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Monthly Archives: May 2018

20 takeaways from Meeker’s 294-slide Internet Trends report

May 31, 2018 No Comments

This is a must-read for understanding the tech industry. We’ve distilled famous investor Mary Meeker’s annual Internet Trends report down from its massive 294 slides of stats and charts to just the most important insights. Click or scroll through to learn what’s up with internet growth, screen addiction, e-commerce, Amazon versus Alibaba, tech investment and artificial intelligence.


Social – TechCrunch


Google Search Labeled the California GOP as Nazis, But It’s No Conspiracy

May 31, 2018 No Comments

No, Big Tech isn’t trying to defame conservatives. But Google did make a big mistake.
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Getting Started with Programmatic Advertising

May 31, 2018 No Comments

Learn what programmatic is, the top 4 questions to ask when getting started, and learn about 3 resources to help guide your programmatic next steps.

Read more at PPCHero.com
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The Top 25 Most Influential PPC Experts of 2018

May 30, 2018 No Comments

Every summer, we release our newest edition of the Top 25—it’s a nice way to honor some of the hardest workers in our tight-knit PPC community. The new list has been released. Find out this year’s Top 25 Most Influential PPC Experts and the Top 5 Rising Stars in PPC!

Read more at PPCHero.com
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Snap CEO Evan Spiegel says letter about ‘toxic’ culture was a wake-up call

May 30, 2018 No Comments

Snap CEO Evan Spiegel spoke a bit about some of the cultural issues at the company, going public and competition with Facebook at Recode’s annual Code Conference this evening in Rancho Palos Verdes, Calif.

Earlier today, Cheddar reported how a former Snap engineer criticized the company for a “toxic” and “sexist” culture that is not welcoming to women and people of color. In an email former Snap engineer Shannon Lubetich wrote in November, she described how Snap is not adequately promoting diversity at the company.

“The letter was a really good wake-up call for us,” Spiegel said.

Spiegel described how, in light of the letter, Snap hired external consultants to help the company figure out areas in which to improve. Snap also ran a company-wide survey and changed its promotion structure, Spiegel said. He later added that he’s “proud” of the progress Snap has made over the last few months.

In the letter, Lubetich also described a scenario in which scantily clad women, hired by Snap, dressed up in deer costumes.

“People are going to make mistakes and I was frustrated, to say the least, to see people dressed up as deer at a holiday party,” Spiegel said.

In addition to cultural issues, Snap has also struggled on the public market. Snap’s Q1 2018 earnings, for example, showed lackluster user growth numbers amid a rocky redesign and increased competition from Facebook. Still, Spiegel said the redesign was the right way to go, as was going public.

“I think this was the logical step forward in being an independent company,” Spiegel said about going public.

Meanwhile, Snap is constantly fending off competition from Facebook. Spiegel initially joked, “I think it bothers my wife more than it bothers me.”

But in all seriousness, Spiegel said Snap’s values of deepening relationships with the people closest to you is “really hard to copy.” Facebook, on the other hand, is more about having people compete online for attention, Spiegel said.

He also joked, in light of Cambridge Analytica scandal, that Snap would “appreciate it if [Facebook] copied our data protection practices as well.”


Social – TechCrunch



Movable Ink now lets developers build custom email applets

May 29, 2018 No Comments

Movable Ink has always prided itself on providing marketers with a way to deliver highly customized emails, but today the company decided to take that one step further. It announced an SDK that enables developers to build custom applets to add their own unique information to any email.

The company has always seen itself as a platform on which marketers can build these highly customized email marketing campaigns, says Bridget Bidlack SVP of product at Movable Ink.

“We built our business on making it easier for marketers to add intelligent content into any email campaign through a library of hundreds of apps. With our [latest] launch, we’re really opening up our development framework to agencies and system integrators so that they can create those apps on their own,” Bidlack explained.

This means companies are free to create any type of data integration they wish and not simply rely on Movable Ink to supply it for them. Bidlack says that could be anything from the current weather to accurate inventory levels, loyalty point scores and recent purchase activity.

What’s more, Movable Ink doesn’t really care about the source of the data. It could come from the company CRM system, internal database or offer management tool. Bidlack says Movable Ink can incorporate that data into an email regardless of where it’s stored.

This all matters because the company’s whole raison d’etre is about providing a customized email experience for every user. Instead of getting a generic email marketing campaign, you would get something that pulls in details from a variety of sources inside the company to build a custom email aimed directly at the individual recipient.

