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Are you looking at the wrong Black Friday metrics?
Paying attention to the right ecommerce metrics can help you establish the best customer base and shopping experience for long-term growth. But many retailers still focus only on the most popular metrics -- especially during the online shopping craze of Black Friday and Cyber Monday (#BFCM). Over the next few weeks ecommerce managers will be obsessing over data, but which stats are the most important? Two popular metrics -- ecommerce conversion rate and average time on site -- may be misleading, so I recommend looking instead at longer-term benchmarks. Here's how it all breaks down. Littledata's ecommerce benchmark data now contains indicators from over 12,000 sites, making it an ideal place to get a realistic view of Black Friday stats. Last year we found that the impact on Black Friday and Cyber Monday was larger in 2017 than in 2016. Using that same data set of 440 high-traffic sites, I dove into the numbers to see how this affected other metrics. Metrics to avoid I think that overall ecommerce conversion rate is a bad metric to track. From the leading ecommerce websites we surveyed, the median increase was 30% during the BFCM event last year...but nearly a third of the stores saw their conversion rate dip as the extra traffic didn’t purchase, with this group seeing a median 26% drop. Some stores do extremely well with deals: four sites from our survey had more than a 15-fold increase in ecommerce conversion rate during BFCM, and nearly a quarter saw more than double the conversion rate over the period. But the real question is: will tracking conversion rate hour-by-hour help you improve it? What could you possibly change within in day? Another misleading metric is average time on site. You may be looking for signs that the the extra traffic on the holiday weekend is engaging, but this is not the one to watch. The time on site for visitors who only see one page will be zero, which will mask any real increase from engaged visitors. Where to focus instead Now, do you know what good performance on funnel conversion metrics would look like for your sector? If not, have a look at Littledata’s industry benchmarks which now cover over 500 global sectors. Littledata’s benchmarks also include historic charts to show you how metrics such as add-to-cart rate vary for the average retailer in your sector month by month. Next try the ‘speed’ performance page to see how fast a user would expect a site in your sector to be. If you see site speed (as measured in Google Analytics) drop below average during Black Friday trading it’s time to pick up the phone to your web host or web operations team. Then, are you tracking return on adverting spend for extra Facebook Ads you're running during the quarter? Ad costs will spike during the peak trading period, and you make not be getting the same volume of traffic conversion into sales. Here are some quick pointers. Facebook Ads. Littledata’s Facebook Ads connection will ensure accurate data, with a dedicated Facebook report pack for automated insights. Shopify. If you're running your site on the Shopify platform, read up on which metrics are most important for Shopify stores and check out Shopify's BFCM Toolbox for seasonal online marketing. Missions. Use Missions in the Littledata app to make permanent improvements to your user experience. BFCM may be over before you can make the changes, but customers will keep buying the rest of the year. For example, can you increase add-to-cart rate with tips such as highlighting faster selling items or recommending an alternative to out-of-stock products? So focus on some clearer metrics and I hope Black Friday brings you every success! [subscribe]
For every retail loser there's a retail winner
Today PwC's retail survey found the British high street is being reshaped as shoppers shift online - especially in fashion, where a net 100 high street stores closed. This misses the positive side of the story: all those shoppers are buying from independent UK brands online instead, which is one of the fastest growing area of the UK economy. We looked at 30 mid-sized online fashion retailers (with average sales of £1m per month) who get a majority of their traffic from the UK. This collection had grown their sales by an aggregate 21% from October 2017 to October 2018 (year on year). Fashion shoppers love to browse unique designs on Instagram and Pinterest, compare prices and get easy home deliveries. Independent ecommerce brands are bringing original designs to the British wardrobe, and we should celebrate their success. Behind the research Littledata gathers benchmark data from Google Analytics on over 12,000 websites, including many types of ecommerce businesses. Our customers get insights into their performance and recommendations on how to improve online conversion. [subscribe]
Categorising websites by industry sector: how we solved a technical challenge
Are you benchmarking your ecommerce site in the right sector?
Littledata launched benchmarks for websites two years ago. They quickly became a key feature of our app, and as customers became more engaged, so did ideas for how to improve our benchmarking and the algorithms that learn from those benchmarks. In response to customer feedback and deeper research into industry sectors, we've made some really exciting improvements over the last few months to make the comparisons even more useful -- and even more dynamic. The changes are five-fold. Detailed sectors and sub-sectors. Almost every customer we talked to said the benchmark comparison was most useful if it was for very similar sites. Previously we only had 50 high-level sectors to compare with; now we have hundreds of low-level sectors. You can visualise the full range. Smarter auto-categorisation of your website. Our machine learning process now has a 95% chance of finding the best sector for your website, meaning you can compare against the most useful benchmark without filling in a single form! Ability to manually change industry sector. And of course, if you're in that 5% that needs human input, then you (or your Enterprise account manager) can pick a better sector in the general app settings page. You might also want to change sectors just to see how you compare. No problem. Benchmarks for technology stacks. Want to see if you are making the most of a technology such as Shopify or Yieldify? Now you can compare with other sites using the same technology, making our ecommerce benchmarking even more powerful for agencies and web developers. Benchmarks for starter websites. Previously we only generated benchmarks for sites with at least 500 monthly visits. We dropped that to 200 monthly visits, so starter websites can see a comparison - and see more detail as they grow. We've launched a live visualisation of how our AI-based website categorizer is mapping a range of industry sectors. It offers a full overview of website categories and segments. And you can drill down to see more details. For example, we've seen a big rise in wine, coffee and health shake retailers this year, many of whom are using our ReCharge integration to get insight into their subscription business models. As our algorithms learn about ecommerce businesses selling beverages of many varieties and automatically categorises sites accordingly, you can now look closely at a particular segment to see how your site compares. Littledata is an Agile company. We're constantly iterating, and continuously improving the benchmarks to make them more actionable, so please give us feedback if you'd like to see more. Happy benchmarking! [subscribe]
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. Littledata's Facebook Ads connection audits your Facebook campaign tagging and pulls ad cost daily into Google Analytics. This automated Facebook-Ads-to-Google-Analytics integration is 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 is included with all paid plans. You can activate the connection from the Connections tab in 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?
