Introducing Shopify Flow connectors for Google Analytics

Littledata has launched the first Shopify Flow connector for Google Analytics, enabling Shopify Plus stores to analyse customer journey using a custom event in Google Analytics. In addition to Littledata's native connections with Shopify, Shopify Plus, Facebook Ads, ReCharge, etc., we have now launched a beta version of a Flow connector for Google Analytics. What is Shopify Flow? Flow is an app included with Shopify Plus, which enables stores to define automation pathways for marketing and merchandising. Think of it as an ‘If This Then That’ generator just for Shopify. For example, after an order is marked as fulfilled in Shopify’s admin you might want to trigger an email to ask for a review of the product. This would involve setting a ‘trigger’ for when an order is fulfilled and an ‘action’ to send an email to this customer. How do you use Littledata Flow actions? You install Littledata's Shopify app along with Shopify Flow Every time an order is created in your store we send it to Google Analytics, along with information about which customer ID made the order (nothing personally identifiable) You add Littledata's actions to your Flow Every time the order or customer event is triggered, even for offline events, the event is linked back to Google Analytics In Google Analytics you can then: Segment the customer base to see if these actions influence purchasing behaviour Visualise when these events occurred Analyse the customers making these actions: which geography, which browser, which marketing channel (in GA 360) Export the audience to retarget in Google Ads (in GA 360) Export the audience to run a website personalisation for using Google Optimize How do you set the actions up in Flow? Google Analytics customer event – can be used with any customer triggers, such as Customer Created Google Analytics order event – can be used with any order triggers such as Order Fulfilled, Order Paid, How else could I use the events? You can now link any of your favourite Shopify Apps with Flow connectors into Google Analytics. Some examples would be: Analyse if adding a product review leads to higher lifetime value    Retarget in Google Ads after a customer's order is fulfilled   Set up a landing-page personalisation for loyal customers (using Loyalty Lion connector) How much does this cost? The Flow connectors are included as part of Littledata’s standard subscription plans. You’ll need Littledata’s app to be installed and connected to link the events back to a customer – and to get reliable data for pre-order customer behaviour. Can Littledata set up a flow for a specific app? Our Enterprise Plans offer account management to help you configure the Littledata Shopify connection, including the Shopify Flow connectors. Get in touch if you have a specific app you'll like to make this work with.  

2018-12-17

Why don't my transactions in Google Analytics match those in Shopify?

