Category : Futurology
The year in data: 2018 in ecommerce statistics
How did ecommerce change in 2018? Let's take a look at the data. Littledata benchmarks online retail performance in Google Analytics, and with over 12,000 sites categorised across 500 industry sectors we have a unique insight into ecommerce trends in 2018. The pattern we're seeing is that web sessions are becoming ever shorter as users split their attention across many ads, sites and devices. Marketers need get visibility across a range of platforms, and accept that a customer purchase journey will involve an ever greater number of online touch points. In the following analysis, we look at how performance changed across 149 ecommerce sites in 2018, and how these trends might continue in 2019. Ecommerce conversion rate is down Ecommerce conversion rate has dropped by an average of 6 basis points, not because of a drop of online sales - but rather because the number of sessions for considering and browsing (i.e. not converting) has risen. This is partly an increase in low-quality sessions (e.g. SnapChat ads preloading pages without ever showing them to users), and partly an increase in users from platforms like Facebook (see below) which bring less engagement with landing pages. See our mission to Increase Ecommerce Conversion Rate for more details. Revenue per customer is up Revenue per customer is the total sales divided by the total number of users which purchased online. The increase of $16 USD per customer per month shows that many stores are doing better with segmentation - ignoring all those sessions which don't convert, and retargeting and reselling to those that buy lots. The growth in subscription business models is also fuelling this trend. Getting a customer to commit to a regular payment plan is the most effective way of increasing revenue per user. See our mission to Increase Average Order Value for more details. Reliance on the homepage is down Content marketing became mainstream in 2018, and no self-respecting brand would now rely on the homepage alone to drive interest in the brand. The percentage of traffic coming 'through the front door' will continue to fall. In building out a range of keyword-specific landing pages, stores are harnessing a wider range of Google search queries, and providing more engaging landing pages from Google Ad and Facebook Ad clicks. Usage of internal site search is up Along with fewer visitors coming through the homepage, we are seeing fewer browsers use traditional category navigation over internal search. We think this is partly to do with younger consumers preference for search, but also probably reflects the increasing sophistication and relevance of internal search tools used by ecommerce. Referrals from Facebook are up Even after Facebook's data security and privacy embarrassments in 2018, it continues to grow as the 2nd major global marketing platform. Although few sites in our benchmark rely on Facebook for more than 10% of their traffic, it is a significant driver of revenue. As merchants continue to come to Littledata to find out the real ROI on their Facebook Ads, check back next year for a new round of analysis! How did your site perform? If you're interested in benchmarking your ecommerce site, Littledata offers a free trial to connect with Google Analytics and audit your tracking. You can see ecommerce benchmarks directly in the app, including 'ecommerce conversion rate', 'referrals from Facebook' and 'reliance on the homepage', to know exactly how your site's performing. Sign up today to benchmark your site and import Facebook Ads data directly into Google Analytics. [subscribe] For this article we looked at Littledata's private, anonymized benchmark data set, selecting ecommerce sites that had a majority of their traffic from the US and more than 20,000 sessions per month. We measured the change from 1st December 2017 to 31st December 2017 to the same month in 2018.
