relevant reach - oracle · the importance of maximizing reach ... the opposite of maximized reach...

RELEVANT REACHGet personal at scale

JANUARY 2017

Identity crisis?Bridging the gap

Win the online grocery shopper

The fundamentals of measurement

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Oracle Data Cloud | @OracleDataCloud 1

As a strategic partner in helping to drive your business forward, it is imperative for us to discuss industry issues, share thought leadership, build relationships that foster a collaborative approach, and create data-driven digital media solutions for our industry.

This year is geared toward pressing forward and focusing more on those challenging topics that are part of the natural maturation of digital media. These include getting to accurate cross-channel consumer identification, managing data overload, understanding relevant reach and the impact of globalization. We also strive to develop best practices from all our media platform partners to ensure optimal campaign execution to achieve your goals.

A letter from leadership

This publication of thought leadership highlights some of the key themes and strategies that we believe to be essential for a successful data-driven marketing strategy.

We are excited by this unprecedented time in our industry and the ability for data to unlock insights and drive connections to your consumers. We look forward to our continued partnership in driving data innovation to success.

Sincerely,

Brenda Tuohig VP, General Manager CPGOracle Data Cloud

WELCOME

Quick answers & the audiences you need to win [email protected]

The notion of “Reach” has been around as

long as mass media. The more reach you

had, so the thinking went, the more likely

your message was to reach the maximum

number of future buyers. The importance of

maximizing reach among potential buyers of

products is also well-supported in research

literature as a key driver of brand growth.

The opposite of maximized reach is precision

targeting. Just ask our offline marketing

friends in the catalog and DM businesses.

They want to be as focused and waste-free

as possible given the high costs of delivery,

and they target accordingly. These marketers

demand that their impressions go to people

most likely to make a purchase in the future,

and therefore they are constantly refining

their lists and strive to achieve a strong return

on ad spend.

Many digital marketers today believe they have to choose between maximizing reach or focusing on precision targeting for their online campaigns. The good news is that they don’t! At Oracle Data Cloud, we know that today’s online marketing capabilities can allow advertisers to reach audiences at scale, while using data to prioritize reaching potential buyers. We call it Relevant Reach.

The what and why of relevanceWe define “Relevant” quite simply. It’s the universe of people who represent the highest potential pool of buyers for your brand.

What does that identifying signal look like? Actually, it varies by industry. In Automotive, there are ~20MM people in the U.S. at any given time who are in-market for a new car in the next 12 months. Knowing this, why would a mass market car maker want to pay ads that reach the 200MM+ people who have no interested in buying a new car, (much less a luxury car)? It’s a little

Let’s look at the rise of Relevant Reach

Jim Greco Senior Analytics Director CPG, Oracle Data Cloud


different in consumer packaged goods, where regular buying of a category is a strong signal of future purchase potential. If 50% of U.S. households buy granola bars each year, is it wise to run a broadly targeted ad to the 50% of U.S. households who DON’T buy granola bars? Not so much.

We’ve found, via our norms data, that brand and category buyers are driving an overwhelming percentage of sales despite an underrepresentation when compared to less relevant non-category buyers.

How do you make Relevant Reach work? By carefully crafting an audience of high-potential buyers based on the right purchase signal. From there, the more relevant your reached audience becomes, the better your advertising will perform at driving sales for your brand. It’s not rocket science, it’s data science. And using data in a smart way is key to finding the most relevant audiences.

Advertisers have traditionally relied on marketing research to choose which audience and media characteristics are most likely to be correlated with buyers. Today, sophisticated data modeling can vastly improve the precision of this process by ranking thousands of audience options on their ability to find buyers.

Let’s look at the chart below where we compared two examples of a relevant Oracle Data Cloud target audience versus a broad demographic target alternative. In both examples, data assets and applied data science do a better job finding the relevant buyers these advertisers should aim to reach.

Random DemoAudience

Data-drivenAudience

Random DemoAudience

Data-drivenAudience

1x 1.25x

5.5x

1x 1.09x

5.2x

Leading Bleach Brand – Buyer ID RateAudience Size = 20MM

Luxury Import Car Brand – Buyer ID RateAudience Size = 1.7MM

DATA-DRIVEN AUDIENCES FIND MORE POTENTIAL BUYERS


reach requirements. We can do better than this with more intelligent, data-driven audience recommendations. In our pickup truck example, the five audiences with the highest buyer identification rate of eventual half-ton pickup buyers, based on data in the BlueKai Marketplace include:

• Motorcycle Owners• ATV Owners• Bought Coleman Products• In-Market for Texas Acreage • In-Market for an RV • The highest-ranking demographic

audience was 1,096th.

