When Littledata first started working with benchmark data we found the biggest barrier to accuracy was self-reporting on industry sectors. Here’s how we built a better feature to categorise customer websites. Google Analytics has offered benchmarks for many years, but with limited usefulness since the industry sector field for the website is often inaccurate. The problem is that GA is typically set up by a developer or agency without knowledge or care about the company’s line of business - or understanding of what that industry sector is used for. To fix this problem Littledata needed a way to categorise websites which didn’t rely on our users selecting from a drop-down list. Google Analytics has offered benchmarks for many years, but with limited usefulness since the industry sector field for the website is often inaccurate. [subscribe] The first iteration: IBM Watson NLP and a basic taxonomy Our first iteration of this feature used a pre-trained model as part of IBM Watson’s set of natural language APIs. It was simple: we sent the URL, and back came a category according to the Internet Advertising Bureau taxonomy. After running this across thousands of websites we quickly realised the limitations: It failed with non-English websites It failed when website homepage was heavy with images rather than text It failed when the website was rendered via Javascript Since our customer base is growing most strongly outside the UK, with graphical product lists on their homepage, and using the latest Javascript frameworks (such as React), the failure rate was above 50% and rising. So we prioritised a second iteration. The second iteration: Extraction, translation and public APIs The success criteria was that the second iteration could categorise 8 sites which the first iteration failed with, and should go on to be 80% accurate. We also wanted to use mainly public APIs, to avoid maintaining code libraries, so we broke the detection process into 3 steps: Extracting meaningful text from the website Translating that text into English Categorising the English text to an IAB category and subcategory The Watson API seemed to perform well when given sufficient formatted text, at minimal cost per use, so we kept this for step 3. For step 2, the obvious choice was Google Translate API. The magic of this API is that it can detect the language of origin (with a minimum of ~4 words) and then provide the English translation. That left us focussing the development time on step 1 - extracting meaningful text. Initially we looked for a public API, and found the Aylien article extraction API. However, after testing it out on our sample sites, it suffered from the same flaws as the IBM Watson processing: unable to handle highly graphical sites, or those with Javascript rendering. To give us more control of the text extraction, we then opted to use a PhantomJS browser on our server. Phantom provides a standard function to extract the HTML and text from the rendered page, but at the expense of being somewhat memory intensive. Putting the first few thousand characters of the website text into translation and then categorisation produced better results, but still suffered from false positives - for example if the text contained legal-ease about data privacy it got categorised as technical or legal. We then looked at categorising the page title and meta description, which any SEO-savvy site would stuff with industry language. The problem here is that the text can be short, and mainly filled with brand names. After struggling for a day we hit upon the magic formula: categorising both the page title and the page body text, and looking for consistent categorisation across the two. By using two text sources from the same page we more than doubled the accuracy, and it worked for all but one of our ‘difficult’ websites. This hold-out site - joone.fr - has almost no mention of its main product (diapers, or nappies), which makes it uniquely hard to categorise. So to put it all the new steps together, here’s how it works for our long-term enterprise client MADE.com's French-language site. Step 1: Render the page in PhantomJS and extract the page title and description Step 2: Extract the page body text, remove any cookie policy and format Step 3: Translate both text strings in Google Translate Step 4: Compare the categorisations of the title vs page body text Step 5: If the two sources match, store the category I’m pleased that a few weeks after launching the new website classifier we have found it to be 95% accurate. Benchmarking is a core part of our feature set, informing everything that we do here at Littledata. From Shopify store benchmarks to general web performance data, the improved accuracy and deeper industry sector data is helping our customers get actionable insights to improve their ecommerce performance. If you’re interested in using our categorisation API, please contact us for a pilot. And note that Littledata is also recruiting developers, so if you like solving these kind of challenges, think about coming to join us!

