Black Friday discounting increases next season’s purchasing

I knew Black Friday had reached ‘late adopter’ stage this week when a company I’d bought fencing panels from - fencing panels – emailed me their holiday season promotions. But the real question is whether all these promotions serve to drive customer loyalty or just attract bargain hunters? At Littledata we looked at aggregate data from 143 retailers who participated most in 2016 Black Friday, versus 143 retailers who did not. For the first 23 days of November 2017 – before Black Friday – the median year-on-year increase in sales was 13% for those pushing discounts the previous year, versus only 1% growth for those avoiding Black Friday discounting *. Our conclusion is that retailers who discounted most heavily on Black Friday 2016 saw a lasting benefit in extra sales a year after the sales period. However, we don’t know whether these extra sales were profitable enough to pay for the seasonal promotions. Another possible explanation is that higher-growth retailers are more active in marketing Black Friday, but in either event the discount season has done them no harm over the following year. In a follow up post next week we’ll compare the peak discount trading – and see if on average these same stores increased their participation this year or reigned it back. Looking at 2016, it seems Black Friday was bigger than the year before for our cohort of 270 UK retailers – but at the expense of sales later in the season. Yet in the UK we are not close to US-levels of hysteria yet, where a much greater proportion of the last quarter’s sales are done on that weekend. The other interesting question is what sectors does Black Friday affect? Reflecting back on my 2016 post, it may be a surprise that the biggest boost of over 100% average increase in sales comes for Health & Beauty stores; whereas technology and computer stores on average saw a boost of 40% for the week. (The graph shows the difference with the average sales volumes in November & December, by sector, for 3 selected weeks.) And perhaps I shouldn’t have been surprised by those fencing panels: business and industrial sites saw a big boost too! Interested in tracking online sales activity for your own site this holiday shopping season? Littledata's ecommerce analytics software provides accurate data and automated reporting to help you track promotions and drive conversions and customer loyalty. [subscribe] * The statistical detail I took a group of 573 retailers we have tracked for at least 2 years, and looked at the ratio of Black Friday weekend sales (Friday, Saturday, Sunday, Monday) to the 2 month average for November and December. Those in the top quartile (trading 2.6 times above average during the Black Friday season) were deemed to have participated; those in the bottom quartile, showing a dip in trading over that weekend were deemed not to have participated. I then looked at the year-on-year growth in revenue between November 2016 (first 23 days) and the same period in November 2017, for the discount versus non-discount group. A t-test between the groups found a 18% probability that the two groups had the same mean, not allowing us to dismiss the null hypothesis.  