The more relevant your audience, the stronger your campaign performanceAnd we can prove it because we’ve measured it. Our experience measuring the incremental sales lift from online campaigns strongly supports the conclusion that reaching relevant audiences drives superior lift. Need more proof? In a tightly controlled experiment conducted across 24 CPG online campaigns, we found only:

~11%Approximately 11% of demographic audiences (on average) consisted

of individuals who were also members of a relevant purchase-based audience simultaneously deployed in the same campaign. The maximum overlap was 20%.

We then looked and found in a broader analysis conducted across 116 campaigns with diversity of advertisers, publishers and categories that:

+80%Relevant, data-driven audiences drove +80% higher incremental

sales on average relative to their native target counterparts within

the same campaign.

How to harness the power of Relevant ReachThere are a few pretty simple things advertisers can do to drive Relevant Reach in their campaigns.

Avoid going too narrow. The availability of data often tempts advertisers to define target audiences very narrowly—which by definition makes them too small to stand alone in a single campaign and may result in higher media costs. To avoid this, audiences are usually ‘extended’ to achieve a scale that fulfills the overall campaign reach objective. We believe there is a compelling case for taking a data-driven approach to inform intelligent audience extension. It assumes that you:

• Start with the most relevant audience for your brand and campaign objective

• Extend that audience by using the next most relevant audience available

• Repeat the process until you achieve your campaign reach target

So let’s put this to work with an example. Say you’re a media planner, trying to identify audiences that will reach the most people in-market for a half-ton pickup truck. You’d typically start with obvious choices like, “In-Market for pickup trucks”, followed by a demographic audience (e.g., Male 24-35) to achieve campaign

IT’S NOT ROCKET SCIENCE, IT’S DATA SCIENCE.

DATA-DRIVEN RECOMMENDATIONS

For the most part, advertisers have not yet adopted this type of audience planning, though that’s changing. For those that aren’t, they are leaving significant money on the table.

Look at your audience and media costs separately. It’s only natural that advertisers would carefully evaluate the price premium of a data-driven audience relative to its performance. This lets the advertiser make an informed choice whether to pursue additional volume, or forego the volume to maximize the return on ad spend.


But it’s not always so straightforward. The complex and sometimes opaque auction-based systems in the digital ecosystem can make things harder to analyze. Programmatic campaigns that underbid in their auctions may fail to achieve sufficient reach into their desired audiences, saturate reached individuals with too many impressions, and consequently depress audience performance. Ouch.

On platforms where there is a fixed supply of ad inventory, the competition (and required bid) for specific users in your audience grows as the pool of unreached users in that target shrinks. Advertisers that are committed to an ‘audience-first’ strategy need to work with their media partners and agencies to understand underlying media costs and ensure they get the most value out of their chosen audiences.

Relevant Reach lets you get personal and that makes you more effective. Personalization is powerful stuff. For brands that compete in ‘ubiquitous’ categories that everyone purchases, such as garbage bags, toothpaste or ready-to-eat cereal, advertisers may dismiss the usefulness of targeting because these are products every household buys. Not so fast. Relevant targeting strategies still pay dividends in these categories. If I run an ad for toothpaste, I may want to target that ad broadly.

But what if I run an ad for whitening toothpaste? Or toothpaste for sensitive teeth? In both of these cases, reaching a relevant audience allows for ad personalization, to ensure the right message is delivered to the right household—at scale.

RELEVANT REACH LETS YOU GET PERSONAL

Why settle for reach when you can have Relevant Reach?Reach matters. Reach is here to stay. But the smart money is on Relevant Reach and not inefficient, broad target marketing. And why wouldn’t it be? Use data to help you identify the scaled audiences that are most relevant and represent the highest potential pool of buyers for your brand. Then, let’s use data to help you identify the scaled audiences that are most relevant and represent the highest potential pool of buyers for your brand.

So next time you are faced with a choice between achieving maximum campaign scale and exploiting the performance advantage of targeting, don’t choose. Take both.


“Deterministic” isn’t the same as “True”The industry has reduced cross-device accuracy to a single question: “How many of your IDs are deterministic and how many are probabilistic?”

