Link Analytics to AdWords with our new Google Ads connection

To target -- and retarget -- the right shoppers, ecommerce sites need to connect customer behaviour and ecommerce data from Google Analytics with their Google Ads (AdWords) accounts. But until now that was a complicated process, to say the least. Marketers have spent years going through detailed setup steps to connect the platforms, or wading through spreadsheets with manual imports and exports, building custom audiences and segments. It was an ongoing headache, but they did it because connecting shopping behaviour data with AdWords campaigns gets big results. Now there's a better way. Littledata's new Google Ads connection makes it easy to link Analytics to AdWords. Ecommerce sites are using the connection for smarter targeting that increases online sales and customer LTV. Why should you link Analytics to AdWords? In past posts we've highlighted the benefits of linking Analytics with AdWords for a mutually beneficial relationship. Littledata's new connection automates the process to ensure accurate tracking and more targeted campaigns. Benefits include: Online sales data in AdWords reports, and visa versa. Add sales columns to reports in Google Ads and view Google Ads costs in Google Analytics. Abandoned cart campaigns. Get higher ROI with targeted PPC campaigns based on shopping cart activity. Ecommerce hyper-segmentation, especially for Shopify stores and enterprise clients. Since Littledata fixes ecommerce tracking across the checkout flow, the Google Ads connection is especially powerful for marketers looking to retarget with granular user behaviour data, such as product list views, product detail pages and adds-to-cart. Multiple accounts. Multiple views. Our Google Ads connections lets you link multiple AdWords accounts to multiple Google Analytics views. It's that simple. Wait, do you mean Ads or AdWords? Have you heard the news? Google AdWords is now Google Ads. Google pitched the switch to Ads as a large-scale rebrand for simplicity, but it's clearly targeted in part at bumping up competition against other 'ads' in common parlance: Facebook Ads, Instagram Ads and Twitter Ads, with Reddit Ads quickly gaining pace among SaaS companies in particular. We still talk about AdWords a bit on the blog (as does the rest of the internet, such as Search Engine Land), but soon we'll all have to adapt to the change. So we're calling this new connection a Google Ads connection, but we don't expect marketers to stop chatting about AdWords any time soon. How does it work? After you sign up for Littledata, you can connect Analytics to AdWords from the Connections tab in the Littledata app. Just follow a couple of setup steps and the app makes the connection for you. No more manual connections. Plus, we audit your analytics setup continually to ensure consistent ecommerce tracking, campaign tagging and UTM parameters. So what are you waiting for? Those products aren't going to retarget themselves... And don't forget to try our Facebook Ads connection to complete your marketing analytics stack. It's an easy way to link Facebook Ads to Google Analytics. All paid plans in the Littledata app include a variety of Google Analytics connections for Shopify, Shopify Plus, ReCharge, Refersion, CartHook and more. PS. The next iteration of our Google Ads connection will provide automation for retargeting using ecommerce segments. Sign up for Littledata today so you're first in line!

