Category : Littledata
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. 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.
Introducing Buyer Personas
This week we're excited to introduce Buyer Personas, a game-changing new feature for marketers and ecommerce teams that are serious about hacking growth at a major scale. Do you know which types of customers are most likely to convert? Gathering customer data is one thing, but turning it into actionable insights is another. We've found that Littledata users are often struggling to find the exact differences between web visitors that buy and those that don't buy, especially when it comes to particular marketing channels. Littledata's new Buyer Personas feature automatically generates user personas based on your particular Google Analytics ecommerce setup or conversion goals, making it easier than ever to target your marketing and on-site content at those shoppers most likely to engage, convert, and grow with your online business in the long term. For example, if you know that users who arrive on your site on the weekend, in the afternoon are more likely to buy, then you should allocate more of your budget to those times. Or if users on tablets are most likely to convert, then target campaigns and ad formats most relevant for that screen size. Accurate Data If you have a decent Google Analytics setup it is possible to look at how different attributes of the user (age, browsing device, time of visit, etc.) affect their likelihood of converting. The better the data setup for your 'people analytics', the more detailed the report can be – when's the last time you audited your website's Google Analytics setup? Buyers or Users? We’re calling the new feature Buyer Personas since this is often requested by retail customers, but it is equally relevant if you have another conversion goal (eg. registrations, event bookings). In all of these cases, your customers are essentially 'buying in' to your product or service. You can switch the conversion metric at the bottom of the Buyer Personas page in the app. Marketing Channels Buyer personas give you actionable insights on particular channels, such as paid search, while also improving your overall understanding of your ideal customer base. The feedback is split out by channel so you can action it more easily: how you would re-organise your paid search marketing is very different to how you re-target your email marketing, but both are needed. The reality is that most smaller websites won’t have any of the ideal people of their site. We are not saying that only that exact profile will convert but that, by targeting the marketing on those who convert most easily, you can improve your return on investment. Pick the category with the biggest potential audience first. The first iteration of the new feature is live in the app this week. We look forward to hearing your feedback! Note that to generate Buyer Personas, you will need an active conversion goal or ecommerce tracking setup, and a minimum of 50 conversions in the previous month. Don't have a Littledata account yet? Sign up today to fix your Google Analytics setup for free and start generating buyer personas.
How Google Analytics works
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.
Why do I need Google Analytics with Shopify?
If the lack of consistency between Shopify’s dashboards and the audience numbers in Google Analytics is confusing, you might conclude that it’s safer to trust Shopify. There is a problem with the reliability of transaction volumes in Google Analytics (something which can be fixed with Littledata’s app) - but using Shopify’s reports alone to guide your marketing is ignoring the power that has led Google Analytics to become over by over 80% of large retailers. Last-click attribution Let’s imagine your shoe store runs a Google AdWords campaign for ‘blue suede shoes’. Shopify allows you to see how many visits or sales were attributed to that particular campaign, by looking at UTM ‘blue suede shoes’. However, this is only capturing those visitors who clicked on the advert and in the same web session, purchased the product. So if the visitor, in fact, went off to check prices elsewhere, or was just researching the product options, and comes back a few hours later to buy they won’t be attributed to that campaign. The campaign reports in Shopify are all-or-nothing – the campaign or channel sending the ‘last-click’ is credited with 100% of the sale, and any other previous campaigns the same customer saw is given nothing. Multi-channel attribution Google Analytics, by contrast, has the ability for multi-channel attribution. You can choose an ‘attribution model’ (such as giving all campaigns before a purchase equal credit) and see how much one campaign contributed to overall sales. Most online marketing can now be divided into ‘prospecting’ and ‘retargeting’; the former is to introduce the brand to a new audience, and the latter is to deliberately retarget ads at an engaged audience. Prospecting ads – and Google AdWords or Facebook Ads are often used that way – will usually not be the last click, and so will be under-rated in the standard Shopify reports. So why not just use the analytics reports directly in Google AdWords, Facebook Business, Twitter Ads etc.? Consistent comparison The problem is that all these different tools (and especially Facebook) have different ways of attributing sales to their platform – usually being as generous as possible to their own adverting platform. You need a single view, where you can compare the contribution of each traffic source – including organic search, marketing emails and referrals from other sites – in a consistent way. Unfortunately, Google Analytics needs some special setup to do that for Shopify. For example, if the customer is redirected via a payment gateway or a 3D secure page before completing the transaction then the sale will be attributed to a ‘referral’ from the bank - not the original campaign. Return on Advertising Spend (ROAS) Once you iron out the marketing attribution glitches using our app, you can make meaningful decisions about whether a particular form of marketing is driving more revenue that it is costing you – whether there is a positive Return on Advertising Spend. The advertising cost is automatically imported when you link Adwords to Google Analytics, but for other sources, you will need to upload cost data manually or use a tool like funnel.io . Then Google Analytics uniquely allows you to decide if a particular campaign is bringing more revenue than it is costing and, on a relative basis, where are the best channels to deploy your budget. Conclusion Shopify’s dashboards give you a simple daily overview of sales and products sold, but if you are spending more than hundreds of dollars a month on online advertising – or investing in SEO tactics – you need a more sophisticated way to measure success. Want more information on how we will help improve your Shopify analytics? Get in touch with our experts! Interested in joining the list to start a free trial? Sign up! Get Social! Follow us on LinkedIn, Twitter, and Facebook and keep up-to-date with our Google Analytics insights.
