Key takeaways for brands after Google Marketing Live 2019

Digital giant bets big on shoppable ads, cross-app campaigns and real-time intelligence.

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There were a couple of telling stats from this week’s Google Marketing Live event, which included many digital ad product announcements and was attended by around 5,000 industry players in San Francisco. In a Google-led study, the tech giant sussed out one particular shopper who wanted to buy a single pair of jeans—the person spent 73 days looking and interacted with more than 250 digital touchpoints (searches, video views and page views) before making a purchase. The modern customer journey can be long and complicated, indeed.

This reality underscores the need for a wide range of customer intelligence—from social media listening and email insights to call data—so brands can act with as much relevance and real-time empathy as possible. Google, as much as any martech or adtech player, understands this need all too well and wants to make it easier for marketers to meet customers where they are at in the shopping cycle.

Now that Google Marketing Live is coming to a close, let’s take a look at the new ad products, stats and takeaways that marketing practitioners need to know.

Ads get more visual across apps

Google Discover, which has been the search engine’s news feed since September, now offers brands ad placements that are swipeable, carousel-style images that Instagram initially popularized a few years ago. Marketers can place the ads on not only Google Discover but also the YouTube home feed and the Gmail promotions tab.

Google also promises that these ads will get smarter and smarter due to machine learning. All told, these developments should be attractive if you’re a brand marketer who wants to run cross-app initiatives that strategically use the Alphabet-owned platforms’ wealth of data.

Advertisers should also pay attention to Gallery ads. Also similar to Instagram’s carousel ads, they are designed to be visually stimulating promos and will render at the top of mobile search results. They entail a scrollable gallery that will include four to eight images and up to 70 characters available for every photo. (Search Engine Land first reported on the emergence of these ads in February.)

Advertisers gain control over KPIs

Notably, Google has made moves on the data front to help ad buyers feel more in control over their campaigns. You can now choose what kinds of conversions (sales, lead-gen, email signups, webinar registrations, etc.) you want as your key performance indicator (KPI) at the campaign level.

Additionally, you can adjust conversion values based on the audiences you want to target. This ability will let you better tweak your ad bidding, which should improve ROI.

Ad tools improve efficiency for marketers on the go

The entire digital advertising ecosystem has gradually moved toward the smartphone mindset, letting you manage your campaigns from almost anywhere. In a growing number of instances, all you need to build and buy ads is a wireless signal. These mobile features help busy, often-traveling campaign managers get their work done in an efficient way.

With all of that in mind, Google now lets you build responsive search ads directly from its Google Ads mobile app. En route to a client meeting across town in a taxi cab but need to launch a last-minute holiday campaign? Google’s Android and iOS app now lets you write the search copy, optimize the headline, place bids and set budget constraints from your smartphone.

Timely data and alerts boost performance

Once again, Google recognizes that marketers aren’t always going to be in front of their laptop or at work. The Google Ads mobile app will now send notifications that alert you of a campaign’s performance as well as when better ad opportunities may be afoot.

Google clearly wants ad buyers to make use of their real-time intelligence. For instance, when certain keywords are performing poorly, you will be able to pause part or all of a campaign. And the app will offer you recommendations that can help drive sales. As one possible example, if you are a sneakers retailer and inventory for the white-hot shoe “Nike Air Presto” is unusually abundant—and therefore lower in cost on the bidding platform—the app will ping you to let you know of the opportunity. Google ad buyers of all sizes should appreciate such information, and the feature underscores how data is transforming all of marketing.

Local ads prove successful

While more and more sales happen online, 88% of all retail still happens offline. Therefore, retailers want their digital ads to not just drive ecommerce but also foot traffic to stores.

In recent years, Google, Facebook, Snapchat and other digital platforms have been working to prove that their ads help drive bricks-and-mortar sales. So, it was intriguing to see Google trot out brand-based statistics ahead of Google Marketing Live and during the show. The most impressive data point offered: Quick-serve giant Dunkin’ increased monthly store visits in some locations by 400% with Google’s location-based advertising.