Unfortunately, this attempt to quantify accuracy is based on the flawed assumption that any link directly observed is 100% correct. So-called “deterministic” links come from users who have logged-in to a device—a straightforward concept that’s the industry shorthand for high-quality matching. But a login doesn’t always tell the truth.

What about when your child logs into Facebook using your phone? It’s a login, but it’s not you.

And what about when the email login isn’t validated? You register for an account using almost anything—even a bogus email like [email protected]! Yes, we have this email; in fact, more than 280K times.

Here’s a sample of suspicious email addresses that contribute more than 1MM “deterministic” links.

Email login# of unique deterministic links

[email protected] 280,654





Unfortunately, bad data issues are common. Before applying quality controls, 30% of the device IDs we receive across all deterministic providers have two or more emails associated with them. Some are even matched to 10, 30, even 400 emails! While it may be okay to have one or two emails linked to the same device, it’s clear that deterministic isn’t always right.

The truth about cross-device data quality

Audrey Thompson Director, Data Science Oracle Data Cloud


Companies may claim they have many deterministic links, but if they have no way of cleaning up the bad data, the solution starts to fall apart.

A better way to talk about data quality is to acknowledge that everything is probabilistic. Any single observed link can be thought of as a fact. Sure, that email was seen logging in to that device at that time, but not all facts are true. All links, including deterministic links, have a range of probability, from 0 to 100%. Login data tends to be on the higher end of the scale, but rarely is it 100% right.

It’s all probabilistic ... until you have a Truth SetThere simply isn’t enough high-quality deterministic data available to build a cross-device graph at meaningful scale, so even large players like Facebook and Google rely on so-called “probabilistic” techniques.

A probabilistic match means an association is inferred without being directly observed. In other words, you’re making a prediction about the likelihood that two bits of information are connected in real life. With data, technology and math, you can tease out these connections very well—even if they weren’t directly observed.

How does it work?A probabilistic model has three components:

1) The Observation Data—what signals are you using to predict a match?

For a cross-device solution, it’s best to observe each device as many times as possible. Use associated metadata such as IP address, timestamp, application and device type. More data tends to be better.

2) The Algorithm—how are you making your prediction?

This is the math used to identify the correct connections. The methodology can vary, but what matters most is

how well the algorithm can identify correct matches.

Statistician George Box famously said, “All models are wrong, but some models are useful.” How useful is your model? You need a Truth Set to find out.

3) The Truth Set—what will you use to train and validate your algorithm?

A Truth Set, which is a set of links assumed to be 100% true, is required for any training and evaluation of a probabilistic solution. Careful consideration must be given when obtaining a Truth Set: If this is the data your entire solution will be judged against, you have to make sure your Truth Set is, in fact, TRUE!

How true is your Truth Set?It’s not good enough to say “All my data is scored against a Truth Set!” without first understanding what true really means.

Ask the right questions and you’ll quickly learn that most companies have no way to validate their so-called Truth Sets.

In a typical approach, a company will license a Truth Set from a third party with little knowledge of how the data was collected. These Truth Sets are often small—a million or fewer cross-device pairs—and not representative of the general population. And because the email logins themselves are anonymized, there’s no way to know which bogus links should be discarded.

As we saw before, deterministic data cannot be blindly trusted to be accurate. If you train your model against a “dirty” Truth Set, you’re bound to get bad results. It’s garbage in, garbage out.

How do you know if a link is correct and real? In the cross-device space, where groups of devices are linked together through anonymous logins,

validation is hard to find. At Oracle Data Cloud, we go beyond simply trusting an email login and use the power of our offline data assets to tie devices to real people.

We define truth as a Mobile Ad ID linked to the real user or household in the physical world. To obtain this truth, we license data from several companies that have both a mobile app and a direct billing relationship with a customer, giving us Mobile Ad IDs tied directly to full name and postal address. An email address can be fake, but a billing address is much more likely to be you, grounding truth in the real world.

The source of truth matters: you can have far more confidence in Truth Set data sourced directly from companies that charge customers’ credit cards on a monthly basis or regularly ship items to the same address associated with their devices. In the end, we can move beyond an anonymized group of devices to validated devices tied to a real user. For marketers, this is crucial: a streamlined cross-screen experience is impossible if you have the wrong user or device.

WE DEFINE TRUTH AS A MOBILE AD ID LINKED TO THE REAL USER OR HOUSEHOLD IN THE PHYSICAL WORLD.

