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DataIntelligence

What is Customer-Centric Marketing?

January 29, 2018 — by Laura Carrier0

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It’s seems like obvious advice to tell marketers to focus on the customer. After all, the whole idea behind marketing is to influence the customer’s actions and decisions.

In practice though, many marketers aren’t executing customer-centric marketing, they’re employing campaign-focused marketing or maybe marketer-focused marketing. What’s the difference? In customer-centric marketing, you are first and foremost focused on the customer, their behaviors, their experiences, their needs. With campaign-focused marketing, the focus is on you, the marketer, and your business outcomes. It’s about what you did and what results you got.

That may seem like a subtle difference, but right now there’s a split between the two approaches. In his 2016 Letter to Shareholders, Amazon CEO Jeff Bezos dismissed product-focused and technology-focused businesses in favor of “obsessive customer focus.” That focus has certainly helped Amazon shake up the market. Customer-centric marketing doesn’t mean forgetting about your business outcomes. But it does mean first focusing on the customer such that you satisfy the customer first, which will lead to better outcomes for your business. Here’s a look at what you need to bring that kind of customer-centricity to your business.

The Strategy for Customer-Centric Marketing

Customer-focused is the second of our six pillars of effective marketing (the six in order are data-driven, customer-focused, omnichannel, integrated customer engagement, optimized messaging and comprehensive understanding). Of this half dozen, customer focus demands the most major mind shift for marketers.

In short, the idea is to take a step back from standard marketing processes. In terms of planning, optimization and measurement, consider the customer first. For example, stop comparing how this year’s Halloween campaign did versus last year’s or worrying about how much to spend on mobile versus display on Labor Day. Those are the wrong questions since they are campaign-focused. Make planning & optimization decisions at the customer level (and in real time) — should Jane Doe receive a display ad or a mobile ad when she is researching Halloween costumes? Should she be served an ad for BOGO or for free shipping? Instead of looking at campaigns for June and July, for instance, run a longitudinal analysis over a six-month period to see how all of your 18 campaigns performed in the aggregate against your customers’ behaviors. Did you positively influence your customers’ behaviors during that period? And how much did you influence them — did your marketing lead to incremental impact? That will tell you if your marketing was effective.

In general, customer centricity means thinking about the products and solutions you are using in terms of their customer-actionability — for instance cross-device implementation, sequential marketing, predictive analytics, incremental lift, multi-touch attribution. You are looking at the success of your overall marketing engagements, not a snapshot of how one campaign or one aspect of a campaign performed. The goal is to optimize customer behavior.

How to Implement Customer-Centric Marketing

One primary difference between marketer-focused marketing and customer-centric marketing is the metrics you’re tracking. There are three ways to track changes in customer behavior. On the upper funnel, you’re looking at incremental brand perception. Tools for measuring brand perception include brand perception surveys, which should be focused on the perception change, not awareness and consideration changes, as perception change is what drives behavioral change.

On the lower funnel, the relentless focus should be on incremental return on ad spend (ROAS). Such analyses are based on randomized control trials (CRTs) like the ones used in medical testing. A CRT measures the effects of exposure by including a holdout group (often around 20% of the potential audience) to see if the ad actually influenced a desired behavior.

In the mid funnel, customer-centric marketers are looking for key performance indicators (KPIs) that correlate to incremental ROAS. For instance, clicking on a display ad actually has a low or negative correlation to making a purchase. That’s because display clicks are usually fraudulent, accidental or made by serial clickers. Mid-funnel indicators are specific to your business and your customers, so running correlation analysis to understand which customer behaviors/signals lead to incremental ROAS is imperative. And then run campaigns that focus around driving those particular customer behaviors. Opening emails, reading a blog post, visiting a product page and signing up for a loyalty program are all examples of behaviors that could have a positive correlation for your business.

Note that these metrics are very different from the typical KPIs involved in a marketer-focused marketing campaign, where marketers choose to optimize and measure their campaigns against customer behaviors that aren’t driving true business outcomes like click-throughs, engagement and impressions. Focusing on these not only means that you are not driving business outcomes, but it also leads to frustration for your customers as you try to drive them to behaviors that are irrelevant to the intent of their engagement with your business.

Reorienting marketing around this customer-centric mission requires a radical shift in planning, execution and management. It also means more cross-team collaboration than you usually find in marketing departments organized around functions, channels or the funnel.

