Tag Archives: DATA


2017’s SEO Job Trends: What Does it Mean for Your SEO Career? [DATA] by @MyNameIsTylor

What can data from LinkedIn and Indeed tell us about the state of the SEO career? Using nine months’ worth of data for the 75 most populous metros in the United States, I dive into the four most interesting trends and predict how each one may impact your career.

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71% Plan to Spend More on Digital Marketing Activities in 2017 [DATA] by @rinadianewrites

2017 is here! You may be thinking of where to put your hard-earned money, and whether to spend more, less, or the same on your digital marketing activities for 2017. Find out the results of our latest #SEJSurveySays poll and take a look at what to focus on to make the most of your investments in 2017.

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Data-driven attribution: the cure for discount code abuse?

Produced in association with Fospha.

When measuring the effectiveness of discount codes, retailers often get it wrong. In this article, we’ll look at how data-driven attribution can help businesses better understand where discount codes produce the best ROI.

Retailers often don’t consider discount codes in the same way they do traditional marketing spend. On the one hand this seems appropriate; the cost is a percentage of top-line revenue at the point of conversion rather than upfront speculative spend. But viewing them as nothing but a conversion lever can lead to a dangerous disconnect in understanding the true cost of customer acquisition and retention.

In a multi-channel, multi-device world, it’s increasingly hard to acquire and keep customers cost-effectively. Discount codes are an easy and powerful short-term lever for growth that can be activated and ramped up quickly (through deeper discounting and broader availability etc.). But with the temptation their flexibility and impact affords, many retailers now run expansive discount code programmes without understanding their real cost.

The difficulty in tracking the long-term effectiveness of discount codes is compounded by the following factors:

  • A reluctance to adopt a cautious approach (A/B testing etc.), as retailers seek to maximise the returns from push far and wide through multiple marketing channels (with testing particularly challenging with affiliate and offline channels).
  • For businesses that rely heavily on repeat business, the effect on customer lifetime value is unknown, as retailers try to maximise revenue without conditioning their customers to buy only when discount codes are available.
  • Retailers running frequent or overlapping campaigns where it isn’t possible to fully measure the post-discount drop in sales, and it becomes difficult to measure what ‘normal’ performance looks like. This is illustrated in the chart below:

Feast and Famine: A chart showing the effect running discount codes can have on business performance.

The good news for retailers is that there’s an alternative to scaling back their discount code activity and running testing that limits top line impact. Data driven attribution modelling – focused on the end-to-end economics of the customer journey – can identify their true value and cost, stitching together all customer interactions through every marketing channel and platform visit over time and across devices. This helps build a complete understanding of the role each channel and lever plays in conversion (and at what point in the customer journey they play that role), so a business can identify where a discount code genuinely contributes to a conversion and where they are cannibalising (either totally or partly) a full-price sale.

By considering discount codes only one contributor in a complex user journey, retailers get better visibility on their value relative to other marketing channels and can distribute spend more appropriately. They can also use the insights into where and when in the journey discount codes should be used to optimise spend on customer acquisition and retention, leading to a more customer-centric approach to discounting that will yield long-term brand health benefits.

Fospha helps businesses use data insights to optimise their customer journeys. Click here to find out more.

This content has been produced in association with Fospha. Click here to read our collaborative content guidelines. Views and opinions expressed in this article do not necessarily reflect those of ClickZ’s.


50% of Digital Marketers Say Earned Media Will Deliver Best Returns in 2017 [DATA] by @rinadianewrites

If you’re laying out some last-minute marketing strategies, you may be thinking of different types of media — earned, owned, or paid — that you can leverage to get the best results next year. So which of these media can give you the best return in 2017? We have the answers for you based on our latest #SEJSurveySays poll results!

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Mobile Shopping Searches Continue to Surge During Black Friday [DATA] by @MattGSouthern

Google has revealed the latest data on the impact of mobile shopping searches during Black Friday.

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Twitter Is The Most Used Social Share Button [DATA] by @rinadianewrites

Getting more social shares for your content can lead to growth more engagement. But among all the social media platforms out there, which one can get the most number of shares for your content?

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A Google Search Update Appears to Have Occurred on November 10th [DATA] by @MattGSouthern

On November 10th, an update to Google search appears to have occurred, according to reports on the web and our own data

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‘Creepy data’: how to avoid spooking your customers

Halloween is a scary time for me and not because I fear hundreds of kids banging on my door and hollering for candy. I’m terrified that they won’t come, and I’ll have to eat all of the leftovers myself.