The 5 worst arguments for boosting Bitcoin
I’m exasperated reading dodgy logic justifying the heady ascent of Bitcoin. What are the worst 5 arguments I’ve heard? Full disclosure: I don’t own any Bitcoin, or have any bets on its rise or otherwise. 1. Bitcoin is an insurance against the collapse of capitalism The booster The rise of artificial intelligence and mass joblessness will sweep away much of the old order of nation states and their currencies. Bitcoin is independent of government and will survive the coming storm. A grain of truth I believe big change in the relative value of labour and capital, and how they contribute to the tax base, is coming faster than politicians expect. And the reactionary backlash in affected countries, such as those voting for Donald Trump, won’t stop this trend. The sceptic Bitcoin relies on a chain of other technologies which may well get disrupted with the collapse of capitalism: cheap power supply, a global internet and secure online vaults to hold the private keys and transact the Bitcoin. If you’re betting on the end of the world as we know it, hunting and farming skills are going to be more useful! 2. Bitcoin’s limited supply makes it deflationary by default The booster Unlike fiat money (e.g. the US dollar) which can be printed at will by central banks, the total number of Bitcoin is mathematically limited to 21 million. That means, as other currencies inflate, Bitcoin will hold its value – i.e. it’s digital gold A grain of truth As developed countries around the world are forced to borrow themselves out of the hole of shrinking tax bases and increasing healthcare costs, they may try to inflate their currencies to erode the debt. The sceptic Central banks have a positive inflation target for a reason: in a deflationary currency, no-one wants to spend the currency and so there’s no circulation of wealth. If one Bitcoin could have bought me a coffee in 2016, but at the time of writing could have bought a car, why would I ever spend it? And if no one spends the currency then it has no tangible value. 3. Bitcoin is the leader of the blockchain revolution The booster Blockchain is one of the few game-changing technologies to be invented the last two decades. It will revolutionise the world of finance, and you need to own Bitcoin to be part of that. A grain of truth The blockchain ledger, keeping a public record of all transactions, and reducing the possibility for fraud or interception, will certainly change many aspects of finance. There are many projects underway in financial trading and government. The sceptic Just because Bitcoin was the first use-case of the technology, does not make it essential to newer blockchains. Equally, its first-mover advantage may not even make it the winning cryptocurrency. That said, I wouldn’t go out buying a basket of other cryptocurrencies just yet – they are all overinflated by Bitcoin’s rise. 4. The increasing mining cost of Bitcoin underpins its value The booster New bitcoin gets exponentially harder to mine, so since the cost of electricity for the miner’s servers won’t fall, the cost per bitcoin mine is rising all the time. And if you can’t mine them, you’ll have to buy them. The sceptic Yes.. but what if no one needs Bitcoins at all? Mining gold is subject to the same economic forces, but if the gold goes out of fashion as a value store (as it did an the turn of the Millennium) it still had industrial value for conducting electricity and aesthetic value for jewellery. Bitcoin has neither of those. 5. The rise of bitcoin is 2017 shows it has won out as the cryptocurrency of choice The argument Bitcoin is now the established alternative store of value, which is why it has risen so fast in 2017. And what if all the pension funds and institutional investors now buy up a slice to ensure an allocation of this new asset class? A grain of truth There’s no rational way to value Bitcoin: it does not pay dividends or have intrinsic worth (see point 4). So it could be worth anything .. or nothing. The sceptic Every decade a new mania comes along for investors to follow. The vast chatter on LinkedIn, Facebook and other forums only heightens the mania by allowing unchecked falsehoods to flourish. You only have to look at the South Sea Bubble and Tulip mania to see there is nothing new under the sun. Enjoy the roller-coaster ride up .. because everything that goes up, must come down.
The Freemium business model revisited
After I concluded that freemium is not the best business model for all, the continued rise of ‘free’ software has led me to revisit the same question. In a fascinating piece of research by Price Intelligently, over 10,000 technology executives were surveyed over 5 years. Their willingness to pay for core features of B2B software has declined from 100% in 2013 to just over 50% today – as a whole wave of VC-funded SaaS companies has flooded the market with free product. For add-ons like analytics, this drops to less than 30% willing to pay. “The relative value of features is declining. All software is going to $0” – Patrick Campbell, Price Intelligently Patrick sees this as an extension of the trend in physical products, where offshoring, global scale and cheaper routes to market online have led to relentless price depreciation (in real terms). I’m not so sure. Software is not free to manufacture, although the marginal cost is close to zero – since cloud hosting costs are so cheap. The fixed cost is the people-time to design and build the components, and the opportunities for lowering that cost – through offshoring the work or more productive software frameworks - have already been exploited by most SaaS companies. To pile on the pain, a survey of software executives also found that the average number of competitors in any given niche has increased from 10 to 15 over those 3 years. Even if software build costs are falling, those costs are being spread over a small number of customers – making the chance of breaking even lower. And the other big cost – Customer Acquisition (CAC) – is actually rising with the volume of competition. To sum up the depressing news so far: 1. Buyers have been conditioned to expect free software, which means you’ll have to give major features away for free 2. But you’ll have to pay more to acquire these non-paying users 3. And next year another competitor will be offering even more