2017-11-24

6 essential benchmarks for Shopify stores

Understanding how your website performs versus similar sites is the best way to prioritise what to improve. In this post we take a look at 6 top benchmarks for optimising Shopify store performance. Accurate benchmark data is especially useful to the increasing number of ecommerce companies using web performance benchmarks, such as bounce rates and home page reliance, as core elements of their sales and marketing KPIs. Understanding benchmarks is a key to success. To put together this new benchmarking report, we analysed current data from 470 Shopify retailers. If you're wondering how you compare, check out our Shopify analytics app. Average order value Average order value (AOV) or Average revenue per paying user (ARPU) is the total monthly revenue divided by the number of users which transacted that month. It is a measure of how well you are up-selling and cross-selling your products, depending on your product mix. What is a good average order value for Shopify stores? The benchmark is $69. The average is slightly lower ($63.50) if you are a smaller Shopify store. More than $120 AOV would put you in the top quartile, and one of our top-performing stores in the luxury ecommerce sector is averaging $2,080 per order! If your Shopify store has a lower AOV than the benchmark, you might try increasing your average checkout value by cross-selling other products, offering free shipping above a minimum threshold or increasing pricing on selected products. [subscribe heading="How do you compare?" button_text="BENCHMARK YOUR SITE"] Ecommerce conversion rate Ecommerce conversion is the number of purchases divided by the total number of sessions. Most visitors will take more than one session to decide to purchase, but this is the standard measure of conversion rate. It is a measure of how good a fit your traffic is for your products, and how well your site converts this traffic into customers. What is a good ecommerce conversion rate for Shopify stores? The benchmark is 1.75%. Larger stores have pushed this to 1.85%, and if you are more than 2.8% you are in the top quartile. The highest conversion rate we’ve seen on Shopify is 8%. Can you increase the conversion rate with more attractive product displays, or improving the checkout process? Enhanced ecommerce tracking will help you identify exactly where the blockers lie. Bounce rate from mobile search Since more than 60% of Google searches are now done on mobile, ensuring your site design works on a small screen is important for branding and sales. Bounce rate is the percent of visits of only one page – and will be high if your landing pages do not engage. Google will even adjust your mobile ranking for a given keyword depending on what proportion of visitors stick on your page - a good indication that your link was useful. What is a good bounce rate from mobile search for Shopify stores? The benchmark is 47.5%. The biggest Shopify stores have got this below 40%, and overall large retailers have 38% mobile bounce rate. So it’s not a problem with the Shopify platform, so much as a problem with the store theme – or how the options and products are displayed on a smaller screen. Can you improve the first impressions of the landing pages, put key content higher up the page, or decrease the page load speed to reduce that bounce rate? Delay before page content appears The delay between a page request by the user and them being to read or click on that page. This is more important than full page load speed for AJAX / lazy loading sites (also called the ‘DOM Interactive Time’). What is a good delay time before page content appears? The benchmark for Shopify stores is 2.75 seconds. Even larger retailers have this down to 2.8 seconds, so Shopify sites do well on this score. Anything less than 3 seconds is generally acceptable. Internet users are increasingly intolerant of slow sites. Your developers could look at Google PageSpeed Insights for more details. Often the delay will be down to extra scripts which could be delayed or removed. [subscribe heading="How do you compare?" button_text="BENCHMARK YOUR SITE"] Server response time This is the part of the page load speed which is entirely outside of your control – and due to the speed of the servers your site runs on. What is a good server response time for Shopify stores? The benchmark is 322ms. The average for larger ecommerce is 542ms – so Shopify’s server infrastructure is serving you well here. Reliance on the homepage This is the percent of visitors who land on your homepage. If this is below 40% you rely heavily on your homepage to capture brand or paid search traffic. Google increasingly rewards sites with a greater volume of landing pages targeting more specific keyword phrases. What is a good reliance on homepage percentage for Shopify stores? The benchmark is 32%. Larger Shopify stores, with many more landing pages, have reduced this to 7.3% of traffic landing on the homepage on average. Can you build out product landing pages and inbound links to copy their advantage? Ready to benchmark your own website, stop playing guessing games and start scaling your ecommerce business? Our Shopify reporting app is the easiest way to get accurate benchmarking. Install Littledata today and you'll get instant access to up to 20 relevant industry benchmarks for ecommerce sites, plus the tools you need to fix your analytics for accurate tracking, so you'll always know for sure where your website stands. It's all about smart data that helps you focus on making changes that drive revenue and increase conversions. We're here to help you grow!

2017-11-14

Is Google Analytics compliant with GDPR?