Continued on page 6


Truth is a sliding scaleIt’s still not good enough to say “All my data is scored against a Truth Set!” The real question is: “Well, if you can score against a Truth Set, how well are you able to connect a device to the right person?”

In the industry, a typical answer to this question is “our links are X% accurate.” if it were only that easy! Accurate against what? Accurate to a real person? How many links do you have? What do you mean by accurate? Be wary of simple answers: if someone claims 90% accuracy, that may only apply to the top 5% of their population and may not be validated against real people.

To get substantial volume as well as a high degree of correct links, you need lots of data. The Oracle ID Graph™ connects multiple ID spaces through a kind of “partner cocktail,” combining offline purchase data with deterministic and probabilistic links from hundreds of partners, including industry leaders in cross-device. The end result is a cross-device graph—grounded in the real world—that maintains both a large scale and high degree of precision (i.e., how many validated links did the model pick as winners?).

Having so much data from disparate sources is valuable, but after the data comes in it takes an enormous amount of work to discern the good links from the bad ones.

By applying a series of algorithms, we’re able to evaluate the strength of every Mobile Ad ID or cookie to user/household link in the following graph and pick the winners (the highest scoring link for any given ID). The result is a curve, with a portion of the links having a high precision, with precision declining as you add scale.

For 40MM devices, you can map data at 85% precision or higher. As you add more devices to the mix, the overall precision declines. Let’s say you’re a marketer that wants mobile data mapped at 70% precision or higher: the model produces a pool of 120MM devices to work with.

If you can build an audience that performs 10x better than random, then 70% precision isn’t too bad. Your model is still going to outperform by 7x!

Marketers can benefit by recognizing that not all links are created equal. More is simply not better when you are trying to find the right audience. The best identity solution allows for trade-off between precision and scale.

Truth be toldFor companies like Facebook, Google and Twitter, an email address alone can be good enough because it’s more likely to be “you” when using their services. But when it comes to the open web (DSPs, exchanges, etc.), a much higher standard is required.

Accuracy matters! Getting the right user matters! Cross-device mapping impacts everything from retargeting and frequency capping to attribution. It pays to get it right.

So how do you assess the quality of a cross-device solution?

Don’t bother asking how many deterministic IDs there are. Instead, ask what the tradeoff is between precision and scale. Make sure you know what is meant by accuracy. And make sure the Truth Set is actually true.

Here’s an illustrative example of what that output could look like:


Answering cross-device identity solution questions with the Direct Marketing Association’s RFI TemplateOracle Data Cloud is a proud participant in the Direct Marketing Association’s

(DMA) XDID initiative to solve key issues faced by data-driven marketers today

and that includes creating greater understanding of cross-device identity

solutions. The DMA explains, “A typical consumer connects to content and

advertising across three or more media devices. Consumers are becoming

accustomed to seamless cross-device experiences, while marketers and media

companies strive to deliver on this growing expectation.”

Ajit Thupil, Business Lead for Oracle ID Solutions, elaborates on a core issue marketers are facing and how the RFI template seeks to solve it. “One of the key problems we face in a still evolving space is lack of standardization around the definitions and terminology,” Ajit says. “This leads to buyers facing difficulty in comparing different providers and picking the one most suited to solving their problem.“

According to Ajit, “The RFI template is meant to provide brands and agencies a template that articulates the questions to the appropriate level of granularity to help them make a truly educated choice of identity partner.”

The DMA’s structured innovation program reveals these challenges and potential solutions.1

Cross-device identity technology is still developing. And often, marketers, agencies and publishers (the buyers) aren’t fully aligned on what’s achievable from cross-device solution providers (the sellers). Here are some of the issues we face.

• Not everyone speaks the same language. Important terminology is defined differently by parties in the cross-device ecosystem.

• Buyers don’t always know all the questions to ask, creating a further gap between expectations and delivery.

• Sellers with distinctive methodologies rightfully promote the benefits of their solutions, but in this market, one size does not fit all. What may work for one marketer or publisher may not be an appropriate solution for another.

These factors are contributing to frustration between cross-device solution buyers and the technology sellers who want to deliver powerful solutions their clients will love.

Bridging the gapWith a goal of accelerating cross-device identification business partnerships and ROI between technology buyers and sellers The DMA along with partners on their XDID steering committee including Oracle Data Cloud 1 http://www.b2bmarketingdatareport.com/

2 DMA Cross Device Identity Solutions RFI Template Public Exposure Draft

released The RFI Template.The RFI Template is a tool that outlines the questions marketers, agencies and publishers should be asking cross-device vendors and the simple terms and definitions that everyone in the identity-based marketing and media ecosystem should understand. The template is meant to be a quick-start guide to broader RFPs for all organizations that span the advertising ecosystem.

