Archiv für den Monat: Mai 2018

Where we’re going, we won’t need websites 

As voice becomes the dominant force in search and people spend more time consuming content via social media, the future for the humble home page looks very bleak.

If comScore is correct and half of all searches by 2020 are made via voice, a crucial question arises: will we still need websites?

Even if the research is over-egged and the tipping point is reached a year or two later, the question still remains.

As consumers increasingly get used to asking Alexa, Siri or Google for the news headlines, a dinner recipe or flight options for a weekend away, answers will not be provided by ten blue SEO links. Rather, the options will be weighed up by an algorithm before what is considered to be the best answer is read out.

Remember Lycos and AltaVista?

New technology can always delight early adopters, but as it becomes more mainstream, seasoned observers know some huge names may become casualties as the public adopts new behaviors. Remember AltaVista, AskJeeves and Lycos, as well as when Yahoo! was a force in search? Read these names out loud and you may be less inclined to wonder whether voice will have an impact and shift focus to picking winners and losers.

Make no mistake, this is happening: a tide of disruption heading for search. Canalys estimates 56.3 million smart speakers will ship this year alone. The Amazon Echo has first-mover advantage and so has a 69% share. Google is in second spot with 25%.

However, given the core function of these speakers (beyond playing audio) is to perform voice searches, it would take a brave digital marketing executive to bet against Google closing the gap and even coming out on top – eventually.

Brands rush to the call of Alexa

To get an idea of how this impacts search, as well as consumers‘ interaction with their favorite brands, one need only look at the early rush to set up Alexa skills.

In travel, Expedia and Kayak can find flights and trips via voice search; an Uber or Lyft ride can be hailed too. Capital One lets users check out their balance and Vitality has recipes and health advice available. If that sounds too healthy for a Friday night, both Pizza Hut and Domino’s are set up to receive an order via Alexa. On the other hand, Vitality allows users to find their own recipes and discover a workout to shift the calories.

Then, of course, there are the weather, travel and news travel updates that can be handled via voice rather than a visit to a website.

VR keyboard, anyone?

It isn’t just voice. Canalys is predicting that this is the year when VR headset sales will increase five-fold as the sector moves towards shipping almost 10 million units per year by 2021.

It’s hard to imagine VR users typing a search enquiry into a virtual keyboard in the air. Even harder to imagine that they will scan through a list of blue links to no doubt pick out a text-heavy page.

Results will be aggregated through a dominant source of information in the each vertical: taking a tour of your next house will likely be made possible by Zoopla, or a similar aggregator; picking out a hotel via a VR version of Expedia; test-driving your next car perhaps via something like AutoTrader. Content would be coming from multiple sources, but will likely be accessed through a single aggregator: no need to type in a query and certainly no blue links to choose which home page to visit.

Is the home page already dying?

This is already starting to happen in news and media. Alarm bells no doubt started to ring when a chart for the New York Times showed how bad things had got with direct traffic.

Source: New York Times.

The dates are old, but that underlines how this trend for news sites to lose direct traffic has been developing for at least 5 years.

Look at the latest figures for two British newspapers, The Times and The Telegraph, and the trend seems very clear. Even though the sites are subscription-based (presumably giving users an impetus to get the most from their monthly fee and bookmark the home page), direct traffic accounts for one third and one fifth of all visitors respectively. This is dwarfed by search, with social bringing up the rear.

Source: SimilarWeb

If you then compare these paid-for sites with two free resources, The Mirror and, the trend becomes even more notable. When people have no need to validate paying a monthly fee to get their money’s worth, both sources of free news sink to just one in five visitors arriving direct. Here social is far closer to direct traffic in importance, with search still way out ahead as the number one source of visitors.

Source: SimilarWeb

Putting the data to one side and asking consumers where they get their news results in a huge spike in favor of social media. GlobalWebIndex results from 2017 revealed nearly half, or 44%, say they get news from social media while 37% said they go direct to a news website. This 37% is matched by those who reveal they get their news via referrals from ‘somewhere else‘ and a news aggregator service. The overall percentage exceeds 100% because of mixed behaviors.

People say they access news mostly through social, but the traffic-monitoring data says mostly through search. Either way, going direct to the home page is a habit the majority of people no longer have.

The mobile factor

It’s also clear that mobile websites‘ importance is beginning to fade. App usage has now overtaken the mobile web, suggesting that although people still use mobile sites, they have favorite apps for brands or key tasks.