From May 2018 the new General Data Protection Regulations (GDPR) will come into force in the European Union, causing all marketers and data engineers to re-consider how they store, transmit and manage data – including Google Analytics. If your company uses Google Analytics, and you have customers in Europe, then this guide will help you check compliance. The rights enshrined by GDPR relate to any data your company holds which is personally identifiable: that is, can be tied back to a customer who contacts you. The simplest form of compliance, and what Google requires in the GA Terms of Use, is that you do not store any personally identifiable information. Imagine a customer calls your company and using the right of access asks what web analytics you hold on them. If it is impossible for anyone at your company (or from your agencies) to identify that customer in GA, then the other right of rectification and right of erasure cannot apply. Since it is not possible to selectively delete data in GA (without deleting the entire web property history) this is also the only practical way to comply. The tasks needed to meet depends on your meaning of ‘impossible to identify’! Basic Compliance Any customer data sent ‘in the clear’ to GA is a clear break of their terms, and can result in Google deleting all your analytics for that period. This would include: User names sent in page URLs Phone numbers captured during form completion events Email addresses used as customer identifiers in custom dimensions If you’re not sure, our analytics audit tool includes a check for all these types of personally identifiable information. You need to filter out the names and emails on the affected pages, in the browser; applying a filter within GA itself is not sufficient. But I prefer a belt-and-braces approach to compliance, so you should also look at who has access to the Google Analytics account, and ensure that all those with access are aware of the need not to capture personal data and GDPR more generally. You should check your company actually owns the Google Analytics account (not an agency), and if not transfer it back. At the web property level, you should check only a limited number of admins have permission to add and remove users, and that all the users only have permission to the websites they are directly involved in. Or you could talk to us about integrations with your internal systems to automatically add and remove users to GA based on roles in the company. [subscribe] Full Compliance Other areas which could possibly be personally identifiable and you may need to discuss are: IP addresses Postcodes/ZIP codes Long URLs with lots of user-specific attributes The customer’s IP address is not stored by Google in a database, or accessible to any client company, but it could potentially be accessed by a Google employee. If you’re concerned there is a plug-in to anonymise the last part of the IP address, which still allows Google to detect the user’s rough location. ZIP codes are unlikely to be linked to a user, but in the UK some postcodes could be linked to an individual household – and to a person, in combination with the web pages they visited. As with IPs, the best solution is to only send the first few digits (the ‘outcode’) to GA, which still allows segmenting by location. Long URLs are problematic in reporting (since GA does not allow more than 50,000 different URL variants in a report) but also because, as with postcodes, a combination of lots of marginally personal information could lead to a person. For example, if the URL was mysite.com/form?gender=female&birthdate=31-12-1980&companyName=Facebook&homeCity=Winchester This could allow anyone viewing those page paths in GA to identify the person. The solution is to replace long URLs with a shortened version like mysite.com/form And for bonus points... All European websites are required to get visitors to opt in to a cookie policy, which covers the use of the GA tracker cookie. But does your site log whether that cookie policy was accepted, by using a custom event? Doing so would protect you from a web-savvy user in the future who wanted to know what information has been stored against the client ID used in his Google cookie. I feel this client ID is outside the scope of GDPR, but guaranteeing that the user on GA can be linked to opt-in consent of the cookie will help protect against any future data litigation. The final area of contention is hashing emails. This is the process used to convert a plain email like ‘me@gmail.com’ into a unique string like ‘uDpWb89gxRkWmZLgD’. The theory is that hashing is a one-way process, so I can’t regenerate the original personal email from the hash, rendering it not personal. The problem is that some common hashing algorithms can be cracked, so actually the original email can be deduced from a seemingly-random string. The result is that under GDPR, such email hashes are considered 'pseudonymized' - the resulting data can be more widely shared for analysis, but still needs to be handled with care. For extra security, you could add a ‘salt’ to the hashing, but this might negate the whole reason why you want to store a user email in the first place – to link together different actions or campaigns from the same user, without actually naming the user. There are ways around that strike a compromise. Contact Littledata for a free initial consultation or a GDPR compliance audit.

2017-10-19

Littledata at Codess

I was proud to be invited by Microsoft to speak at their Codess event in Bucharest last week to encourage women in software. We talked about how Littledata uses Meteor, Node and MongoDB to run scalable web applications; slightly controversial because none of these are Microsoft technologies! The event was well run and well attended, so I hope it inspires some of the attendees to start their own projects...or to join Littledata (we're hiring).

2017-10-17

The end of the ecommerce 'thank you' page

For two decades the ecommerce customer journey has stayed roughly the same. Customers browse, add to cart, checkout, and then see a page confirming their purchase: the 'thank you' page. That last step is changing, and this is no small change as it threatens to break how many sites measure purchases. Ecommerce stores that stop using a final 'thank you' page without adjusting their analytics setup accordingly are in danger of getting inaccurate purchase data, or even losing track of shoppers altogether. In order to help our customers get ahead of the curve, we've gone through a number of test cases to find short and long term fixes to this issue. But first, a little history. In the old days... In the early days of ecommerce the biggest barrier during checkout was trust. Retailers paid to be certified as ‘hack-proof’ and customers wanted to make quite sure when and how their money was taken. Fast forward twenty years to today, and in the developed world most consumers have transacted online hundreds of times. They are familiar with the process, expect a seamless user experience, and confident that when they click 'buy' their payment will be taken and the products delivered. Online shoppers are so confident, in fact, that an increasing number we observe don’t even bother waiting for that ‘thank you for your order’ page. That page is becoming redundant for three reasons: Almost every checkout process captures an email address to send an order receipt to, and the email acts as a better type of confirmation: one that can be searched and referenced. Seriously, when was the last time you opted to ‘print the confirmation page’ for your records? Many retailers are forced to compete with the superb customer support offered by Amazon. This includes refunds for products that were ordered in error, and quick handling of failed payments. So from a customer's perspective there’s little point in waiting for the confirmation page when any issues will be flagged up later. Which leads to the third reason: as retailers improve the speed of checkout, the payment confirmation step is often the slowest, and so the one where customers are most likely to drop out on a slow mobile connection. This is no small issue, as mobile revenues are expected to overtake desktop revenues for ecommerce businesses globally this year. What does this mean for ecommerce sites? The issue is that for many sites the linking of sales to marketing campaigns is measured by views of that ‘thank you' page. In the marketing analysis, a ‘purchase’ is really a view of that 'thank you' page - or an event recorded on the customer’s browser with the sale. If customers don’t view the page, then no sale is recorded. If you have ever been frustrated by the lack of consistency between Google Analytics and your own payment/back-end records, this is the most likely issue. A dependency on viewing the 'thank you' page brings other problems too: a buggy script, perhaps from another marketing tag, will block the recording of sales. This is another source of the type of analytics inaccuracy which the Littledata app combats automatically. [subscribe] How to adjust your ecommerce tracking The short-term fix is to tweak the firing order of marketing tags on the 'thank you' page, so that even customers who see the page for fractions of a second will be recorded. Sites with a large number of marketing tags will have the greatest room for improvement. But in the long term, as this trend continues, the analytics solution is to link the marketing campaigns to the actual payments taken. This removes the need for the customer to see any type of 'thank you' or confirmation page, and also removes discrepancies between what your marketing platform tells you was purchased and what actually got bought. This is known as server-side tracking. The good news for those of you on the Shopify platform is that our Shopify reporting app does this already - and solves a lot of other analytics problems in one install. For those on other stores, please do contact us for advice. The Littledata team has worked with ecommerce businesses to set up integrations with Magento, DemandWare and numerous custom platforms. Not only can we help fix your analytics setup for accurate tracking, but our app then automates the audit and reporting process for all of your sites going forward.

