Make your remarketing more effective and less annoying with call tracking data

If your customers frequently purchase on the phone, you might be sitting on a goldmine of remarketing data.

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It’s estimated that most Americans are exposed to around 4,000 to 10,000 ads each day. That’s a whole lot of opportunities to acquire new customers, and just as likely, annoy the everloving snot out of thousands of others. When you use remarketing to stay top-of-mind with customers, you’re walking a fine line between drawing in potential customers and infuriating your audience. Remarketing can and does work, but only if you can put customer experience above short-term vanity KPIs. Here’s how to do it and how to make the customer’s experience better using call tracking data.

Remarketing, retargeting, and why people hate it

What’s the difference between retargeting and remarketing? Remarketing is your overall strategy of reconnecting with customers and prospects after they have interacted with your brand. This could be a combination of email, paid digital media, direct mail and more. Retargeting refers to the cookie-based ads used to remarket to people after they have left your site on other sites as part of an ad network, such as Google Display Network ads. 

Your typical non-marketer consumer may not know these terms or the inner workings of remarketing. They just know them as ads that seem to follow them everywhere they go after visiting your website, and they have some good reasons to hate them. 

Ads are out of context

Have you ever been shopping for some kind of martech product and then get retargeting ads for it on your favorite hockey blog? If you’re a marketer, you probably just sigh and nod your head in shame that someone’s doing it wrong. Displaying ads out of context is one of the big reasons why consumers feel like they’re being “followed” by you. It sticks out like a sore thumb because it’s just the wrong place and the wrong time. However, if you can contextualize your remarketing, the ads will seem natural and do what they’re supposed to do — keep your brand top-of-mind. When you see ads for the hockey gear you’ve been shopping for on the hockey blog and email automation on marketing industry websites, you nod your head in approval and think “YEAH, these folks know what they’re doing!” Then you buy that 12-pack of pucks and call back that martech SDR who has been hounding you for the last six weeks. Mission accomplished! 

Your ads are absofreakinlutely everywhere, forever

The more times someone sees your ad, the more likely they’ll remember you, right? That might be the case, but they’ll probably be remembering that they’d like to strangle you. A study performed by Skin Media and RAPP Media aimed to find out how this repetitiveness affects consumers. In the study, they found that people think that seeing a retargeted ad five or more times is “annoying,” while seeing it ten or more times makes them “angry”. Not the experience you’re looking for. More than half of the visitors polled said that they may be interested in the ad the first time they see it, even though only 10% report making a purchase as a result of seeing a remarketed ad. Think carefully when you are setting your frequency caps and make sure you are not inundating (and annoying the hell out of) your customers with ads. 

Getting retargeted for stuff you already bought

Step 1: Buy a new power drill. Step 2: See millions of retargeting ads for the same darned drill. Step 3: Scream at your computer “GAWD, fix your suppression, dummies!” The average consumer may also find this rather inept, but more likely, they’re going to be turned off by it. Proper post-conversion ad suppression makes your marketing much more efficient and saves your customers from the agony of being reminded of their purchase for six weeks, or worse, seeing an ad with a lower price than they paid and making them feel conned. 

How call tracking data can make the remarketing experience better

Particularly in the post-cookies age we live in, where the use of third-party cookies for remarketing is being smashed by new regulations and browser-level cookie-blocking, using every source of first-party data you have at hand for remarketing is critical. If your business gets a lot of sales inquiries from inbound phone calls, your remarketing picture gets even muddier. A potential customer may have navigated to your website and clicked on a page or product before calling you and either asking a question or ultimately making a purchase. Either way, you are left with a data gap that leaves you open for making bad remarketing decisions that will annoy your customers and waste your marketing budget.