for free What is the route of this economic hole? Focussing on monetising a few existing customers for one. Most SaaS executives were focussed on acquiring new customers (more logos), probably because with a free product they expected to sweep up the market and worry about monetization later. But this turns out to be the least effective route to building revenue. For every 1% increment, Price Intelligently calculated how much this would increase revenue. i.e. If I signed up 101 users over the year, rather than 100, that would increase revenue by 2.3%. Monetization – increasing the Average Revenue Per User (ARPU) – has by far the larger impact, mainly because many customers don’t pay anything currently. In contrast, the impact of customer acquisition has fallen over 3 years, since the average customer is less likely to pay. Monetization is not about increasing prices for everyone – or charging for previously free features – but rather finding the small number who are willing to pay, and charging them appropriately. My company, Littledata, has many parallels to Profit Well (launched by Price Intelligently). We both offer analytics and insights on top of existing customer data – Littledata for Google Analytics behavioural data, and Profit Well for recurring revenue data from billing systems. And we have both had similar customer feedback: that the perceived value of the reporting is low, but the perceived value of the changes which the reporting spurs (better customer acquisition, increased retention etc) is high. So the value of our software is that it creates a requirement – which can then be filled by consulting work or ‘actionable’ modules. For myself, I can say that while focusing on new customer acquisition has been depressing, we have grown revenues once a trusted relationship is in place – and the customer really believes in Littledata’s reporting. For Littledata, as with many B2B software companies, we are increasingly content that 80% of our revenue comes from a tiny handful of loyal and satisfied users. In conclusion, while the cover price of software subscriptions is going to zero, it is still possible to generate profits as a niche SaaS business – if you understand the necessity of charging more to a few customers if the many are unwilling to pay. Freemium may be here to stay, but if customers want the software companies they rely on to stay they need to pay for the benefits. Would you like to further discuss? Comment below or get in touch!
Online reporting: turning information into knowledge
Websites and apps typically gather a huge flow of user behaviour data, from tools such as Google Analytics and Adobe Analytics, with which to better target their marketing and product development. The company assumes that either: Having a smart web analyst or online marketer skim through the reports daily will enable management to keep tabs on what is going well and what aspects are not Recruiting a ‘data science’ team, and giving them access to the raw user event data, will surface one-off insights into what types of customers can be targeted with which promotions Having worked in a dozen such companies, I think both assumptions are flawed. Humans are not good at spotting interesting trends, yet for all but the highest scale web businesses, the problem is not really a ‘big data’ challenge. For a mid-sized business, the problem is best framed as, how do you extract regular, easy-to-absorb knowledge from an incomplete online behavioural data set, and how do you present / visualise the insight in such a way that digital managers can act on that insight? Littledata is meeting the challenge by building software to allow digital managers to step up the DIKW pyramid. The DIKW theory holds that there are 4 levels of content the human mind can comprehend: Data: the raw inputs; e.g. the individual signals that user A clicked on button B at a certain time when visiting from a certain IP address Information: provides answers to "who", "what", "where", and "when" questions Knowledge: the selection and synthesis of information to answer “how” questions Wisdom: the extrapolation or interpretation of this knowledge to answer “why” questions Information is what Google Analytics excels at providing an endless variety of charts and tables to query on mass the individual events. Yet in the traditional company process, it needs a human analyst to sift through those reports to spot problems or trends and yield genuine knowledge. And this role requires huge tolerance for processing boring, insignificant data – and massive analytical rigour to spot the few, often tiny, changes. Guess what? Computers are much better at the information processing part when given the right questions to ask – questions which are pretty standard in the web analytics domain. So Littledata is extending the machine capability up the pyramid, allowing human analysts to focus on wisdom and creativity – which artificial intelligence is still far from replicating. In the case of some simpler insights, such as bounce rates for email traffic, our existing software is already capable of reporting back a plain-English fact. Here’s the ‘information’ as presented by Google Analytics (GA). And here is the one statistically significant result you might draw from that information: Yet for more subtle or diverse changes, we need to generate new ways to visualise the information to make it actionable. Here are two examples of charts in GA which are notoriously difficult to interpret. Both are trying to answer interesting questions: 1. How do users typically flow through my website? 2. How does my marketing channel mix contribute to purchasing? Neither yields an answer to the “how” question easily! Beyond that, we think there is huge scope to link business strategy more closely to web analytics. A visualisation which could combine a business’ sales targets with the current web conversion data, and with benchmarks of how users on similar sites behave, would give managers real-time feedback on how likely they were to outperform. That all adds up to a greater value than even the best data scientist in the world could bring. Have any questions? Comment below or get in touch with our team of experts! Want the easier to understand reports? Sign up! Get Social! Follow us on LinkedIn, Twitter, and Facebook and keep up-to-date with our Google Analytics insights.