 

Such revelations signal that hyperlocal marketing has gone multichannel, and advertisers of all sizes are now using digital to not only drive store visits but also sales in other offline channels like inbound phone calls.

Retail ads expanded

It’s clear Google wants a bigger chunk of retail advertising budgets as it competes with Amazon’s growing ad business.

Google revealed that its Showcase Shopping Ads, first debuted in 2017, have gone from being available for regular search results to the image search results, the discover search results and YouTube.

Showcase Shopping ads are similar to Galley Ads in that they offer the ability to include multiple product images that are scrollable from left to right. The ads also offer an easy way for consumers to click through to a product page and then commence to check out.

Marketers: stay ahead of the digital game

Google Marketing Live 2019 shows the brand marketing community continuing to march toward shoppable ads, tools for the mobile-minded practitioner, and improved targeting that leverages location data and granular performance metrics. For Google’s part, the ad products shown off represent the search engine giant’s desire to become a bigger player in retail.

It’s clear that Google is trying to advance how competitive it will be with Facebook, Amazon, and others for brand marketers’ ad dollars in the coming months—especially the holiday season. For all nearly all marketers, it’s imperative to keep pace as the available tools and best practices change at lightning speed.

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Google is focused on Ether and Ozone rather than on Amazon

Google journeys into uncharted territory with offline TV efforts and disruptive purchasing habits – countering competition from Amazon.

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We were marveled at Google Marketing Live. Fascinating demos and ground-breaking announcements like the Discovery Ads and the Bumper machine. The keynote also emphasized that Google is taking privacy seriously, which I was particularly pleased to hear. And in the ad innovations keynote, there was an overwhelming feeling that Google takes competition from Amazon more than seriously without ever mentioning it.

“Did they just say that?” was one of my common reactions during the keynote. I like the format of their presentations and the fact that you can go back and review presentations via the online portal almost immediately after they are finished. “Yes, they did!”, they said they were going to allow advertisers to book campaigns on national broadcast networks and local TV stations programmatically later this year. Google is reaching into the Ether. They also said they wanted to enable purchasing from a whole host of places within the Google properties; via voice commands, in images, in videos, in cars, in search results.

Wait, in search results? Did they just say that?

Buying functionalities will be available everywhere you use Google, a bit like the ozone gas which is distributed in the air around us in the atmosphere. Ozone is present in different doses but everywhere to be found. And it is, of course, the ozone layer that protects us from strong radiation from the sun. Fun fact, ozone which is composed of oxygen, is also lethal to humans if the concentration is too high.

Not only is Google working on Shopping Actions, which you can read more about here [https://searchengineland.com/new-personalized-shopping-actions-enabled-google-shopping-debuts-in-france-314458/], a functionality whereby you can compare products and buy from shops either within Google, by going to an online store or by going to a physical store. Initially, I found this surprising – and even a bit of a fuzzy positioning: buy either here or there or offline in a shop – buy wherever you see fit. It makes a little more sense when you consider that they are also activating the shopping experiences within all their properties and in future projects like in cars which were mentioned several times during the day. Will they be changing their mantra from Mobile First to Shopping First, I wonder? This impressive host of shopping-related initiatives is clearly aimed to defend Google from the rise of Amazon. Put up an ozone layer to protect them from Amazon radiation.

Why is Amazon such a danger to Google?

We currently observe a user behaviour by which an increasing number of people end their user journey on Amazon, whether they start it on Google, Facebook or somewhere else. 

If this user behavior expands further, then Google risks being excluded from the strong monetization related to e-commerce and limited to generating advertising revenues which can’t be connected directly to sales. Due to the way the digital marketing ecosystem works, this is increasingly important.

What originally made Google advertising so compelling was exactly the fact, that an advertising campaign could be directly connected to a conversion. This was what made Google Ads become such a dominating part of the marketing mix, and in turn, this, is what made Google rich.