The benefits are tremendous in terms of less waste, enhanced customer relationships and, ultimately, strong business growth. If you’re frustrated with your current marketing, then maybe it’s time to try a new approach and shift the focus to the customer first.

DataIntelligence

How to Tell AI Hype from Reality

January 11, 2018 — by Todd Wasserman0

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In 2017 it was hard to escape talk of artificial intelligence. Gartner claimed that AI had reached the peak of its hype cycle and deep learning and machine learning were at the “peak of inflated expectation.”

Such excitement is par for the course in tech. Some have compared AI in 2017 to cloud computing a decade or so before. Though the cloud was hyped relentlessly, actual adoption was slow. AI may be on a similar path.  A 2017 SAS survey of European business execs, for instance, showed just 24% had the right infrastructure in place for AI.

Is the market ready for AI? That’s a moot question because marketers are already using many AI technologies. Some have been using for years without calling it AI. But other AI applications are still years or even decades away. Here’s how to tell the difference:

AI reality

Most AI applications are improved versions of what we would have called “data mining” a few years ago. The difference is that there is often a predictive layer. Marketers are now using these technologies in varying degrees:

• Content curation/recommendations: IBM Watson teamed up with UnderArmour in 2016 to create a personal health consultant that uses a particular consumer’s data to craft a personalized training routine. For instance, a 45 year-old man training for a 10K could use UA’s Cognitive Coaching system to get advice on diet and exercise goals, which are based on data from other consumers in the same demo. More recently, IBM conducted a pilot that involved integrating its Watson Cognitive Bidder with MediaMath’s DSP to incorporate more unstructured data like the weather or location to fine-tune real-time buying decisions that address consumers’ current states of mind.

• Natural language processing: Consumers telegraph their needs via search queries and social media chatter. But because of the way we express ourselves, translating that language into data can be tricky. For instance, as Stanford computer scientist Chris Manning recently pointed out, “terrific” used to mean “to terrify” and has only fairly recently taken on a positive meaning. Thanks to wide daily exposure to natural language, machines are getting much better at picking up on such nuances. Some companies even analyze incoming sales calls to unlock patterns and opportunities.

• Image recognition: This is another AI technology that has become so common that it’s mundane. We take it for granted, for example, that Facebook will be able to “tag” people in photos we post. For marketers, image recognition provides analysis of visual imagery. U.S. consumers now post some 3.25 billion photos a day. Marketers can analyze such images to get clues about behavior and brand affinity.

• Ad targeting. Of course, one of the most popular deep learning applications right now is ad targeting. By analyzing data, machines can optimize ad performance via targeting. Marketers use this tool to improve their ROAS. In theory at least, your ad targeting keeps improving over time.

• Customer service/chatbots. One bad customer service experience can undermine millions in advertising. That’s why some brands are turning to chatbots to handle such inquiries. Experiments with voice-based chatbots have found that they can solve consumers’ queries 82% of the time and leave humans to handle the more complex queries. In addition, brands like 1-800 Flowers and Whole Foods have used chatbots to establish a brand presence on Facebook’s Messenger app.

AI hype

While those technologies are in use right now, some AI being discussed won’t be of use for years — maybe even decades. Those include:

• General intelligence: This is the type of AI usually portrayed in Hollywood movies and sci-fi novels. In reality, today’s AI is “narrow AI,” meaning the intelligence is focused on a single task, like playing chess or understanding spoken language. Exhibiting a well-rounded intelligence — one that can make quick connections and exhibit wisdom — is still a long way away, if it is ever possible.

• Complete automation: Another fear about AI is that it will monopolize all jobs handled by human workers. While some tasks — like customer service — are increasingly going this way, they are often shifting the work of human workers, rather than replacing it completely. As a corollary, look at the spread of ATMs. While you might think that they would have replaced human tellers, instead there were more because ATMs made it cheaper to open new branches and more branches meant more jobs for human tellers.

• Creativity: In advertising, AI can be used for “dynamic creative,” which swaps in marketing messages based on available data. (For instance, you might see an ad for a sunny vacation in Rio if the temperature has just plummeted near you.) But what machines are really doing is grabbing elements that a human has created. While AI systems can now compose music and even write Harry Potter fan-fiction and create movie trailers though, humans are still required to exercise judgment. An AI system is great at presenting random combinations based on exposure, but a human is still needed to make sense of it. Making unusual connections (like Jay-Z’s sampling of “It’s a Hard Knocks Life” from Annie) is still beyond even the most advanced AI system.