2017-08-30

Custom reporting for marketing agencies

Are you a digital marketing agency looking for new reporting solutions? As our agency partnerships continue to grow, we thought it would be useful to outline how Littledata's custom reporting helps forward-thinking agencies cut down on reporting time, visualise data and improve performance for their clients. The marketing landscape is complex, but your reporting doesn't have to be overly complicated. With such a wide range of channels and sites to track, many agencies struggle to find the best analytics tools. To you we say: Welcome, you've finally found a solution that both simplifies and enhances the reporting process. Smarter reporting and accurate analytics Do you produce regular campaign performance reports in Excel or Google Sheets for your clients? Have you rejected other reporting solutions as being too rigid or complex for your needs? Then Littledata’s custom reports could work well for you and your clients. We automate the data fetching and calculations you currently run manually, and display the results to clients in a streamlined web app. We'll even show you the most important metrics, and report on key changes - automatically. One key advantage over tools such as Tableau, Data Studio or Chartio is that you can define a template report and then roll it out for many different web properties (or segments of websites) with the click of a button. Compared with other solutions you may have considered we also offer: Full support in data setup, report design and client onboarding Branded report packs for your clients and customers Complete life cycle data on your clients' customers, from marketing attribution to repeat purchases (including for subscription-based businesses) 1st line support to end users Flexibility to calculate any metrics (using Google Sheets in our processing pipeline) Comparison to industry benchmarks for sales, marketing and web performance - or create private benchmarks amongst your own client base Actionable insights for any online business to improve marketing ROI and increase conversions, whether one large ecommerce site or a series of micro-sites Integration of Google Analytics with Google Search Console data for powerful SEO reports [subscribe] We’re also open to discussions about white-labelling the Littledata app. This type of partnership works best for agencies with at least 20 clients ready to take advantage of our intelligent analytics tools. Please contact us if you’d like a demo, to see how this has worked for existing customers, or to discuss a particular client’s needs. Get ready to love your analytics :)

2017-08-09

How to add tracking for multiple websites or apps (VIDEO)

If you're tracking multiple sites or apps in Google Analytics, you can connect all of these views to your Littledata account and easily switch between them. Watch this quick video to learn how to add or remove a Google Analytics data source in the Littledata app. [embed]https://www.youtube.com/watch?v=xoISTTx1zlw[/embed] FAQs - Working with multiple Google Analytics views How do Littledata reports link to Google Analytics views? When you click to set up another site you will see a list of all the Google Analytics properties and views linked to your Google account. Typically you will only be interested in one of the views, which contains data for the site or app you are working on. When you select a view, Littledata fetches the data it needs to enable core features such as our intelligent Google Analytics audit and industry benchmarking. Note that this doesn't commit you to purchase anything. The underlying data in your Google Analytics account is not affected unless you opt-in to our automated fixes, which let you automatically fix particular aspects of your Google Analytics setup. [subscribe] How many websites or apps can I track? You can set up standard reporting for as many websites as you like. However, if you're using Littledata's Pro services for advanced custom reporting, this is priced per view or data source. You can switch between these sites using the drop-down menu in the top bar. Does your reporting work with mobile app properties? Right now, some of the features will work - such as dashboards, alerts and buyer personas - but audit and benchmarking are specifically for websites. How do I add or remove a site? Once you've connected multiple web properties to your Littledata account, you can manage them using the My Sites page under the profile photo drop-down menu in the upper right. Can Littledata handle micro-sites? Yes. If each micro-site have it's own Google Analytics view, then go ahead and connect them all to your Littledata account. If the micro-sites are all under one web view, then ask the Littledata team about custom solutions to create a multi-site dashboard that lets you visualise Google Analytics data from many micro-sites and benchmark against each other. We have done this for a range of customers and are happy to discuss the details of what is involved in reporting on multiple micro-sites, whether just a few or several hundred!