You can bridge this data gap and get your hands on precise first-party data for remarketing by using a call tracking and conversational analytics platform. When your customers call you, they are literally telling you what they want and how they talk about it. To feasibly classify customer conversations into useful digital datasets, you need an automated system that can understand what’s being said and accurately derive meaning from it. Your call tracking platform should be able to accomplish a few things: 

  • Automatically determine the outcome of inbound phone calls 
  • Predict and classify call type (e.g. sales call, service call, etc.)
  • Collect digital journey data such as UTM, keywords, and GCLID
  • Push marketing intelligence collected from calls to your martech stack in real time

With this type of functionality, you can fine-tune your remarketing campaigns without doing a lot of heavy lifting.  The data can be fed to your DMP and/or ad network to automate the process in real time. And when you understand the nature of a call, you can optimize your media for higher ROI, which can be particularly helpful when you are nailing down the next best step in your marketing, whether that be retargeting ads for someone who did not make a purchase, or suppressing ads for someone who did. You can also use call data to feed to Google’s automated bidding algorithm to adjust your bids according to what is (or isn’t) happening on the phone. 

Conversational analytics tools like Invoca’s new Signal Discovery take this to a new level of precision and granularity, as they can help you find out things about phone conversations that you don’t even know to look for. Over 56% of marketers have no idea what’s said during the calls that they drive or what the outcomes of those calls are. It’s a big data gap that marketers shouldn’t have to live with. “Conversations are overflowing with insights that don’t always see the light of day outside the contact center. As a result, many companies are missing out on opportunities to create a more consistent and positive customer experience across human and digital touchpoints,” said Dan Miller, lead analyst and founder at Opus Research. 

Signal Discovery solves this issue by enabling marketers to quickly gain new insights from tens of thousands of conversations and take action on them in real time. From there, you’re able to drill down into each topic to understand caller behavior and then create a “signal” that Invoca will listen for in future calls so you can see exactly when a specific topic is discussed and can automate your marketing based on this data. No more guesswork, no more risky call assumptions.

With all this data, you can make your remarketing efforts more targeted, relevant, efficient, and above all, less annoying. 

Get the Call Tracking Study Guide for Marketers to learn more about how to use call tracking data to improve your remarketing strategy. 

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CPG marketing platform Quotient buys location data provider Ubimo

The deal continues Quotient’s evolution beyond its origins as Coupons.com

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Quotient announced last week that it’s buying Ubimo, an Israeli location intelligence company. Ubimo translates location data and history into audience segmentation, activation and campaign attribution, connecting digital campaigns to in-store results.

DSP was the attraction. Quotient and Ubimo had worked together for several years, utilizing the latter’s data for Quotient customer campaigns. But the primary rationale behind the acquisition was Ubimo’s DSP, according to Jason Young is Quotient’s Chief Marketing & Media Officer.

Quotient intends to offer its CPG brand and agency clients a self-service DSP. “The acquisition will accelerate Quotient’s product development of a self-service platform, where marketers can plan, buy, and optimize media campaigns directly from an automated platform,” according to the Quotient’s press materials.

Coupons.com evolved. Quotient began life as Coupons.com in 1998, which it still owns and operates. In 2017, the company acquired mobile marketing company Crisp Media for roughly $53 million.

Quotient distributes digital coupons through its network, which includes Coupons.com and a wide range of retailers and grocery store properties. The company also makes programmatic media buys on behalf of customers.

Quotient influencer marketing campaigns (2019)

Through loyalty cards and point-of-sale (POS) redemption data, Quotient is able to deliver closed loop reporting as well. It also uses POS data for campaign targeting.

For retailers, Quotient offers a range of ad and media solutions. For example, it enables retailers to sell ad space on their sites and apps and distribute digital circulars on social media. It also operates an influencer marketing platform.

Utility of location data. Brands that don’t have access to coupon or loyalty card POS data, have increasingly used store visitation as an attribution metric. That’s one of the primary capabilities Ubimo offers its customers.

But Ubimo also uses location data, which can be combined with other data sets, to enable highly specific audience segmentation and targeting. Quotient will bring Ubimo’s technology into its platform and combine its own shopper data with Ubimo’s location data and analytics, which Quotient “expects to meaningfully improve campaign performance for customers.”

With Ubimo’s assets and customer relationships, Quotient intends to expand beyond its traditional CPG and retail customer/partner base and move into “adjacent markets, such as Out-of-Home.”

Why we should care. Quotient’s main reason for buying Ubimo was the company’s DSP. But from a larger market perspective, the deal shows the increasingly mainstream use of location intelligence, both for targeting and attribution. It also shows a growing recognition of the power of location data for merchants and brands — unless they sell exclusively online — to maximize targeting effectiveness and to demonstrate the real-world impact of digital campaigns.