As we move on from a year in which AI hype has peaked, 2018 will likely be a building year in which businesses and marketers pragmatically apply the technologies that affect real-world outcomes. Meanwhile, AI itself will keep improving, expanding the palette of what’s possible and what will be possible in decades to come.

IntelligencePeopleTrends

MathCapital: A Venture Capital Fund to Support the Next Wave of Marketing Innovation

January 8, 2018 — by Eric Franchi0

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The year 2018 finds us at the beginning of the next wave of innovation in digital. Billions of connected devices, the emergence of identity-based targeting and measurement, new consumer interfaces such as AR, VR and voice and all forms of Artificial Intelligence will make digital marketing more effective and digital media more engaging than ever.

It’s an exciting time to be a marketer, media creator and consumer. But it’s never been a more exciting time to be a startup focused on addressing the needs of this giant, and growing, market. That’s why we are thrilled to announce the launch of MathCapital, an early-stage venture capital firm focused on the digital transformation of marketing and media. We created MathCapital to help identify and support the startups that will become the next generation of industry leaders.

As long-time entrepreneurs ourselves — Joe Zawadzki is founder and CEO of MediaMath, and founder of [x+1], and I previously co-founded Undertone – our team is often approached for advice and investment by startups. This led to notable personal angel investments in names many are familiar with, such as AppNexus, Moat, Integral Ad Science, BounceX and mParticle.

We’re also excited to have the support of MediaMath. While MathCapital is a separate entity, MediaMath has committed resources via its in-house innovation group that has incubated a number of profitable agencies. This group will serve as an access point to MediaMath’s 4,500+ clients, 350+ partners and 600+ employees, helping to accelerate all facets of our portfolio companies’ businesses out of the gate.

We’re a startup of four partners ourselves, so we can’t wait to roll up our sleeves and get to work. Our first investments will be announced here soon, so watch this space. And if you’re a funder who would like to learn more about working with us, please contact us here.

DataIntelligenceMedia

Incrementality is the Best Way to Prove Your Advertising is Working. Here’s How to Measure It

December 27, 2017 — by Natraj Ramachandran0

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The number one question advertisers are asking now is, “How can I measure incrementality?” In other words, “Did my ad campaign cause the consumer to convert?”

There have been many methods for measuring incrementality, including looking at conversions pre-and post your digital marketing efforts or using a complicated model. But in recent years, marketers have understood that the only true way to measure causality is to use a scientific approach.

In public health, the gold standard in measuring the efficacy of any intervention is a randomized control trial (RCT). For example, if we take two equal populations who have similar behavior and give one population a pill and another a placebo we can then measure the effectiveness of the pill we administered.

The same can be done in marketing. In Incrementality Testing, users are separated into two equal populations. One group (test) receives an ad and another group (control) doesn’t. Once we observe the conversion rates of both these populations we can measure their incremental conversions. More importantly, we can accurately measure the cause and effect of our marketing efforts.

In order to create solid lift measurement tests, you need to follow an experimental design approach:

Setting up your test

Measuring incrementality is simple if you follow these steps:

1) Randomization
Randomization ensures that the populations in test and control are statistically equivalent. This allows us to compare behaviors of the groups.

2) Hypothesis
The key to a strong Lift Measurement test is creating a hypothesis. This can be created by looking at previous observational data. Without a strong hypothesis, the results of your test will be pointless.

3) Primary outcome
Decide the behavior you are trying to observe. For example, if we show an ad for pants, we hope that the desired behavior would be the customer would then buy pants. In your test that would be the conversion event.

4) Length
Select a start and end date of a reporting cycle to ensure that reporting results align to the hypothesis of the test (minimizing distortions due to influences of time).

5) Expected use of outcome
The results of your study should contribute to the advancement of knowledge of your marketing campaign. You will use results to make strategy, budgetary and/or optimizing results.

Understanding your results

1) Lift, incrementality, incremental conversion, confidence, confidence intervals what do they all mean?
A common confusion with any test are the metrics you receive. It is essential that you have defined your results before you receive them.

a) Lift
This will give you the likelihood to convert. For example, let’s say your lift is 30%, this would mean that if we show your ad to a consumer they are 30% more likely to convert.

b) Incrementality

The percentage of conversions you received because of your ad. Let’s say we run our test and get a 20% incrementality, this means we received 20% more conversions because we showed an ad. Or alternatively we would have lost 20% had we not shown this ad.

