Beyond real-time: How location based marketers look to the past to predict the future

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How location based marketers predict the future
Jamie Crespi

The iPhone–which is turning ten this month– transformed the way people communicate, shop, and travel. It also transformed the way marketers reach their ideal audiences. For the first time, they could use the smartphone’s location data to find out where consumers were—and buy impressions accordingly.

Today, advertisers continue to use real-time location data to target consumers. But the industry has come a long way since 2007. Advertisers are moving beyond the real-time capabilities of location technology towards more advanced applications. Brands can boost campaign performance and ROI, by using historical data and AI-powered solutions to discover where consumers have been and where they are going.

Back to the Basics, Location 101

Location-based advertising means using real-time location data to reach out to consumers based on where they go. A common example of Location 101 is targeting consumers based on their distance from a given location in real-time, known as proximity targeting. For example, if a pharmacy wants to target young women inclined to purchase its products, they can collect mobile devices seen in similar or related locations. Using location technology, the pharmacy brand can retarget that audience group whenever they come within feet of one of the pharmacies with products they have in stock or specific promotions. The same example applies to many verticals, not just CPG.

But even proximity targeting has matured beyond these basic abilities. More advanced applications involve layering real-time location data with other valuable data for more granular targeting.

Let’s say a high-end retailer on a busy shopping street is using proximity targeting to reach consumers within 100 feet of their store. That brand could be wasting impressions on people who wouldn’t shop at a luxury store in the first place. But how would they know? If the brand pairs its proximity data with consumer information such as shopping histories, demographic and geographic details, they can more accurately target the right person. For instance, they can improve campaign performance by reaching out only to high-income earners who have previously shopped in a different luxury store—individuals who are not just nearby, but also likely to buy.

From Mobile to Mobility

Brands need to remember that they aren’t advertising to a mobile device, rather, they are advertising to a mobile person. Therefore, the importance of looking at historical location data to get to know their consumers and their preferences is paramount. For instance, sports enthusiasts could be identified by the quantity of trips they make to sports fields each month. Their habits can be identified by the types of hotels they stay at while on the road to the games. Based on this historical information, advertisers can utilize this data to target this audience with ads for fan gear when close to sporting goods stores or hotel deals when traveling on the road to games. This type of data builds rich audience targeting segments for marketers so they can advertise based on consumer behaviour.

Places we go over time reveal more than just consumers’ preferences; historical location data can also tell advertisers things like when consumers tend to go to the gym, how often they go shopping, or how frequently they are heading to the airport—from specific location, the day of week, time, to duration of time spent in a location. Such insights can be used to retarget consumers not only at the right place, but also at the right time.

For example, someone who visits the airport monthly is likely a frequent flyer, but that doesn’t mean they’ll be receptive to an airline ad while boarding a competitor’s plane. Instead of using proximity targeting to reach this flyer at the airport, the airline can analyze historical location patterns to reach out after their travel when they are back at work or at home—and before they book their next flight.

AI and Advanced Analytics

While many brands focus on real-time location and historical behavioral patterns, other advertisers are looking into the future. With machine learning and predictive analytics, these advertisers lean on their technology partners’ knowledge of behavioral patterns and advanced algorithms to drive in-store foot traffic.

Brands drive foot traffic by targeting only those with strong purchase intent at exactly the right time and place. Brands can now tell their tech partners how many visitors they need in what locations and buy them on a cost-per-visit model; only being charged once a store visit occurs.

For example, a fast-food chain can identify its ideal audience by looking at individuals who visit this chain a few times a month. Then, they can deliver ads to these individuals at the time and place they are most receptive based on past behavior patterns to drive them in store. Such advanced techniques increase foot traffic as well as in-store sales.

From basic applications to more advanced use cases, location data and technology continue to alter the advertising world. As the industry evolves, brands should look beyond where consumers are in the moment, to where they’ve been and where they’re going in order to improve conversation rates, waste less ad dollars, and increase sales.

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Jamie Crespi is VP, Americas Marketing. She is responsible for leading the US marketing efforts by providing creative market planning and execution that maps to the sales and product strategy. She is responsible for all client and prospect marketing communication in North America and working hand in hand with the US sales team to drive revenue. She has over a decade of experience working in marketing and advertising as well as an extensive adtech background.
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Question 1: How long have you been at RSi and what is your role?
For the past three years, I have been responsible for creating and scaling Ansa, a web-based solution from RSi – Retail Solutions, Inc., that has enabled over 75 of the world’s largest CPG companies and their agencies to build, measure and maximize the performance of their shopper marketing campaigns running in support of the nation’s leading retailers. I am responsible for all aspects of business development, partner and agency relationships and the overall revenue growth of Ansa.