The World Cup guide to marketing attribution
It’s World Cup fever here at Littledata. Although two of the nationalities in our global team didn’t get through the qualifiers (US & Romania) we still have England and Russia to support in the next round. And I think the World Cup is a perfect time to explain how marketing attribution works through the medium of football. In football (or what our NYC office calls 'soccer'), scoring a goal is a team effort. Strikers put the ball in the net, but you need an incisive midfield pass to cut through the opposition, and a good move starts from the back row. ‘Route one’ goals scored from a direct punt up the pitch are rare; usually teams hit the goal from a string of passes to open up the opportunity. So imagine each touch of the ball is a marketing campaign on your site, and the goal is a visitor purchasing. You have to string a series of marketing ‘touches’ together to get the visitor in the back of the net. For most ecommerce sites it is 3 to 6 touches, but it may be more for high value items. Now imagine that each player is a different channel. The move may start with a good distribution from the Display Ads defender, then a little cut back from nimble Instagram in the middle. Facebook Ads does the running up the wing, but passes it back to Instagram for another pass out to the other wing for Email. Email takes a couple of touches and then crosses the ball inside for AdWords to score a goal – which spins if off the opposing defender (Direct). GOAL!!! In this neat marketing-football move all the players contribute, but who gets credit for the goal? Well that depends on the attribution model you are using. Marketing attribution as a series of football passes Last interaction This is a simplest model, but less informative for the marketing team. In this model the opposing defender Direct gets all the credit – even though he knew nothing about the end goal! Last non-direct click This is the attribution model used by Google Analytics (and other tools) by default. In this model, we attribute all of the goal to the last campaign which wasn’t a Direct (or session with unknown source). In the move above this is AdWords, who was the last marketing player to touch the ball. But AdWords is a greedy little striker, so do we want him to take all the credit for this team goal? First interaction You may be most interested in the campaign that first brought visitors to your website. In this model, Display ads would take all the credit as the first touch. Display often performs best when measured as first interaction (or first click), but then as a ‘defender’ it is unlikely to put the ball in the net on its own – you need striker campaigns as well. Time decay This model shares the goal between the different marketing players. It may seem weird that a player can have a fraction of a goal, but it makes it easy to sum up performance across lots of goals. The player who was closest to the goal gets the highest share, and then it decays as we go back in time from the goal. So AdWords would get 0.4, Email 0.5 (for the 2 touches before) and Instagram gets 0.1. [subscribe] Data-driven attribution This is a model available to Google Analytics 360 customers only. What the Data-driven model does is run through thousands of different goals scored and look at the contribution of each player to the move. So if the team was equally likely to score a goal without Facebook Ads run down the wing it will give Facebook less credit for the goal. By contrast, if very few goals get scored without that pass from Instagram in the midfield then Instagram gets more credit for the goal. This should be the fairest way to attribute campaigns, but the limitation is it only considers the last 4 touches before the goal. You may have marketing moves which are longer than 4 touches. Position based Finally you can define your own attribution weighting in Position Based model, based on which position the campaign was in before the goal. For example, you may want to give some weight to the first interaction and some to the last, but little to the campaigns in between. Still confused? Maybe you need a Littledata analytics expert to help build a suitable model for you. Or the advice of our automated coach known as the analytics audit. After all, every strategy could use a good audit to make sure it's complete and up-to-date. So go enjoy the football, and every time someone talks of that ‘great assist’ from the winger, think of how you can better track all the uncredited marketing campaigns helping convert customers on your site.
Google Analytics Data Retention policy - which reports does it limit?
From 25th May 2018 Google allowed you to automatically wipe user-level data from the reporting from before a cut-off date, to better comply with GDPR. We made the change for Littledata's account to wipe user-level data after 26 months, and this is what we found when reporting before February 2016. Reports you can still view before the user data removal Audience metrics Pageviews ✓ Sessions ✓ Users X Bounce rate ✓ Audience dimensions Demographics X OS / Browser X Location X User Type X Behaviour Pageviews ✓ Custom events X
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