Using Google Analytics in store - online and offline
Let’s say I am a retailer selling LEGO®. I have an offline store and I would really like to track my performance. This article will show you that online and offline have a lot in common. The KPI’s are almost the same. You just need to find the right tools to track each channel. I’m an online geek so I would like to track all my activity in my Google Analytics account. At this point, you might already think that it can't be done. But just this morning when I saw this quote it became clear: It always seems impossible until it’s done. So let’s dive in on how an offline store KPI’s can by tracked via Google Analytics. I chose LEGO for two reasons. One, I love LEGO, and second, I love the uniformity of a LEGO store. All LEGO stores have the same structure, philosophy, almost the same products (differs by approx 10% in each country) and the same management control. That made my example so easy to picture. A LEGO store has in it the following and it can be translated in an online store the following way: Offline Online Collections of products Category Main products Products Complementary products Complementary / Accessories products Every product comes in a box The main photo of the product Every shelf has only one collection The listing page The LEGO catalog The online catalog (newsletter) Facebook page / Find shop page Facebook page / Find shop page Tablets with video of the products Video on product page Giant statues Banners Marketing events Campaigns Marketing assets ( rollup, banners, mash) CPM campaigns Traffic sensors Google Analytics tracking code Cash register Checkout page VIP cards UserID tracking A tablet for surveys Exit survey or email survey A tablet for VIP registrations Register section Video cameras Hotjar :) The first step to monitoring something is to choose which tool we are going to use. In my demo, we will use Google Analytics. We are going to create a Universal Google Analytics account with Enhanced Ecommerce tracking set up. In a normal website, we will implement this tracking code on each page of our website. In a retail store, we will have like a single page website because LEGO usually has only one room for the stores. Now comes the fun part. When a loyalty card means a UserID I mentioned above that we have a 'website' for the LEGO store with the new Universal Google Analytics script. We also have a VIP club because all LEGO stores have a program called LEGO VIP Club. This club is a program designed to engage customers and increase sales. With each sale, a customer is encouraged to become a part of the VIP Club. They will get a card, like a credit card design, and a unique ID. With this ID, we will be able to unify the customer's activity on online and in offline stores. Sessions: or “traffic” in retail For traffic control lots of retail stores have implemented sensors to track the amount of people coming in and walking out. Such a solution is usually called “counting visitors” or “footsteps counting”. A retail store can implement a "counting visitors system" in 2 parts of the stores for collecting the maximum amount of data: outside the store, and just inside the entrance / exit. This way you can measure the amount of people who saw your store and the amount of people who actually came in / left the store. You could then further divide the amount of people leaving the store in shoppers and visitors. Which in online translates to conversion rate. Here we will then add the amount of people that saw LEGO ads via marketing efforts. To make things interesting, we can put a contactless device by the entrance, to track the number of VIP customers that enter the store. Here you can extract data from your counting visitors system and send this information via the Measurement Protocol to Google Analytics. I will not get technical on this, due to the fact that it is just an idea and not a case study. But for more information feel free to contact us. At the entrance gate, you can send GA the information that a customer entered the LEGO store from Happy Street, give him a generic userID from the counter and if he taps the VIP card send the VIP Club ID also. Category and products Every brick and mortar store has an inventory of products. And every product in LEGO Store has a single category. If you've never seen a LEGO Store, let me show you what order means in products and category. [embed]https://youtu.be/lAaE-pxNB1w[/embed] The products and categories can be imported to Google Analytics using data import function in the admin section. In a LEGO store, you can track the On Shelf activity by using both traffic sensors and track events on digital assets. The sensors can track and send GA, the traffic on a specific section of the store. And since we are talking about LEGO this will be easy because the products are not mixed up. LEGO has implemented Digital Boxes in US stores. Digital Boxes are an emulator that takes the image of an object you have in your hand and projects a new image on top of it. This Digital Box can be seen in the video above, and could be treated like a view of a specific product page. Another cool asset LEGO has is the video player on the shelf. This video player shows