Company co-founder and CEO Vivek Sharma says that when they launched in 2010, service providers at the time were focused on how many people they could reach and open rate, but nobody was really thinking about the content. His company wanted to fill that gap by focusing specifically on building emails with customized content.

As Sharma said, they didn’t try to take on the email service providers. Instead they wanted to build this intelligent customization layer on top. They have grown increasingly sophisticated with their approach in the last 8 years and count companies like Dunkin’ Donuts, Bloomingdale’s, Comcast and Delta among their 500+ customers. They also have strategic partnerships with companies in the space like Salesforce, Oracle, IBM, Cheetah Digital, Epsilon and many others.

The approach seems to be working. The company has raised a modest $ 14 million since it launched in 2010, but today it boasts $ 40 million in annual recurring revenue, according to  Sharma.


Enterprise – TechCrunch


Five ways to use predictive analytics

May 29, 2018 No Comments

The era of graphs and spreadsheets as a way of thinking about analytics is beginning to approach its end. Predictive analytics, along with associated artificial intelligence (AI) and machine learning technologies, are changing the way in which we deal with data. These tools are becoming more accessible, and ‘big data’ thinking is no longer limited to firms with billion dollar budgets.

Predictive analytics provides a glimpse into the future, as well as access to strategic insights that can open up new opportunities. Here are five ways you can put predictive analytics to use, and how you can change the way you think about data.

Qualifying leads

According to Forrester research, predictive analytics has found three main use cases for dealing with  leads. Specifically:

  1. Predictive scoring: This method analyzes how leads are responding to your marketing attempts and how likely they are to take action based on that information. In this way, you can more quickly identify which leads to focus more resources on and which to divert resources from.
  2. Identification models: This use case is an approach that focuses on comparing leads to customers who have taken actions in the past. In doing so, you can divert resources to those leads who are most promising based on previous actions they have taken, as well as identify new markets that you weren’t previously aware of.
  3. Personalization: In concert with predicting which leads are most likely to take which actions, the same data can be used to determine which leads respond best to which types of messaging. This advanced form of segmentation can take things deeper than simply splitting leads into groups – instead sending them much more personalized messages.

One prominent example of this was covered in the Harvard Business Review, detailing how a Harley Davidson dealership increased sales leads by 2930% using an AI named Albert.

The AI crunched CRM data to identify characteristics and behaviors of previous buyers. It then split them into micro-segments based on those characteristics. For each segment, it tested different combinations of headlines, visuals, and other elements to determine which worked best for each segment.

The value of your lead qualification is highly dependent on the value and quantity of your data. No matter how good your statistical models are, their abilities are still very limited without access to the information that they need to learn about your customers.

In the digital space – particularly if you are not using a CRM – the best place to start with predictive analytics will almost certainly be an integration of Google Analytics and Google BigQuery.

Modeling customer behavior

While lead qualification and conversion is the most obvious use-case for predictive analytics, and likely the one worth looking into first, it’s far from the only marketing application of this emerging technology. But virtually any use is going to have customer modeling at its core.

You can divide customer modeling into three basic types: cluster models, propensity models, and collaborative filtering.

Cluster models

Clustering is a way of segmenting customers into groups based on many variables. A cluster model looks for correlations between various attributes and identifies a number of equilibria in which certain types of attributes tend to accumulate. What makes clustering special, compared with traditional segmentation, is the sheer number of variables involved. Clusters often use 30 variables or more, far more than would be possible if you were manually segmenting customers, or even if they were manually segmenting themselves.

Clusters come in three forms:

  1. Product clusters: These are clusters of customers who tend to only buy specific types of products, ignoring other things in your catalog
  2. Brand clusters: These customers tend to buy from a specific collection of brands
  3. Behavioral clusters: These are segments of customers with a specific collection of behaviors, such as frequent buyers who place small orders, or customers who prefer the call center over the checkout cart.

What’s important to recognize about these clusters is that they enable predictions about which clusters people belong to – even with limited information. If they buy one product with a specific brand, your brand cluster can predict what other brands they may be interested in, rather than just the more obvious recommendation of simply offering everything else by the same brand.

Propensity models

A propensity model is one that makes future predictions about customer behavior based on correlations with other behaviors and attributes. This may be accomplished using regression analysis or machine learning. A good propensity model controls for as many variables as possible so that correlations aren’t confused for causes.