This RFI Template has been developed through the efforts of more than a dozen respected cross-device ID (XDID) technology buyers and sellers. These organizations, all listed below, are members of the DMA’s XDID Structured Innovation Advisory Council.

This RFI Template outlines the key questions the council recommends every XDID technology buyer ask before selecting a XDID solution provider. Further, the RFI Template outlines certain background information we recommend the buyer make available to the seller. Doing so will make it easier for XDID solution providers to respond with more realistic expectations about how their solutions can deliver against buyer needs.2

Get started with the RFI Template by visiting the DMA online at https://thedma.org/structured-innovation/cross-device-id/cross-device-identification-solutions-rfi-template/


Digital marketing offers a seemingly limitless number of different measurement metrics to evaluate campaigns. Some are confusing and worthless, others offer useful insights, but only one gets a unanimous nod of approval from marketers of all stripes: return on investment (ROI), the causal lift showing advertisers how much more consumers spent because they were exposed to the campaign.

However, when evaluating best practices to drive ROI, the answer often varies (predictably) depending on the person answering the question. A publisher is likely to explain the virtues of high-impact units and viewability rates, while a creative agency may stress the importance of design and aesthetics. Data companies and ad networks will tout the importance of machine learning and programmatic optimization.

All of these will improve campaign performance against traditional performance-oriented metrics. To increase sales, however, advertisers should focus on four fundamental factors.

Aligning campaign objectives with strategiesThis isn’t just corporate jargon. The primary reason that campaigns don’t meet their ROI goals is misalignment between the campaign’s goals and its substance, including elements like creative, channels, targeting and execution.

For example, an advertiser may implement a campaign strategy that drives upper-funnel activity, such as increased awareness of the brand, but express surprise when subsequent ROI measurement shows limited or no short-term sales lift. You wouldn’t evaluate marathon runners by their 100-yard dash times. So why structure a campaign to achieve one objective and then judge its effectiveness with another?

As another example, a campaign designed to convert users of competing brands should focus on the number of new or lapsed buyers added—and their expected lifetime value—rather than pure ROI.

Target the right audience A beautiful creative, compelling call to action and optimized delivery will fail every time with the wrong audience. For example, the smartest “Buy one, get 10 free” mascara ad on Earth just isn’t compelling to me, while a simple no-graphics ad for a Snickers

bar at 5:30 p.m. on a workday could influence my behavior.

Brands that can segment their audiences to reach new buyers, existing but infrequent buyers and frequent buyers—at both the brand and category level—can measure and optimize their campaigns to drive sales lift for each group. Significant performance improvements are available to marketers who are more thoughtful when defining their digital audience targets.

Know how a product is consumed to determine how it is soldHow customers purchase a product should drive the design for its campaign. A relatively inexpensive product with less than 5% market penetration and a 12-month average purchase cycle should have a very different campaign—and different ROI expectations—than a highly discretionary product with 40% penetration and a monthly purchase cycle.

Rather than creating cookie-cutter campaigns, marketers should use product nuances to their advantage, both in expectation setting within their organizations and in designing their campaigns.

Marketers can’t drive sales lift without the fundamentals and execution

Robin Opie VP of Data Science, Oracle Data Cloud


Measurement makes better creativeIt may sound counterintuitive, but great creative goes hand in hand with great statistical analysis. Rather than following a tired aphorism—“humor works best” or “ads need offers”—marketers should start with a strong creative strategy, then rigorously test it.

It’s possible to measure sales lift down to the audience and creative level. The real key to success in this area is to create an intelligent learning agenda, measure audience response as much as possible and continue to adjust.

Dodge the execution torpedoes that sink success While strategic alignment, audience targeting, product consumption analysis and creative optimization greatly improve the odds of driving sales lift, there are a number of campaign execution issues that can torpedo an otherwise healthy program.

One issue: impression dumping. Publishers unable to exhaust a budget may sometimes open the frequency cap near the end of the campaign, dumping a significant amount of your budget in a short time to users. Advertisers should evaluate impression frequency per user at the distribution level and not settle for an average, which could hide the underlying drivers.