Top 5 Google Analytics metrics Shopify stores can use to improve conversion
Stop using vanity metrics to measure your website's performance! The pros are using 5 detailed metrics in the customer conversion journey to measure and improve. Pageviews or time-on-site are bad ways to measure visitor engagement. Your visitors could view a lot of pages, yet be unable to find the right product, or seem to spend a long time on site, but be confused about the shipping rates. Here are the 5 better metrics, and how they help you improve your Shopify store: 1. Product list click-through rate Of the products viewed in a list or category page, how many click through to see the product details? Products need good images, naming and pricing to even get considered by your visitors. If a product has a low click-through rate, relative to other products in the list, then you know either the image, title or price is wrong. Like-wise, products with very high list click-through, but low purchases, may be hidden gems that you could promote on your homepage and recommended lists to increase revenue. If traffic from a particular campaign or keyword has a low click-through rate overall, then the marketing message may be a bad match with the products offered – similar to having a high bounce rate. 2. Add-to-cart rate Of the product details viewed, how many products were added to the cart? If visitors to your store normally land straight on the product details page, or you have a low number of SKUs, then the add-to-cart rate is more useful. A low add-to-cart rate could be caused by uncompetitive pricing, a weak product description, or issues with the detailed features of the product. Obviously, it will also drop if you have limited variants (sizes or colours) in stock. Again, it’s worth looking at whether particular marketing campaigns have lower add-to-cart rates, as it means that particular audience just isn’t interested in your product. 3. Cart to Checkout rate Number of checkout processes started, divided by the number of sessions where a product is added to cart A low rate may indicate that customers are shopping around for products – they add to cart, but then go to check a similar product on another site. It could also mean customers are unclear about shipping or return options before they decide to pay. Is the rate especially low for customers from a particular country, or products with unusual shipping costs? 4. Checkout conversion rate Number of visitors paying for their cart, divided by those that start the process Shopify provides a standard checkout process, optimised for ease of transaction, but the conversion rate can still vary between sites, depending on payment options and desire. Put simply: if your product is a must-have, customers will jump through any hoops to complete the checkout. Yet for impulse purchases, or luxury items, any tiny flaws in the checkout experience will reduce conversion. Is the checkout conversion worse for particular geographies? It could be that shipping or payment options are worrying users. Does using an order coupon or voucher at checkout increase the conversion rate? With Littledata’s app you can split out the checkout steps to decide if the issue is shipping or payment. 5. Refund rate Percent of transactions refunded Refunds are a growing issue for all ecommerce but especially fashion retail. You legally have to honour refunds, but are you taking them into account in your marketing analysis? If your refund rate is high, and you base your return on advertising spend on gross sales (before refunds), then you risk burning cash on promoting to customers who just return the product. The refund rate is also essential for merchandising: aside from quality issues, was an often-refunded product badly described or promoted on the site, leading to false expectations? Conclusion If you’re not finding it easy to get a clear picture of these 5 steps, we're in the process of developing Littledata’s new Shopify app. You can join the list to be the first to get a free trial! We ensure all of the above metrics are accurate in Google Analytics, and the outliers can then be analysed in our Pro reports. You can also benchmark your store performance against stores in similar sectors, to decide if there are tweaks to the store template or promotions you need to make. Have more questions? Comment below or get in touch with our lovely team of Google Analytics experts! Get Social! Follow us on LinkedIn, Twitter, and Facebook and keep up-to-date with our Google Analytics insights.
It’s Black Sunday – not Black Friday
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.
The Black Friday Weekend of 2015
Shoppers on Black Friday are becoming more selective – with a decrease in the number of retailers seeing an uplift in Black Friday sales, but an increase in the purchase volumes seen at those selected stores. Littledata looked at the traffic and online sales of 84 ecommerce websites* over the Black Friday weekend (four days from Friday to the following Monday), compared with the rest of the Christmas season (1st November to 31st December). 63% of the surveyed retailers saw a relative increase in traffic on Black Friday weekend 2015 versus the remainder of the season, compared with 75% of the same retailers seeing traffic rise on Black Friday 2014. This implies some decided to opt out of Black Friday discounting in 2015 or got less attention for their discounts as other retailers spent more on promotion. The same proportion of retailers (60% of those surveyed) also saw a doubling (on average) in ecommerce conversion rate** during Black Friday 2015. In 2014, over 75% of retailers saw an improved conversion rate during Black Friday, but the median improvement over the rest of the season was just 50%. 61% of websites also saw an increase in average order value of 16% during Black Friday 2015, compared with only 53% seeing order values increase the previous Black Friday. We predict that this trend will continue in 2016, with a smaller number of websites benefiting from Black Friday sales, but a greater increase in ecommerce conversion rate for a select few. Be sure to check back for what the actual trends will be for 2016! Let us know what you think below or get in touch! * The surveyed websites were a random sample from a group which got a majority of their traffic from the UK or the US. The data was collected from Google Analytics, and so represents real traffic and payments. ** The number of purchases divided by the total number of user sessions Image credit: HotUKDeals Get Social! Follow us on LinkedIn, Twitter, and Facebook and keep up-to-date with our Google Analytics insights.
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