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Neura converts mobile-location data into time and consumer attention

The company announced its app for Salesforce Marketing Cloud and fully automated marketing using real-world behavioral insights.

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Location data has come a long way. From the early days of radius targeting, which is still happening, mobile-location and location history are now being used for sophisticated AI-driven personalization and customer engagement, increasingly without any reference to location at all.

Neura, which describes itself as “a leader in real-world customer intelligence,” announced it’s available as an app for Salesforce Marketing Cloud. I spoke with Amit Hammer, CEO of Neura, about the practical mechanics of what the company announced.

Converting location into consumer attention. Neura promises brand marketers that they will be able to use its Salesforce app to create customized audience segments and then market to them when they’re most receptive (via app notification, email or text) based on real-world activities and movement patterns. Neura’s data can also be combined with other insights in Salesforce to launch fully automated, personalized campaigns from within Marketing Cloud.

This sounds like the familiar “right message, right time, right place” refrain that has annoyingly appeared in so many mobile marketing presentations. But Hammer convincingly unpacked it for me.

Customer-journey builder incorporating behavioral “triggers” for personalized messaging

Hammer argued that app engagement is generally poor – Neura says notifications have average engagement rates below 8% – because messages are delivered at the wrong time, when users’ attention is not available (e.g., at work, sleeping, working out). He says that behavioral insights from offline movement patterns (and related customer inferences) are a much more reliable guide to customer openness to marketing messages.

This is the alchemical transformation of location data into attention-availability (i.e., time).

Agencies or in-house marketers handle all the creative. Neura’s system identifies when each person in each audience segment may be most open to the marketing message. Two “business travelers,” for example, may still have very different work-leisure schedules and corresponding attention patterns. Neura’s system can accommodate those differences. Users may receive the same messaging creative but potentially at very different times of day or days of the week.

SDK integration into enterprise mobile app. Neura works predominantly with mobile-first brands that have app-based audiences. If users don’t have the brand’s app it’s much tougher to gain these insights and the system doesn’t work as well — although there can be some lookalike modeling.

Neura’s enterprise/brand customers install the company’s SDK in their app. Then Neura starts building behavioral profiles of the brand’s audience from scratch.

Privacy is much less of an issue (or perhaps not an issue) here because this is permission-based first-party data. Neura is analyzing data on behalf of the brand, which has a direct relationship with its consumers. In addition, users must affirmatively opt-in to allow use of location.

The system doesn’t rely on pre-defined personas (e.g., working parent, business traveler) and then seek to find those people in the world, but creates customer personas and profiles based on their individual behaviors. As indicated, there is some lookalike modeling but Neura is more often delivering deterministic data.

Why marketers should care. Location is a critical source of data signals about customers. In many cases, offline activities are much more reliable indicators or predictors of preferences, identity, and intent than online signals. However, all of this must be handled transparently.

But when location is ethically and reliably sourced, it can be the cornerstone of relevance and personalized marketing efforts. And the combination of this data with machine learning technology does bring us much closer to – dare I say it – one-to-one marketing.

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How marketers confront the obstacles of digital customer engagement

Is your organization struggling to deliver a fluid customer journey? You’re not alone.

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IDG Connect and Siteimprove conducted a survey of over 100 marketers worldwide, working at companies with at least 1,000 employees in various industries, and discovered a mixed picture: while modern marketers are committed to digital tools, many admit to having sub-optimal online presences where budgets, team structures, infrastructure, and content quality and freshness are questionable.

The results point to a desire for marketers to optimize the customer experience across every touchpoint. That aim is commendable but, as the research makes clear, there are serious obstacles along the way.

Read this report and to learn more about the latest research on digital customer journeys and marketing optimizations. Visit Digital Marketing Depot to download “Digital Insights 2019: How marketers confront the obstacles of digital customer engagement,” from Siteimprove.

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MasterCard buys SessionM for tighter credit card-loyalty program integration

The deal will provide customer insights for personalized offers and targeting and enable closed-loop measurement at the point of sale.

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Mastercard has purchased customer data and loyalty platform SessionM. Terms weren’t disclosed but the startup raised almost $100 million over four rounds. 