Physical or digital? We found merchants doubling down on both at Paris Retail Week. At the big event in Paris last month, we found retailers intent on merging the online and offline shopping experience in exciting new ways. See who we met and what the future of digital might hold for global ecommerce. Representatives from our European team had a great time at the big ecommerce event, one of the 'sectors' at Paris Retail Week. Outside of the event, it was great to have a chance to catch up with Maukau, our newest Shopify agency partner in France. (Bonjour!) Among the huge amount of digital sales and marketing trends we observed throughout the week, a few emerged again and again: mobile-first, phygital experience, and always-on, multi-channel marketing. Getting phygital Phygital? Is that a typo? Hardly. It’s the latest trend in ecommerce, and it was prevalent everywhere at Paris Retail Week. Phygital combines “physical” and “digital” experiences in a new ecosystem. This offers the consumer a full acquisition experience across different channels. From payment providers to marketing agencies, everyone was talking about going phygital. One of our favourite presentations was by AB Tasty. They focused on how optimising client experience can boost sales and conversions in the long-term. It’s not enough to promote your products, nor to link to an influencer for social proof -- you need to create a full customer experience. Starbucks and Nespresso are good examples of how this works offline, assuring that a customer who comes in to drink a coffee will linger around for the next 20-30 minutes. By keeping the customers in the shop, they will eventually order more. The goal is to reproduce this immediately sticky experience online too, and focusing on web engagement benchmarks is the best way to track your progress here. Using the example of conversion rate optimisation (CRO) for mobile apps, AB Tasty's Alexis Dugard highlighted how doing data-driven analysis of UI performance, on a very detailed level, can help clarify how mobile shopping connects with a wider brand experience. In the end, customer experience means knowing the customer. 81% of consumers are willing to pay more for an optimal customer experience. Brands that are reluctant to invest in customer experience, either online or offline, will hurt their bottom line, even if this isn't immediately apparent. Those brands that do invest in multi-channel customer experience are investing in long-term growth fuelled by higher Average Order Value (AOV). 81% of consumers are willing to pay more for an optimal customer experience -- the statistic speaks for itself! Another great talk was from Guillaume Cavaroc, a Facebook Academie representative, who discussed how mobile shopping now overlaps with offline shopping. He looked at experiments with how to track customers across their journeys, with mobile login as a focal point. In the Google Retail Reboot presentation, Loïc De Saint Andrieu, Cyril Grira and Salime Nassur pointed out the importance of data in retail. For ecommerce sites using the full Google stack, Google data represents the DNA of the companies and Google Cloud Platform is the motor of all the services, making multi-channel data more useful than ever in assisting with smart targeting and customer acquisition. The Google team also stated that online shopping experiences that don’t have enough data will turn to dust, unable to scale, and that in the future every website will become, in one way or another, a mobile app. In some ways, "phygital" really means mobile-first. This message that rang out clearly in France, which is a mobile-first country where a customer's first encounter with your brand or product is inevitably via mobile -- whether through a browser, specific app or social media feed. [subscribe] Multi-channel experience (and the data you need to optimise it) Physical marketing is making a comeback. Boxed CEO Chieh Huang and PebblePost founder Lewis Gersh presented the success of using online data for offline engagement, which then converts directly back on the original ecommerce site. Experimenting heavily in this area, they've seen personalised notes on invoices and Programmatic Direct Mail (with the notes based on viewed content) generate an increase of 28% in online conversion rate. Our real-world mailboxes have become an uncluttered space, and customers crave the feel of a paperback catalogue or simple postcard, to name just a bit of the physical collateral that's becoming popular again -- and being done at a higher quality than in the years of generic direct mail. Our real-world mailboxes have become an uncluttered space, and customers crave the feel of a paperback catalogue or simple postcard. However, data is still the backbone of retail. In 2017 Amazon spent approximately $16 billion (USD) on data analysis, and it was worth every penny, generating around $177 billion in revenue. Analysing declarative and customer behaviour data on the shopper’s path-to-purchase is a must for merchants to compete with Amazon. Creating an omni-channel experience for the user should be your goal. This means an integrated and cohesive customer shopping experience, no matter how or where a customer reaches out. Even if you can't yet support an omni-channel customer experience, you should double down on multi-channel ecommerce. When Littledata's customers have questions about the difference, we refer them to Aaron Orendorff's clear explanation of omni-channel versus multi-channel over on the Shopify Plus blog: Omni-channel ecommerce...unifies sales and marketing to create a single commerce experience