 

c) Incremental Conversions/Revenue

Generally, a portion of sales would have occurred despite the campaign. Your incremental conversions/revenue are conversions that occurred as a result of your campaign. These conversions wouldn’t have occurred if your ad was not shown.

1) Confidence %

Your confidence is the % of distribution that sits above zero. For example, if we get a 90% confidence We are 90% sure that the lift is > 0.  This is a good, positive signal we look for to validate expected results.

2) Confidence Intervals

This provides more detail into the health of a measurement. A confidence interval is a range of values that you can be certain contains the true mean of the population. If for example you use a 95% confidence interval, we are saying that we are 95% certain that interval we calculated has the true lift of the population, the narrow the confidence interval the better. The confidence interval is a measure of how precise your lift/incrementality are in your test.

Guardrails/Mitigating factors

1) Why do my lift/incrementality numbers vary so much?

a) Sensitivity — Lift/Incrementality are calculated based on small conversion rates. In a recent campaign the measured conversion rate for the test group was .03% and .02% for the control group. This would equate to a 33% incrementality.

If the test group conversion rate increased to .04% this equates to a incrementality of 50%.

a) Seasonality – The control group response rate can vary drastically depending on different buying seasons. For example, if we ran a test during Black Friday, the control group response rate may be higher than other times of the year as users are more likely to purchase during this time. This would in turn dampen the influence of your ad campaigns.

b) Brand Awareness – Some products are more familiar to consumers than others. If we ran a test for Snapple and another test for Honest Tea, we should expect larger lift/incrementality for Honest Tea vs Snapple. Snapple is more recognized by consumers than Honest Tea. This means that Snapple consumers are more likely to convert even without the influence of an ad campaign. This means the ad campaign run for an Honest Tea would have a larger impact on consumer behavior.

1) Can you tell me results at the strategy or tactical level?

This is an almost impossible question to answer unless your test is set up appropriately and you have a well-defined hypothesis. Take for example a prospecting and remarketing campaign that are run in tandem. In this scenario, there will be a natural flow of users from the prospecting campaign to the remarketing campaign, causing overlapping populations. As a result, there may be some users who receive two ads then convert (see below).

It becomes almost impossible to understand and attribute the conversion to the appropriate ad. Additionally, this type of behavior isn’t seen on the control side as they are held out to all ads.

 

In order to understand tactic/strategy level results it is best to test each strategy one at a time to avoid overlapping audiences and audience flow.

Conclusion

Incrementality testing can be an incredibly powerful tool if implemented correctly. Many public health organizations continue to use it to measure causality for clinical interventions. Testing in marketing is still nascent and still needs more exploration.

In order to have an effective test you need to:
1. Ensure you determine what you would like to learn from the test before campaign and incremental lift test(s) are set up.
2. Determine the primary goal you are testing (Visits, Checkouts, Signups).
3. Ensure the results of your study contribute to the advancement of knowledge of your marketing campaign.
4. Make sure the results will be used to make strategy, budgetary and/or optimization decisions.

Understand that your results will continue to change because the behaviors of your clients are also constantly changing. In order to make your tests useful have a test plan at the beginning of ever year and continue to iterate on your hypothesis. Running one or two tests won’t provide you with the answers you need. Measurement for measurement’s sake is doesn’t advance learning.  Don’t just measure to say you did it, and then let the results linger in a PPT print out on your desk, measurement should be actioned on.  If you can’t make a decision or take an action based on what you have learned, then that measurement isn’t useful.

Intelligence

In 2018, Marketers Will Discover More AI Applications in Programmatic Advertising

December 14, 2017 — by Amarita Bansal0

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This Q&A with Chris Victory, VP Partnerships at MediaMath, originally appeared on eMarketer.

Even when digital ads are highly targeted down to individual users, it’s hard to get consumers to pay attention to them — let alone interact with and enjoy them. Using artificial intelligence (AI) to further optimize digital ad spending is the next step marketers are taking to make ads a more welcome part of consumers’ digital lives. eMarketer’s Tricia Carr spoke with Chris Victory, vice president of partnerships at demand-side platform MediaMath, about the demand for AI in the programmatic advertising space and the core elements of his company’s partnership with IBM’s Watson.

eMarketer: Are marketers typically aware the benefits of AI in programmatic settings, or are you doing a lot of education on this front?