Today, the user journey is not as linear as it was back then, and it has many more touch-points as the Ads innovation presentation on Google Marketing Live further illustrated: a purchase decision can take a user through 50 to 250 touchpoints and run over long periods of time. In parallel, organisations are increasingly measuring and monitoring the performance of their campaigns based on the impact they have on sales.

Facebook is generating powerful influence on buying decisions but it is a challenge to connect that influence to sales. The same goes for display and video advertising which is the reason why improved integration and measurement between channels is so important. If a sale takes place in a different Walled garden (Google, Amazon, Microsoft, Apple, …) than the one which generated the decision to buy, connecting influence to action is difficult. As we saw in the presentations yesterday, Google aim to make it easier to track and monitor behaviour among their own properties and more difficult to track from other properties – in the name of privacy.

We found in our research at Innovell, that search & shopping strategies involving both Google and Amazon are already a winning approach for leading paid search teams around the world. Approximately 80% of these teams include shopping services in their offering, and 32% of them have already started working with Amazon Ads despite limited availability around the world.

With growth in searches slowing down and market share projected to recede in 2019, Google has chosen to take up the challenge. Growth is to be found in shopping and Google is going all in.

Google today master the entire user journey except for the final sales transaction. They are reaching into the ether to connect with one of the last offline media outlets, TV broadcast. And at the other end of the user journey, rather than trying to do what Amazon does, they have chosen to do like the ozone gas, dilute their shopping capabilities everywhere around us when we are in touch with products or services via a Google service. Everywhere to be found, and aiming at disrupting the user journey to their advantage.

2019 is Ether and Ozone.

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Salesforce announces Pardot Business Units for enterprise marketers

Salesforce is launching Pardot Business Units, a new feature for digital marketers looking to segment audiences across different areas of an enterprise. The solution, announced Monday, aims to provide agile functionality and analytics to global marketing teams for account-based marketing efforts. The tool leverages Pardot Einstein, Salesforce’s AI, seeking to help sales and marketing teams […]

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Salesforce is launching Pardot Business Units, a new feature for digital marketers looking to segment audiences across different areas of an enterprise. The solution, announced Monday, aims to provide agile functionality and analytics to global marketing teams for account-based marketing efforts. The tool leverages Pardot Einstein, Salesforce’s AI, seeking to help sales and marketing teams interpret digital engagement metrics and understand what type of content effectively resonates with the individuals who compose enterprise buying teams. The AI analyzes engagement metrics from across an entire enterprise, rather than the business units, giving digital marketers access to enterprise-level data and connect with their global marketing partners to share insights.

Why we should care

Enterprise digital marketers are familiar with the challenges of operating in an environment with limited access to different parts of the business. For companies composed of sub-brands, business units and across multiple geographies, most team have their own siloed data, best practices and processes. The lack of visibility and processes can hinder teams from sharing data and aligning messaging across the organization. Pardot Business Units seeks to allow users to break down those silos.

It also addresses privacy and compliance regulations by allowing teams in different geographies to see when a customers has provided explicit permission. “Compliance is such an important part of this capability,” says Nate Skinner, Pardot vice president. “We’re focused taking care of compliance to help marketers manage it.”

More on the news

Digital marketers using Pardot can now:

  • Segment audiences by line of business, sub-brand or geography for targeting.
  • Understand what customers and leads have provided consent for marketing.
  • Create visual reports for engagement metrics across multiple domains.

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Why you want ‘clumpy’ binge-buying customers

By understanding binge purchasing, you can uncover a new metric to measure and predict CLV – and choose which customers to focus on and when.

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We’ve all heard of the term the “hot hand” in the context of sports. Basketball players go from missing every shot, to scoring in streaks. Sometimes players are in such a “zone” that he or she seemingly can’t miss a shot. Baseball players also tend to hit home runs in bunches.