The truth is that Google Analytics and Shopify need a little help to play well together. Most marketers use Google Analytics to track performance, but having a good data collection setup -- even for basic essentials like transactions and revenue -- is harder than it looks. As a Partner Manager at Littledata, I work with a wide variety of apps and agencies, especially Shopify Plus Partners, who are in turn working with marketing managers and ecommerce directors. One of the most frequently asked questions I get from those marketers is “Why don’t my transactions in Google Analytics match those in Shopify?” So in this article I’d like to take you on a journey, explaining what could cause this, how it can affect marketing and how to get accurate data that matches your actual money in the bank. Top 6 reasons for inaccuracy There are many reasons for differences in tracking results, but let’s take a look at the top 6 reasons. 1) Some orders are never recorded in Google Analytics Usually, this happens because your customer never sees the order confirmation page, and most commonly this is caused by payment gateways not sending users back to the order thank you page. 2) The Analytics / Tag Manager integration has some errors Shopify has an integration with Google Analytics but it is a pretty basic one, tracking just a few of all the possible ecommerce events and micro-moments required for a complete picture. Although Shopify’s integration is meant to work for most standard websites, there are those who build a more personalised theme. In which case they would require a custom integration with Google Analytics. (Here’s what you can track with Littledata’s Shopify app) 3) A script in the page prevents tracking to work on your order thank you page Many websites have various dynamics on the thank you page in order to improve user experience and increase retention. But these scripts can sometimes fail and create a domino effect preventing other modules to execute. Such errors can stop Google Analytics from tracking the event. 4) The user has opted out from Google Analytics tracking This instance is not encountered as often, but it’s worth mentioning that some users can opt out of Google Analytics tracking with the help of a simple browser add-on. Features like this work by adding bits of JavaScript code into every website the user visits which will prevent the Google Analytics tracking code from capturing user-related data. This also means that GA will not drop any cookie nor will send any data to its servers. [subscribe heading="Fix tracking automatically" button_text="Get the Littledata app"] 5) Too many products included in one transaction Every time a page on your website loads, Google Analytics sends a hit-payload to its servers which contains by default a lot of user data starting from source, path, keywords etc. combined with the data for viewed or purchased products (name, brand, category, etc). This data query can get quite long if the user adds products with long names and descriptions. But there is a size limit for each hit-payload of 8kb, which can include approx. 8192 characters or information for about 20 products. Where this limit is reached, Google Analytics will not send the payload to its servers, resulting in lost purchase data. 6) Too many interactions have been tracked in one session This inconsistency is not encountered as often, but it needs to be taken into account when setting up Google Analytics tracking. One of Google Analytic’s limitations for standard tracking is that a session can contain only 500 hits. This means that interactions taking place after the hit limit is reached will be missed by Google Analytics. How a data mismatch damages your bottom line We have found that 8 out of 10 Shopify merchants have only a 70 - 80% accuracy rate for transactions and revenue in Google Analytics mostly due to the reasons mentioned above. In other words, 80% of Shopify merchants are missing at least 20% transaction data! Statistically, small or even medium-sized merchants dealing with four-figure monthly revenue can be very affected by the missing data because they are more likely to take bad marketing decisions based on segmented data. Hyper-segmentation is counterproductive if you’re working with bad data. And for larger business which rely heavily on Google Analytics to make data-driven decisions, accuracy is an absolute must. Imagine having a 20% inaccuracy margin when dealing with six or seven figure monthly revenue! It kind of puts things into a different perspective, right? It would be quite impossible to know how much to invest & re-invest in marketing without knowing the actual ROI. But wait! There’s an easy fix Littledata’s Shopify app can automatically fix most of the tracking inconsistencies mentioned above. Here’s how our app works, it's like magic. First, the app adds a DataLayer on your website containing all the Enhanced Ecommerce events. Then it inserts a tracking script on each layout which captures every fired event as soon as it occurs, and then using Server Side tracking, the app listens for all transactions to ensure 100% accuracy. In addition to the guaranteed transaction accuracy, Littledata’s tracker attributes each sale by source together with granular user and product data. The app also sends custom information in 4 custom dimensions to understand KPIs regarding lifetime value (LTV). Sound pretty geeky? It is. But the cool part is that the app uses automation and machine learning to do all the heavy lifting for you, so you can focus on growing your business instead of worrying about tracking issues. And the tech extends to all the apps you use. We include smart connections with apps like ReCharge and Refersion, to ensure accurate data about every marketing channel and product mix, including subscriptions. For example, our ReCharge connection automatically tracks both first-time payments and recurring transactions. This gives you accurate sales data and marketing attribution for those sales. Compare different tracking methods I know it may sound too good to be true, and this is why we offer a 14-day free trial so you can test the results by creating a Test Property in your Google Analytics account and compare data between Shopify’s standard tracker and Littledata’s advanced solution. Once you have accurate data, you can start benchmarking against other Shopify sites and optimising your website with data-driven decision making. Questions? Littledata is here to help. We built our smart ecommerce analytics app to simplify everything, and with a clear picture of your ecommerce data and access to automated optimization tools you can truly take your business to the next level. Are you ready for accurate data?