But, for marketers, there’s something even scarier than a stoop full of Elsas and Annas, Spidermen and Minions. It’s the dreaded “creepy data” problem.

‘Creepy data:’ Data your customers don’t expect you to have on them

Back in the 2008 presidential election, then-candidate Obama sent an email to campaign contributors and others who signed up for messages. The email said something like “Make sure you’re registered to vote.”

“This would be cool,” I thought. I clicked through, gave the campaign access my Facebook and then watched as the message came up: “Congratulations! You’re registered to vote!”

That is not creepy data.

The notice also had a share-with-friends button. As a loyal American, I encourage everyone to vote, then as well as today. So, I clicked on the “share” button, expecting to see a post reminding my friends to vote. Instead, I saw a stream showing all of my friends who weren’t registered to vote.

That is creepy data.

Making data less creepy

Third-party data is a vastly richer source of information that just the clickstream and preference data we collect on our customers. But it’s also a double-edged sword. Your customers don’t know how much data we do have on them.

Some big data houses have 300-plus data points on each person in their database: demographic, psychographic, behavior and more. Customers don’t expect that data to be out there for marketers to access and use.

Many companies buy third-party data for profiling, segmentation or modeling. These are proper uses of data, but it’s easy to overreach.

After all, you can be too smart in your data use. Remember the dad who found out his teenage daughter was pregnant because she started getting pregnancy-related mail from Target?

You can avoid this by first asking yourself, “Do my customers know I have this data, and would they want me to use it this way?”

What’s scary for a brand using third-party data is using too much data and using it the wrong way. The media blowback can be massive if customers take their grievances to social media, as Target found out.

Data misuse affects people lives and tarnishes your brand image. None of us want that.

How not to be a creep

These three steps can help you know if you have strayed into the creepy-data zone:

1.You’re describing a new marketing program, and you get a funny feeling in the pit of your stomach, or if you look back at a strategy, and it just feels wrong.

In my years of working with third-party data and hundreds of companies, I’ve learned that when someone gets that funny feeling, we usually find we did overreach. We either used too much data or forgot the customer doesn’t know we had that data.

Trust your instinct, and change course.

2. Never refer directly to data you get from a third-party source if your customers didn’t give it to you.

They don’t expect you to have this data. It applies to Facebook, mobile apps and the like. Have you ever looked at the app permissions on your phone?

As a marketer, you might, but the average user doesn’t look.

Forcing permission in your app’s tiny-type user agreement and telling the customer you’re using it are two different things. Use the “common person” test. Would this person expect you to have the data you’re using?

That’s why you should not refer to data unless your customers know for certain that you have it.

That’s where Target went wrong. The company was too blatant about letting its customers know how much data it had on them. A marketing piece – an email message or a direct-mail piece – is no place to brag about how smart your modeling is.

3. Develop a customer advisory group

Consult this group when planning marketing programs using complex data integrations or advanced segmentation. Discuss what you want to do, and ask their opinions. In other words, get a reality check on what regular people would find creepy or cool.

Naturally, you have to build your group carefully. Nondisclosure agreements and other security measures are key. You don’t need a large group, but the membership should constitute a good cross-section of your customer base.

Preview marketing materials and campaigns, brief them on big-concept ideas, and ask them whether they would expect you to know the data you present in the email.

I’ve worked with marketers who got ahead of themselves with data and made grand assumptions about how their customers will react. We all need a governor to tell us when we’ve gone too far too fast.

Conclusion: Don’t be creepy

Creepy data doesn’t go away like Halloween. Data gets creepier the more often we use it recklessly. Always recognize your data can hurt real people’s lives. No fancy data integration is worth that.

39% of SEOs Tackle Technical SEO First [DATA] by @MattGSouthern

After conducting an SEO audit, the next step for a majority of SEOs is to tackle the technical side first.

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5 SEO Myths Debunked by Data [SPONSORED] by @lauracruzspeaks

This is a sponsored post, the data mining of which was done by the SimilarWeb team, using the SimilarWeb PRO platform. Remember those SEO “truths” that everyone used to believe in? Like how keyword stuffing and amassing loads of inbound links was the way to go? Over the years, we have learned ranking number one  is not the most crucial aspect for SERP, and buying ads won’t necessarily help you climb to the top. There are tons of myths still out there, and it’s high time we started dismantling a few. Here’s a look at some of the more recent myths circulating in […]

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