Throughout my career and through my research at Wharton, I’ve studied the phenomenon of the “hot hand” as it relates to the way consumers tend to buy products and services or consume content. Simply put, customers who consume or buy content in bunches, then go away and come back and buy in bunches, are more valuable to companies than customers who buy at a steady pace.

Don’t believe me? Let’s take a deeper look at how measuring binge consumption by customers, or what I call “clumpiness,” can be applied to maximize Customer Lifetime Value, yielding stronger sales and marketing ROI over time.

Maximizing Customer Lifetime Value with clumpiness

CLV is universally accepted as a central tenet of marketing today. In both academia and practice, it is looked upon as a goal of firm value maximization. That is, more profitable firms recognize that CLV maximization yields greater cash flows and higher long-run profits.

Relatedly, mathematical models that allow these firms to predict CLV are commonly based on a framework commonly called RFM.

  •       Recency – How recently did a given customer make a purchase?
  •       Frequency – How often they made a purchase?
  •       Monetary Value – How much did they spend?

These are the cornerstones of CLV calculations and segmentation used by countless marketers and I’m here to tell you: They’re wrong!

Well, sort of. They are incomplete.

Through research, I have demonstrated and introduced that not only are RFM crucial components to calculating CLV; there is one additional dimension that MUST be factored in: clumpiness (C) or as some refer to it, binge consumption.

The hot hand

Let’s go back to the hot hand example and the player who is scoring points in bunches. Now, juxtapose over the world of marketing and consumers and you have clumpiness, AKA consumers who buy in bunches.

My research shows those who consume or buy content in bunches, then go away and come back and buy in bunches, are more valuable than other customers.

Let me put that another way. If a given brand knew both – how clumpy a consumer’s behavior is AND how frequently they buy – the better predictor when it comes to their future CLV is their clumpiness. I realize that may seem shocking, but it’s true. My research clearly illustrates that brands/marketers should be tracking someone’s clumpiness over time because that’s extraordinarily predictive of their CLV.

Across the board, marketers see far stronger results when they use RFMC data versus only using RFM. By focusing on clumpy consumers as their most valuable customers, brands can realize far stronger CLV and profitability.

With that overview in mind, let’s take a deeper look at what various brands have done to improve CLV and better target their marketing to encourage binge purchases by consumers.

Digital consumers behave more clumpily

We’re all familiar with binge-watching a series on Netflix, or other binge consumption of content from YouTube to gaming. But consumers have expanded this behavior beyond digital content and we’re now seeing it everywhere — from shared services such as AirBnB, Lyft and Uber to retail and online purchases.

A variety of different factors can drive clumpy behavior. In the case of content, the key driver is availability. For example, Netflix releases a new season of a given show, and suddenly everyone wants to watch it ASAP. They literally plan their lives around it.

Consumers can go weeks in between major purchases and then get the “hot hand” making multiple purchases or consuming an unusual amount of goods or services in a short period, or spending more money in a concentrated time.

The two sides of being clumpy and the demographic view

There are two types of clumpiness when it comes to consumers – visit clumpy and purchase clumpy. Consumers who are visit-clumpy are akin to the classic “window shoppers” of yesteryear. They visit both online and offline channels without necessarily making a purchase. In contrast, purchase-clumpy shoppers are far more valuable over time.

As a part of our research, we examined multiple retailers in specific product categories. Among the key findings were that millennials are more clumpy than other generations and that women are clumpier than men.

With marketers struggling to figure out how to market to millennials, this information can be helpful. By understanding clumpiness as a key facet of CLV, brands are turning the corner and seeing better results.

By understanding clumpy behavior, knowing to look for it and analyzing the level of clumpiness, marketers and other key decision makers gain a new metric for measuring and predicting CLV and choosing which customers to focus on and when. They can also gain a better understanding of customer satisfaction and react to it faster.

Defying the odds

When I first set out to conduct the research, I would have bet that the, findings would indicate that regular buyers were more loyal than those who buy in clumps. Well it turns out that my research, as well as others, suggests that regular buyers are in fact not more loyal.