by Ari
2019-02-28

Why you should link Google Ads with Google Analytics

Google Ads (formerly Google AdWords) and Google Analytics have constantly proved their worth as valuable tools for ecommerce marketers to get insights and detailed reporting on advertising ROI. But why should you link Google Ads and Google Analytics together? What does it mean to connect them? Our enterprise ecommerce customers in particular have seen a major benefit of linking Analytics with Ads. Those who linked these two platforms have seen a significant improvement in reporting and it made it much easier to retarget ads to clients that have forgotten or abandoned their services but shown intent to purchase a particular product or type of product. Why connect? Is it really necessary to connect Google Ads linked with Analytics? Let's get down to basics. In the Ads platform you can’t see what your users do after they click on your ads or if said ads led to a sale, you can’t see their path on your website, so you are basically losing the big picture of your customer’s behaviour after they see the ad. In short, without connecting the two technologies together, your shopping funnel is incomplete. You can't see Google Ads performance compared with other marketing channels, or how those Ads actually contribute to revenue. Both Google tools have their individual strengths but you can see their real power once you have linked both of them. If you are already using both Analytics and Google Ads but haven’t linked them yet, then you are missing a lot of valuable information about how to connect marketing with revenue -- and where to optimise. [subscribe button_text="Free Google Analytics Connection"] With the two platforms tied together, they will be able to communicate much more efficiently and provide more granular data in your reporting. Google Analytics has a dedicated section within the Acquisition reports solely detailing Google Ads performance which you cannot obtain unless you have linked your Google Ads and Analytics accounts and are using auto-tagging in Google Ads. These reports share some common information with the types of data that can be found in Google Ads, but here you are able to combine and link the Google Ads data with all the data available in Analytics to find more meaningful insights and potentially make better decisions. Moreover, you are able to leverage these insights into a number of different goals that you wouldn’t be able to easily see in Google Ads. Surprisingly though, a full connection doesn’t happen automatically. Yes, they are both Google products, but you need to do some work to connect the platforms and then take action based on that data. Google's thoughts on connecting Google Ads with Google Analytics This quick video highlights the benefits of linking the two platforms together (whether you call them Ads or AdWords is up to you...marketers are still a bit confused by Google's rebrand). https://www.youtube.com/watch?v=8EmXFM1_xEo Top four benefits of linking Google Ads with Google Analytics for ecommerce 1. Retarget based on checkout steps in ecommerce The most effective way to grab these customers is to target them based on where they dropped off. Luckily, Google lets you do exactly that: with the right analytics, you can set up retargeting campaigns based on checkout behaviour. We highlighted this in a more comprehensive blog post on how you can improve Google Ads retargeting by analyzing the customer behavior during checkout. Our customers have been applying those tips and seeing results in less than a week. Learn more about how to improve AdWords retargeting using ecommerce checkout steps. 2. Retarget based on users who reach a Google Analytics goal You can set up a simple or complex goal and then target that audience with the right messaging. For example, even a newsletter subscription can lead to a goal completion. That user showed interest in your product and with a bit of persuasion and smart ad targeting, you’ll most likely succeed in transforming that lead into a buying customer. PRO TIP: Watch our video on troubleshooting your Google Analytics goals setup if you're having issues with goals. 3. Block advertising to people who have previously purchased An effective retargeting audience setting is crucial. There is no need to spend money on retargeting ads for people who will not be convinced to buy by them. If someone has already purchased a product from your online store, then the chance of them buying the same product in the next few days is nihil. If you don’t set an effective retargeting audience, you are more likely to spend way more money for with no result. The solution is to exclude people who recently bought your products from retargeting for a certain period, and you’ll be able to retarget them again after a certain time frame. That means that if John from California just bought a shirt from my website, I will not retarget him for the next month; he will not see any ads of the shirt appearing on his browser for that period. [subscribe "button_text="Free Google Analytics Connection"] If you want to see the best result of with your retargeting campaign then keep this in the back of your mind when making campaign planning. You will be left with more budget to spend on retargeting ads that are actually effective and most important of all, a happier audience. 4. Send different adverts to different segments of customer lifetime value (LTV) Our biggest customer segment right now is automated analytics for ecommerce subscription businesses. It should come as no surprise that subscription ecommerce merchants get a special benefit from linking Ads with Analytics. Littledata's ReCharge connection enables you to see customer lifetime value and create different audiences based on a customer’s last purchase or the number of orders placed. By this segmentation of audience you can customise your PPC ads and reach the right people who are already loyal to your brand and know your products. Your ROAS will be amazing and you won’t have to make huge efforts to get major results. Questions? The benefits above speak for themselves, so what are you waiting for? Especially if you run an ecommerce site, the time to connect is now :) If you’re trying to connect Google Analytics with AdWords for an ecommerce site, it should go smoothly. But sometimes an account manager can help with custom setup and reporting, or simply check to make sure you’re tracking things correctly. Littledata’s pricing options include various levels of support to fit every business size and goals for growth. Check out our free guide on how to connect your Google Analytics and AdWords accounts.

2019-02-25

How to calculate customer lifetime value (CLV) for subscription ecommerce in Google Analytics