2018-12-14

Getting started with Universal App Campaigns

With 3.8 million apps available for Android users and 2 million apps in Apple's App Store, it can be tough for an app developer to stand out among the competition. But with Google's Universal App Campaigns (UAC), developers have an opportunity to market their mobile apps with targeting options based on audience demographics and behavior. It all happens automatically -- as long as you set up the campaigns correctly. In this post I take a look at how you can put machine learning to work for you, using the power of Google’s Universal App Campaigns. Campaign set up Getting started with a UAC is relatively easy. The three steps are to identify an audience, ensure conversion tracking is set up correctly, and relevant text, video, and images are available for the campaign. The two major actions for UACs are to find new users who will install the app or those who will perform an action inside the app, such as making an additional purchase. One the UAC is set-up, it is eligible to show on Search, Display, YouTube and the Play Store. The initial setup is straightforward. The advertiser only needs to provide four lines of text with images and with machine learning, Google decides which combination to show to a particular user. Goals When you consider goals for your UAC, the install action is an obvious one regardless of the app category. Targeting options includes people who are likely to install the app or who are likely to install it and perform in app action. It is up to the advertiser to determine what a valuable action looks like and ensure conversion tracking is set up before launching a campaign. In-app actions, or goals, or can be either success actions or proxy actions. With a success action, the app user makes a purchase inside the app, upgrades the service, or signs up for a paid subscription; something that generates revenue. Assuming success actions happen at least ten times a day with users, the system has enough data to identify and target the right audience for your UAC. If volume of success actions is low, there is not enough data for machine learning to make decisions. In that case, the advertiser can identify a proxy action which is a behavior that is likely to lead to success action. An example of this is someone who added payment information to upgrade service but did not follow through with upgrading. Or it could be tracking which of your users share incentives with their network. Advertisers need to think carefully about what a proxy action truly is. When it it is too early in the funnel, it includes people who are less likely to convert and not a good representation of those who will later perform a success action. If a mid funnel behavior is identified as a proxy action, rather the the top of the funnel, it may better represent people who are closer to converting so it is more likely to later result in a success action. Conversions Setting up and collecting conversion data is a crucial piece to success because these campaigns look at past searches, browsing behavior, and other apps used to determine who is most likely to convert. Before launching a UAC, ensure this conversion tracking is set up correctly or your will not be measuring goals that matter. For e-commerce sites, the primary conversion is clearly to drive revenue in the form of an in-app purchase or perhaps subscriptions. With luxury retail, it is especially important to have conversion recording correctly because of the multiple touch points. And Shopify users can use the Littledata reporting app to gain even more insight on the user journey through that platform. Measurement and optimization There are immediate metrics to monitor - app installs and in-app purchase - but there are also long term considerations such as the customer lifetime value (CLV), that should be part of your overall strategic marketing plan. A single user who makes a purchase provides direct revenue. If they refer someone to your app, that is considered indirect revenue. The first number is clear-cut revenue and easy to measure. The second is one that you determine based on your internal data, meaning what type of behavior and interaction with customers generally leads to a sale. The value of both of these actions contribute to the CLV. Lifetime is the length of time they interact with your app. If they install the app and use it to buy things over the course of a year, then stop, their CLV time period is one year. Once you have identified your CLV, use this value to set your target CPA and optimize it based on performance. Decide what you are willing to pay for a success action and what you will pay for a proxy action, knowing that number will likely change over time. As data comes in from your UAC, you can compare the lifetime value of your different customers through segments. Segments help you uncover those customers who purchase every couple months compared to those who only make an initial purchase. Those the make multiple purchases represent segments with a higher value. Drilling into data with segments allows you to see who gives you the best return for your investment. This level of detail helps you identify how much you paid in your UAC for to acquire each type of customer so you can adjust accordingly. Review what you paid initially for the type of users that you bring in and compare that to their lifetime value. Are you investing your budget in a UAC that brings in users that generate recurring revenue? When you bid strategically based on a lifetime value, you are not overly focused on short-term transactions. It is less expensive to keep a customer than to acquire a new one so you want to think in those terms. What next? Decide on UAC goals that make sense for the purpose of your app. What should users do in addition to downloading the app and what behaviors indicate they are getting close to a conversion? Gather assets - text, video, and image - that are enticing for users and ensure conversion tracking is setup properly. Without proper conversion tracking, you miss out on the data you need to determine success. Monitor performance of your campaigns, and if you run an ecommerce site, track a wealth of data with the Littledata app. Think about the CLV and optimize your campaigns to reach the right users rather than any users. Your bottom line is generating revenue so keep that in mind with every UAC. With careful planning and well managed campaigns, your app can stand out in a crowded marketplace.