Chris Victory: Marketers who use programmatic advertising have seen the benefits of implementing machine learning into their decisioning and spend of their marketing dollars. AI is the next step in this journey, and everybody is super excited about it. But the challenge lies in delivering something that goes beyond what machine learning does today and is truly artificial intelligence. Those in the programmatic setting are perfect candidates to use the next generation of AI, because machine learning is core to what they do and it’s much more palatable to them.

eMarketer: How are you helping to propel the use of AI in this sense through your partnership with IBM Watson?

Victory: There are three main areas where we focus on the power of AI. The first is infusing AI into the decisioning process on how to better spend your money. Today that’s based on machine learning, but think about all the additional data that can be ingested, like sentiment and mood of the user, and a deeper contextual analysis of a page. These types of things are driven by AI and can be infused into the bidding process to make better decisions.

The second is AI-driven ad creative — it’s like the next-gen version of dynamic creative optimization — to deliver things like interactive ad units. For example, you can interact with an ad by asking a question, and it will give users different answers based on the question asked.

The third is using AI across customer analytics and marketing insights. Today you can look at analytics through a number of different technologies that give insights into your customer base. But the next generation of that is using AI for more predictive analytics. For example, don’t just tell me who my lapsed customers are, but show me who my potential lapsed customers are based off of behaviors. Then you can create user segments to target with specific messages related to their potential of becoming a lapsed customer.

eMarketer: Are there any roadblocks in the industry preventing marketers from experimenting with AI to optimize their programmatic advertising?

Victory: There’s a lot of noise out there — this doesn’t just apply to AI, but marketing technology in general. AI is the buzzword now, and there are a lot of companies getting money from venture capitalists because they have AI in their strategy. But it’s getting harder for a marketer to cut through the noise and understand exactly what they can do with AI — what’s real and what’s not.

eMarketer: Will consolidation happen in 2018, or is it a long way off?

Victory: I think there will be a proliferation of companies before there is consolidation. Next year will be interesting. You’ll see a lot of companies coming in.

There will be a real need for agencies to play a role here and become experts in the use and the application of AI, because agencies are engaged with their clients. It’s very similar to the evolution of the agency role in the programmatic world.

eMarketer: What else do you expect to happen in this landscape in 2018?

Victory: There will be a lot more marketers and advertisers jumping in headfirst to embrace and adopt AI. Once they start seeing the results of some of the applications of AI, there will be a snowball effect and people will want to do more.

There will also be continued proliferation of companies in this space and in the different applications of AI. Virtual reality has been the hot topic for the last year and a half, but now AI is. It’ll be interesting to see where venture capitalists make their nest here, and where that directs the market.

DataIntelligence

1995-like Media Buying Would Bring 1995-like Outcomes

December 6, 2017 — by Lewis Rothkopf0

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This article, written by Lewis Rothkopf, General Manager of Supply at MediaMath, originally appeared on LinkedIn. 

Like many of you, I read with interest Augustine Fou’s piece, “What a Concept! Buy Media As If It’s 1995,” in which he shares some disturbing examples of middlemen subtracting disproportionate value from media transactions. Also like many of you, I consider Dr. Fou to represent the very best of our industry — he’s a much-needed independent voice in cybersecurity and fraud research and someone whose perspective I hold in the highest regard.

Our opinions differ, however, when it comes to some of the solutions advocated in his latest piece. To be sure, there is no legitimate place for vendors who add no value to the ecosystem — they are a drag on publisher RPMs and do not promote better business outcomes for marketers. Simply put, if you don’t add value to the media transaction, you don’t deserve to be a part of it.

But to paint all intermediaries — “from agencies to ad tech vendors” — with the same brush would be a mistake. When looking at those entities who play a role in the value chain, it is important to separate Black Holes from Shining Stars.