Many times these are subscription customers and in fact, just buy without even thinking about their repurchase decision. A lot of research shows right now this is how you lose money. You take someone that buys in a regular pattern and try to upsell them because they don’t even think that they’re buying in a regular pattern.

We call it “poking the sleeping bear.” You poke somebody who’s just using your service regularly but isn’t even consciously … let’s say monthly making the decision to do so. And by your saying “Hey, why don’t you also buy …product?” “Holy cow! You mean I’m spending $300 a month on your product? Forget it! I cancel!” But your goal was to upsell them and instead you made them churn. So I’m not a strong believer in just observed loyalty. What appears to be observed loyalty over time, that’s not actually loyalty.

Final thoughts

I’m sure many of you reading this will have doubts. Many of you will want to stick to the tried-and-true RFM method and you are of course more than welcome to continue to do so. But I can tell you, without reservation, that if you do not begin to also factor in C (clumpiness), you will never get a true read on your customers.

Although recency/frequency/monetary value (RFM) segmentation framework, and its related probability models, remain a CLV mainstay, companies need to extend the framework to include clumpiness to predict future customer behavior successfully.

After studying thousands of data sets from companies across categories, we’ve found that C adds to the predictive power, above and beyond RFM and firm marketing action, of both the churn, incidence, and monetary value parts of CLV. Hence, we recommend a significant implementation change: from RFM to RFMC.

Measuring clumpiness has huge practical value. Clumpy consumers are worth more money and firms need to find them, and use marketing to drive customers to binge consume.

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Here’s how to get the most out of your marketing analytics investment

Build organizational structure and develop analytics leaders who bridge data science with marketing strategy to improve your return on investment.

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Gartner recently published their Predicts 2019 research report, outlining several converging trends that pose a threat to CMOs and marketing organizations. The report also makes several bold predictions including that “by 2023, 60 percent of CMOs will slash the size of their marketing analytics departments by 50 percent because of a failure to realize promised improvements.”

The number one success factor for CMOs today is the ability to effectively leverage customer data and analytics. And yet, according to Gartner’s report, companies today are clearly not demonstrating consistent return on that investment, a problem which often stems from a lack of marketing analytics leaders and the organizational structure necessary to effectively translate data and insights into action.

To discuss in more detail, we chatted with one of the authors of the Gartner report, Charles Golvin, to explore what CMOs and marketing leaders can do to buck the prediction and drive stronger results for their marketing analytics investment.

Our conversation, coupled with my own experience, solidified five ways CMOs can improve return on their marketing analytics investment, while also reinforcing why it matters:

1. Build organizational structure to apply better data

Knowing how to effectively leverage customer data and analytics is the number one success factor for CMOs today. And yet, to fully leverage the power of analytics, companies need to develop organizational structure and processes to be able to identify, combine and manage multiple sources of data.

As Golvin puts it, “companies need to build a better pipeline of carrying data from its raw state to decision and action systems for data science leaders to apply insights and powerful analysis to determine the right action and right strategy.”

To build these pathways, companies need a strong methodology coupled with an approach for how data gets aggregated, digested and applied to their various marketing systems.

2. Develop analytics leaders who bridge both data science with marketing strategy

Another key success factor for companies is developing and hiring the right leaders who can bridge both data science and business strategy. Simply put, analytics leaders need to know enough about business to ask the right questions of data. Only then, can they apply data and models to yield better decisions and drive sustainable growth.

This is our philosophy at Wharton – preparing well rounded, analytically-adept business leaders who don’t ask what data can do for them, but what data is needed to increase customer lifetime value (CLV) and how to apply data and customer insights to shape brand strategy.

Gartner regularly conducts surveys about different challenges that CMOs and marketers face, and every year, the one that rises to the top is finding skilled data and analytics leaders to hire,” shares Golvin. “Companies also struggle to find those ‘unicorns,’ or people able to command both data science and business strategy.”