2018-10-31

How to increase Average Order Value (AOV) on your ecommerce site

Average order value (AOV) is a bona fide north star metric for Shopify stores, and ecommerce companies more broadly. Increasing AOV is a priority goal for ecommerce teams as it directly boosts revenue (and profits, if you’re doing things right). Growing revenue often requires retailers to acquire more traffic, but with AOV you can increase sales simply by convincing shoppers to spend that little bit more. AOV can be improved by adopting a number of proven optimisation techniques. Many of these have their roots in offline retail, where price, promotion, placement and merchandising all play a part in persuading customers to buy additional - or more expensive - items. We’ll get onto these tactics soon enough, but for now let’s start at the beginning. What is average order value? AOV is the average amount spent by customers when they place an order. To calculate AOV you divide total sales by the total number of orders (typically over a certain period of time). You can monitor AOV via Google Analytics. If you’re using Littledata then you’ll see it on your dashboard and in ecommerce report packs. Why is average order value important? AOV is one of the primary KPIs in ecommerce. It is a measure of sales trends and reflects customer behaviour and buying preferences. This insight can be used to optimise your website, pricing strategy, and guide decisions on what you choose to sell. It is also a good indicator of your ability to optimise ROI, as your marketing budget will go that much further if you increase AOV. It is worth investing time and money into moving the AOV needle, as it will create universal uplift. Implement the right kind of tactics - and technology - and we are sure that you will see some positive results, especially if this is an activity you haven’t yet spent too much time on. The results? New and existing customers are likely to spend more with you whenever they buy. Better sales numbers, bigger profits, and various additional benefits. Just like the other ecommerce KPIs, it is best not to view AOV in isolation. Related metrics include customer lifetime value (CLV) and customer acquisition cost (CAC), particularly for ecommerce subscription businesses. How do I know if my average order value is in good shape? Littledata has robust benchmarking data from a sample of 12,000 ecommerce sites. You can drill down by category and revenue to see how you compare vs your peers. For example, we analysed AOV across 379 medium-sized ecommerce sites in September and found that $123 is the typical amount spent. But average is relative - it very much depends on the sector. Start a free Littledata trial to see your AOV alongside the benchmark for your sector (we will show you some other juicy metrics and benchmarks too). It will look like this: Pretty cool, huh? If you happen to be underperforming in any area then our app will suggest some proven optimisation ideas to help you improve your store. Other stores have used our ecommerce benchmarks to grow sales, and we're confident that you will experience similar results. What affects average order value? Lots of things influence how much people spend when they buy from your site. Consider the last time you bought a higher priced item, such as a TV, laptop or mobile phone. More often than not there are upsells and cross-sells as you progress down the purchase path. You end up buying related items (mobile phone cases), upgrading your initial choice (256MB memory vs 64MB), purchasing add-ons (extended warranty), or clicking on a compelling product bundle (phone + case + warranty = 15% off). This kind of buying behaviour helps ecommerce teams to sleep soundly at night. It is to be encouraged. A real world example Apple is an absolute master of maximising AOV. Let’s take a quick walkthrough of one of its purchase pathways. First, we’ll select a Macbook Pro and will then see the following page, which invites us to customise our order. Add a little more memory and one item of software, and the order value increases by about 30%. Boom. Now let’s click the ‘Add to Bag’ button. We’ll progress to an ‘Essentials’ page. Yet more ideas to help us spend extra money. Think we’re all done? Not so fast. Click on ‘Review Bag’ and you’ll enter the checkout. Note the ‘Related Products’ that appear underneath the