hit according to the timeline.
Additionally, because it was important in this category to focus more on refills than the actual product, capturing those device IDs also gave them the ability to connect back with shoppers who had initially purchased the items.
Bridging the Online/Offline Divide
Before going into the details of this particular case study, Fleisch shared some background and history on the art and science of location tracking. According to Fleisch, the reason location has grown so much as a tactic and as an industry is because it bridges the online and offline gap.
Location informs targeting, insight, and attribution, allowing us to understand how online messages and digital ads impact real-world behavior. It can also help gauge how a campaign is performing, which, taken together with the tracking, is an important data set because in the end, 90% of all CPG sales still take place in the store.
The Evolution of Location Tracking
There are two things that any location provider has to do really well. They must be able to verify physical places and they must be able to verify signals from smart phones. Fortunately, as technology has evolved, so has the accuracy of location tracking, and through this evolution there have been a number of different methodologies employed to verify physical buildings or physical places.
- Address-based Radius
In the early days of location, companies used address-based radius, which defined store locations based on store address. This usually entailed dropping a pin on a map and drawing a radius or geofence around it. It was pretty easy to get your hands on a store list, so this method was fairly easy to do.
Unfortunately, this method wasn’t very accurate, often not even capturing the store. And if the circle was expanded to actually include the store, it also captured a lot of other data along with it. Basically, this methodology said that anyone who entered that circle or geofence would be considered a Walmart visitor, which resulted in a lot of waste.
- Parcel Data
So, then people began using parcel data, also known as land parcel data, which produced fairly good results in most cases.
Land parcel data, which is used a lot in real estate, identifies the plot that the land sits on. In cases where, for example, Walmart owns most of its lot, if someone is on the property there’s a pretty good chance they intend to visit the store.
However, in the case of strip malls and other multi-tenant properties, there may be one piece of land with six different businesses on it. So, even though you know with a high degree of accuracy that someone is on that lot, you don’t necessarily know which store they’re in.
Another common and still widely used methodology is store-based radius, which defines store locations based on store center. In store-based radius, a geofence basically manipulates the pin to drop it on the center of the store and draw a circle. But while this method is much better than the address-based radius, it can still result in some inaccuracies.
The most recent, and what is considered industry wide as the best way to measure or verify physical places, is the polygon, which defines store locations based on land boundaries. Polygons use latitudes and longitudes associated with the actual footprint of the store to physically draw a perimeter around the footprint of the building itself. Lat/longs can also define the dedicated parking lot for that store and the retail block – the shopping center where that building sits.
This method provides highly accurate data in terms of the latitude and longitude information which comes from a mobile phone, pinpointing whether that lat/long is inside the store, in the parking lot, or even across the street.
Verification and Accuracy
While polygons are the most accurate way to verify the physical place, there is also another half to the equation – verifying the signal itself. Mobile phones don’t always function properly, and according to Fleisch, there is a wide range of accuracy in terms of lat/long signals his company gets.
Most location tracking signals come from consumer apps, where users give permission to use their location for advertising purposes. But in order to make sure the signals received from these phones are accurate, every one must be put through a verification process.
And while most location tracking companies do this to some extent, Fleisch says that at GroundTruth they check for everything, including how fast the person is traveling, the horizontal accuracy, and the degree of accuracy. They’ve even ingested store hours so they know whether the store is open or not. If they see a signal from the store at 2:00 in the morning it’s likely someone stocking shelves rather than a shopper, and they wouldn’t want to include that person in the data.
When deciding whether the signal is useful for the purposes of targeting someone with an ad or for attribution, it’s important to determine if it was actually a store visit. Fleisch says there are a number of different things they check to make sure a particular signal is really accurate and precise, including:
- Whether the signal is inside the store Blueprint
- Whether the visit is during open hours
- Employee status
- Accurate GPS signals
- Speed of device (filter for drive by)
- Filter out derived or state signals
- Filter out outlier signals from the same device
- Filter out fraudulent or suspect devices
Location and Personalized Creative
According to Fleisch, the ability to really understand the context of where customers and shoppers were physically was invaluable to this program and allowed them to present a campaign that reached specific consumers based on the following criteria.