Golvin also pointed out that once a company does hire an analytics leader, companies need the right foundation in place to foster their success. “There’s no value to hiring a data scientist whose output leadership doesn’t understand or know how to implement.”

Too often, we see traditional marketing organizations that aren’t able to effectively apply analytics or don’t understand how to frame the questions for data scientists on their team. The reverse is also a common challenge: analytics leaders don’t grasp how to use data to shape the broader business and brand strategy.

3. Hire a Chief Analytics Officer, or up-level the importance of analytics

So how do companies up-level the importance of analytics and develop the data-driven culture, capabilities and leaders needed to successfully transform their organization? One trend we are seeing is the emergence of the Chief Analytics Officer or Chief Data Scientist across more organizations.

As Golvin notes, “we’re already starting to see the emergence of Chief Marketing Technology Officers, who are focused on deployment of the right technology, architecture and capabilities. The next trend may be marketing analytics leaders at the c-level, who are purely about analytics and understanding the data.”

When companies empower analytics leaders to lead strategy, it can transform the culture, providing a clear vision for what customer data will be used and how to reach the desired business impact. When companies fail to make this investment, it leaves high-caliber professionals in a quandary.

“Too often data science leaders end up doing grunt work such as basic data processing and preparation, rather than using their analytics mindset and abilities to drive actionable marketing strategy, separate the signal from the noise and improve marketing outcomes,” notes Golvin.

4. Focus on better data, not big data

An ongoing challenge organizations face today is what we call “better data, not big data.” Too often we see companies that are collecting data for data’s sake, rather than taking a lean approach where they only collect data when it helps to optimize the experience for their target customers or better prediction of future behaviors.

“As data becomes more integral to marketers, a ‘more is better’ attitude develops, without necessary consideration given to the downside risks,” notes Golvin. “Companies need to do a better job of being transparent about what data they use and how, as well as considering the pros/cons, and risks of incorporating that data into a profile of their customers. More data does not necessarily lead to greater business intelligence – and in many cases can expose the brand to issues that impact customer trust.”

Data collection is in no one’s interest when it’s not meaningfully tied to strategy.

5. Separate the signal from the noise to predict and optimize business outcomes

Improving ROI for marketing analytics requires constant learning and experimentation to separate the signal from noise. There’s no better way to learn about your customer than to see what works and what doesn’t.

While big data and machine learning are great to business intelligence, a well-controlled experiment can deliver far more value. Finding the most impactful experiments to run starts with asking the right questions and maintaining a test and learn mindset where you’re constantly evolving to improve the experience for customers. The iterative adaptation based on these experiments builds momentum.

Many marketers know the “Holy Grail” phrase “deliver the right product to the right person at the right time.” In the past, this was more difficult because we didn’t know where consumers were. Now when marketers use better data, they know where the customer was and is more likely to be – providing the foundation for the ultimate in contextual 1:1 marketing.

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AI-powered surveys: Hyped or helpful?

Some wonder if AI-powered surveys are over-hyped vaporware that legitimizes bad science. Here’s how they really work.

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For marketers with an interest in research, it’s a good time to start talking about AI-facilitated online surveys. What are those, exactly? They’re surveys that use machine learning to engage with respondents (think of a chatbot) which then manage a lot of the back-end data involved with implementing and reporting (think of pure drudgery). We have had a great experience with GroupSolver, and other examples include Acebot, Wizer, Attuned (specific to HR) and Worthix. There are others out there, and probably even more by the time you read this.

The good news is that a conversation about AI-facilitated online surveys is well underway. The bad news is that it’s rife with exaggerated claims. By distinguishing between hype and genuine promise, it’s possible to set some realistic expectations and tap into the technology’s benefits without overinvesting your time and research dollars in false promises (which seems to happen a lot with AI).