SessionM is behind loyalty programs for a wide range of companies including Coke, L’Oreal and Chipotle. It uses customer data and numerous behavioral and intent signals to deliver personalized (primarily) mobile offers.

Loyalty 2.0. Mastercard said in its press materials, that “The addition of SessionM will enhance Mastercard’s ability to help brands around the world deliver personalized, real-time offers and comprehensive campaign measurement based on robust, data-driven insights . . . SessionM helps brands create and manage consumer engagement and loyalty programs with industry-leading technology that powers a complete loyalty solution — from data management to campaign execution to program measurement.”

Moving beyond the marketing jargon, why did Mastercard buy SessionM? The deal actually makes perfect sense, as Mastercard seeks to gain an edge against payment card rivals and offer value-added services to its B2B customers.

The acquisition’s rationale. It’s about bringing a lot more data, targeting sophistication and measurement to branded credit card loyalty programs. A SessionM blog post reveals the rationale behind the acquisition:

  • 74% of Americans possess a store credit card; Cardholders receive rewards, discounts and exclusive experiences, while merchants receive a free ad in the customer’s wallet, an additional stream of revenue through credit card fees, and in theory, more ‘sticky’ customers . . . Just having a card to use will increase store sales by some 28% to 30%.
  • Brands can enhance their retention strategy by combining branded credit card + loyalty program [and] gain the ability to recognize, reward and improve communications with cardholders, improve customer experience for non-card members, and acquire more cardholders with personalized interactions.
  • Brands that combine a branded label credit cards with a loyalty/rewards program achieve greater results because a single program better reflects the simplicity that customers want and eliminates pain points, such as redemption limitations.

Why we should care. Mastercard has been working with digital marketing platforms for some time, using its transaction and POS data to enable targeting and attribution by third parties, including Google. Now the company will be able to offer a powerful data-driven loyalty program along with branded credit cards to its B2B customers.

That program will not only provide customer insights for personalized offers and targeting to retailers and brands, it will enable closed-loop measurement at the point of sale. SessionM will also drive additional revenue for Mastercard. It’s a pretty compelling proposition all the way around.

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Deliver More Relevant Website Experiences by Leveraging Analytics

Join us on Thursday, October 31 at 1:00 PM ET (10:00 AM) for this live webinar.

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Live Webinar

Formica, the world’s leading manufacturer of high-pressure laminate, noticed that its website was not meeting customer expectations. With a complex digital portfolio, covering 28 country sites, 14 languages and 450,000-plus products, the company had to act, before customers went elsewhere for their residential and commercial flooring needs.

Join us as Formica executives share how the company is now using AI-powered search, recommendations and data analytics to redirect sales leads, and compensate for regional differences in its product catalog and content. You’ll hear how Formica manages two go-to-market models (B2B and B2C) on a global basis, plus an overview of the internal processes the company has set up with its global teams and partners.

Attend this webinar and learn how to:

  • Develop a unified strategic plan to deliver more relevant visitor and customer experiences
  • Evolve your end user experiences using Coveo and SiteCore
  • Increase personalization to provide effortless customer self-serve options
  • Scale your sales efforts more effectively

Register today for “Deliver More Relevant Website Experiences by Leveraging Analytics,” sponsored by Coveo.

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The Truth About Personalization: Using a CDP to Personalize Marketing on the Channels That Really Matter

Join us on Wednesday, October 23, at 1:00 PM ET (10:00 AM) for this webinar.

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For the last several years, pundits have predicted that THIS would be the year mass personalization takes off. Industry giants like Facebook and Amazon have been held up as shining examples of how to personalize at scale. But is this level of personalization really needed for all companies on all channels?

In this webinar, Tom Treanor from Arm Treasure Data shares the results of a new Forbes personalization survey with industry benchmarks collected from marketing leaders from global 2000 companies to help better define what’s working, what’s not, and how a Customer Data Platform can be used to maximize personalization results on the right channels that matter for your business.

Register today for “The Truth About Personalization: Using a CDP to Personalize Marketing on the Channels That Really Matter,” sponsored by Arm Treasure Data

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With loss of Yahoo and image search, Google Shopping search partner traffic nosedives

While it is a small fraction of Shopping traffic, the partner network can help advertisers currently excluding this traffic to grow moving forward, particularly in a competitive Q4 holiday season.