- Reaching Consumers Based on Who They Are
Recent Walmart Shoppers – Because this was a test for 500 Walmart stores, Fleisch and Watkins knew it was important to make sure they were only talking to people who shopped those 500 stores. So, they took the signals of people who had been inside the innermost section of the polygon – those 500 Walmart stores – to build a loyal audience.
These people were served very targeted ads, driving them to Walmart, because they knew these consumers were loyal to Walmart and that they shop there, rather than at the other locations.
- Reaching Consumers Based on Where They Are
Proximity to Store – Fleisch says they see the most conversions when they can reach people throughout multiple points of the physical journey to store. Because these ads are basically mobile banner ads, it’s important to hit them a few different times in order to really drive that consideration and ultimately the conversion.
To reach them pre-shop, Fleisch’s company used a tactic they call Neighborhoods, which utilized heat map technology to identify residential areas that have the highest potential to shop one of those 500 stores.
So, instead of drawing a 10-mile radius around the store, they only focused on those residential blocks that they knew had the highest propensity to shop there. This let them know they were reaching the right shoppers pre-shop.
Then they did what Fleisch refers to as a “last mile strategy.” Consumers within a mile or so of the store were served a location aware message, letting them know that this product was available within that distance, with the purpose of getting them into the store.
On Premise – These messages are delivered all the way down to what Fleisch says they call “on premise” or “on lot.” At this point, they know the consumer is in the shopping center, in the parking lot or in the store, so they serve an ad intended to drive to the specific aisle or an offer the shopper could redeem in-store.
According to Fleisch, these on premise messages are your moment to really close the deal, to get shoppers into the aisle and to drive that conversion.
Fleisch also pointed out that when trying to drive offline behaviors and results, they generally don’t look at click through rate because it doesn’t really tell them much. Instead, they optimize toward two main metrics:
- Store Visit Rate
With store visit rate, if you reach 100 people, and 20 of those people show up in the store within a few days of seeing the ad, your store visit rate is 20%. So, Fleisch says their goal is always to drive store visit rate up. If they have a high store visit rate, this tells them that they are reaching the right shoppers and those shoppers are showing up in stores as a result.
- Cost Per Visit
Cost per visit illustrates how efficiently you are driving those shoppers. You always want to drive cost per visit down, so, if you’re spending $1,000 to drive a thousand visits your cost per visit is $1.00.
According to Fleisch, their goal, as always, was to get the store visit rate up and the cost per visit down, allowing them to understand store by store which tactics were most effective in terms of generating the visit.
At this point, they also used POS data to determine where they wanted to drive customers so they could “heavy up” the top performing stores and pull back on lower performing stores. This was important during the campaign because when going back with the line review for the buyer, they were able to walk into the conversation knowing which stores were most receptive to the brand and the product, and in which stores the product performed best.
Having those analytics behind them also allowed them to make recommendations for the future, knowing which stores performed best and using the store traits to replicate that strategy across the country.
According to Watkins, it was a successful new product launch for the brand and for the customer through Walmart, other retailers, and in other markets as well.
For this particular program, the company saw double digit category growth. Watkins was unable to share full details due to an NDA, but at the end of the day, the client was very pleased with the activation and the teams were able to deliver the customer goals for the retailer, the shopper goals, and the brand goals.
Additionally, Watkins stated the client was able to take some of the elements back into their brand team. This client, who began the process with no insights and little understanding of the category or the consumer, through the details and rich data provided by Watkins and GroundTruth, was able to gain a better understanding of the reporting process, how rich media works, who its consumer/shopper really was, and of the shopper journey as a whole.
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