basket summary. Is it any wonder that Apple is valued at more than one trillion dollars? How can I increase my average order value? The million dollar question (or maybe a few billions, in the case of Apple). The researchers for our newest product feature - called Missions - have discovered plenty of ideas for you to try out. Littledata Missions provide step-by-step guides to help ecommerce teams optimise performance, and AOV was one of the very first metrics we wanted to explore. The following ideas are taken from our Average Order Value Fundamentals mission. There are a bunch of others in there to try too. Missions automatically generate based on your ecommerce benchmark data in the Littledata app (try Missions for free today). I’ll wager that at least one of the following will help you to grow AOV. And a super combo might seriously move the dial. Once you’ve optimised AOV - and there might be a ceiling - you can work on increasing purchase frequency, customer referrals, and then scale up your customer acquisition efforts. So then, here are 12 ideas to help you start to grow average order value... 1. Provide free shipping for orders above a certain amount Betterware grew AOV by 20% after introducing free shipping for orders above £30. M&S also provides free standard delivery for orders that exceed £30, as seen in the screenshot. A study by UPS found that 58% of consumers would add extra items to their cart in order to qualify for free delivery. As such this is a great way of increasing average order value. Free delivery is an expectation these days, so if you're late to the party - and concerned about margins - then a minimum threshold is worth testing. 2. Offer minimum spend discounts Much like introducing a free shipping threshold, you can provide a discount if the customer spends a certain amount on your site. Although it might seem to go against the goal of increasing average order value, setting offers such as this can tempt visitors into spending whatever is necessary to achieve the discount, because it appears like a deal. There are a number of ecommerce plugins to help with this. A lot of happy Shopify stores use the Product Discount app. 3. Make the most of up-selling Up-selling is the art of convincing prospective customers to increase their spend, typically by buying a more expensive item to the thing they're looking at. For example, in the screenshot below we can see how Amazon shows higher priced TVs to the one initially selected. By listing out the features side by side it may be enough to convince the prospective buyer that the next model up is a more attractive option. This is a sure-fire way to increase average order value, though it's not without its risks as you'll need to change the shopper's mind about something ("You don't really want that, you want this."). So be careful when experimenting with up-selling techniques. 4. Embrace cross-selling Amazon has attributed around 35% of its revenue to cross-selling. Not exactly small change. As such it is crucial to find a cross-selling strategy that works for your website. Cross-selling is the science of persuading customers to buy additional products related to the thing they’re about to purchase. For example, buy a camera and you might see recommendations for camera cases, bigger memory cards, battery chargers, etc. Adding items to the basket in this way is highly likely to increase average order value. However, it is important to specify which customers receive cross-sell offers. You should certainly think twice before cross-sells to customers who regularly return items. 5. Allow customers to use live chat A Forrester study found that there is a 10% increase in order value from customers who used the live chat function. The study also discovered that live chat helps to increase revenue by 48% per chat hour, and increased conversion rate by 40%. The business case for live chat would appear to be strong. Why is this? Mainly because customers like the immediacy - and familiarity - of chat. It has been reported that 73% of consumers who have used live chat were pleased with the experience. So, live chat is good for AOV, sales, conversion rates and customer satisfaction. What's not to like? 6. Show how others have enjoyed the product Average order value is 6% higher among shoppers who read reviews, compared with those who don't bother, according