the content of a product on a tablet (usually 7” wide). This tablet is put next to a selected product and the customer has the opportunity to virtually see the content of the LEGO box. We can now send to GA this interaction of a user with the video. Here we can use Google Tag Manager to catch the user interaction with this digital asset. For the products that have no video on the shelf or a digital box projection, we can use a smartphone along with an improved version of the LEGO app called LEGO 3D Catalog. This app can be downloaded from the Android store or Apple Store, and in the same manner, as the digital box, it will project an animation of the product on the image of the product box. To enter the app you must be logged in, so we can use the User ID, and we can make use of the GPS position to be certain that he is in our store. Online meets offline All online marketing activities are easy to send to Google Analytics if we use a system to track them. You can build your social campaign in a way that will be shown in Google Analytics in a very detailed way. Littledata provides a template to build powerful URL’s that can be used in your social campaigns. The role of this URL is to tag your traffic with the campaign information. Download Littledata's campaign tracking sheet with a URL builder. Online marketing activities mean Facebook, AdWords, mall website, PR communication, partners and mail exchange. You can connect the tablet, which you have in your store for surveys, to Google Analytics and get interesting reports based on that data and act quickly with the alerts from Google Intelligent Alerts. Impressions or proximity to marketing assets The easy part in offline marketing is to track the impressions. Two words: proximity sensors. By using these sensors you can track the amount of people that came close to your marketing asset and send it to GA (or, as we say, make it fire to GA). A marketing asset can be a banner, a statue, a mash or a roll up. And now let’s take the game to the next level. Track the promotions interactions. Let’s say you have a LEGO photo booth. Within the photo booth, you could place a QR code that will automatically share your customers' photo on social media and, in the same time, send a hit to GA or add a hashtag. Purchases The complex structure of a purchase in Google Analytics is this: 'id': 'P12345', // Product SKU 'name': 'Android Warhol T-Shirt', // Product name 'category': 'Apparel', // Product category 'brand': 'Google', // Product brand - in our case is super easy “LEGO” 'variant': 'black', // Product variant - on LEGO we have products like mugs red, green, yellow 'price': '29.20', // Product price (currency). 'coupon': 'APPARELSALE', // Product coupon - We can put here the coupon from our campaigns. And for the general campaigns like LEGO has a full month 30% off at City collection. Put the LEGO City sales that meet the conditions (like 1+1, or 2+1, or 2 +50%) a LEGO City identifier. 'quantity': 1 // Product quantity (number). All of these can be sent to GA on the purchase. Also, we can add custom dimensions like payment method and we must not forget about the VIP Club ID. Incomes and outcomes all in one place Data import lets you upload data from external sources and combine it with data you collect in Google Analytics. You could then use GA to organise and analyse all of your data in ways that reflect your business better. Data imports join the offline data you've uploaded with the default hit data being collected by Google Analytics from your websites, mobile apps or other devices. Imported data can be used to enhance your reports, segments and re-marketing audiences in ways that reflect your own business needs and organisation. The result is a much fuller, more complete picture of your users' online and offline activity. You can import you banners costs, traffic data from mall reports or refunds that maybe you are not tracking in your accounting software connected to Google Analytics. Data imports let you manually do a few of the things I detailed in this article. Big DATA in useful reports At this point, you have a bunch of data. All you need is some simplification. As already Littledata showed you, the final reports are the ones that really matter. Now, you just need to take a seat, grab a pencil and draw the KPI’s that matter to your business. I have some retail KPI’s that can be relevant to your business as they are for a LEGO store: customer retention, cost of goods sold, customer satisfaction incremental sales, average purchase value sales per square foot, cross devices and offline/online, conversion rate in store, track sales target, track bundle performance, employee sales performance, VIP enrollment target. "Sky is the limit" when it comes to understanding your customers. Even if you are a big store or a little one, your company will be able to make adjustments to various strategies and budgets, improve your activity and bring customers better services. Want more information on this blog post? Contact one of our lovely experts for details! Further reading: What is CRO, conversion optimisation, for ecommerce? Image Credit: Image courtesy of http://eveash.com
Will a computer put you out of a job?