Many of Littledata's subscription customers come to us with a similar problem: how to calculate return on advertising spend, considering the varying customer lifetime value (CLV) of subscription signups. Calculating marketing ROI for subscription ecommerce is a big problem with a number of potential solutions, but even the initial problem is often misunderstood. In this post I break down what the problem is, and walk through two proven solutions for getting consistent, reliable CLV reporting in Google Analytics. What is customer lifetime value? I work with all kinds of subscription ecommerce businesses: beauty boxes, nutritional supplements, training courses and even sunglasses-by-the-month. All of them want to optimise customer acquisition costs. The common factor is they are all willing to pay way MORE than the value of the customers' first subscription payment... because they expect the customer to subscribe for many months. But for how many months exactly? That's the big question. Paying for a marketing campaign which bring trial customers who cancel after one payment - or worse, before the first payment - is very different from paying to attract sticky subscribers. A marketing director of a subscription business should be willing to pay WAY more to attract customers than stay 12 months than customers who only stay one month. 12 times more, to be precise. So how do we measure the different contribution of marketing campaigns to lifetime customer value? In Google Analytics you may be using ecommerce tracking to measure the first order value, but this misses the crucial detail of how long those shoppers will remain subscribers. With lifetime customer value segments we can make more efficient use of media, tailor adverts to different segments, find new customers with lookalike audiences and target loyalty campaigns. There are two ways for a marketing manager to see this data in Google Analytics: one is a more difficult, manual solution; the other is an easier, automated solution that ties recurring payments back to the original campaigns. A manual solution: segment orders and assign a lifetime value to each channel It's possible to see the required data in GA by manually segmenting orders and assigning a lifetime value to each channel. For this solution you'll need to join together: (a) the source of a sample of first orders from more than a year ago, by customer number or transaction ID and (b) the CLV of these customers The accuracy of the data set for A is limited by how your Google Analytics is set up: if your ecommerce marketing attribution is not accurate (e.g. using Shopify's out-the-box GA scripts) then any analysis is flawed. You can get B from your subscription billing solution, exporting a list of customer payments (and anonymising the name or email before you share the file internally). To link B to A, you'll need either to have the customer number or transaction ID of the first payment (if this is stored in Google Analytics). [subscribe] Then you can join the two data sets in Excel (using VLOOKUP or similar function), and average out the lifetime value by channel. Even though it's only a sample, if you have more than 100 customers in each major channel it should give you enough data to extrapolate from. Now you've got that CLV by channel, and assuming that is steady over time, you could import that back into Google Analytics by sending a custom event when a new customer subscribes with the 'event value' set as the lifetime value. The caveat is that CLV by channel will likely change over time, so you'll need to repeat the analysis every month. If you're looking to get away from manual solutions and excessive spreadsheets, read on... A better solution: tie recurring payments back to the original campaign(s) What if you could import the recurring payments into Google Analytics directly, as they are paid, so the CLV is constantly updated and can be segmented by campaign, country, device or any other standard GA dimension? This is what our Google Analytics connection for ReCharge does. Available for any store using Shopify as their ecommerce platform and ReCharge for recurring billing, the smart connection (integration) ties every recurring payment back to the campaigns in GA.  Here's how the connector works The only drawback is that you'll need to wait a few months for enough customer purchase history (which feeds into CLV) to be gathered. We think it's worth the wait, as you then have accurate data going forward without needing to do any manual imports or exports. Then, if you also import your campaign costs automatically, you can do the Return on Investment (ROI) calculations directly in Google Analytics, using GA's new ROI Analysis report (under Conversions > Attribution), or in your favourite reporting tool. Do you have a unique way of tracking your marketing to maximise CLV? Are there other metrics you think are more important for subscription retailers? Littledata's connections are growing. We'll be launching integrations for other payment solutions later this year, so let us know if there's a particular one you'd like to see next.

2019-02-05

Shopify's 'sales by traffic source' report is broken

If you're a Shopify store manager, one of your biggest questions should be 'which campaigns lead to sales?'. We looked at data from 10 Shopify Plus customers to see whether the sales by traffic source report can be trusted. Under the Shopify store admin, and Analytics > Reports tab, you can (in theory) see which sessions and sales came from which traffic sources. BUT this sales by traffic source report is broken. Looking at 180,000 orders for 10 stores in Q4 2018, here are the marketing channels which Shopify Analytics says brought the traffic: Direct 83.5%Social 9%Search 4.5%Unknown (other websites, not social or search) 3%Email ~0.1% And using comparative data from Google Analytics we know this is all wrong. Here's a comparison of Shopify's attribution to Google Analytics last-click attribution of sales for one of these customers: Marketing attribution comparison for 700 orders Shopify Google Analytics Direct 99% 43% Search (Paid + Organic) 0.6% 7% Social 0.4% 10% Email - 25% Affiliates - 15% Here's why it's broken 'Direct' traffic is when the source is unknown. But for Shopify's report this means where the source of the last session is unknown - the user most probably visited a search ad or product review previously. Having only 1% visibility on your marketing performance is just not acceptable!We know that tagged Facebook traffic alone represents 7% of traffic for the average store, so 10% of sales from Social is more normal. Social also brings more than the actual sales in terms of visibility and influencers.Google generates billions of pageviews a month for ecommerce stores. If your site gets only 1% of its traffic from search, we'd be very surprised! Including paid search this site is still well below the 40% average. (Check out our 6 essential benchmarks for Shopify stores.)Monthly emails and personalised retargeting emails are now a staple of online marketing, and we know all the customers in this analysis use email marketing of some form - including for new product launches, discounts and cart abandonment campaigns. The problem is, it's unlikely to be the only campaign which brought customers, so it gets drowned out by other 'last click' channels. The solution: multi-channel attribution.Affiliates are a really important channel to get right, as they are paid based on the sales attributed to them. Why should you rely solely on the report the affiliate marketer gives you, and not see the same numbers in Google Analytics? So don't leave your marketing analytics to guess-work! Try the Littledata app to connect Shopify with Google Analytics on a free trial today. All paid plans include unlimited connections, to ensure accurate marketing attribution for sales via ReCharge (subscriptions), CartHook (one-page checkout), Refersion (affiliates) and more.

2019-02-04

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. [subscribe] 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. [subscribe] 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? [subscribe] 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

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