Question 2: How does RSi help solve marketer challenges?
Shopper marketers’ biggest challenge is to connect their online campaigns to in-store results. RSi’s Ansa solution provides the intelligence they need, based on daily, store-level POS-data from the largest US retailers in order to plan, target, and measure the impact of their shopper marketing campaigns. Retail Solutions Inc. has partnered with the leading ad networks in Shopper Marketing, such as Blis, to make Ansa’s automated analytics available for the world’s largest CPG companies and their agencies. To measure and maximize their digital ad campaigns, all they need to do is ask for Ansa inside their next campaign.

Question 3: What benefits does the partnership with Blis bring to buyers as well as the adtech ecosystem?
With RSi’s Ansa solution, building, dynamically optimizing, and reviewing attribution measures for every digital ad campaign has never been so simple. Here is how it works:
1. STORE-LEVEL TARGETING: automatically get from Ansa your store targeting data as store addresses, lat/longs or by Ansa Digital ZIPs to identify stores with the greatest sales potential prior to launching hyper-local media.
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3. MEASUREMENT & INSIGHTS: get access via the online portal to end of campaign analysis just days after the media campaign is over. Visualizations give you a standardized set of analytics, such as sales lift, incremental dollars and units, confidence level, weekly lift, characteristics of high performing stores, etc. Prove and improve your media to help you fine-tune strategies for your future campaigns.

Question 4: What are use cases for the Blis + RSi partnership? (Please provide a few examples from different verticals).
If you are a shopper marketer, maximizing your budgets, understanding performance of your marketing tactics and generating key learnings from those marketing tactics are tasks that are essential to your business.

Running a digital marketing campaign with Blis, and Ansa’s daily, store-level sales intelligence helps make that extremely for the CPG community and shopper marketers specifically.

For existing products, Blis campaigns using Ansa targeting can reach a targeting efficiency of 2:1 vs. campaigns that do not use Ansa store-level targeting thereby ensuring that every dollar is spent driving sales to your most important retailer locations.

Blis campaigns optimized with Ansa typically identify and heavy up investment around 16% of stores that are trending significantly ahead of the average store during a campaign and identify and decrease investment around 14% of stores that are trending significantly behind the average store, therefore ensuring that your budget is being optimized surrounding stores that are over-performing during a given campaign.

After each Blis campaign, Ansa automatically generates measurement of Featured Item Lift and Halo Item Lift at both the total event and week levels. Results are completed 5 business days after the end of each campaign and allow you to learn quickly and improve continuously, all at an amazingly affordable price.

Question 5: What shopper marketing measurement trends do you predict for 2018?
Optimization in-flight based on store sales trends during campaign. Optimizing on engagement, intent and / or clicks may be ok for some campaigns but more and more frequently shopper marketers are tasked with driving sales at their most important retailers. And understanding how their marketing tactics performed 5-6 weeks after a campaign has finished is just not fast enough anymore in today’s fast paced world and puts media providers at a severe disadvantage. By utilizing automated reporting that allows Ansa partners like Blis to understand and optimize their media in-flight based on daily, store-level POS sales data you now empower your media partner to act on supporting the stores that are driving your product sales which can ultimately provide a powerful boost to a shopper marketing campaign.

Question 6: If there was one piece of content you think every marketer should read, what is it?
(Other than this blog post of course!)

Think with Google and Facebook IQ are two fantastic sources of resources. Articles, trends, case studies, POVs, insights, etc… pretty much everything you need to read to keep you up-to-speed in this very fast-paced environment.

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Today, mobile devices are like mini retail stores we carry around in our pockets: places where consumers can browse merchandise or place orders almost instantly.

But mobile devices also give consumers something they can’t get in stores: personalized marketing. Collecting data like shopping histories and browsing patterns, mmobile devices provide retailers with detailed insight into individual consumers and a means of communicating with them directly.

How can retailers use mobile insights and capabilities to craft effective, one-to-one messaging?

1. Get personal.

Today, consumers want—and expect—ads to speak directly to them. In fact, 74% of customers feel frustrated when their online experiences aren’t personalized.

The easiest way for retailers to personalize content is by harnessing their first-party data. If a customer purchases a dress online, the brand can use what they know about her (her fashion interests, browsing history and email address) to customize subsequent content. For example, the brand can serve an ad via email that suggests a pair of shoes to go along with the new dress.

With CRM data, the retailer can see what the woman bought online, but do they know what she’s purchased elsewhere? Or what she does when she’s not shopping? This is where location data comes in. Retailers that layer location-based insights on to other sources of data can get to know where and when consumers shop at brick and mortar stores. They can also identify other behavioral patterns, including which day of the week and time of day they like to go shopping—data can enables greater levels of personalization.