It’s perfectly reasonable to assume this behavior will tap in to the trend for brands to make their content voice-friendly. If a consumer has a preference to book hotels on Expedia and dinner with Domino’s, they will likely ask Alexa or Google to look for a Paris weekend deal or a two-for-one pizza offer through these favored brands. No need for a home page, though the app might be required to give an order reference or calendar reminder for peace of mind.

No more home pages?

If you look at the direction of travel, the future of the home page appears bleak.

Within 2 years we’ll hit a tipping point in voice search and this year should see a spike in sales of VR headsets – the former having far more immediate effect on search than the latter.

Also, in a mobile-first world, consumers are steering towards apps where they already know which brand they want to interact with, or trust an aggregator to come up with the right offer.

I’d suggest this means the home page will still limp on for a few years, providing information to voice search algorithms, as well as being a resource for information and ecommerce.

Ultimately, the job of a search marketer is going to shift towards getting their clients‘ products and services in front of consumers via voice, and perhaps VR. There is no need for a home page here and we’re already seeing, particularly in news, how home pages are increasingly not the first port of call.

Consumers are increasingly looking for the simplicity of using voice and brands must adapt to find the best ways to make their ‘skill‘ used for those searches or to craft their data so it becomes the top answer.

This will mean websites will eventually fall into disuse and become redundant. Not so much a fall off a cliff, but a long march into obscurity.


Four ways Google is making SEO easier

One of the easiest ways to understand SEO’s importance to the marketing mix is to pay attention to what Google says and does. Google is very keen on good SEO because it makes the internet a better place for users. If the internet is a better place for users, then Google can sell more ads.

Here are four things Google has said and done to help marketers improve SEO that you may not be aware of.

Google added an ‘SEO‘ audit to its Lighthouse extension

Google is actively giving developers advice on how to improve the sites they work on: its Lighthouse auditing tool now has an SEO component that can analyse any page for basic SEO competency and tell you how to make it better.

This is a nice change for search marketers, who have for a long time made up for Google’s radio silence with research and educated guesswork. Some of the tips offered by the audit extension are fairly obvious and well known (tile tag exists, canonicals not broken, etc.), but others give an interesting insight into how Google assesses a page – such as the importance of making sure your text is big enough. Beyond being useful to marketers, it’s interesting to see how many different factors contribute to a positive user experience and correlate with a higher search engine ranking.

Google made significant improvements to Search Console

Search Console – formerly known as Webmaster Tools – helps you understand what’s going on beneath the hood of your website. It’s a comprehensive piece of software that, in its latest beta version, allows you to immediately index blogs and view up to 16 months of data in the search analytics (Performance) report.

For search marketers, this is particularly important; just think back to the days before ‘(not provided)‘ was your most common GA keyword. Now you have a rich bounty of keywords, just waiting to be incorporated into your search strategy.

It’s worth mentioning that Google is taking Search Console seriously: it’s actively asking for suggestions and potential improvements, and even implementing some of them.

Google has revamped its SEO guide

By relaunching its SEO starter guide, Google is offering newbies an easy way to improve the quality of their websites. If you’re reading this, you’re probably a bit beyond starter guides, but it never hurts to brush up on the basics, especially when they’re directly from the horse’s mouth – after all, who knew text size was such a big deal?

It’s a useful primer for anyone looking to brush up on their on-site optimization, and a strong indicator that Google is taking organic search as seriously as ever. With content, for example, it dedicates a whole section to advice on organising topics, understanding readers‘ desires, optimising copy, images, and headlines for users (not engines), writing link text, and generally creating blogs and web pages that your target audience actually wants to read.

Google has hired a new public search liaison

Finally, Google’s hiring of a public search liaison suggests not only that organic search is here to stay, but that the company is willing to be more open and transparent about it.

When Matt Cutts – who led Google’s WebSpam team and served as a kind of unofficial liaison between the company and the SEO community – resigned in 2016, search marketing professionals started communicating with Google in a number of different ways. They popped up in Google hangouts with engineers, asked questions in official Google Threads, and turned up to conferences where Google’s employees were present.

Google, in turn, started communicating more with them via the Google Security Blog, the Google Chrome blog, the general Google blog, the Google Webmaster Central Blog, the Google Analytics blog, and the Google Search blog. It then appointed its first public liaison for search in October 2017: Danny Sullivan, a former SEO journalist and analyst.

No doubt he’ll prove a useful resource for the SEO and marketing communities. But more importantly, perhaps, is what Sullivan’s appointment says about Google’s shifting philosophy to search marketing. If it was once obscure and opaque about organic search, it’s now open and consultative.

Luke Budka is director at integrated marketing agency TopLine Comms.



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Five ways to use predictive analytics

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.