I see a two tier economy opening up in England, and it’s not as simple as the haves and have-nots. It’s between those that build machines, and those that will be replaced by them: between those that can code, and those that can’t. We’ve seen the massive social effects that declining heavy manufacturing jobs since 1970s have had on much of the North of England and Scotland, and I believe we’re at the start of a similar long-term decimation of service industry jobs – not due to outsourcing to China, but due to automation by computers. Lots of my professional friends in London would feel they’re beyond the reach of this automation: their job involves being smart and creative, not doing production-line tasks. But it is these jobs, which currently involve staring at numbers on a screen, which are most at risk from computer substitution. If your job involves processing a load of data into a more presentable format (analysts, accountants, consultants and some types of traders) then a computer will eventually - within the next 20 years - be able to do your job better than you. In fact, within 20 years computers will be much better than humans at almost every kind of data processing, as the relentless extension of Moore’s law means pound-for-pound computer processing will be 1 million times cheaper than it is now. As Marc Andreessen put it, ‘Software is eating the world’, and we’re only just beginning to work through the implications. This worries me. With the greater and greater levels of automation of the working world, what happens to employment? Last year we saw an incredible event in the sale of WhatsApp to Facebook: massive wealth creation ($17bn) accompanied by almost no job creation (33 employees at the time of sale). If a tiny number of highly skilled people can create a service with 300m paying customers, why do companies need to hire lots of people? In the utopian view of future work we give up all boring admin tasks to the machines, and focus on face-to-face interaction and making strategic decisions based on selected knowledge fed to us by our personal digital agents (like Google search on steroids). Lots more thinking space leads us to be more productive, and more leisure time makes us happier. But 30 years ago they thought computers would evolve into very capable personal assistants, when in fact office workers are chained to the screen for longer hours by the tyranny of email and real-time information flow. Look at Apple’s forecast from 1987 of what computing might look like in 2006: the professor is freed from the tedium of typing or travelling to the library. Yet they didn’t consider whether the professor himself might be needed in a world where students could get their lectures as pre-recorded videos. So the cynical view is that more volume of data will require more humans to interpret, and the technology will always need fixing. As companies become more automated there will be more and more jobs shifting into analysis and IT support; analogous to how, as postal mail has been replaced by email, jobs in the company post room have shifted into IT support. The problem is that there really are a limited number of humans that can set up and maintain the computers. I’d love to see society grappling with that limitation (see grass-roots initiative like CoderDojo) but there are some big barriers to retraining adults to code: limited maths skills, limited tolerance for the boredom of wading through code, and limited opportunities for people to test their skills (i.e. companies don’t trust this most critical of job roles to new apprentices). So those that have commercial experience in programming can command escalating day rates for their skills – and this is most apparent in London and San Francisco, while pay in other skilled areas is not even keeping up with core inflation. That leads us to the dystopian view: that the generation starting their working lives now (those 10 years younger than me) will see their prospects hugely diverge, based on which side of the ‘replace’ or ‘be replaced’ divide they are. If companies akin to Google and Facebook become the mainstay of the global economy, then they’ll be a tiny number of silicon sultans whose every whim is catered for – and a vast mass of technology consumers with little viable contribution to the workplace. Let’s hope our politicians start grasping the implications before they too are replaced by ‘democracy producing’ software!
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