2017-08-02

TechHub London demo roundup

Last night we gave a live demo of the Littledata app at TechHub London's Tuesday demo night. It's always exciting to share Littledata with other entrepreneurs and business owners, and to get their feedback about Google Analytics issues (everybody has some!). But in this post I'm putting our app aside for a moment in order to share some thoughts on the other company demos from the event. After all, isn't sharing feedback and ideas what the TechHub community is all about? My Film Buzz MyFilmBuzz is an early stage mobile app – launched eight weeks ago with 150 users. The user interface is really intuitive; making use of great visuals from movies and Tinder-style swiping to rate movies. The commercial problem is competing with established players like Rotten Tomatoes with big established audiences. Can a better interface tempt film viewers away? HeathClub TV HeathClub TV offers personalised training videos and exercises, selling via personal trainers who create their own profile and packages. A bit like Udemy for personal training courses, the trainers take a cut of the course fees. Again personal fitness is a very competitive market – the founder said one competitor spent £1.5m on their first version mobile app. I’ve personally enjoyed the 8-fit mobile app, with a similar mix of video exercises but without the marketplace for trainers to produce content. It will be interesting to see if the user generated content model wins out in this market. Trevor.io Trevor helps companies visualise data sources from their own business, such as SQL databases. The user interface makes a good job of simplifying a complex task, switching between table and graph views. As a data geek, I love it! We thought about a similar product in the early stages of Littledata, so my big question is: how many users have the analytical knowledge to create the data integrations, but aren’t comfortable using SQL or similar. At Littledata, most of our analysts progress to coding, because it makes them quicker to do the analysis – but then we are an unusually techy company. Grocemania Grocemania allows customers to place orders from local retailers, charging a small delivery fee (£2.50) and small minimum order (£10) subsidised by 15% commission from the retailers. They have launched a pilot in Surrey with nine retailers. The strategy seems to be to undercut other delivery companies, with lower delivery costs from freelancers and passing stock control onto the retailers. The presenters got a groan for highlighting how they reduce employment costs, but my real concern is how they can profitably undercut companies like Amazon who are ruthless pros at retail and delivery. [subscribe] Worksheet Systems Similar to Trevor, Worksheet Systems aims to solve the problem of storing lots of data in interconnected spreadsheets. Their idea is to split the user interface and database inherent in a complex spreadsheet, and present as a kind of Google Sheet – rather than the customer building an actual database. It looks really powerful, but I wasn't clear what it can do that Google Sheets doesn’t; we use Sheets for lots of smaller ‘databases’ in Littledata, and it’s both simple and powerful. Crowd.Science Crowdfunding for scientific projects, helping scientists raise money from individual donations, business sponsorship and charitable trusts. They take 5 – 10% commission of the money raised. It seems like a great model: crowdfunding is well proven in other areas, and some scientific projects have real public benefit. As the trustee of a grant-giving trust, I know the way we find projects is fairly inefficient, so this platform would be a great benefit as it takes off. Realisable Realisable is an Extract, Transform and Load (ETL) tool, with a visual business rules editor to transform a data source. Their live demo uses a job to transform unshipped orders from Shopify into a format that can be exporting to an accounting package, adding a customer ID to the transactions. I investigated this market in 2016, and there are some very big companies in the ETL market. Many of their products suck - a great opportunity - but there are ones with better user interfaces like Stitch Data. Talking to the founders afterwards, their strategy is to dominate a channel (in their case, Sage consultants); I know this has really worked for another ETL tool, Matillion for Amazon RedShift. Conclusion What’s my favourite idea (outside of Littledata)? Crowd.Science has the biggest potential commercially I think, but I do love Trevor’s product.

2017-07-05

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