Another problem is audience broadening. Audience targeting specificity is sometimes “relaxed” if a campaign is underpacing. Advertisers should monitor the “within target audience” impression rate and compare publishers to ensure their “in-market chocolate bar” audience doesn’t become “all U.S. consumers who shop at a grocery store.”

There is also viewability and pub quality. Enough has been said on this issue that it doesn’t need rehashing, except to emphasize how important it is.

Finally, there is geotargeting accuracy. If a campaign is geospecific, in terms of story proximity or region-level applicability, advertisers need to monitor impression distribution by geography.

Marketing is never completely predictable, but a well-designed, rigorously analyzed and continuously improved campaign can help unlock the sales mystery originally posed by John Wanamaker, of which half of marketing actually drives sales.

Marketers who follow the fundamentals while proactively identifying and addressing execution issues will improve their odds of driving sales lift.

Follow Oracle Data Cloud (@OracleDataCloud)

and AdExchanger (@adexchanger) on Twitter.


In the age of the connected consumer, retailers have a big opportunity to increase loyalty, win incremental trips and grow their eCommerce business.

But it isn’t about how the retailer wants to sell It is about how consumers want to buy, which is where online grocery shopping & curbside pickup come in. For today’s grocery shopper, ease, convenience and quality are key. And for retailers who want to win this space, that means innovating and investing in the future state user-friendly online shopping.

Start with the essentialsTo support the owned investment of an online shopping cart, paid digital media is essential to driving qualified traffic. The objective is to surprise, delight and convert the shopper with every visit to your site. This is what we refer to as the “Click/Touch path to purchase.”

A Retailer that wins in this competitive space needs to clearly communicate the key benefits of their service, lead

with relevancy (always) and leverage addressability to its fullest potential. Oracle Data Cloud sees that with a strategic trifecta plan in place as mentioned here, targeted digital ad exposure drives incremental online shopping engagement across all key metrics.

Add the right media mixDigital media will reach the shopper wherever and whenever they show up online. To maximize relevant channels to drive the click/touch path to purchase, utilize programmatic display ads across mobile, tablet and desktop/laptop.

Add in social media ads across Facebook, Instagram, Twitter and other social channels and incorporate direct mail for top loyal shoppers and/or high value offers to drive conversion. Then, monitor the media spend and conversions by channel and audience and begin to target, message and channel optimizations based on the most efficient cost per order.

Remember, relevancy is king Many Retailers go to market with their online shopping media support with a one-size-fits-all approach: High basket offers, delivery subscriptions, whatever they can do to lure the shopper in.

What are the online shopping unique selling propositions for each individual shopper? We find the most impactful

media spend and performance is built around relevancy.

Use these four targeting and content recommendations to keep relevancy your focus.

1. Look for loyalty in moms and young families (looking for ways to save time and find harmony in their day), urban professionals and value-sensitive shoppers.

2. Acquire loyal prospects (model current active online shoppers within your Loyalty universe to convert current loyal shoppers to online) and pure prospects (model current active online shoppers outside of your Loyalty universe to convert shoppers into your brand).

3. Focus on small businesses (B2B taps into a new stream for turnkey, consistent revenue) and retailer strategic corporate partners and alliances.

4. Aim for content tailored by segment that is both promotional and equity driven.

Baseline metrics for performance Have you spent the time to understand how valuable your digitally engaged shoppers are? The answer is very valuable and loyal and you’ll see that proof in the results for online shopping.

Best practice is to implement a pixel to capture shopping-cart conversions. This enables tracking of all basket

How to win with online grocery shoppers

Blake Eisler Director, Client Solutions, Oracle Data Cloud


confirmations and the ability to optimize your media efforts during the media flight. From there you can identify different variables to pass back through the pixel code to report on additional conversion metrics. Oracle Data Cloud has reported conversions yielding between $7-$12 on average spanning across all audience segment types.

Additionally we also measure the actual causal event of online shopping confirmations and its associated performance via DLX ROI. We’ve observed lifts on average from our forensic control over exposed ranging from 3% upwards of 10%. Finally, promotion of online grocery shopping offering and capability also yields a positive halo effect on total store (including in-store) sales so the media is working double time for your brand.

If you’re ready to win with the online grocery shopper, Oracle Data Cloud provides measurement options to support online grocery shopping ad campaigns. Email [email protected] to get started.


Notes


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https://blogs.oracle.com/OracleDataCloud/http://bit.ly/oracledatacloud

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relevant reach - oracle · the importance of maximizing reach ... the opposite of maximized reach...

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