to a Bazaarvoice study. Positive social proof is incredibly powerful. It goes a long way towards encouraging people to progress to the checkout. Social proof comes in many forms, from reviews and ratings to testimonials and buyer videos. Make it highly visible at key points in the buyer journey, to build trust and reinforce the decision to buy. 7. Offer financing for high-ticket items Analysis by Divido has shown that sales can increase by 40% when high-ticket items are offered in monthly instalments. Your most expensive items are the ones which can be heavily responsible for driving up your average order value. If you offer customers the option to pay in instalments it can help you sell more of these higher valued products. For example, Goldsmiths offers shoppers 0% interest-free credit on purchases which total more than £750. This may appeal to people looking at items in the £500+ range - they might end up being tempted to spend more once they see the financing available. 8. Offer volume-based discounts Office supplies company Paperstone generated a 19% average order increase when a bulk discount deal was offered. As well as helping to grow AOV, strategic discounting can be great for clearing out excess inventory. However, remember that it is important to calculate bulk discounts very carefully. You need to offer deals that attract customers, but which do not hurt your profit margins. 9. Use dynamic retargeting to increase average order value Stella & Dot increased AOV by 17% after experimenting with dynamic retargeting, which allows ecommerce firms to show shoppers the right kind of ads during the shopping journey (such as product recommendation ads, based on their browsing behaviour or purchase history). This technology also recaptures lost sales from visitors who leave websites, by showing them personalised offers to re-engage them. 10. Send personalised emails OneSpot found that average order value increased by 5% upon the personalisation of emails. Simply put, customers are more likely to feel valued by your site if you provide them with messages that are relevant to their specific interests. Personalisation often starts at the customer's name ('Dear sir' won't cut it), but extends to the content of the email. If this is based on prior browsing and purchase history then you're more likely to engage the shopper, to reinforce - or complete - the purchase. 11. Offer a gift card or loyalty scheme By offering customers rewards for shopping with you, you’re likely to see an increase in orders, as well as an increase in the size of those orders. It has been shown that offering rewards for purchases 15-20% above average order size can increase the amount people are willing to spend. Encouraging big spenders to buy more frequently will also have the effect of increasing AOV. A study by BigDoor found that loyal customers make up 70% of total sales in some cases, so it is important to give something back to those customers once in a while. 12. Create product packages A case study into BaubleBar, a jewellery site, showed that average order value increased significantly when product bundling was offered. One pair of its earrings costs $30, but a bundle of three is just $48. This bundle screams “deal” to a customer. BaubleBar saw its average order increase by $22 in a matter of days. Bundling reduces cognitive load. If you can help shoppers avoid thinking too much then you're onto a good thing. Bundles can be viewed by visitors as a valuable deal, especially if they contain products which supplement the one they are already interested in. Packaging up items this way can be incredibly persuasive, particularly when you're offering a discounted price. They can also save the shopper time - no need to browse for add ons. Start the AOV mission today In summary, trying to increase average order value is worth the effort, and will be a gift that keeps on giving once you move the dial in the right direction. You can launch the Average Order Value mission directly from your Littledata dashboard. Our app will track your progress as you test ideas to discover what works best for your site. People trust Littledata to audit, fix and automate reporting. They use our benchmarks to check and compare their performance, relative to their peers. And now, with Missions, digital teams can actively set about increasing ecommerce revenue.