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Click traffic from the Google search partner network took two major blows in early 2019. The first was Yahoo’s move to begin showing only Microsoft Ads-powered sponsored listings following a more than a three-year stint in which some of Yahoo’s listings were powered by Google. The second was Google’s update to bring ads featured in image search out from the partner network and into the core Google Search Network.

Here we evaluate a sample of long-standing Tinuiti (my employer) advertisers to assess the effects of these changes to the share of Google Shopping traffic coming from search partners, the relative value and cost of that traffic, and what it all means for advertisers.

Search partner click share falls dramatically across device types

As you can see from the chart below, search partner traffic once accounted for a significant share of Google Shopping clicks, and in August 2017 was at 16% for desktop. In August 2019, that figure was just 3%, with share on tablets and phones at 2% and 1%, respectively.

The timing of the dip seems a bit delayed from what we might have expected given the details of the two announcements ostensibly driving this trend.

In the case of Yahoo, it announced in January that it would only serve Microsoft Ads, but the change was said to have rolled out through March. For image search, Google announced that it would be integrated into the core Search Network in late March. As such, April would have presumably been when much of the decrease occurred.

However, our numbers show that traffic share really took the biggest month-to-month dip from June to July. It’s not entirely clear why there seems to have been a delay, but the decline is certainly what we expected in light of these two changes, and it’s possible Google’s change to image search took longer than expected to roll out. There may have also been other less publicized updates to the partner network affecting these trends.

Some advertisers choose not to allow Shopping ads to show on the search partner network, owing to the lack of controls available in terms of bidding and where ads are shown. However, our research shows that the Google Search Partner Network is usually an efficient way to extend the reach of Shopping campaigns.

Search partner clicks convert at a lower rate than core search, but cost less too

Looking at the conversion rate of search partner traffic relative to core search, partners clearly convert at a significantly lower rate.

In July and August, search partner conversion rate improved relative to core search across device types. This makes sense if the image search change really did take a few months to roll out, since the transition of image search clicks from the partner network to core search would likely put downward pressure on core search conversion rate.

Regardless, the disparity in conversion rate might be enough to send some advertisers running to Shopping campaign settings to shut down the partner network. However, looking at relative CPC, search partner traffic also consistently tracks well below core search in the price paid for clicks as well.

All told, the median advertiser saw no difference in the cost per conversion of search partners versus core search network in August 2019. As such, opting Shopping campaigns into the partner network garners incremental traffic without harming ROI for many advertisers.

Conclusion

These updates meaningfully reduced the importance of the partner network to Google Shopping campaigns, and it seems unlikely that we should ever expect partner click share to regain its former heights. There just aren’t many properties out there for Google to partner with that can produce the kind of click volume that Yahoo and Google image search provide.

Still, it remains the case that the partner network is typically a worthwhile investment for retailers looking to maximize the reach of their Google Shopping campaigns. While it may only be a small fraction of Shopping traffic, it can certainly help advertisers that are currently excluding this traffic to grow moving forward. Particularly in the competitive Q4 holiday season, it would be a shame for brands to leave this opportunity on the table.

Of course, Google didn’t actually lose image search ad traffic, and those impressions and clicks are now just a part of its core Search Network. Advertisers that were already targeting the Search Partner Network shouldn’t have seen much of a change to overall Shopping traffic as a result of this update specifically, though the change may have forced competitors that were formerly excluding partners into competing for these image search placements.

Yahoo’s move did give Microsoft Ads traffic a boost, and while Google will likely continue to account for the vast majority of paid search traffic in the U.S., Microsoft Ads is still a crucial part of reaching searchers who might not turn to Google with their queries.

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Get lead scoring data right in Google Analytics with Google Tag Manager

Ruth Burr Reedy, VP of strategy at UpBuild, on the benefits of setting up lead scoring in Google Analytics and the steps to get there.

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Ruth Burr Reedy, VP Strategy at UpBuild
Ruth Burr Reedy, VP strategy at UpBuild speaking at MarTech Conference 2019 in Boston.