across your brand. Multi-channel ecommerce...while less integrated, allows customers to purchase natively wherever they prefer to browse and shop. Definitions aside, the goal is to reduce friction in the shopping experience. In other words, you should use anonymous data to optimise ad spend and product marketing. For marketers, this means going beyond pretty dashboards to look at more sophisticated attribution models. We've definitely seen this trend with Littledata's most successful enterprise customers. Ecommerce directors are now using comparative attribution models more than ever before, along with AI-based tools for deeper marketing insights, like understanding the real ROI on their Facebook Ads. The new seasonality So where do we go from here? In the world of ecommerce, seasonality no longer means just the fashion trends of spring, summer, autumn and winter. Online events like Black Friday and Cyber Monday (#BFCM) define offline shopping trends as well, and your marketing must match. "Black Friday" saw 125% more searches in 2017, and "Back to School" searches were up 100%. And it isn't just about the short game. Our own research last year found that Black Friday discounting is actually linked to next-season purchasing. Phygital or otherwise, are you ready to optimise your multi-channel marketing? If not, you're missing out on a ton of potential revenue -- and shoppers will move on to the next best thing.

Your website is an essential tool for attracting and converting customers. Driven by the uptake in online shopping, having a well-designed ecommerce site is no longer a luxury. It’s now a necessity -- you need to regularly convert browsers into buyers. Web design has the power to really grab your customers attention and portray your messaging. But when it goes wrong, the customers you lose will rarely come back. In this post I take a look at common web design fails that drive customers away, so you can avoid them. They may be common mistakes, but they're often overlooked! Fail #1: The CMS, plugins and theme are outdated You don’t need to modernize your website every day, or even every week, but you do need to make sure it doesn’t feel outdated. That means you should regularly update your website theme, your plugins and your content. Updating your theme and plugins will ensure you have the latest features and boost your security, while regularly updating your content will improve your SEO ranking and make your website more interesting for repeat visitors. Fail #2: Your website is not mobile responsive Over 50% of online traffic is from mobile phones and tablets, so having a website that properly displays itself on those devices is essential. If your website is non-responsive, you’ll be missing out on a massive amount of potential business. Below is the website Dribble, a powerful example of a responsive website (here's a big list of mobile-responsibe web design done well). Plus, your SEO will suffer and it makes your business look unprofessional. Common issues with non-responsive websites are text being displayed too small to read, irregular formatting, un-clickable links and images not loading. How many of your customers are shopping on mobile? Where are they falling out of the checkout funnel? Use this tool to find out. Fail #3: Stock photos and generic content Building customer loyalty and trust -- both of which are vital for repeat business -- begins with establishing credibility and authenticity. Nobody wants to read the same blog they have already read 50 times on your website, or look at stock photos they have seen on other brands websites. Good writing should be original, punchy and relevant to your target audience. And copy should be matched with credible, original imagery. Stock photos are easy to spot a mile off. Using original imagery significantly helps to build a website design that stands out and wins customer trust. [subscribe] Fail #4: It’s slow and your bounce rate is high Speed matters. If your website loads too slowly, you can say goodbye to the impatient modern-day consumer and watch your bounce rates rise. First impressions of a website are made immediately, so if your website takes more than a few seconds to load, your content and design won’t be given the chance to see the light of day. Make sure your images are compressed, limit the amount of videos and animations published within, make sure your hosting provider can handle fluctuating amounts of traffic, and disable any plugins you aren’t actually using. Then make sure to check your speed and performance rates against other sites. Benchmarking is the most accurate way to do this, so you can see how you compare to similar sites in your industry. Fail #5: Your site is unbranded and doesn’t stand out The minute a possible customer comes to your website, they should know exactly whose website they are on. Having a nicely designed logo is, therefore, critical for making a good first impression and improving brand awareness. And best of all, it’s really easy to do. Online tools are readily available to create stunning high-resolution logos in second, such as Shopify’s logo maker. Fail #6: Face it, your site's just not that interesting There is nothing worse than going on to a website and finding it incredibly boring. Content needs to compliment design, so it’s vital you have interesting content throughout to keep your customers engaged and coming back for more. Using banners, photos and graphics, along with authentic and interesting copy is the right way to grab your customers’ attention and encourage them to make a purchase or opt-in via a form. Fail #7: It’s not made for converting If your website doesn’t have clear calls to action (CTAs), then it’s not going to have good conversion rates. Plain and simple. This 'fail' can easily be eradicated by using smart opt-in offers, having clear navigation menus ('nav menus' in designer jargon), and writing relevant, targeted content. Evernote use an excellent CTA.   