2018-10-25

Categorising websites by industry sector: how we solved a technical challenge

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. 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!

2018-10-16

Ecommerce trends at Paris Retail Week

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. 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.

2018-10-09

What's the real ROI on your Facebook Ads?

For the past decade Facebook’s revenue growth has been relentless, driven by a switch from TV advertising and online banners to a platform seen as more targetable and measurable. When it comes to Facebook Ads, marketers are drawn to messaging about a strong return on investment. But are you measuring that return correctly? Facebook has spent heavily on its own analytics over the last three years, with the aim of making you -- the marketer -- fully immersed in the Facebook platform…and perhaps also to gloss over one important fact about Facebook’s reporting on its own Ads: most companies spend money with Facebook 'acquiring' users who would have bought from them anyway. Could that be you? Here are a few ways to think about tracking Facebook Ads beyond simple clicks and impressions as reported by FB themselves. The scenario Imagine a shopper named Fiona, a customer for your online fashion retail store. Fiona has browsed through the newsfeed on her Facebook mobile app, and clicks on your ad. Let’s also imagine that your site -- like most -- spends only a portion of their budget with Facebook, and is using a mix of email, paid search, affiliates and social to promote the brand. The likelihood that Fiona has interacted with more than one campaign before she buys is high. Now Fiona buys a $100 shirt from your store, and in Facebook (assuming you have ecommerce tracking with Pixel set up) the sale is linked to the original ad spend. Facebook's view of ROI The return on investment in the above scenario, as calculated by Facebook, is deceptively simple: Right, brilliant! So clear and simple. Actually, not that brilliant. You see Fiona had previously clicked on a Google Shopping ad (which is itself powered by two platforms, Google AdWords and the Google Merchant Center) -- how she found your brand -- and after Facebook, she was influenced by a friend who mentioned the product on Twitter, then finally converted by an abandoned cart email. So in reality Fiona’s full list of interactions with your ecommerce site looks like this: Google Shopping ad > browsed products Facebook Ad > viewed product Twitter post > viewed same product Link in abandoned cart email > purchase So from a multi-channel perspective, how should we attribute the benefit from the Facebook Ad? How do we track the full customer journey and attribute it to sales in your store? With enough data you might look at the probability that a similar customer would have purchased without seeing that Facebook Ad in the mix. In fact, that’s what the data-driven model in Google Marketing Platform 360 does. But without that level of data crunching we can still agree that Facebook shouldn’t be credited with 100% of the sale. It wasn’t the way the customer found your brand, or the campaign which finally convinced them to buy. Under the most generous attribution model we would attribute a quarter of the sale. So now the calculation looks like this: It cost us $2 of ad spend to bring $1 of revenue -- we should kill the campaign. But there's a catch Hang on, says Facebook. You forgot about Mark. Mark also bought the same shirt at your store, and he viewed the same ad on his phone before going on to buy it on his work computer. You marked the source of that purchase as Direct -- but it was due to the same Facebook campaign. Well yes, Facebook does have an advantage there in using its wide net of signed-in customers to link ad engagement across multiple devices for the same user. But take a step back. Mark, like Fiona, might have interacted with other marketing channels on his phone. If we can’t track cross-device for these other channels (and with Google Marketing Platform we cannot), then we should not give Facebook an unfair advantage in the attribution. So, back to multi-channel attribution from a single device. This is the best you have to work with right now, so how do you get a simple view of the Return on Advertising Spend, the real ROI on your ads? Our solution At Littledata we believe that Google Analytics is the best multi-channel attribution tool out there. All it misses is an integration with Facebook Ads to pull the ad spend by campaign, and some help to set up the campaign tagging (UTM parameters) to see which campaign in Facebook brought the user to your site. And we believe in smart automation. Shhhh...in the past few weeks we've quietly released a Facebook Ads connection, which audits your Facebook campaign tagging and pulls ad cost daily into Google Analytics. It's a seamless way to pull Facebook Ads data into your overall ecommerce tracking, something that would otherwise be a headache for marketers and developers. The integration checks Facebook Ads for accurate tagging and automatically pulls ad cost data into GA. The new integration will normally only be available in higher-tier plans, but we're currently offering it as an open beta for ALL USERS, including Basic plans! For early access, just activate the Faceb|ook Ads connection from your Littledata dashboard. It's that easy! (Not a subscriber yet? Sign up for a free trial on any plan today.) We believe in a world of equal marketing attribution. Facebook may be big, but they’re not the only platform in town, and any traffic they're sending your way should be analysed in context. Connecting your Facebook Ads account takes just a few minutes, and once the data has collected you’ll be able to activate reports to show the same kind of ROI calculation we did above. Will you join us on the journey to better data?

2018-09-20

Intro to the Littledata app (VIDEO)

How does the Littledata app work? It's magic! Or at least it feels that way. This new video gives a quick overview of how it all fits together. Our ecommerce analytics app is the only one on the planet to both fix your tracking and automate reporting. Our customers see dramatic growth, from higher add-to-cart rates to better return on paid search. But what happens first, and what happens next? If you're an ecommerce marketer using Google Analytics, Littledata will make your job a whole lot easier. The process breaks down to four core steps, which you can repeat as often as you'd like. First you connect your analytics account, marketing channels like Google AdWords and Facebook Ads, and website data from tools like Shopify, ReCharge and CartHook. (And yes, we'll help you comply with GDPR). Then you use the Littledata app to audit your analytics setup and fix your tracking. Shopify stores can fix tracking automatically -- other sites get clear recommendations on what to do. If your goals include higher marketing ROI and increased conversions, the next step is to automate reporting with report packs and a smart dashboard, available directly in the app. And then it's time to optimise revenue with industry benchmarks, enhanced reporting and buyer personas, all built automatically. Sign up today for a free audit of your analytics setup, or book a demo to learn more. A complete picture of your ecommerce business is just around the corner!

by Ari
2018-08-14

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