Black Holes:

• Roll up, or aggregate publisher supply without adding a layer of value or differentiation such as data enrichment, geolocation or fraud protection
• Misstate the provenance of the supply or their right to resell it
• Are highly discrepancy-prone
• Are often several layers separated from the underlying publisher inventory

Whereas Shining Stars:

• Offer a layer of protection for buyers by offering assurance — and sometimes a guarantee — that the supply is legitimate, safe and fraud-free
• Add value to otherwise-commoditized supply with the addition of data
• Help identify the most appropriate demand for particular inventory
• Innovate on formats with marketers and publishers
• Normalize disparate pools of supply to make them addressable across channels
• Improve measurement and accountability
• Work to keep costs low

Additionally, though it can be tempting to dispose of baby along with bath water and flee from programmatic and its defense mechanisms (and the associated costs of same), doing so is not without significant risk. Dr. Fou acknowledges this in his piece, in which he says, “Of course, the publishers have to be vetted…” and that marketers shouldn’t simply take their word for it.

To this point, I’ve spoken with several publishers in recent months who have a strong understanding about where invalid traffic comes from and who have taken the necessary steps to avoid it. But we’ve interacted with many others who do not yet have such an understanding. One media property whom most would consider to be of the highest quality recently told us that invalid traffic is not preventable by publishers. This is most certainly not an accurate statement, and it was asserted in response to a suspicious traffic inquiry that we raised on behalf of our customers. Of course, when you buy inventory you are also effectively transacting with everyone from whom that publisher has sourced traffic in the past!

Dr. Fou raises several important — and downright horrifying — examples of non-value adding middlemen sucking trust and credibility out of the ecosystem. But turning back the clock to a time when there were no intermediaries facilitating media transactions is not the answer. Lots of things must happen in order to make a transaction possible, safe and effective — including things that can’t be taken for granted, such as data-decorating inventory to make it more addressable, ensuring that the inventory is free of fraud and that the supply source has a right to sell it, and intelligently marrying the right supply with the right demand. These things cost money, and should be considered every bit as much a part of a marketer’s investment in “working media” as the final act of displaying the ad on the web page.

It is in the interest of every legitimate actor in digital media to eliminate Black Holes from the ecosystem. By working together, we can continue to reduce costs and remove points of friction from media transactions, and reach a near future state in which good actor intermediaries are able to compete on their technical capabilities and their ability to drive business outcomes, and not only on their existential legitimacy relative to bad actors.

DataIntelligence

How to Kick Start Your Holiday Campaign this Season

November 30, 2017 — by Laura Carrier0

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This article originally appeared in MarTech Advisor. 

MediaMath’s VP of strategy and measurement Laura Carrier explores how marketers can ensure their advertising campaigns are timed in accordance with the height of consumer holiday spending.

Holiday season is the largest retail season of the year and as the gift giving traditions get underway, now is the time for advertisers to start detailed planning of how they’ll effectively target and reach holiday shoppers. According to eMarketer, Holiday sales will total $923.15 billion, representing 18.4 percent of US retail sales for the year.

To help advertisers make the most of holiday shopping budgets, we looked for trends in the way our best brands and retailers made the most of this season, including the way they think about timing and key dates, budget, media, targeting and more. Consider the following best practices:

Market to Your Audience Based on Deep Understanding

Marketers know it’s important to understand how audiences demonstrate different shopping behaviors – that’s nothing new. But it’s what marketers do with these insights which matters. Ask yourself, does your ad spend correlate to consumer’s shopping behavior? According to our own analysis, 2016 ad spend lagged behind the time frames consumers expected to do most of their online shopping. Over half of consumers plan to start holiday shopping no later than Black Friday, yet marketers had only spent 25% of their campaign budget by that time last year. This year, make sure to pace your holiday budget before customers do their shopping (while they are researching & planning)!

Get Creative Right 

Knowing who you’re targeting on an individual level, as opposed to different segments of customers or audience groups, will help fine tune your creative and targeting strategies this season. Executing true customer-centric marketing with a single view of the customer will allow marketers to optimize against all marketing touch points. Using this approach, dynamic creative optimization, which updates creative elements on the fly without advertisers having to manually build or modify new assets, will allow for more relevancy in the conversations you have with consumers.

When it comes to optimizing campaigns, marketers should take into account differences consumers shopping habits on key holiday dates when deciding on content. For instance, if you’re marketing to someone who is shopping the weekend before Christmas, getting an item to them as quickly as possible is much more important than the price, e.g. offering free shipping or in-store pick up. On the other hand, if you’re marketing to somebody who is shopping on one of the major one-day sales, like Black Friday, Cyber Monday or any retailer’s one-time sales, content around price would take priority, e.g. Buy one, get one free. Knowing the different types of consumption patterns will help advertisers optimize their Holiday campaigns.