Here are a few examples of propensity models:

  • Propensity to unsubscribe: A model like this allows you to determine the appropriate email frequency, weighing the possibility that a recipient will unsubscribe against any possible positive outcome
  • Propensity to churn: These are customers who are likely to move on if you don’t take action, but who may be high value otherwise
  1. Lifetime value: Modeling the lifetime value of a customer can help you make strategic marketing decisions if it leads you to customers with more lifetime value, or leads to behaviors that extend lifetime value.

Other propensity models include predicting how far through somebody’s lifetime value you are, and how likely they are to convert or buy.

Collaborative filtering

If you’ve seen Amazon’s “customers who liked this product, also liked…” recommendations, you know what type of model this is. At first glance collaborative filtering might sound similar to product-based cluster models, but collaborative filtering is a bit different. Rather than grouping customers by the types of products they are likely to buy, collaborative filters make recommendations based on aggregate behavior.

In other words, this is less about the user’s product preferences and more about the behaviors that products tend to cause for users.

There are three types of collaborative filters:

  1. Up-sell recommendations. These are recommendations for a higher tier version of a product before the sale is made
  2. Cross-sell recommendations. Also offered before the sale is made, this is a recommendation for a product that is often bought at the same time as the initial one
  3. Follow-up recommendations. These are recommendations for products that people tend to buy a certain time period after buying a prior product, such as replacing a product that runs out, or buying dishes after buying a table.

Connecting the right product to the right market

Working backwards from customer modeling, it’s possible to identify markets for your products that you may not have been aware of. Here are just a few examples of how this use case can play out:

  • Incorporate referral sources into your cluster models. This will allow you to identify which traffic sources correlate with which types of products, brands, or behaviors. From this, you can immediately identify a new market for these products or brands
  • Incorporate referral sources into your lifetime value propensity models. This will allow you to determine which locations to invest more of your marketing resources into, since you roughly know what the ROI will be
  • Look for correlations between traffic sources and success with up-sells, cross-sells, and follow-up recommendations
  • Look for correlations between keywords and your customer models
  • Analyze the attributes that are strong predictors of buying specific types of products and brainstorm other markets that might share those attributes that you have not yet targeted
  • Investigate high performing outliers where limited data is available and investigate whether expanding in those markets is a good option.

Connecting the right users to the right content

There are a number of ways that you can leverage your customer models to connect prospects with content in ways that move you toward your goals, some of them more obvious than others. Here are a few examples:

  • Matching content related to products or brands based on the appropriate clusters
  • Matching users to conversion copy when propensity models predict they are most likely to buy
  • Recommending content to users that improves their propensity scores
  • Recommending content to users that enhances their likelihood of responding well to an up-sell or cross-sell
  • Matching traffic sources to the content that tends to produce high propensity scores for each particular traffic source.

As you can see, the number of approaches you can take here grows pretty quickly. Think strategically about how best to put your models to use and make the most of your models.

Discovering strategic marketing insights

While some predictive analytics tools can automatically streamline your marketing process and generate results (like Albert did for Harley Davidson), it’s important to remember that human decisions still play a very important part in the process.

Where predictive analytics and related AI tools often fail is in a propensity to ‘over-fit’ the data. They can get stuck at local maximums and minimums, incapable of making the leap to new terrain.

Escaping from traps like these, and making the most of these tools in general, requires you to find strategic insights from within your predictive analytics models.

For example, suppose you discover that a specific piece of content has a tendency to raise your prospects’ propensity scores; any automation you have in place can be applied to customize how your users are marketed to, and push them toward that piece of content. But what predictive analytics can’t tell you is whether there might be other traffic sources you haven’t tried yet that would be a good fit for that content. Using your experience and brainstorming capabilities, you can identify other potential markets for that content, feed them into your model, and see how the exposure changes things.

Your goal in working with these kinds of models must always be to find insights like these and test them to see if the results are as expected. If your model runs on autopilot it will not discover any new opportunities alone.

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How WIRED Lost $100,000 in Bitcoin

May 28, 2018 No Comments

We mined roughly 13 Bitcoins and then ripped up our private key. We were stupid—but not alone.
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A Buyer Persona Based Approach to Mapping Your Customer Journey

May 27, 2018 No Comments

Explore how a persona-based approach is used to enhance customer experience, improve targeting accuracy, and harness behavioral analytics to their business objectives.

Read more at PPCHero.com
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