“These are the wrong kind of leads.”

Sound familiar? If you’re a lead generation marketer, it’s an unwritten right of passage to get that complaint from your sales team.

Perhaps you are generating more leads, but they’re coming from higher funnel campaigns, and sales isn’t seeing them convert like quickly enough. “Top of funnel marketing means you’ll get top of funnel leads,” said Ruth Burr Reedy, VP of strategy at digital marketing agency UpBuild, during a talk at our Martech Conference in Boston last month. Those higher funnel leads will, by their very nature, need more touches to convert to sales. “If the sales team is not expecting them, they’ll be unprepared to deal with them,” said Burr Reedy.

Expectation-setting is critical when marketing teams run higher funnel lead gen campaigns. To help marketers get a claear sense of how their campaigns are performing, the touches involved in converting certain leads and other insights, Burr Reedy laid out a framework for setting up lead scoring for attribution in Google Analytics. This can provide a better picture than what you get in your CRM. “Attribution in CRM can be really confusing and not snapshot of reality,” she said.

How to get started

First, talk to the sales team about how they qualify leads. “If you press them,” said Burr Reedy, “they’ll tell you they look at one or two dimensions — often title, company revenue or company size.” Then agree on the thresholds for those dimensions that qualify a lead as hot, warm or cold. Be sure you’re capturing these criteria in your forms.

Establish with sales the criteria for each lead type.

Once you know the fields you’ll be tracking, using your browser developer tools, get the field ID for each. Then, in GTM create a custom JavaScript variable for the ID with getElementById or getElementByName.

Test your custom variables in the GTM console and in preview mode to be sure they’re returning the data you want. (If you want to track fields from a dropdown list on your forms, Burr Reedy recommends Simo Ahava’s blog post for tips.) Of course, be very sure you’re not collecting personally identifiable information (PII).

Next, in GTM, create Triggers for each lead type — hot, warm, cold — and then Event Tags for each one.

Configure Triggers in Google Tag Manager for hot, warm, cold leads.

Establish and document naming conventions for capturing your lead criteria. Burr Reedy suggests putting lead type criteria right in your Event Labels in GTM for clearer reporting and continuity.

Document your naming conventions.

How to use the lead scoring data in Google Analytics

Once you have this set up, you’ll be able to get a much better picture of how these leads perform from within Google Analytics.

See customer pathing to understand how long the leads take to convert. Share this information with sales to help set expectations as well as get a better understanding of where you should focus your efforts by seeing which referral sources drive a disproportionate share of hot/warm leads that convert. You can also use this information to find on-page optimization opportunities. Look at landing page reporting in Analytics to see which pages drive hot/warm leads and which pages only drive cold leads.

Capture lead scoring data in Google Analytics to better inform your marketing efforts and communication with sales.

To make this work consistently, said Burr Reedy, “You need to have a good system for managing all of your IDs. When a form is changed, be sure there is a process for notifying and capturing those changes. Be consistent with naming conventions.” This requires tight orchestration between any internal and external teams involved in any piece of the process.

Once it’s up and running, marketing will have a much more accessible and real-time view into the lead performance to inform their campaigns, site content and communication with sales.

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14 industry experts on the future of attribution

Competition for customers’ attention has never been so relentless, or so complicated.

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Consumers interact with your brand across a dizzying number of channels, touchpoints and devices. The average person today has four to six connected devices and switches constantly from one to the other. It’s harder than ever to know whether your ads and other marketing tactics are reaching the right audience—or making an impact. The average marketer doesn’t have the ability to consolidate data to understand the influence of touchpoints across digital and traditional channels.

At the same time, an industry-wide focus on enhancing consumer privacy and data security has raised the bar for trust and transparency. New regulations and technology changes make collecting, tracking, measuring and other data-related practices more challenging.

For this report, Nielsen asked 14 industry experts two questions to help marketers navigate this challenging environment:

  • How should marketers prepare for an increasingly complex customer journey?
  • What measurement strategies and tactics do marketers need to be successful today and in the future

Use their answers as a resource to help improve marketing effectiveness and develop strategy so your brand can thrive. Visit Digital Marketing Depot to download “14 Industry Experts on the Future of Attribution,” from Nielsen.

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