Without a clear CTA, how are your customers meant to know what you want them to do? Simply put, they won’t - they will leave. Every page (including your blog posts) should have a clear CTA to guide your online visitors down the buyer journey. Fail #8: It’s not optimized for SEO Optimizing each aspect of your website begins with understanding what works well and what doesn’t. The only way of doing this accurately is by using analytics to get deeper insights into how your potential buyers are using your site. You’ll be able to see which pages perform well, which keywords attract the best traffic (SEO is an area that you should be continually optimizing), which promotions work best, and which images resonate with your customers the most. As search engines become smarter, continually optimizing for SEO is an excellent way to get a clearer view of what's working and clarify anything that isn't clear. Then you'll be on the road to becoming an SEO-driven business - an easy way to improve revenue. Fail #9: It’s cluttered and noisy If your website is too cluttered, it will create a bad customer experience for any visitor. It will also distract potential buyers away from doing what you want them to do, such as making a purchase, filling out a form or requesting more information via chat. Don’t make the mistake of cramming too much into each page, or filling your web pages with in-your-face advertising. Your website should be easy to navigate, simple and concise. Customers should be able to convert with minimal effort. Conclusion The bottom line: if your ecommerce site has many design fails that impact the user experience, your company may lose out on potential profits. Use the tactics mentioned in this article to get started on improving the design of your website today!   Michelle Deery is the content writer for Heroic Search, a digital marketing agency based in Tulsa. She specializes in writing about eCommerce and loves writing persuasive copy that both sells and educates readers.

Littledata launched benchmarks for websites two years ago. They quickly became a key feature of our app, and as customers became more engaged, so did ideas for how to improve our benchmarking and the algorithms that learn from those benchmarks. In response to customer feedback and deeper research into industry sectors, we've made some really exciting improvements over the last few months to make the comparisons even more useful -- and even more dynamic. The changes are five-fold. Detailed sectors and sub-sectors. Almost every customer we talked to said the benchmark comparison was most useful if it was for very similar sites. Previously we only had 50 high-level sectors to compare with; now we have hundreds of low-level sectors. You can visualise the full range. Smarter auto-categorisation of your website. Our machine learning process now has a 95% chance of finding the best sector for your website, meaning you can compare against the most useful benchmark without filling in a single form! Ability to manually change industry sector. And of course, if you're in that 5% that needs human input, then you (or your Enterprise account manager) can pick a better sector in the general app settings page. You might also want to change sectors just to see how you compare. No problem. Benchmarks for technology stacks. Want to see if you are making the most of a technology such as Shopify or Yieldify? Now you can compare with other sites using the same technology, making our ecommerce benchmarking even more powerful for agencies and web developers. Benchmarks for starter websites. Previously we only generated benchmarks for sites with at least 500 monthly visits. We dropped that to 200 monthly visits, so starter websites can see a comparison - and see more detail as they grow. We've launched a live visualisation of how our AI-based website categorizer is mapping a range of industry sectors. It offers a full overview of website categories and segments. And you can drill down to see more details. For example, we've seen a big rise in wine, coffee and health shake retailers this year, many of whom are using our ReCharge integration to get insight into their subscription business models. As our algorithms learn about ecommerce businesses selling beverages of many varieties and automatically categorises sites accordingly, you can now look closely at a particular segment to see how your site compares. Littledata is an Agile company. We're constantly iterating, and continuously improving the benchmarks to make them more actionable, so please give us feedback if you'd like to see more. Happy benchmarking! [subscribe]