Let’s say a CPG brand wants to reach out to a previous customer who hasn’t been seen in store lately. The marketers can use their knowledge of the consumer’s daily commute to deliver the ad just before he leaves work, suggesting he stop by on his way home. They may even offer him a discount on the product he previously purchased.

2. Market to individuals, not devices.

Once retailer marketers have identified their ideal audiences on mobile, they shouldn’t see phones as the only means of communication. Consumers own an average of 3.6 connected devices, so retailers should communicate with consumers across the devices they use, including tablets, laptops, desktops and addressable TV.

However, if a retailer sees a user reading political news on the tablet all day but watching cartoons in the evening, it might not be the same same person. With families and partners sharing devices at home, marketers need to make sure they are constructing nuanced consumer profiles across devices in order to reach out to individuals, not just devices.

3. Don’t be creepy.

Personalized, cross-device marketing is on the rise in part because consumers are increasingly willing to disclose their data to retailers. After all, purchase histories and location data are essential for useful or interesting ads.

But how retailers use that data is key. Consumers want to feel understood, but they don’t want to feel like ads are invasive or drawing on data that’s simply too personal and private. Marketers need to make sure they aren’t crossing any personal boundaries or making consumers feel uncomfortable.

If marketers want to turn heads or, more importantly, turn consumers into buyers, they’ll need to do more than blast out generic ads to the masses. When retailers personalize ads with these three tips, they’ll see huge improvements in campaign performance.

But how, exactly, do they measure these improvements? Find out next week when we assess the best metrics for retailers.

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Embracing the Retailer’s Dream Metric: Cost Per Visit

The twentieth-century American engineer and statistician W. Edwards Deming once said, “Just because you can measure everything, doesn’t mean that you should.”

This applies to retailer struggles today as marketing executives need to decide what they should measure and how. Do they care about impressions, views or click-through rates? And once they figure that out, how can they make sure their ad dollars are really working? The Partnership predicts that ad fraud will cost brands over $16 billion this year alone, while Infectious Media suggests that over half of all digital ads aren’t seen at all.

Fortunately for retailers, there’s a new metric in town—one designed to eliminate waste and increase sales. With a cost-per-visit (CPV) model, retailers pay only when a consumer sees an ad and visits a specific location. Here are four ways retailers are benefiting from this cutting-edge new metric.

1) Increased Foot Traffic

With the National Retail Federation predicting eight to 12 percent e-commerce growth this year alone, no one can deny the rapid rise of online sales. However, 85 percent of consumers still prefer to shop in brick-and-mortar stores, where 94 percent of all sales are generated. That’s why it’s vital for retailers to keep their physical stores alive and continue to enhance their in-store experiences.

With the explicit goal of bringing visitors into physical store locations, CPV is a metric for retailers wanting to increase foot traffic—and pay only for successful conversions. While there are many ways to boost in-store visits, today’s leading location data solutions use predictive location modeling. With Blis Futures, we choose to charge on a CPV-basis because we are completely confident in this approach.

2) Greater In-store Sales

Driving consumers into brick-and-mortar locations may also encourage consumers to buy more than they anticipated. It gives retailers the opportunity to upsell consumers so they need to make sure they clearly advertise their promotional pricing, point-of-purchase displays and loyalty programs. Once you have a potential customer in the store, you can push tailored messaging in real-time and create personalized promotions. As anyone that has ever been into a Target retail location can attest – you may go in for one specific item but end up unable to leave the store for less than $100! So only paying when a consumer sees an ad and then visits a physical location reaps multiples rewards for a marketer.

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When retailers buy ads on a cost-per-visit basis, they don’t pay if the consumer sees the ad but doesn’t come into the store. That means the retailer also benefits from ad views and branding. In fact, a consumer may see the ad and make a purchase online rather than in-store, but the marketer still pays nothing for that conversion. At Blis, we are willing to take that risk and allow marketers “free” branding messages. Our confidence in the technology behind our CPV metric allows us to think of marketers first.

4) Risk-free Investing

CPV transfers the risk from buyer to partner, so retailers don’t have to worry about wasted ad spend: They’re making a completely risk-free investment. With free branding and zero downside, retailers have nothing to lose.

When Blis became one of the first tech partners to offer the CPV model earlier this year, we sent a critical message to both retailers and the wider industry: We’re ushering in a new era of transparency and accountability in advertising.

Check back again next week when we switch gears to discuss how retailers can use mobile to boost engagement, retention and acquisition.

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