M-commerce Market Grows

On the busiest shopping days of the season, customers are reaching for their phones first. Site traffic is just as likely to come from cellular devices as it is desktop site visitors with 47% of mobile share occurring on Black Friday and 49% of mobile share on Cyber Monday out of all total site traffic by device.

Increasingly, customers are continuing to buy sale items on their phones and check out one-day sales. According to eMarketer, US m-commerce sales will rise by 38% this year, and sales via smart phones will increase by 57.8%. With that in mind, marketers should be adopting an ominchannel approach when making marketing channel decisions.  Consumers are influenced by all of the various different media & channels available to them, so understanding behaviors across devices is becoming even more paramount today than it has historically been.

Online vs. Offline Shopping

The share of eCommerce is growing as 55.6% of US consumers plan on doing most of their holiday shopping online . Marketers will make smarter decisions if they understand the influence of online marketing on offline purchases, without ignoring the fact that offline marketing also influences online purchases.

Online shopping is growing at a faster pace than anything else, now 16.6% in 2017, compared to 3.1% for in-store retail. With Holiday shopping beginning earlier, coupled with the growth of online shopping, it’s important to remember that consumer research and holiday purchase planning is happening a lot earlier, too.To fully market across the customer journey, marketers must speak to consumers online in efforts to influence offline store sales, and measure the impact of those marketing touch points on offline behaviors. This will allow for true customer-level understanding, and ultimately the closing of the loop-optimizing marketing to those consumer behaviors.  As a result, brands and retailers alike will benefit from building out a digital strategy that includes both online and offline presences as one strategy-not as two separate tactics.

Intelligence

51 Artificial Intelligence (AI) Predictions for 2018

November 28, 2017 — by Amarita Bansal0

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This article originally appears in Forbes.

It is somewhat safe to predict that AI will continue to be at the top of the hype cycle in 2018. But the following 51 predictions also envision it becoming more practical and useful, automating some jobs and augmenting many others, combining machine learning and big data for fresh insights, with chatbots proliferating in the enterprise.

“Making smart marketing decisions across all customer touchpoints, using all available data, to drive complex business outcomes is a herculean task — and artificial intelligence is an absolute requirement for making it all work. In 2018, we’ll finally start to see AI deliver on the omnichannel promise to make marketing that consumers — and others in the value chain — love. The technology is there — from players like IBM Watson and others — and now is the time to rally the right processes and people to put it in action.” Dan Rosenberg, Chief Strategy Officer, MediaMath

For the full article, click here!

IntelligenceMedia

Bolstering Brand Safety with Contextual Pre-Bid Segments

October 25, 2017 — by John Van Antwerp0

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Seventy-eight percent of marketers report their brand reputation has been harmed in the past by “unintended” ad placement adjacent to inappropriate content, according to a CMO Council survey. In the contextual pre-bid landscape, our partner Grapeshot has focused on brand safety and their unique take on keyword segments — Predicts, which dynamically adapt to the relevant conversation happening on web pages, social and elsewhere. At a time when one of the largest perils of digital advertising is having your ad appear next to offensive or controversial content, Grapeshot’s brand safety segments are sought after by some of the largest advertisers in the industry. Their Predicts segments are a creative adaptation of keyword segments, along with an interesting application of technology, to create a differentiated and useful product in the market.

Today, we’re proud to announce our newest integration with Grapeshot, unlocking the entire Grapeshot portfolio of contextual pre-bid products in our platform to provide more choice and flexibility to our clients. The launch is the culmination of one of the largest integrations the Grapeshot team has performed in two years. Contextual pre-bid segments are available for both web and in-app including:

  • Brand Safety — make sure your ads only run alongside content that is appropriate for your brand
  • Standard Segments — target content based on a static set of keywords defined by the experts at Grapeshot
  • Standard Predicts — target content based on a dynamic set of keywords determined algorithmically and by following the social conversation across the web, defined by the experts at Grapeshot
  • Language — target based on page languages
  • Custom Segments and Custom Predicts — target content based on a static or dynamic set of keywords (respectively) created by you, tailored to your unique marketing needs

We’re excited to have Grapeshot available within our platform and look forward to hearing how it’s improved your targeting experience.