"Is my conversion rate good or bad?" We built Littledata's benchmarking feature to help you say goodbye to guessing games and start automatically benchmarking your site against top performers. Now that our benchmark tool has been around for awhile, we've started to get a sense for which ecommerce sites are using it most effectively. In other words, we've seen how benchmarks can help websites increase revenue - not in theory but in actual practice. Littledata has now helped hundreds of companies understand where their performance is compared with other websites in their niche, using our benchmarking algorithms and clean user interface. But can benchmarks really help you grow sales? I understand if you want to see the data for yourself. One of our long-term customers makes for an ideal case study. Case study - Pufushop Over the course of 2017, we helped Pufushop, a Romanian ecommerce site, understand if their website changes were helping to increase performance - and where they still had work to do. Pufushop is a retailer of baby goods, with a main focus on baby carriers. The products in their store are all premium quality and from top vendors, so comparing them with just any other baby store wouldn't have been relevant. Instead, we compared their ecommerce metrics with specific benchmark segments that were most relevant to their market landscape and business goals. Ecommerce benchmark segments Benchmarking is used to measure and compare the performance of a specific indicator, and it's most useful when you map that data onto your internal KPIs and compare performance against similar sites. Littledata specialises in ecommerce analytics and our benchmark population now includes Google Analytics data from almost 10,000 sites. We break that data into specific categories, such as Marketing, Ecommerce and Speed (site performance), and within each category you can filter by industry, location, website size, and more. Littledata aggregates reliable data from those thousands of high-performing websites so that you can focus on results. In this customer's case, we analysed their website and business model to provide 5 relevant benchmark segments: Romanian websites to compare KPIs across regional market Small SEO websites because 60% of Pufushop's traffic comes from search engines SEO-driven online stores (more generally, to see how they compare) General online shopping websites across the globe, to get a sense for how their funnel compares And a specific revenue per customer category based on shoppers' average basket spend (sites with a similar average order value, no matter the sector) Key metrics Web behaviour is not necessarily consistent across industries. We started Pufushop's analysis by looking at key ecommerce KPIs such as Checkout completion rate, Ecommerce conversion rate and Add-to-cart rate, but we didn't just pull these metrics blindly. Starting with the first month, February 2017, we looked at how other stores with a similar average basket value were performing. This helped our client establish what was working and what could be improved. As we worked with them to make sure everything was tracking correctly (after all, benchmarks are only as useful as your data is accurate), they could also check these benchmarks directly in the Littledata app. Results Now for the first time, both Pufushop's Marketing Director and Senior UX Designer had clarity on which areas of the website could be improved to increase sales. Based on the benchmark data they could see that the main places to improve were: The checkout process (to increase the checkout completion rate) Product pages (to increase the add-to-cart rate) Resolving those two main issues will automatically resolve the e-commerce conversion rate KPI and will indirectly influence the Revenue per customer. Pufushop decided to use Google Optimize in order to improve the checkout completion rate. Using Google Optimize is an easy-to-use, fast and scalable tool in order to A/B-test different experiences on the checkout page. Pufushop conducted a variety of targeted experiments, including: Shortening the checkout process Eliminating unnecessary fields Testing variants of checkout pages Split-testing different product pages Testing a variety of shipping costs After a couple of months of testing, the results were significant: The add-to-cart rate grew from 3.7% to 5.5% The checkout completion rate jumped from 52.8% to 89.7% Now those are some real results! Having a direction as well as a target helped Pufushop's digital team to focus on clear, achievable goals. As they continue to grow, we're glad to have them as a part of the Littledata family. [subscribe] Ready to benchmark your site? If you're in the same place as Pufushop was a year ago, here's a quick guide for how to use ecommerce KPI benchmarks to improve your store performance. Sign up for Littledata's main app or Shopify app Look at the benchmark data and pick an industry and a set of KPIs - the right sectors and segments will help you optimise campaigns Use tools like Hotjar and Littledata's automated reporting to analyse user behaviour around those benchmarks and define a short list of actions you're going to take Use Google Optimize or hire a developer to put those actions into place Monitor how users are interacting with the changes When you have sufficient data to see a clear relationship between those changes and an increase in traffic, revenue or conversions, make those changes permanent and move on to focus on a new set of KPIs Keep in mind that there are situations where the KPIs will show you issues of wrong messaging, for example of a product page or advertisement - technical issues where the change is fairly easy to make. In other cases, you will need to develop a long-term strategy for radical changes to your website, such as altering your checkout process. The online environment is a fast-moving industry, so you need to be agile and ready to change accordingly. Either way, we're here to help you scale with data-driven strategies for sustainable growth. Now stop reading this post and start benchmarking your site!   Note: In order to maintain data-confidentiality, KPI values have been altered in this case study (the results are real, only the benchmarks have been adjusted).