As Dr. Melanie Mitchell puts it in “Artificial Intelligence Hits the Barrier of Meaning,” AI is outstanding at doing what it is told, but not at uncovering human meaning. If that’s true, what possible use can AI have for online surveys? There are five themes that need to be addressed.

Reduced customer fatigue

One misconception is that AI surveys reduce fatigue because traditional surveys are too long. Not quite. Surveys are only too long if they are poorly crafted, but that has nothing to do with how the instrument is administered. Where AI does help is in creating an experience that is very comfortable for the respondent because it looks and feels like a chat session. The informality helps respondents feel more at ease and is well-suited to a mobile screen. The possible downside is that responses are less likely to be detailed because people may be typing with their thumbs.

Open-ended questions

There are three advantages to how AI treats open-ended questions. First, the platform we used takes that all-important first pass at codifying a thematic analysis of the data. When you go through the findings, the machine will have already grouped them according to the thematic analysis the AI has parsed. If you are using grounded theory (i.e., looking for insights as you go), this can be very helpful in getting momentum towards developing your insights.

Secondly, the AI also facilitates the thematic analysis by getting each respondent to help with the coding process themselves, as part of the actual survey. After the respondent answers “XYZ,” the AI tells the respondent that other people had answered “ABC,” and then asks if that is also similar to what the respondent meant. This process continues until the respondents have not only given their answers but have weighed in on the answers of the other respondents (or with pre-seeded responses you want to test). The net result for the researcher is a pre-coded sentiment analysis that you can work with immediately, without having to take hours to code them from scratch.

The downside of this approach is that you will be combining both aided and unaided responses. This is useful if you need to get group consensus to generate insights, but it’s not going to work if you need completely independent feedback. Something like GroupSolver works best in cases where you otherwise might consider open-ended responses, interviews, focus groups, moderated message boards or similar instruments that lead to thematic or grounded theory analyses.

The third advantage of this approach over moderated qualitative methodologies is that the output can give you not only coded themes but also gauge their relative importance. This gives you a dimensional, psychographic view of the data, complete with levels of confidence, that can be helpful when you look for hidden insights and opportunities to drive communication or design interventions.

Surveys at the speed of change

There are claims out there that AI helps drive speed-to-insight and integration with other data sources in real-time. This is the ultimate goal, but it’s still a long way off. It’s not a matter of connecting more data pipelines; it’s because they do very different things. Data science tells us what is happening but not necessarily why it’s happening, and that’s because it’s not meant to uncover behavioral drivers. Unless we’re dealing with highly structured data (e.g., Net Promoter Score), we still need human intervention to make sure the two types of data are speaking the same language. That said, AI can create incredibly fast access to the types of quantitative and qualitative data that surveys often take time to uncover, which does indeed bode very well for increased speed to insight.

Cross-platform and self-learning ability

There is an idea out there that AI surveys can access ever-greater sources of data for an ever-broader richness of insight. Yes, and no. Yes, we can get the AI to learn from large pools of respondent input. But, once again, without two-factor human input (from respondents themselves and the researcher), the results are not to be trusted because they run the likely danger of missing underlying meaning.

Creates real-time, instant surveys automatically

The final claim we need to address is that AI surveys can be created nearly-instantaneously or even automatically. There are some tools that generate survey questions on the fly, based on how the AI interprets responses. It’s a risky proposition. It’s one thing to let respondents engage with each other’s input, but it’s quite another to let them drive the actual questions you ask. An inexperienced researcher may substitute respondent-driven input for researcher insight. That said, if AI can take away some drudgery from the development of the instrument, as well as the back-end coding, so much the better. “Trust but verify” is the way to go.

So, this quote from Picasso may still hold true: “Computers are useless. They can only give you answers,” but now they can make finding the questions easier too.

Summary

The good news is that AI can do what it’s meant to do – reduce drudgery. And here’s some more good news (for researchers): There will always be a need for human intervention when it comes to surveys because AI can neither parse meaning from interactions nor substitute research strategy. AI approaches that succeed will be the ones that can most effectively facilitate that human intervention in the right way, at the right time.