I’ve read many articles stating a link between faster page loading and better user engagement, but with limited evidence. So I looked at hard data from 1,840 websites and found that there’s really no correlation between page load speed and bounce rate in Google Analytics. The oft quoted statistic on page load speed is from Amazon, where each 100ms of extra loading delay supposed to cost Amazon $160m. Except that the research is from 2006, when Amazon’s pages were very static, and users had different expectations from pages – plus the conclusions may not apply to different kinds of sites. More recently in 2013, Intuit presented results at the Velocity conference of how reducing page load speed from 15 seconds to 2 seconds had increased customer conversion by: +3% conversions for every second reduced from 15 seconds to 7 seconds +2% conversions for every second reduced from seconds 7 to 5 +1% conversions for every second reduced from seconds 4 to 2 So reducing load speed from 15 seconds to 7 seconds was worth an extra 24% conversion, but only another 8% to bring 7 seconds down to 2 seconds. Does page speed affect bounce rate? We collected data from 1,840 Google Analytics web properties, where both the full page load time (the delay between the first request and all the items on the page are loaded) and the bounce rate were within normal range. We then applied a Spearman’s Rank Correlation test to see if being a higher ranked site for speed (lower page load time) made a site more likely to be a higher ranked site for bounce rate (lower bounce rate). What we found is almost no correlation (0.18) between page load speed and bounce rate. This same result was found if we looked at the correlation (0.22) between bounce rate and the delay before page content starts appearing (time to DOM ready) So what explains the lack of a link? I have three theories 1. Users care more about content than speed Many of the smaller websites we sampled for this research operate in niche industries or locations, where they may be the only source of information on a given topic. As a user, if I already know the target site is my best source for a topic, then I’ll be very patient while the content loads. One situation where users are not patient is when arriving from Google Search, and they know they can go and find a similar source of information in two clicks (one back to Google, and then out to another site). So we see a very high correlation between bounce rate and the volume of traffic from Google Search. This also means that what should concern you is speed relative to your search competitors, so you could be benchmarking your site speed against a group of similar websites, to measure whether you are above or below average. [subscribe]   2. Bounce rate is most affected by first impressions As a user landing on your site I am going to make some critical decisions within the first 3 seconds: would I trust this site, is this the product or content I was expecting, and is it going to be easy to find what I need. If your page can address these questions quickly – by good design and fast loading of the title, main image etc – then you buy some more time before my attention wanders to the other content. In 2009, Google tried an experiment to show 30 search results to users instead of 10, but found the users clicking on the results dropped by 20%. They attributed this to the half a second extra it took to load the pages. But the precise issue was likely that it took half a second to load the first search result. Since users of Google mainly click on the first 3 results, the important metric is how long it took to load those – not the full page load.   3. Full page load speed is increasingly hard to measure Many websites already use lazy loading of images and other non-blocking loading techniques to make sure the bare bones of a page is fast to load, especially on a mobile device, before the chunkier content (like images and videos) are loaded. This means the time when a page is ready for the user to interact is not a hard line. SpeedCurve, a tool focussed entirely on web page speed performance, has a more accurate way of tracking when the page is ‘visually complete’ based on actual filmstrips on the page loading. But in their demo of The Guardian page speed, the page is not visually complete until a video advert has rendered in the bottom right of the screen – and personally I’d be happy to use the page before then. What you can do with Google Analytics is send custom timing events, maybe after the key product image on a page has loaded, so you can measure speed as relevant to your own site.   