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Using technology to plan an effective people-based audience activation strategy

A well-designed identity strategy using audience analytics and insights can tie disparate forms of ecosystem data together for a global view of consumers.

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As the marketing world has grown increasingly “digital-first,” data has become more pervasive, and as it surges in scale, marketers find it more difficult to ascertain its value. One would think that with more data, understanding value would be much easier; however, many know this isn’t true. When your audiences range from first-party CRM data to third-party acquired data and everything in between, without a defined identity strategy, you will lack clarity around whom you are talking to and how they prefer to communicate.

The influences of a digital-first world are having a direct impact on advertisers. The promise of more accurate and scalable data is becoming a reality through people-based marketing but understanding how to best leverage identity across various data sources, platforms, and customer touchpoints are becoming increasingly complex.

Typical challenges faced by advertisers include:

  • How do I activate my offline CRM data for digital marketing and maintain a persistent view of my customers?
  • What is the best way to leverage CRM data in digital marketing tactics, such as display, where cookies are mixed in?
  • How can I leverage my website data to better personalize digital marketing, while maintaining knowledge of the user’s identity?
  • Is third-party data a worthwhile investment, and how do I best utilize this data for prospecting tactics?

While this list is only a small sample, it illustrates the complexity of audience activation. A well-designed identity strategy that is enabled by technology can tie disparate forms of ecosystem data together. Data Management Platforms and the newer space of Customer Data Platforms can make this a reality when combined with a persistent global view of people.

You can think of an identity strategy as a playbook, structured around insights based on audience analytics to assess value, which all ladder up to tactical business use cases. This might sound daunting, but it’s not rocket science.

Let’s discuss how you can best plan this out.

1. Start with the technology

Without getting too deep, DMPs are cookie-based data platforms that maintain first- and third-party data not tied to a known identity. CDPs, on the other hand, are good at maintaining known first-party personally identifiable information (PII), such as first name, last name, address, email, etc. You can think of a DMP as the central command center for cookie-based activation and driving connected experiences across paid media and the website. A DMP can help augment your prospecting use cases with third-party cookie data from a variety of vendors in the market.

A CDP will bring together your website data and cross-channel media data, and (most importantly) it will enable you to link this all to a known view of a customer. Whether it be someone who has raised her hand to receive an email communication or someone who purchased a product, this view of identity can be stitched together in a secure (and anonymous) way (i.e., any reference to PII is removed).

The interplay between a DMP and CDP is critical and centralizing all of this data allows you to focus on studying audience segments across various data sources to find pockets of value.

2. Understand audience use cases

Now that you have a basic understanding of the technology, you should understand use cases around audiences and how to start segmenting your data. There are general buckets of data you will come across. Everyone has different versions of these buckets; however, they represent the most common audience use cases.

  • First-party offline CRM – This is your most valuable data asset.
    • Examples: Account records, call center data, loyalty data
  • First-party online authenticated
    • Examples: Website conversion data linked to a logged-in account, email, and CRM loyalty data
  • First-party online anonymous
    • Examples: Digital media impressions, Website visitation data, and mobile data
  • Second-party
    • Examples: Direct purchases of audiences (e.g., cookies) from another data provider and publisher, where you now own the audience
  • Third-party
    • Examples: Audiences data purchased to target against and use for analytics rights; however, unlike second-party data, you do not own the rights to the audience (e.g., cookies)

3. Assess the value of this data

Now that you have a basic knowledge of different types of data and how they can be bucketed, it is time to start assessing value – a key to defining a people-based audience strategy. Value can be assessed in multiple ways, varied by industry-specific KPIs. As you will come to see, scalability is the largest driver impacting value for any data asset.

Assessing value requires building an analytics framework, which we will discuss in the next part of this series, to start evaluating your data and make recommendations on the best way to activate it across your organization.

Sunil Rao, Vice President of Analytics at Merkle, also contributed to this piece.

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