But doesn’t speed still affect my Google rankings? Yes, slightly. But when Google incorporated speed as a ranking signal in 2010, their head of SEO explained it was likely to penalise only 1% of websites which were really slow. And my guess is in 7 years Google has increase the sophistication with which it measures ‘speed’. So overall you shouldn’t worry about page load times on their own. A big increase may still signal a problem, but you should be focussing on conversion rates or page engagement as a safer metric. If you do want to measure speed, try to define a custom speed measurement for the content of your site – and Littledata’s analytics experts can work with you to set that custom reporting up.

Many businesses spend a disproportionate amount of time tweaking copy, design and interactive content for their homepage. Yet they miss the fact that the action is increasingly elsewhere. Homepage traffic has traditionally been seen as a proxy for ‘brand’ searches – especially when the actual search terms driving traffic are ‘not provided’. Now, brand search traffic may be finding other landing pages directly. Our hypothesis was that over the last 2 years the number of visits which start at the homepage, on the average website, are decreasing. To prove this, we looked at two categories of websites in Littledata’s website benchmarks: Websites with more than 20,000 monthly visits and more than 60% organic traffic (227 websites) Large websites with more than 500,000 monthly visits (165 websites) In both categories, we found that the proportion of visits which landed on the homepage was decreasing: by 8% annually for the smaller sites (from 16% of total visits to 13% over two years), and 7% annually for the larger sites (from 13% to 11%). If we ignore the slight rise in homepage traffic over the November/December period (presumably caused by more brand searches in the Christmas buying season), the annual decline is more than 10%. From the larger websites, only 20% showed any proportionate increase in homepage traffic over the 2 years – and those were mainly websites that were growing rapidly, and with an increasing brand. I think there are three different effects going on here: Increased sophistication of Google search usage is leading to more long-tail keywords, where users want a very specific answer to a question – usually not given on your homepage. The increase in mobile browsing, combined with the frustrations of mobile navigation, is leading more users to use search over navigation – and bypass your homepage That Google’s search-engine result page (SERP) changes have made it less likely that brand searches (searching for your company or product names) will navigate to your landing page – and instead browse social profiles, news, videos or even local listings for your company. In conclusion, it seems that for many businesses the homepage is an increasing irrelevance to the online marketing effort. Spend some time on your other content-rich, keyword-laden landing pages instead! And would you like to see if you are overly reliant on your homepage traffic, compared with similar websites? Try Littledata’s reporting suite.   Get Social! Follow us on LinkedIn, Twitter, and Facebook and keep up-to-date with our Google Analytics insights.

The biggest day for online retail sales among Littledata’s clients is the Sunday after Black Friday, followed closely by the last Sunday before Christmas. Which is more important - Black Friday or Cyber Monday? Cyber Monday saw the biggest year-on-year increase in daily sales, across 84 surveyed retailers from the UK and US. In fact, Cyber Monday is blurring into the Black Friday weekend phenomenon – as shoppers get used to discounts being available for longer. We predict that this trend will continue for 2016, with the number of sales days extending before and after Black Friday. Interested in what 2016 will bring? Stay tuned for our upcoming blog post! Want to see how you did against the benchmark? Sign up for a free trial or get in touch if you have any questions!   Get Social! Follow us on LinkedIn, Twitter, and Facebook and keep up-to-date with our Google Analytics insights.